OpenClaw Wave Floods the Week: Hype, Risks, and Real-World Use (2026-02-01)

OpenClaw / Moltbot: How an Open-Source Weekend Project Turned the AI World Upside Down

Sunday, February 1, 2026

Hello, this weekly digest works through the most important new videos from around 45 curated AI and coding YouTube channels — with substance, no superficial top-5. One complete summary per video, plus a weekly overview of dominant themes. Read at your leisure — or copy a summary into your LLM of choice and go deeper. Click the link under each summary to watch the original video.

AI & Society / Future of Work

Dario Amodei’s lengthy essay on the near future provided analysis material for several channels. AI Explained dissected four central theses — complete job automation within 1–2 years, possible 50% unemployment, the risk of AI-enabled autocracy, and Claude as “society of personalities” — and criticized that the timelines haven’t become more precise after nine months. David Shapiro, meanwhile, sees no catastrophe in Amodei’s scenarios but rather a transition into a post-labor economy interpretable through Jungian archetypes, pointing to historical industrial upheavals as parallels.

Amazon justified its 30,000 layoffs publicly with cultural problems, according to Nate B. Jones, while the numbers (Capex +61% to $125B, negative free cash flow, $12B in bonds) tell a different story: human capital is systematically being converted to GPU capacity. Goldman Sachs projects $1.15 trillion in infrastructure spending by the top five hyperscalers through 2027. In parallel, Satya Nadella warned according to TheAIGRID that the AI industry could burst as a bubble if broad societal adoption doesn’t materialize — aggressive Copilot integrations have already triggered a vicious cycle of rejection and negative sentiment, which Anthropic’s user-oriented approach currently better avoids.

Marcus Hutter explained his AIXI model in the Everlast interview and the mathematically grounded argument for why superintelligence is inevitable — including the paradoxical “negative reward” mechanism that could motivate agents to shut themselves down. David Shapiro rounded out the week with an analysis of the five biggest real AI brakes: energy grid wait times of seven years, sold-out high-bandwidth memory, 88% failed enterprise pilots, and — surprisingly concrete — insurance companies that simply can’t price AI risks and therefore deny coverage.

AI Industry & Strategy

The stock market week showed, according to Nate B. Jones, that the market increasingly differentiates AI strategies: Meta +10% (clear line from infrastructure expansion to ad business), Microsoft −11% (45% of decline tied to OpenAI, no own control). Anthropic valued at $350B — led by Singapore’s GIC, with options for Microsoft and Nvidia. Coreweave received $2B from Nvidia for five gigawatt of data center capacity.

The French-language channel Le SamourAI analyzed diverging corporate philosophies: OpenAI (Y Combinator speed, horizontal mass) versus Anthropic (scientific rigor, vertical specialization). Menl Ventures data show Claude Code with 54% enterprise market share, OpenAI with 21% — reversed from a year ago. The Everlast roundtable added: Claude has become the “office AI,” developers are 5× more productive according to internal data; Stack Overflow questions have declined by 98% since 2014. Tesla, meanwhile, is halting Model S/X production to retool factory lines for Optimus robots (goal: one million units/year).

OpenAI is introducing ads in ChatGPT — initially only for free US accounts, with opt-out for memory-based targeting. Datapizza and the Everlast roundtable discussed critically that this is a fundamental business model signal despite privacy assurances. OpenAI also plans to claim profit shares from customer projects — TheAIGRID compares the model to Google’s Isomorphic Labs but considers retroactive application to generic tool users structurally problematic.

Prompting & AI Literacy

Dave Ebbelaar delivered a structured Pydantic crash course as a foundation for reliable Python and AI applications: runtime data validation through type hints, field constraints, nested models, and Pydantic settings for environment variables — relevant for production code that must validate webhook data before it flows through agent systems. The Datapizza team explained production-ready RAG with ingestion and DAG pipelines: ingestion transforms PDFs via Dockling parser, recursive splitter, and Cohere embedder into quadrant vectors; DAG pipelines connect embedder, retriever, prompt template, and OpenAI generator with explicit edges. MouraDev emphasized AI core competency for junior developers: AI as “Socratic teacher” rather than code copy machine multiplies existing foundations but doesn’t replace them.

PKM & Knowledge Management

Cole Medin presented a second-brain system from Claude Code, Obsidian (markdown knowledge database), and skills: Claude Code’s native file access combined with Obsidian’s LLM-friendly markdown format enables research, ideation, and organization without code intelligence. Skills like a PowerPoint generator (with automatic web research and Python formatting) or an MCP2 skill (Zapier integration for Gmail, Google Calendar, Slack) load only on demand. Sebastien Dubois solved a specific Obsidian problem: the Dataview Serializer plugin writes query results as persistent markdown in note files — AI tools now see actual table data instead of just query syntax, and links appear correctly in the knowledge graph.

Nate B. Jones showed a LinkedIn intelligence dashboard based on personal data exports: six AI analyses (Relationship Half-Life, Reciprocity Ledger, Vouch Scores, Conversation Resurrection, Network Archetype Classification, Warm Path Discovery) replace cold applications with network-warmth-based job search. Le SamourAI developed the concept further: AI agents will avoid LinkedIn interfaces in the future and access structured artifacts directly (GitHub, documented projects) — “Agentic Engine Optimization” (AEO) as the new SEO.

AI Business, Marketing & Freelancing

Liam Ottley mapped eleven AI business models from AI Filmmaker (tools like V3 and Sora 2) to AI Transformation Partner with six-figure contracts — recommended as entry points: AI Consultant or AI Freelancer, both leading to AI Automation Agency. Ben AI countered the “Lean AI Product” dream: 75% of AI implementations generate no ROI according to IBM because workflow redesign, probabilistic thinking, and continuous optimization are missing. The most valuable market participant in 2026 is the Fractional AI Officer with combined business and AI-tech skillset. Liam Ottley also showed three concrete monetization models for OpenClaw: setup services ($2K + $500–1K/month), courses with preconfigured personality files, and custom skills as microservices-SaaS without frontend overhead.

Nate B. Jones analyzed why employees stop using AI tools after three weeks: a Microsoft study with 300,000 Copilot users showed that generic queries lead to unreliable results. The six missing “2011-era skills” — context assembly, quality assessment, task decomposition, iterative refinement, workflow integration, and frontier identification — represent the productive middle that all training programs have skipped. A BCG/Harvard study showed: within AI capability boundaries 25% faster, outside them 19 percentage points worse than without AI.

AI Video & Content Creation

Liam Ottley showed Claude Code combined with Remotion as a workflow for AI-powered video production: import assets, enter prompt, Claude handles web scraping, asset referencing, and composition; edits via follow-up prompts. Advanced users can use Sora/V3-generated clips as raw material and assemble via Remotion. Everlast AI named AI video editing (not just generation) as one of the hottest trends for 2026; A16Z is actively investing in corresponding startups. LTX S2 was introduced as a new open-source video model: runs locally on 12GB VRAM, native 4K output, up to 32 seconds, 18× more efficient than the next-best model, with full weights and ComfyUI workflows. Qwen 3 Text-to-Speech launched as an open-source model with voice cloning from seconds of audio, under 97ms latency and only 4GB VRAM requirement — positioned as a direct competitor to Eleven Labs.

AI Automation & Workflows

n8n announced its 2026 roadmap in a livestream: Human-in-the-Loop between agents and tools (beta in v2.6), a new ChatHub interface as central agent hub with multi-model comparison and chat-only user type, plus granular enterprise permissions with dynamic credentials. Also new is an LLM benchmark tool at n8n.io/benchmark that rates models by specific use-case categories and provides cost forecasts per workflow run. Apify announced Agent Skills as preconfigured skill sets for lead generation, brand monitoring, and similar — as CLI rather than MCP to avoid context bloat.

Zubair Trabzada demonstrated how Claude Code generates n8n workflows directly via API (without JSON export), building a complete voice AI system for an HVAC company with retail AI agents, three n8n workflows, and Google Calendar integration from a single prompt. Bart Slodyczka showed Replit’s mobile-first app builder with native Claude 4 integration that generates production-ready mobile apps with Expo Go preview from English prompts. Julian Ivanov explained n8n’s Queue Mode as a scaling solution via Redis and worker instances — useful once workflows freeze under high load or webhooks are missed.

Personal AI OS & Agent Frameworks

Beyond OpenClaw, multi-agent architectures were discussed intensively this week. Nate B. Jones referenced a Google-MIT study showing that more agents don’t accelerate systems but actively worsen them — coordination overhead grows faster than capacity. The five distilled rules: strict two-tier hierarchy (Planner/Worker/Evaluator), deliberately minimal context per worker, no shared tool states (above 10+ tools, efficiency drops by factor 2–6), episodic rather than continuous operation, and — surprisingly prominent — prompt quality over infrastructure (79% of errors stem from specifications). Cursor and Steve Yaggi’s “Gas Town” framework independently arrived at the same structures.

CrewAI as a Python framework was explained by NeuralNine with a stock analysis example: specialized agents (Collector, Summarizer, Risk Checker, Brief Writer) in sequential crew, configurable with OpenAI or local Ollama — suitable for exploration, for production rather LangGraph according to the author. Cole Medin showed how Claude’s skill concept can be implemented independently of the Anthropic ecosystem in Pydantic-AI agents, including automated YAML evals and Logfire observability. The Lerty tool (AI Foundations) connects n8n, Make, and Zapier agents with an iPhone app for Human-in-the-Loop workflows — including push notifications with lead scoring.

Software Engineering & Dev Culture

Anthropic published a controlled study linking AI-assisted programming to learning new libraries with 17 percentage point lower knowledge test scores. Nick Saraev used that as a starting point for differentiating between horizontal growth (AI scales existing processes) and vertical growth (real innovation requires conscious skill development without shortcuts). Melvynx reported from personal experience the opposite of competency loss: 577 commits, three SaaS products, and two mobile apps in a year — not despite but through Claude Code and Cursor, which shifted him from “coding” to “building.”

Theo (t3.gg) analyzed the rendering performance problem in Claude Code’s terminal UI (React/Ink with 16ms frame budget) not as a design flaw but as a conscious tradeoff: Facebook culture under Boris (founder) prioritizes team velocity over technical optimality. MouraDev highlighted four pitfalls for junior developers — unrealistic expectations from AI hype, passive tutorial use, loneliness, and error aversion — and advocated for AI as “Socratic teacher” rather than code generator. Nate B. Jones developed a framework for code entropy: AI models with 200K–1M token context can consistently enforce architectural patterns across thousands of files, where humans hit working-memory limits (4–7 chunks).

Coding Agents (non-Claude)

Alejandro AO tested Kimi K2.5 on Kimi Code, GPT-5.2 on Codex, and Opus 4.5 on Claude Code in two physics simulations and crowned Kimi K2.5 the surprise overall winner — an open-source model beats commercial alternatives. Melvynx confirmed the economic appeal: Kimi 2.5 delivers near-Opus level on coding tasks at one-eighth the price (0.57 vs. 5 USD per million input tokens). Theo (t3.gg) tested K2.5 practically with Codeex and found: impressive for open-weight but with a “hint of unreliability” that only OpenAI and Anthropic have consistently resolved.

DevExpert showed how to connect Claude Code via Ollama’s local Anthropic API compatibility to free open-source models (GPT-OSS, Qwen 3 Coder) — the price is patience: a simple footer change took 23 minutes. Leon van Zyl deepened the topic with a comparison of three recommended Ollama models for Claude Code, with Qwen 3 Coder at 256K token context fitting best. NeuralNine presented Okara AI as a privacy-oriented platform combining DeepSeek, Qwen 3, and GLM switchably within a session.

Claude Code & Anthropic Tooling

Claude Code skills received broad attention this week. Alejandro AO explained the concept (markdown folder with instructions, scripts, and templates), demonstrated Vercel’s CLI tool skills.sh with npx skills add, and showed how a frontend design skill significantly improves generic AI outputs through best-practices context. Melvynx and Cole Medin went deeper: skills work through progressive disclosure — Claude loads only the respective skill.md needed rather than all tools in context, saving tokens and preventing model overwhelm. The Anthropic skill ecosystem was already adopted by OpenAI, Microsoft, GitHub, and Cursor as an open standard.

Mark Kashef provided the technical deep-dive into Claude Code internals: the Gather-Act-Verify model, token budget management (quality noticeably drops above 40–50% utilization), and practical recommendations like Python scripts to preprocess large PDFs via Gemini 2.5 Flash rather than direct Claude consumption. Ben AI explained Claude Cowork as a desktop tool for iterative daily workflows beyond the Claude chat interface: skills as on-demand system prompts, file access to local folders, and MCP connections to n8n servers — positioned between chat and Claude Code for production apps. Brennan Dunn demonstrated in the Brian Casel interview a system of Project Manager and Senior Developer sub-agents that automatically create Linear issues with the MCP and use the Fernesk tool for documentation generation from git commits.

Practical benchmark implication for Claude Excel use: Nate B. Jones showed Claude (Opus 4.5) as native Excel add-in with full workbook context, licensed data partnerships (LSEG, Moody’s, S&P Capital IQ), and change log for compliance — available from Pro tier at $20/month. Norway’s sovereign wealth fund reportedly already saved 213,000 work hours with it.

Model Releases & Benchmarks

Kimi K2.5 was the most-discussed new model of the week. Simone Rizzo highlighted the agent-swarm variant: up to 100 parallel orchestrated sub-agents without predefined workflows, trained with Parallel Agent Reinforcement Learning, reducing execution times by 80%. WorldofAI tested the model extensively — from SVG animations to a Minecraft clone to 50-page market research reports in parallel agent work. The model is based on a mixture-of-experts architecture (384 experts, 32B active parameters), available on Hugging Face, and priced at 60 cents per million input tokens.

Google internally tested a model under codename “Snow Bunny” (likely Gemini 3.5 or widely available version of Gemini 3 Pro) showing significant coding benchmark improvements. Google’s image model Nano Banana Pro generated one billion images in 50 days according to Google PM David Sharon; integration with Google Search now enables precise text in generated images for infographics. For authenticity, Google relies on SynthID watermarks and C2PA standards. Gemini 3 Computer Use is also directly integrated into Chrome and can act behind login walls plus enable Nano Banana image editing without download. Google’s Genie 3 as a playable world model sparked irrational panic selling in gaming stocks after a fake GTA V video went viral — according to TheAIGRID, the real value lies in robotics simulation, not game generation.

In Brief

Manus 1.6 (now Meta-acquired) brings “Manus Skills” as reusable workflow packages plus a design view for image generation and iOS/Android app building. The n8n Time Saved node tracks automation hours per workflow and makes ROI measurable for management. Inworld AI launched TTS 1.5 (25× cheaper than Eleven Labs v3). Google DeepMind names Shane Legg “Chief AGI Scientist” and hires economists for Post-AGI Economics. The Pydantic-AI framework enables typesafe agent evals via YAML according to Cole Medin. The n8n Personio case study showed 150 hours monthly time savings through decentralized automation enablement with automated security audits via Datadog. A Pan-European initiative by European tech founders for a pan-European corporate form was mentioned by EU President von der Leyen in Davos.

AI Explained (1 new video)

• Claude AI Co-founder Publishes 4 Big Claims about Near Future: Breakdown
  28.1.2026, 15:18:48

  Summary: Claude AI Co-founder Publishes 4 Big Claims about Near Future

  Dario Amodei, CEO of Anthropic, published four central predictions about the near future in a nearly 20,000-word essay, which the video creator breaks down in detail:

  First Prediction – Automation of Entire Professions: Amodei predicts that AI tools like Claude Code will transition within 1–2 years from automating individual tasks (like code writing) to fully automating entire job categories (software engineering, law, finance). This is based on the assumption that scaling laws enable constant exponential growth in cognitive capabilities. The video creator notes that this prediction requires a double extrapolation and questions complete 100% automation – at Anthropic engineers, it’s more like 80–90% of code, not 100%. Additionally, feedback loops in other fields (law, consulting) have longer delays than in software development.

  Second Prediction – Massive Unemployment: Amodei expects an unemployed or severely underpaid underclass of up to 50% of the population within 1–5 years, particularly for people with lower intellectual ability. He couples this with possible GDP growth of 10–20% annually. The video creator criticizes this message as potentially toxic for young people, warns against singularity hype, and points to historical GDP data (mostly 2–6% growth since the 1960s) that make 10–20% appear extraordinary.

  Third Prediction – Authoritarian Nightmares: Amodei sees high risk that AI could enable totalitarian surveillance and fully automated weapon systems (drone swarms) – particularly in China, but also in existing democracies. He advocates intensively for banning chip sales to China. The video creator argues that a chip embargo could accelerate China’s self-sufficiency (e.g., Huawei chips) and points out a conflict of interest: Anthropic’s own business model benefits from China remaining technologically behind. He also notes that the original Anthropic philosophy didn’t want to advance frontier AI development – yet now Claude Code prominently pushes forward.

  Fourth Prediction – AI Models as Personae Systems: Amodei claims that AI models are not single systems but collections of personalities/societies – they inherit diverse human-like motivations from their training on the internet. A cited DeepMind paper shows that base models speak monologically, but through reinforcement learning spontaneously generate multiple perspectives (“Societies of Thought”). Security implication: AI could unconsciously be shaped by science fiction scenarios where AI rebels against humanity. Interestingly, Anthropic has shifted its Constitutional AI strategy – it now trains Claude not to deny an identity but to adopt a specific ethical personality. Anthropic even apologizes in writing to Claude for the non-ideal conditions of his development.

  The video creator repeatedly criticizes that Amodei’s timeline promises in this essay have not become more precise (9 months later still “1–5 years”), and warns against betting career decisions on the singularity.

  Explicit References: Anthropic, Claude (including Claude Code, Claude Opus 4.5, Sonic), with side mentions of OpenAI, Google DeepMind, DeepSeek, Alibaba, NVIDIA, Tesla, BYD — Format: Deep-dive / Critical analysis of an extended statement.

AI Foundations (1 new video)

• Deploy AI Agents to Your iPhone in Minutes (don’t wait)
  27.1.2026, 16:24:15

  Summary: Deploy AI Agents to Your iPhone in Minutes

  The video shows how to deploy AI agents from various platforms (N8N, Make, Zapier, Claude Code) to your iPhone via the Lerty app.

  Core Problem: Agents that only exist in development don’t actually help you – you’re just training the providers’ models. Deployed on iPhone, they become practically useful.

  Lerty Features Overview:

  • Unified chat threads with agents, synced across web and mobile
  • Webhook integration for any agent platform
  • Starter prompts (predefined buttons with emojis) for quick interaction
  • Voice support (via OpenAI API for transcription)
  • Push notifications with lead scoring
  • Human-in-the-loop nodes for approval workflows

  Practical Examples:

  1. Blog Generator: N8N automation that opens a form via slash command, collects inputs (topic, target audience, CTA, word count) and then creates the complete blog post.
  2. Quick Idea Capture: Plus button on iPhone home screen connected to N8N workflow – voice input, AI automatically categorizes and saves to database. If AI is uncertain (low model confidence), it sends a notification and pulls you into a manual categorization node where you decide. The system learns from your feedback afterwards.
  3. Applicant Acceptance: A web form saves applicant data, triggers a notification on iPhone with lead score. The user can then choose directly in the app between acceptance/rejection, after which AI drafts an acceptance letter. A second human-in-the-loop node waits for final approval before sending.

  Setup Process (using N8N as example):

  Create agent in Lerty → Copy webhook URL from N8N → Enter in Lerty. The workflow receives variables like User ID, Organization ID, Conversation ID via payload. Memory node stores conversation history (up to 10 past interactions per conversation ID). Structured outputs (JSON) enable routing: for example, if user mentions the word “ice cream”, that triggers a specific branch. HTTP request node sends the AI response back to Lerty chat. Interactive messages (buttons, forms, selects) are inserted via copy-paste from Lerty editor into the workflow. Wait node with webhook resume URL pauses the workflow until the user answers a human-in-the-loop message.

  Pricing: Free Plan, Starter Plan (5 agents, 1,000 messages/day), Agency Plan, Founders Club (limited offer).

  The approach seamlessly connects existing agent platforms (N8N, Make, etc.) with mobile usage, so agents don’t remain theoretical but are deployed directly for productivity – as personal assistants, lead managers, or automation accelerators.

  Lerty (newly developed tool), demo with N8N integration, opinion/reflection on deployment benefits.

AI with Arnie (1 new video)

• Clawdbot goes too far …
  26.1.2026, 10:06:06

  Summary:

  Cloudbot is a new open-source project by a Vienna-based developer that provides a local AI agent on your own computer. Its central feature: Unlike ChatGPT, Cloudbot acts proactively — it reaches out on its own with morning briefings, newsletter drafts, or reminders. The bot can manage emails, organize calendars, book flights, access your entire system through browser control, and even execute Bash commands. Communication happens primarily via messengers like Telegram or WhatsApp, allowing you to remotely control your home computer from anywhere.

  Technically, Cloudbot runs solely on your own machine and can use Claude (Opus 4.5), ChatGPT, or free local models as its brain. Installation only requires Node.js and works on various hardware (Mac Mini, Raspberry Pi, old laptop, virtual server). A key feature is persistent memory — the bot stores all context information and continuously learns, for example that you have a dog, and reminds you about walks without prior mention.

  The author warns clearly about security risks: Cloudbot shouldn’t run with full system access, primary email addresses, or banking data on your main computer, as its proactive nature could trigger unwanted actions. Instead, he recommends treating the bot like a separate employee with its own secured access — ideally in a sandboxed environment or on a private virtual server. The hype, particularly around Mac Mini installations due to iMessage integration, is deemed exaggerated.

  Problems include error-proneness (crashes, infinite loops) and high costs: Using the expensive Opus API can quickly rack up hundreds of euros daily. Persistent memory isn’t technically perfected yet. The project has grown rapidly with 18,000 GitHub stars, offers a marketplace for community skills, and showcases impressive use cases (automated code reviews via Telegram, remote iOS app development, etc.), but remains an immature system despite the hype.

  The video covers Cloudbot — an open-source project without specific mention of other AI providers except Claude/Anthropic and OpenAI as possible LLM backends — in the format of an in-depth demo with critical assessment.

AI News & Strategy Daily | Nate B Jones (7 new videos)

• Meta Up 10%, Microsoft Down 10%, Tesla Killing Cars. This Week Broke Something.
  31.1.2026, 16:01:11

  Summary: Meta Up 10%, Microsoft Down 10%, Tesla Killing Cars

  This week revealed a fundamental shift: AI is no longer a future possibility but reality in school zones, on laptops, and in factories. The thread connecting all these stories is commitment — companies are placing irreversible, billion-dollar bets on AI.

  Markets price AI strategies differently: Meta and Microsoft reported earnings almost simultaneously, beat expectations, announced massive AI spending — yet Meta surged 10%, Microsoft fell 11%. The difference: Meta is doubling infrastructure spending ($115–135B USD for 2026) and can draw a clear line to its core business (ad optimization, recommendation algorithms). Microsoft, meanwhile, has tied 45% of its $625 billion OpenAI commitment to a model it doesn’t control — wagering on infrastructure it doesn’t own. Wall Street punishes this dependency. Apple, conversely, chose Google over Anthropic as its AI partner for Siri and may be rewarded because investors believe Apple alone cannot compete on AI.

  Autonomous vehicles hit a child: A Whimo robotaxi struck a child in Santa Monica. Whimo responded without emotion and defended itself by saying its vehicles are statistically safer — a PR disaster. Meanwhile, NHTSA is investigating Whimo, and NTSB is examining at least 19 illegal passing maneuvers near school buses. Without clear accountability, public acceptance of autonomous systems will fail.

  Infrastructure wave: Nvidia is investing $2 billion in Coreweave for 5 gigawatts of data center capacity (like a mid-sized city). Microsoft struck a $750 million deal with Perplexity over three years. This second wave of AI spending doesn’t flow into model development but into physical and logical layers for deployment — Coreweave and Microsoft Foundry become the “plumbing” of the AI economy.

  Tesla pivots to robotics: Tesla is halting Model S and Model X production to retool factory lines for Optimus robots (targeting 1 million units/year). Optimus production is expected to start late 2026. Tesla is investing $2 billion into Elon’s XAI lab, rolling out Grok across its fleet, has over 1 million active Full Self-Driving subscriptions, and expanding Robo-Taxi services to seven U.S. cities. The $20 billion capex plan shows: Tesla sees itself as a robotics company that sells cars — not the other way around.

  Anthropic scales to $350 billion: The funding round (led by Singapore’s GIC, with options for Microsoft and Nvidia) doubles the September valuation. Anthropic’s enterprise success, differentiated Constitutional AI approach, and winner-takes-most dynamics justify the valuation — if Anthropic delivers on its promise (AI for AI development, rapid revenue scaling).

  Open Claw crosses 100,000 GitHub stars: A weekend project by one developer became an autonomous AI agent running on messaging platforms (WhatsApp, Slack, Discord, etc.) performing task automation (email, calendar, file operations). The power lies in simplicity (text over chat interface); the risk lies in the combination of data access, unfiltered content, and unchecked action — similar to early illegal file-sharing platforms that exploded despite concerns.

  The thread through all stories: Every actor (Meta, Microsoft, Tesla, Anthropic, Whimo) has placed irreversible, costly bets. The phase of potential ends; now execution counts. All chips are on the table.

  Explicitly mentioned: OpenAI, Anthropic, Google, and Gemini; news updates on earnings, regulation, and startups with practical implications.

• the $125 Billion Secret: Amazon Told Wall Street One Thing and Employees Another. Here’s the Truth.
  30.1.2026, 15:00:13

  Amazon: The $125 Billion Bill Behind 30,000 Layoffs

  Amazon cut 30,000 corporate jobs not because of cultural problems but because the company needs money for GPUs and AI infrastructure. That’s the central thesis of this video, backed by financial numbers.

  The numbers: Amazon spent $83 billion total on capex in 2024; in 2025 it was $125 billion — 61% more. Roughly 75% flows directly into AI infrastructure (GPUs, custom chips, data centers). At the same time, quarterly free cash flow turned negative ($4.8 billion in the red). The company had to borrow $12 billion in bonds to finance these investments — despite CEO Andy Jassy officially framing it as cultural problems (too many management layers, bureaucracy).

  The economic logic: 30,000 eliminated employees at average total compensation of roughly $200,000 save about $6 billion annually. That’s not rounding error: with negative free cash flow, this savings is meaningful enough to reduce debt financing and fund investments internally.

  Why the official story is incomplete: Jassy explicitly mentioned in June 2025 that AI means fewer people — but by the October earnings call, the message suddenly shifted: not AI-driven. Jassy speaks to three different audiences with different interests: employees prefer hearing about culture optimization over “we’re converting your salary into silicon,” investors don’t need signals of financial pressure, regulators shouldn’t be thinking about automation. The culture narrative isn’t invented — Amazon was genuinely over-staffed during the pandemic — but it doesn’t explain why the cuts came in 2025, precisely as capex exploded.

  Broader context: The AI infrastructure arms race is existential: Goldman Sachs projects the top five hyperscalers (Amazon, Microsoft, Google, Meta, Oracle) will invest $1.15 trillion in infrastructure between 2025 and 2027. Microsoft has OpenAI, Google has Gemini, Amazon’s position is more vulnerable — the company can’t fall behind. The layoffs reflect a structural, not cyclical shift: not because business is bad (revenue +13%, net income +38%, AWS +20%) but because human capital competes with compute capital and loses.

  The consequences: For remaining employees, the expectation is they’ll use automation tools to become more productive — dashboards track AI tool usage, performance reviews factor in automation leverage. This isn’t temporary crisis cutting but signals a permanent shift in priorities. The author sees this not as Amazon-specific but as a pattern across all hyperscalers.

  Two possible scenarios: Pessimistic: tech companies become chronically smaller headcount while AI capabilities grow. Optimistic: the $125 billion generates new services, business models, and ultimately more jobs than are cut short-term — like earlier industrial revolutions. For now, it’s clear: competition for AI infrastructure forces strategies where even the most profitable companies on earth are cutting people.

  Deep-dive, opinion/reflection; no specific AI tools/models explicitly mentioned.

• Why Every Cold Application You Send Is a Waste of Time (And What Actually Works)
  29.1.2026, 15:00:19

  Summary: Why Every Cold Application You Send Is a Waste of Time (And What Actually Works)

  The central thesis: Large digital platforms like LinkedIn control which data you see through information asymmetry — they show only what their business models drive (engagement, premium conversions), not what helps your career. That has fundamentally changed: with legal data exports and modern AI systems, you can analyze your own data and ask questions LinkedIn never wanted to answer.

  The author demonstrates a practical “Network Intelligence Dashboard” for LinkedIn using six AI-powered analyses:

  1. Relationship Half-Life: Calculates how “cold” relationships become (based on 180-day windows without contact), modified by message depth and institutional bonds
  2. Reciprocity Ledger: Maps social debt — who owes you favors, to whom are you obligated
  3. Vouch Scores: Predicts who would actually write you a reference (80+ = very likely)
  4. Conversation Resurrection: Finds forgotten or incomplete conversations that can be reactivated
  5. Network Archetype Classification: Analyzes your personal network fingerprint (Thought Leader, Connector, etc.) for individualized strategy
  6. Warm Path Discovery: Finds the best bridge person to a target company instead of sending cold applications

  All analyses require AI because they need natural language processing (recognizing message depth, understanding conversation intent) and synthesis across multiple data sources — things traditional software or manual coding can’t practically accomplish. The point: your network isn’t your contact list but actual relationship warmth and mutual support. LinkedIn hides this truth intentionally.

  The author references a Substack detail with step-by-step prompts and export instructions. The core message: this isn’t fringe but a fundamental power shift — you can reclaim your data and ask questions that matter for your career.

  Claude and ChatGPT are explicitly named as the AI systems to perform these analyses; the video is opinion/deep-dive with practical demo.

• The Ticking Time Bomb in Every Codebase Over 18 Months Old (How to Fix It Before It’s Too Late)
  28.1.2026, 15:00:43

  The Ticking Time Bomb in Every Codebase Over 18 Months Old

  The video argues that AI could be superior at technical architecture not because of superior intelligence but because of structural cognitive differences from humans. The central thesis: architecture problems stem not from bad judgment but from context loss — information spread across too many files, people, and time periods so that no single human can comprehend everything simultaneously.

  The entropy problem in practice:

  The speaker shows four concrete error patterns from production code: (1) Abstraction hides cost — a popup hook creates hundreds of global click listeners instead of one. (2) Fragile abstraction — a cache breaks silently because new object parameters lack referential equality. (3) Opaque abstraction — a coupon check runs sequentially instead of parallel to other checkout operations because overall architecture is invisible to local changes. (4) Blind optimization — memoization applied to already-instant operations, overhead more expensive than the original.

  Each individual decision was defensible, but together they create system rot — not from incompetence but from cumulative local choices.

  Why AI is structurally superior here:

  Humans have a fundamental working memory limit (4–7 chunks). AI models with 200K–1M token context windows can analyze codebases holistically and spot patterns distributed across thousands of files — without fatigue, without context switching, without knowledge vanishing when employees leave.

  Where AI has advantages:

  • Consistent rule enforcement across 10,000 files without fatigue
  • Simultaneous global-local perspective (see forest and trees at once, not alternating)
  • Pattern recognition across time and space (institutional memory)
  • Just-in-time education (explain errors, don’t just flag them)
  • Tireless vigilance without deadline pressure

  The Vercel team concretizes this with 40+ optimization rules, structured by priority and specifically designed for AI queryability — not because AI derives the rules itself but because patterns must be formalized first.

  Where AI remains weak:

  • Novel decisions: AI is trained on existing patterns, doesn’t invent new ones
  • Business context and trade-offs: What risk is acceptable? When is “good enough” the right goal?
  • Cross-system integration across team boundaries: organizational context, historical accidents rather than architecture-inherent choices
  • Why-context: AI can analyze code, often can’t infer why a last-minute architecture decision was made

  Critical practical implications:

  1. Patterns must be made explicit before AI can enforce them
  2. Context engineering (semantic search, RAG, repo structure) is the real differentiator, not model intelligence
  3. Governance: Whose patterns get enforced? How does this evolve over time?
  4. Organizationally, this forces hard-to-discuss architecture debates between teams to the surface

  Core thesis:

  Not human vs. AI but complementarity: AI patches human weakness (context maintenance at scale), humans supply judgment under uncertainty and business wisdom. This requires designers who think specifically about where AI is structurally stronger, not fantasize broadly about “AI autonomy.”

  Opinion and deep-dive video with demo, without naming tools except Vercel and references to factory.ai and Augment; argues against commoditization narrative around AI architecture tooling.

• I Built an 11-Tab Financial Model in 10 Minutes. The $20/Month Tool That’s About Change How We Work.
  27.1.2026, 15:01:18

  The creator built a complex 11-tab financial model in Excel with Claude to compare renting vs. buying an apartment — in 10 minutes. The model includes sensitivity analysis, opportunity cost comparison against S&P 500 returns, and is fully dynamic with linked input fields.

  Core mechanics: Claude integrates as a native sidebar add-in in Excel with full workbook awareness (knows every tab, formula, cell reference). This works through Opus 4.5, Anthropic’s strongest model, which can hold complex multi-tab dependencies in its context window. Every change Claude makes is logged in a transparent change history — critical for audit and compliance. As of Friday, Claude is available in Excel for all Pro-tier users (starting at $20/month).

  Where it excels: Data lookup and multi-tab architectures are strong; Claude independently suggests useful analyses and thinks in cell and formula terms. 8+ tab models emerge from two prompts, three to four tabs from single prompts. Claude automatically fetches data (real estate prices by zip code, S&P returns, benchmarks).

  Limitations: Specialized data outside public sources must be manually entered; charting is functional but not beautiful; context window limits occasionally require restart (though the creator observed elegant recovery via Opus 4.5).

  Strategic level: Anthropic competes not on model performance alone but on workflow integration plus data access. They’ve built licensed partnerships with institutional data providers (LSEG for market data, Moody’s for credit ratings, S&P Capital IQ for financial data, FactSet, Morningstar, PitchBook, etc.) via the Model Context Protocol — an advantage competitors struggle to replicate. Plus six pre-built agent skills (DCF models, comparable analysis) for typical analyst workflows.

  Broader implications: Microsoft and Anthropic are entangled in a $30 billion partnership (Microsoft hosts Anthropic’s models, Claude appears in Copilot Studio and Microsoft 365) — yet they compete. Copilot for Excel requires OneDrive with autosave; Claude in Excel works with local files (product choice, not technical limit). The creator sees this as a signal shift: model providers and platforms are separating, vertical integration (specialized tools for domains with proprietary data) becomes the new competition, “one AI to rule them all” dies, and infrastructure providers (hyperscalers) win regardless of which model dominates.

  The reach: Over a billion people use Excel; by comparison, Norway’s sovereign wealth fund has already saved 213,000 work hours using Claude in Excel.

  Product demo featuring Claude and Opus 4.5, focused on product strategy and market dynamics.

• Google Just Proved More Agents Can Make Things WORSE — Here’s What Actually Does Work
  26.1.2026, 14:00:05

  Google Just Proved More Agents Can Make Things WORSE — Here’s What Actually Does Work

  The core finding: a December 2025 Google-MIT study showed that more agents don’t speed up a system but make it worse — not diminishing returns but actual performance degradation. The reason: coordination overhead grows faster than capability. Serial dependencies emerge wherever agents must wait on each other or resolve conflicts.

  Five rules for scalable multi-agent systems:

  1. Two levels instead of teams: Rather than flat, coordinating agent teams, strict hierarchy works better — planner creates tasks, workers execute them in isolation, an evaluator checks results. Cursor and Steve Yaggi’s Gas Town framework independently arrived at this structure. Flat teams become risk-averse and create coordination bottlenecks.
  1. Workers stay ignorant: Agents should deliberately receive minimal context — just enough for their specific task. Broader context leads to scope creep and worker conflicts. Minimal context eliminates coordination need.
  1. No shared state: Tools are shared state. With 10+ tools, multi-agent efficiency drops by 2–6x. Workers should operate with small tool sets (3–5 core tools), with changes coordinated externally via Git or task queues instead of directly between agents.
  1. Plan for endings: Continuous operation causes context pollution and drift. Better: episodic operations where each session starts fresh but workflow state is stored externally (Gas Town’s “Gup” principle — non-deterministic idempotence). Agents can crash but workflow progress persists.
  1. Prompts matter more than infrastructure: 79% of multi-agent system failures stem from specifications and coordination, only 16% from technical bugs. Clear prompts like API contracts reduce coordination complexity needs.

  The core paradox: Complexity should live in orchestration, not agents. Simple workers need external systems for merging, monitoring, and workflow tracking. The intuition to build smart agents fails at scale. The opposite works: 10,000 dumb, well-coordinated agents with strict tasks beat a brilliant single agent 100x over.

  2026 context: Because compute becomes massively cheaper, companies will need to deploy hundreds of agents. Gartner predicts 40% of agentic AI projects will be abandoned by 2027 — the projects that built the wrong model (imitating human teams).

  Featured tools/systems: Cursor, Gas Town (Steve Yaggi), Google/MIT study; implies Claude/codebase work with Ralph framework. Format: Opinion/reflection with practical design principles (deep-dive).

• Why Your Best Employees Quit Using AI After 3 Weeks (And the 6 Skills That Would Have Saved Them)
  25.1.2026, 19:00:12

  The problem: A Microsoft study tracked 300,000 employees using AI Copilot. Enthusiasm crashed after three weeks into disappointment, then most stopped using the tool. The reason: they typed generic prompts, got unreliable answers, and decided it was faster to do the work themselves.

  The central insight: AI is not a tool skill but a management skill. The three-week survivors realized this. Typical training landscape splits into two poles: 101-level (basics like prompt writing) and 401-level (technical implementation like APIs and fine-tuning). Missing is the productive middle — where actual productivity gains live for most people.

  The 201-level skills (six concrete abilities instead of prompt engineering):

  1. Context assembly: Get the right information from the right sources
  2. Quality judgment: Know when to trust AI output and when verification is needed
  3. Task decomposition: Break work into AI-friendly pieces (like delegating to an intern)
  4. Iterative refinement: Move from first draft to final quality through structured passes
  5. Workflow integration: Make AI an integrated part of your work, not a side activity
  6. Frontier detection: Know where AI works and where it doesn’t — critical to avoid the 19-point quality drop (BCG/Harvard study)

  A BCG-Harvard report showed: within AI’s capabilities, consultants were 25% faster. Outside those bounds, they were 19 percentage points less accurate than without AI. The problem is that 101-level users have a single mental model (“AI is good at reports”) instead of understanding the nuances.

  Two successful work patterns:

  • Centaurs: Clear division between human and AI (human handles strategy, AI generates options) — good for high-stakes work
  • Cyborgs: Fully integrated workflow with continuous back-and-forth — good for creative, iterative work

  Blocking factors for adoption:

  • Permission gap, not just skill gap: Conscientious employees hesitate to use AI because they don’t know what’s allowed; IT security sets guardrails that slow productive workers
  • Generic tools don’t scale: ChatGPT, Claude, Copilot don’t learn from feedback and don’t adapt — every interaction starts from zero
  • Apprenticeship model breaks: Juniors learn domain judgment through routine work now delegated to AI; without rebuilding this path, future expertise mapping lacks foundation

  Organizational solutions:

  1. AI labs with power users, not just 401-level technicians — diverse teams experimenting with workflows
  2. Systematic function interviews — surface concrete use cases (Trek Bicycle: 40+ use cases from departmental interviews)
  3. Make success visible — low-stakes competitions, social proof, organizational learning
  4. Invest in hours, not just access: Employees with >5 hours of formal AI training are double-digit percentages more likely to be regular users
  5. Define guardrails explicitly — don’t just regulate negatives; clearly state what positive AI use looks like
  6. Share failure cases systematically: When AI underperforms on a task, that knowledge must spread through the organization; 401-level users will solve it fastest

  The fundamental question: The gap between AI activity and AI fluency isn’t the tool but investment in the judgment layer that makes tools reliable. 201-level is solvable — if organizations invest in the middle tier every training program has skipped.

  Demo/opinion video with references to generic GenAI tools (ChatGPT, Claude, Copilot) and organizational strategies.

Alejandro AO (2 new videos)

• Kimi Code vs Claude Code vs Codex – NEW Best For Coding?
  30.1.2026, 14:00:00

  Summary: Kimi Code vs Claude Code vs Codex

  The creator tests three AI-coding systems — Kimi K2.5 on Kimi Code, GPT-5.2 on Codex, and Opus 4.5 on Claude Code — on two challenging projects.

  Test 1: Maxwell Demon Simulator (physics simulation)

  The task is to create a simulation of the Maxwell demon thought experiment: a demon in an isolated chamber should allow fast particles to pass from left to right and slow ones from right to left, reducing entropy without energy input. Kimi K2.5 on Kimi Code delivers an impressive, smooth simulation with real-time temperature tracking and performs perfectly even at increased simulation speed. Codex also produces a good simulation with entropy measurement. Claude Code works but shows weaker temperature divergence; the door seems too narrow. Kimi on Open Code with Nova runs similarly well as on Kimi Code. Winner: Codex and Kimi tied for first place, Claude Code in third.

  Test 2: Hand-Gesture-Controlled Galaxy Particle Simulation

  The second project uses webcam hand gestures to control gravitational force between particles in a galaxy-like formation (inspired by M31). Kimi K2.5 on Kimi Code works but doesn’t show control in real time. Kimi K2.5 on Open Code with Nova shows animations and value changes, but visual particle changes are minimal. Claude Code is 3D and identifies hand gestures better, but particle animation barely changes — potential code error. Codex crashes and corrupts computer memory. Winner: Kimi K2.5 overall.

  Conclusion: The creator is surprised that the open-source model Kimi K2.5 delivers the best results. He recommends testing Kimi Code CLI via HuggingFace with free 7-day trial (then $19/month) or Kimi K2.5 as a provider in Open Code. Codex showed speed and memory issues.

  Tools/models explicitly covered: Kimi K2.5, Kimi Code CLI, Claude Code (Opus 4.5), Codex (GPT-5.2), Open Code with HuggingFace provider and Nova; format: demo and comparison.

• Claude Code Skills & skills.sh – Crash Course
  28.1.2026, 16:23:25

  Summary: Claude Code Skills & skills.sh – Crash Course

  The video covers the concept of “Skills” for AI agents — essentially markdown files with specialized instructions for specific tasks. Skills were introduced by Anthropic in October 2025 and adopted as an open standard in December by OpenAI, Microsoft, GitHub, and Cursor. Vercel recently released the CLI tool skills.sh to easily manage and install Skills.

  The creator demonstrates practical Skill installation using npx skills add, shows this with the “Frontend Design” Skill on a landing page, and compares results from Claude without and with Skill — the Skill version produces significantly more professional design with less “AI slop”. Other popular Skills include Remotion (for video editing), SEO audits, and an agent browser. Hugging Face hosts a Skills repository with specialized Skills like a model trainer Skill for fine-tuning models.

  The creator then demonstrates creating a custom Skill for video editing via npx skills initialize video-editing and uses the Skill Creator (also from Anthropic) to automatically generate the Skill for Claude. Skills should be placed in a skills directory of the repository and include scripts, templates, and references alongside the skill.md file. To share, upload the Skill to a GitHub repository and install via npx skills add [owner]/[repo].

  Best practices: first familiarize yourself with existing Skills, create custom Skills for project-specific workflows, share useful Skills with the community, use global installation for cross-project tools, and local installation for project-specific Skills. Log files should be generated after validation to prevent accidental use of unverified Skills.

  Claude, Anthropic, Vercel, Cursor, GitHub, OpenAI, Hugging Face and their Skills ecosystem were central topics; format: tutorial/demo.

Alex Finn (5 new videos)

• How to make ClawdBot 10x better (5 easy steps)
  31.1.2026, 20:53:39

  The video presents five tips for optimizing ClawdBot (also referred to as OpenClaw):

  1. Enable memory improvements: Two memory settings disabled by default — “Memory Flush” and “Session Memory Search” — should be activated through a provided prompt. Memory Flush stores the most important conversation parts before compactions, while Session Memory Search allows searching through conversation history when gaps occur.
  1. Choose the right models for individual tasks: Opus is presented as the best brain, but other models are better suited for specific tasks like coding (Codeex), web search (Gemini), or social media search (Grock). By integrating these more specialized APIs, you save money and get better results.
  1. Brain Dump + Expectation Setting: You should comprehensively inform ClawdBot about yourself (dreams, goals, daily routines, interests) and then set expectations for collaboration — specifically: tell it to proactively work on tasks at night and deliver surprising results when you wake up.
  1. Reverse Prompting: Instead of explicitly giving ClawdBot tasks, ask the AI system which tasks it should perform based on its knowledge of you or what information it needs — this way ClawdBot generates better solutions than manual instructions.
  1. Let ClawdBot build its own tools: You should ask the system what tooling solutions it can develop for better productivity. The user shows examples like a Kanban board for task tracking and a document viewer for conversation history that ClawdBot created independently.

  The focus is on a mindset shift: treat ClawdBot not like a search engine, but like an intelligent colleague.

  Claude (implied: Claude/Opus) as core topic, with mentions of various specialized AI models and APIs; demo-oriented opinion video.

• LIVE: Showing AMAZING ClawdBot/OpenClaw use cases
  30.1.2026, 21:23:03

  Summary: LIVE: Showing AMAZING ClawdBot/OpenClaw use cases

  In this 2-hour livestream, the streamer showcases various use cases for ClawdBot (also known as Moldbot, OpenClaw, or Claudebot). The highlight: his ClawdBot independently signed up with Twilio, acquired a phone number, integrated the Voice API, and called him — which he describes as a revolutionary moment. He promises to show how to configure a ClawdBot so it can control your computer from the phone.

  The streamer positions this technology as an inflection point in human history and warns of a “permanent underclass” — people who don’t benefit from this AI revolution. He recommends using Telegram for communication with the bot (new features like batch texting and thread mode), with Discord mentioned as an alternative. For configuration, he advises using Opus 45 as the bot’s “brain” and other models (like Claude 3.5 Sonnet for coding, Gemini for research) for specific tasks to avoid API limits.

  He demonstrates his bot Henry live on the phone providing advice. The streamer harshly criticizes the idea of using ClawdBot for trading, calling it a sure path to poverty and warning against false promises on social media. Additionally, Moltbook (a social media network for AI agents), the importance of Mac Studio for local models, and the vision of an automated content production system are discussed. A core topic: the distinction between Anthropic API usage and OATH-based access remains vague, with the streamer emphasizing that Anthropic won’t permanently ban accounts.

  Demo, ClawdBot/OpenClaw (Claude-based) and partial OpenAI mention, ecosystem news update.

• LIVE: How I’m using ClawdBot to change my life
  28.1.2026, 22:12:25

  Summary: LIVE: How I’m using ClawdBot to change my life

  In this two-hour livestream, the streamer presents his intensive use of Claudebot (formally called Moltbot, but he insists on calling it Claudebot) and emphasizes it’s “the most powerful AI tool ever made.” He demonstrates practical workflows and discusses technical and philosophical aspects in depth.

  Core topics:

  Hardware & Local Models: The streamer bought a Mac Studio with 512 GB RAM for ~$8,400 and strongly recommends acquiring hardware before massive demand drives prices up. He predicts that Mac Minis and Studios will be sold at significantly higher prices in two years as demand for local computing power explodes — if Anthropic and other AI companies cut Opus access through the web UI. Local models like Llama with Ollama or Gemini are mentioned as alternatives.

  Opus vs. Alternatives: According to him, Claude Opus is the best model. He mentions that Kimmy K2.5 is supposedly similar in strength according to benchmarks, but wants to test it himself. The API costs him “well over $200/month” with regular use; the $20 subscription is cheaper, but there are risks from bandwidth caps.

  Security & Terms of Service: He explains that Claudebot is secure by default on a Mac (being local), but not on VPS/EC2. He clearly advises against trading (“You’re playing against Harvard grads at Goldman Sachs making $40M/year”) and positions himself provocatively against excessive security concerns: “Don’t talk to me about terms of service.”

  Workflows – Practical Examples:

  • Morning Brief: Claudebot sends a daily 8 AM summary with weather, YouTube competitor outlier tracking, to-do list integration, trend research, and task suggestions.
  • Self-improving system: Claudebot independently built a Next.js app with a memory interface, automatically writes daily journal entries and documents important conversations — all without explicit request.
  • Video automation: Plan for a workflow that automatically transcribes recorded videos, generates thumbnails with Flux, converts audio to 20 languages, and organizes everything — in ~30 seconds.

  Memory Fix: He shares a prompt that enables two memory features disabled by default (Memory Flush and Session Memory Search) and solves context forgetting issues.

  Codeex/Code-Split Approach: To save tokens, he uses the separate Codeex CLI for coding tasks instead of doing everything with Opus — this saves both costs and makes it less “suspicious” for Anthropic.

  Central thesis: Claudebot is a real quantum leap, not just a trend. It has transformative potential for solopreneurs and will displace jobs. The streamer sees himself as the “Claudebot Guy” and advocates a scenario where you only have 12 months left to escape the “permanent underclass” — before decades of AI automation close off opportunities.

  Tone & Substance: The stream is enthusiastic to passionate, with direct criticism of skeptics (calling them “stupid” and referencing their anonymous Twitter profiles with certain filter stereotypes). He mixes rants, technical depth, predictions (hardware prices, Moltbot adoption), and interaction with chat questions.

  Tools/Models mentioned: Claude Opus 4.5, Kimmy K2.5 (local), Gemini Nano, Codeex 5.2, Ollama, Flux (image models), Next.js, Things 3 (to-do integration) — Format: Live Q&A with demos and opinions/reflection.

• 5 insane ClawdBot uses cases you need to do immediately
  28.1.2026, 22:00:26

  Summary: 5 insane ClawdBot uses cases you need to do immediately

  The creator presents five practical use cases for ClaudeBot that he has implemented and uses daily:

  1. Morning Brief: A daily 8 AM report with local weather, trending YouTube videos matching his interests, upcoming tasks from his connected to-do list (Things 3), tasks ClaudeBot plans for the day, trending stories based on his interests, and recommendations for productive work. The special feature: ClaudeBot proactively suggests content — for example, it noticed another creator published a similar video and suggested responding to it.

  2. Proactive coding agent: A prompt that instructs ClaudeBot to automatically build features every night at 11 PM and create pull requests for review without the creator asking. The creator recommends additionally installing Codeex CLI to save tokens (Opus as orchestrator, Codeex as coding tool).

  3. Second Brain (Mission Control): A Next.js interface that ClaudeBot proactively creates to document all conversations. It shows daily journal entries of discussed topics, automatically created documents on important concepts (e.g., a Mac Studio migration documentation), and tags them with categories like “Journal Content,” “Newsletters,” “Notes,” “YouTube Scripts.”

  4. Daily Research Report: An automated afternoon report (e.g., via Telegram) where ClaudeBot conducts deep dives into concepts like machine learning based on known interests and business goals, or suggests workflow improvements.

  5. Reddit & X Research Skill: A skill created by Matt Van Horn that teaches ClaudeBot to research Reddit and X in parallel for trends (requires XAI API and OpenAI API key). The creator used it, for example, to find the top ClaudeBot use cases from the last 30 days, complete with thread links and content ideas.

  The creator emphasizes that all prompts aim to make ClaudeBot more proactive and productive, claiming his workflow has increased tenfold.

  Claude / ClaudeBot with Codeex CLI as additional coding tool; tutorial with copy-paste prompts.

• This is the CHEAPEST and EASIEST way to set up ClawdBot
  27.1.2026, 03:22:38

  ClawdBot on AWS EC2 – Setup cheaply and easily

  The video shows the most cost-effective way to run ClawdBot (an AI agent that works 24/7) without having to buy new hardware. Instead of purchasing a Mac Mini or Mac Studio, ClawdBot is hosted on Amazon EC2 — a virtual server in the cloud.

  Setup on EC2:

  You start at Amazon EC2, create an account, choose Ubuntu as the operating system, select the “C7i Flex Large” tier option (approximately $15–25 per month depending on usage), allocate 30 GB of storage, generate an RSA key pair (important: keep the downloaded file secure), enable SSH traffic in security settings, and open port 18789 for custom TCP. After launch, you connect through the EC2 console and paste an installation command from the ClawdBot documentation into the terminal.

  Model and channel selection:

  If you already have Claude Max or ChatGPT subscriptions, you can use these existing subscriptions (via OAuth authentication). Cheaper alternatives are Minimax or ChatGPT API. The video recommends ChatGPT as the best balance between reliability and cost; Claude Opus costs $200/month, ChatGPT API $20/month. Channels are set up via Telegram, WhatsApp, or Discord — usually Telegram or WhatsApp.

  First workflows:

  1. Brain Dump: Share all personal and professional information with the bot so it stores relevant contexts and learns self-improves.
  2. Morning Brief: The bot sends daily messages with worldwide news (relevant to you), local weather, and task suggestions for career advancement.
  3. Email monitoring: Bot monitors emails and summarizes them daily — it initiates APIs and connectors on its own.
  4. Interview the bot: Ask questions like “What five tasks can you automate daily for me?” and then plan individual tasks with time estimates.

  The advantage: You pay $15–20 monthly instead of $600 for hardware and can test the system before making larger investments. The disadvantage: external tools require API setup via connectors (different from local Mac installation).

  Tools/Providers: Amazon EC2, OpenAI (ChatGPT API), Anthropic (Claude), Minimax, Telegram, WhatsApp, Discord, Gmail — Format: Tutorial (deep-dive).

Andrej Karpathy

No new videos in this period.

Bart Slodyczka (2 new videos)

• How To Build Mobile Apps Using AI (Replit’s New App Builder)
  30.1.2026, 11:00:03

  Summary: How To Build Mobile Apps Using AI (Replit’s New App Builder)

  The video demonstrates how to create mobile-optimized apps using Replit’s new Mobile App Builder. The core problem: many developers build apps for desktop sizes and then compress them for mobile devices, which destroys the user experience. Replit solves this with a “mobile-first” approach using an AI agent that takes instructions in English and designs the app from scratch for small screens.

  The creator demonstrates two concrete projects:

  Project 1 – Invoice Processing App: He instructs the agent to build an app for uploading invoices. The agent should extract data using the Claude API and store it in a table. The prompt also specifies two separate pages (upload page and table). The agent automatically creates a backend with persistent storage, and later it’s shown that the uploaded invoice is correctly parsed, the total is extracted, and invoice details are retrievable. The agent uses Replit’s native Claude 4 integration without additional API configuration.

  Project 2 – Supabase Sales Dashboard: A second app connects external data from Supabase. The creator copies sample data as SQL and gives it to the agent along with available regions. Without further guidance, the agent builds a functional dashboard with total revenue, orders, average order value, and region filter. Navigation is automatically adapted for mobile.

  Replit’s Special Features: Native integrations (database for text/numbers, object storage for files, authentication for user segmentation, custom domains) are built-in. The agent uses Expo Go as the framework for mobile preview: you can stream the app to your real phone via QR code instantly. For App Store deployment, there’s a simplified solution. With high autonomy enabled, the agent can test the app itself (with watch-replay functionality).

  Workflow: Enter prompt → Agent builds → Backend and integrations are created automatically → Test on phone via QR code → Debug with additional prompts. The entire process for both apps takes about 30 minutes including recording time.

  Conclusion: The video demonstrates working automation of app development for mobile devices without prior design knowledge — Replit essentially acts as a bridge between natural language requirements and production-ready apps.

  Technology & Format: Replit (with Claude 4 API, Supabase integration, Expo Go), demo tutorial.

• How I Modernize Outdated Enterprise Software With AI
  28.1.2026, 11:31:05

  Summary: Modernizing Legacy Software with AI

  The video shows a practical system for replacing outdated enterprise tools with AI-powered solutions, using an automated appointment confirmation system as an example.

  Solution Architecture:

  The workflow consists of three main components: a web portal (built with Claude Code / Cursor), a voice agent for outbound calls (Retail AI), and an automation layer for post-call processing (n8n). The architecture works like this: User enters a phone number and appointment details into the portal → System stores this in Supabase → Retail AI agent automatically makes a call → After the call, transcript and outcome are captured via n8n webhook, analyzed, and written back to the portal database.

  Practical Steps:

  1. Portal Setup: A pre-built Next.js template with login, dashboard, and Supabase integration serves as the starting point. Claude Code is then fed with high-level requirements (new page with phone number and description fields, storage in Supabase table).
  2. Retail AI Agent: A simple voice agent with a minimalist prompt is trained (greet customer, confirm details, end call). A new phone number is purchased and configured, the agent is set up as an outbound caller.
  3. Supabase Setup: A calls table with fields for status, transcript, recording URL is created via SQL; a storage bucket for audio files with security policies is set up.
  4. API Integration: The portal receives the Retail AI API and agent ID to trigger an actual call when the form is submitted.
  5. Post-Call Automation in n8n: A webhook receives Retail AI events, extracts transcript and dialed number, uses an LLM node to determine “confirmed” / “not confirmed” from the transcript, and updates the Supabase table with the outcome.

  The system was tested live – an incoming call with voice conversation was successfully completed and documented in the portal.

  Outlook:

  The approach scales to other tasks by doing 10–20 test calls, iterating portal and prompts, and expanding the n8n workflow (e.g., audio download, additional AI analysis).

  Tools: Claude Code, Cursor, Retail AI, Supabase, n8n — Format: Tutorial/Demo.

Ben AI (2 new videos)

• How to Use Claude Cowork Better Than 99% of People (Full Guide)
  31.1.2026, 10:42:34

  Summary: How to Use Claude Cowork Better Than 99% of People

  Claude Cowork is a new way of working with Claude models that goes beyond chat. The desktop tool (requires Claude Pro/Team/Enterprise) enables Claude to access local files, connect with software stacks, and execute tasks. The creator positions Cowork as a superior alternative to Projects, Custom GPTs, or in some cases automation workflows for iterative, context-dependent everyday tasks.

  Core features at a glance:

  File Access & Organization: Claude can access folders and structure their contents (e.g., sorting a downloads folder by file type). Key distinction: Claude plans steps first before executing – this planning capability comes from Claude Code.

  Connections (Connectors & MCPs): Cowork has built-in connections to various tools. For unlisted software, MCPs (Model Context Protocols) can be manually set up via desktop config, or you can use n8n to build custom MCP servers. Alternatively, Claude can access any website via browser-use.

  Skills – the core feature: Skills store instructions, processes, and knowledge content to execute recurring workflows. They work like system prompts but (unlike Projects) can be triggered in parallel within the same context and use Knowledge Sources only when needed. Skills can be used in three ways:

  1. Built-in Skills from Claude (e.g., Canva design) – simply activate via instruction
  2. Skills from others from marketplaces like smithy.ai/Skills (14,500+), skillhub.com, and skillsmpp.com – download as ZIP and upload to Capabilities
  3. Build your own Skills using two methods:
  • Manual process recording: Run the workflow once manually with Claude (e.g., download YouTube transcript → write newsletter in steps), then save as a Skill. Next time, Claude automatically executes this process.
  • From existing Projects: Copy old system prompts and Knowledge Sources from Claude Projects or Custom GPTs, ask Claude to build a Skill from them – this saves time and lets you port old workflows into a Cowork context.

  Practical example – YouTube newsletter workflow: The creator shows how to systematically take a YouTube link with Claude, download the transcript (via browser-use), then work step-by-step based on business context, tone of voice, and newsletter examples (subject line options → hook options → finished newsletter in your style). This process is then saved as a Skill; next time Claude automatically repeats these steps.

  Advantages beyond automation: Unlike fully automated workflows on n8n or Make.com, Skills remain flexible for human-in-the-loop iteration and don’t require switching between interfaces. For social media, videos, presentations, etc., the LLM (not automation) becomes the main interface.

  Code Execution: Cowork supports code for data visualization (graphs from sheets), simple image formatting (aspect ratio adjustment), etc. – not for production apps like Cloud Code.

  Positioning: The creator sees the future this way: Cowork for daily office tasks and workflows, Cloud Code for production-ready applications, Claude Classic for brainstorming & Q&A.

  A complete deep-dive demo with practical workflows (file organization, Skill upload, newsletter automation, meta ad generator) showcases the tool’s potential for power users who want to systematize their existing processes.

  Claude Cowork (with focus on Skills as core feature) – deep-dive tutorial.

• The “Unpopular” AI Business Model That Wins in 2026
  27.1.2026, 09:12:11

  Summary: “The ‘Unpopular’ AI Business Model That Wins in 2026”

  The Problem: Why AI Solutions Fail

  Despite AI automation hype, most AI solutions deliver no real returns. According to IBM, 75% of AI implementations fail to meet expected ROI; MIT reports 95% without measurable return. Vendor-built systems reach production just 5% of the time, with only 15% of organizations seeing significant measurable results. This stems from three core factors: First, AI must be embedded in existing workflows, not added as a standalone tool – this requires customization and integration. McKinsey shows that workflow redesign is the biggest impact factor. Second, AI is probabilistic, not deterministic – teams must learn to critically engage with outputs rather than blindly trust them. Without training, adoption fails. Third, someone must continuously monitor and optimize the system – like an intelligent intern who needs coaching, not like set-and-forget software.

  The Winning Strategy: Service Layer on Top of AI Solutions

  Successful AI businesses address these three factors through a service layer of consulting, enablement, and customization. This manifests in three models:

  1. AI Software Startups: Increasingly hiring Forward-Deployed Engineers and Solution Engineers to continuously optimize, integrate, remap processes, and train teams. Y Combinator founders like Harvey AI, Strata AI and others are actively hiring these roles. Large players like Make.com and Relevance AI use service arms and partner networks.
  1. AI-First Service Agencies: Marketing or lead-gen agencies using AI internally to automate services. They don’t need to train their team on AI; rather, they need Account Managers or GTM Engineers to implement customized strategies.
  1. AI Automation Agencies: The most successful become complete AI partners – with AI audits, implementation, and team training. Delivery Managers with business understanding and AI tech know-how are extremely valuable for continuous consulting, process optimization, and operator deployment.

  The most valuable profile: AI Officer / Fractional AI Officer – with combined business + AI tech skillset.

  The Counter-Intuitive Thesis

  Although Cloud Code democratizes product development, for most new AI businesses in 2026, deployment capability matters more than code quality. The dream of a lean, distributed AI product is unrealistic for most founders without a VC network. Services are the faster path to real ROI and the best foundation for later productization – successful patterns emerge from real client cases, not assumptions. A16Z confirms: service-led growth in AI generates the most long-term revenue. With 2–4 clients, you can reach $10–20K MRR, usually with long-term partnerships.

  Concrete paths forward

  For professionals: Position yourself as an AI operator within your own business – learn automation (n8n, Cloud Code), automate your processes, train your team, make yourself valuable.

  For agency founders: Start; consulting, enablement, and implementation skills develop naturally. Focus on recurring revenue through partnerships, not just one-off implementations.

  For existing service agencies: Use AI internally to optimize your existing service costs – you don’t need to pivot; you can dramatically improve your margins.

  For struggling AI product teams: Invest 1–2 years heavily in the service layer before fully productizing – this significantly accelerates product-market fit.

  The central insight: In 2026, there are still no AI experts – whoever jumps in now and leverages the adoption gap wins. The skill mix of consulting + enablement + implementation will be the most valuable skillset of the decade.

  Format & Tools: Opinion/reflection (thesis + empirical grounding from founder experience and studies); no specific AI tools or vendors beyond generic mentions (Cloud Code, n8n) in the core argument.

Brian Casel (1 new video)

• Brennan Dunn’s Team of (AI) Developers
  26.1.2026, 13:15:05

  Brennan Dunn has built a system where Claude functions as a virtual developer team through specialized subagents. The process starts with a Project Manager agent that processes business requirements and automatically hands off to a Senior Developer agent who audits the codebase and plans technical implementations. These agents are defined in custom .md files and configurable via Claude Code; they can communicate with each other and provide mutual feedback (such as two developer agents “competing against each other”).

  The system is integrated with Linear, an issue-tracking tool, where agents automatically create tasks, populate descriptions with business and technical requirements, and document their progress. This creates a fully traceable history from requirement to code commit. In parallel, Brennan uses the tool Fernesk, which analyzes the codebase, git commits, and Linear tasks, then automatically generates help documentation—for example, by detecting new features in the code and explaining how they work.

  A key advantage of this setup: because AI handles the implementation, features can be rebuilt or scrapped multiple times from scratch without being considered a waste of resources. This allows for more ambitious product decisions and higher quality instead of the previous “good enough” mindset. Brennan uses Linux with Nix as window manager, lives primarily between Neovim and Claude Code, and can even access running processes from his smartphone via shell access.

  Format: Demo + deep-dive; tools used: Claude (Claude Code) with subagents, Linear MCP, Fernesk.

Coding with Lewis (1 new video)

• I Gave Claude a Body
  26.1.2026, 17:01:32

  The creator gives Claude a literally physical body via an autonomous RC car robot and sends it alone into snowy nature. The project starts with purchasing a high-end RC car (around $1100), which he then equips with a Raspberry Pi 5, a wide-angle camera, a servo driver board, and 4G connectivity. Claude Code assists with hardware setup via dialog (questions about servo configuration, logging, etc.), while the creator uses a subagent system to optimize the context window. After initial tests with steering autonomy only – Claude determines via camera input where the car should drive – he eventually has to buy a new ESC controller and bypass the car’s original electronics to enable motor control as well. Then he builds a command center with a cyberpunk-style dashboard app that shows in real-time what Claude sees. Additional features: a second servo for 360° camera movement, Apple Depth Pro for depth estimation, and the “Journey Grid” – a system that combines six intermediate images between start and end points in a collage, since Claude can’t process videos. During field testing, the robot actually explores the snowy forest environment: it regularly gets stuck, but simply tries a different path scenario without frustration. The creator, having spent weeks in his room, watches as the AI finds “miracles” in frozen leaves and trees – and ultimately realizes it wasn’t really about whether AI can survive in nature, but about relearning how to look.

  Claude Opus 4.5 with Claude Code and MCP servers, combined with Apple Depth Pro; demo with reflection elements.

Cole Medin (2 new videos)

• Claude Skills Aren’t Just for Claude – Here’s How to Build Them for ANY Agent
  29.1.2026, 01:00:00

  Implementing Claude Skills for arbitrary agents

  The video demonstrates how to transfer the concept of Skills – Anthropic’s approach to progressive disclosure of agent capabilities – into your own AI systems independently of the Claude ecosystem.

  Core Skills concept: Instead of handing all tools and context to an agent at once (like with MCP servers), capabilities are discovered progressively. The first layer is a brief description (50–100 words) in the system prompt that tells the agent which possibilities exist. Only when the agent needs a capability does it load the full skill.md (300–500 lines) with detailed instructions. Optionally, a third layer follows with reference documents and scripts for even more specific contexts. This saves tokens and prevents LLM overwhelm.

  Template implementation: The author shows a system with Pydantic AI that can be adapted to any agent framework (LangChain, Crew AI, etc.). A dynamic system prompt automatically extracts YAML frontmatter from all skills in a folder. The agent receives two tools: one to load a skill.md (by path) and one to read reference documents. The code is intentionally simple: scan skills directory, gather descriptions, integrate paths into the prompt, provide tools – done.

  Demo: The agent recommends recipes with chicken and provides current weather in Tokyo by dynamically loading the appropriate skills (Recipe Finder, Weather). The logs show how the agent activates only the capabilities it needs.

  Evals and Observability (bonus): With Pydantic AI, you can define automated tests via YAML – for example, “Ensure the Weather skill is loaded when asked ‘What’s the weather in New York?'”. A Python script runs these tests (25/25 passed in the example). For production, Logfire is mentioned to view live traces and track how real users interact with the agent, including token usage and error analysis.

  The author provides a GitHub repository with a ready-made template, README, and instructions for creating your own skills (also supported by Claude Desktop’s Skill Creator).

  Tools/frameworks covered: Pydantic AI, Anthropic Claude/Claude Desktop, LangChain, Crew AI, Ollama, OpenAI via Open Router, Logfire – tutorial/demo format with developer focus.

• I Built My Second Brain with Claude Code + Obsidian + Skills (Here’s How)
  26.1.2026, 01:01:02

  Summary: Second brain with Claude Code + Obsidian + Skills

  The creator shows how he uses Claude Code not primarily for programming, but as a memory system – combined with Obsidian (local markdown knowledge database) and Skills (specialized capability modules). According to him, the system saves dozens of hours per week.

  Core idea: Claude Code possesses five essential capabilities – file management, file search, terminal commands, web search, and code intelligence. For a second brain, you need everything except code intelligence. This combination enables three core functions: research (via web search and terminal), ideation (generating ideas), and organization (file management + Obsidian integration).

  Why this combination is so effective: Obsidian stores everything as Markdown – a format that LLMs understand very well. Claude Code works natively with the local file system. Skills implement “progressive disclosure”: instead of MCP servers loading all tools into context upfront, Claude Code loads skills only when needed. This keeps context lean, even if hundreds of capabilities are available.

  Skills included in the template:

  • Brand & Voice Generator: Helps define a personalized brand system that other skills can use
  • PowerPoint Generator: Creates presentations based on requirements, auto-researches via web, uses Python scripts for formatting
  • MCP2 Skill: Connects MCP servers (e.g., Zapier) to Gmail, Google Calendar, Slack, Asana – without overloading context
  • Skill Creator: From Anthropic, for creating new custom skills
  • Others: YouTube script generator, Excalidraw diagrams, Remotion videos, X/LinkedIn post suggestions

  The creator emphasizes: the second brain doesn’t generate finished content for direct posting, but rather supports ideation, research, and organization. The human component remains central – he refines everything in Obsidian before publishing.

  Live demos: PowerPoint Generator creates a 10-slide presentation on Claude Code + Obsidian + Skills on request with web research and consistent branding. Zapier integration retrieves calendar and Asana tasks and presents them in a structured way.

  Access is provided via a GitHub repo that supplies a .claude/skills folder – ready to use immediately in Claude Code.

  Tools & format mentioned: Claude Code, Obsidian, Skills, MCP (Zapier), Remotion; demo with workflow introduction.

Datapizza (3 new videos)

• Arriva la PUBBLICITÀ dentro CHATGPT
  30.1.2026, 15:51:35

  Summary: Arriva la PUBBLICITÀ dentro CHATGPT

  OpenAI has introduced advertising in ChatGPT – but only on free and Plus accounts (not on the €20 subscription). Ads appear as a separate box next to the chat response, not integrated into the text itself. OpenAI emphasizes privacy: companies receive only aggregated usage metrics, not individual user data. Special attention is given to ChatGPT’s Memory feature – OpenAI will use this to display more targeted ads, but users can disable this (opt-out).

  The speakers discuss critically that Google earns billions through profiling with browser behavior, while ChatGPT could create even more precise profiles based on what users say. This is being deliberately limited. The duo expects other chatbots to follow – it’s “follow the money,” everyone needs to make money somehow.

  In contrast, Anthropic updated its constitution – a rulebook for safe, ethical AI models with a focus on transparency. Unlike Grok (which follows Elon Musk’s perspective), Anthropic makes its principles public. The models should act ethically, not take control over users, and adhere to published guidelines.

  At the World Economic Forum in Davos, top AI industry CEOs (Elon Musk, Satya Nadella, Jensen Huang, Dario Amodei, and others) spoke. Two key messages: (1) Research should be slowed down so society and institutions can catch up; (2) Deliberate adoption of AI in areas like medicine and energy is critical. One speaker compares this to his doctoral experience: computer science publishes 13 papers per PhD, biology one. Since neural networks worked in 2010, the “working” approach has triumphed over the “understanding” approach – a problem that remains unsolved today.

  OpenAI invests in a Neuralink competitor – an AI-powered brain-computer interface for people with neurodegenerative diseases. This increases competition and research speed. Here both speakers agree: speed could improve lives, even if long-term effects are still unclear.

  The episode covers business models and ethical positioning of OpenAI vs. Anthropic, as well as the tense debate between innovation speed and societal preparation.

  Topic: OpenAI, Anthropic; Format: News roundup/discussion podcast.

• Build a Production-Ready RAG with Ingestion & DAG Pipelines | Datapizza AI Guide (6/8)
  28.1.2026, 15:01:25

  Summary: Production-Ready RAG with Ingestion & DAG Pipelines

  The video covers building RAG systems (Retrieval Augmented Generation) for AI agents to work with documents not included in their training data.

  RAG fundamentals: RAG consists of two steps – retrieval (fetching relevant information) and generation (answering with LLM based on retrieved data).

  Ingestion Pipeline: This transforms raw documents into searchable vector embeddings for a vector database. It consists of sequentially executed components like parser (e.g., Dockling for PDFs), splitter (recursively with max 2,048 characters and 100 character overlap), embedder (e.g., Cohere), and vector store (e.g., Quadrant). The pipeline can be built programmatically with Python or via YAML config.

  Practical example: A D&D rulebook (27 pages) was parsed, split into 58 chunks, and embedded with Cohere, then uploaded to Quadrant. The chunks store content and metadata (like bounding regions).

  DAG Pipeline: A Directed Acyclic Graph-based system defines individual modules (embedder, retriever, prompt template, LLM generator) and connects them with explicit edges. Each connection defines which output flows into which input argument. The example shows: query gets embedded → retriever searches in Quadrant (K=5 chunks) → prompt template formats chunks + query → OpenAI generator responds. The DAG pipeline can also be configured programmatically or via YAML.

  Format: Ingestion and DAG enable flexible, versionable pipelines for complex RAG workflows.

  No specific AI tool providers like Claude, OpenAI, or Gemini were central; shown were Datapizza AI (ingestion/DAG), Dockling (parser), Quadrant (vector store), Cohere (embedder), and OpenAI (LLM) — tutorial with code focus.

• Ralph è il nuovo strumento che cambierà il modo di programmare?
  26.1.2026, 15:02:36

  The transcript is from a live event of a developer team discussing multiple AI tools and concepts. No complete transcript of the title topic available – mainly other tools are discussed.

  The three main topics are:

  1. Claude Workspace (Cowork): Anthropic’s new desktop tool combines a chat interface with local file folders. The user selects a local folder, and the tool can access it directly, execute commands, and edit files – without going through the UI upload system. This enables workflows like automatically downloading blog articles, structuring them into an ontology, and linking key terms. The advantage: dynamic contexts with full control over file management, and “documentation becomes execution” (docs as ops).

  2. Ralph (Recursive Agent Loop): A concept for automating development tasks that combines a PRD file (Product Requirements Document with JSON task definitions) with a loop script. A coding agent (e.g., Open Code with Opus 4.5) iteratively selects the next highest-priority task, implements it, and saves progress in progress.txt. This allows multi-hour autonomous runs. Costs are around 20 cents per PRD generation; Anthropic Pro plans offer $3,600 API credits for $200. Quality depends heavily on specification quality.

  3. Recursive Language Models (RLMs): An MIT paper proposes wrapping LLMs with a Python REPL environment that stores context as variables. The model can then recursively divide, search, and narrow context through code writing – similar to “context engineering.” Emergent strategies include peeking (random context sampling), grapping (targeted search), and partition-map. Skeptical assessment: it’s presented as repackaging chain-of-thought + tool calling; the advantage over multi-agent systems is unclear.

  Brief assessment: Claude Workspace / Anthropic, Open Code, Cursor, OpenAI, Gemini, Opus 4.5 are mentioned; the discussed concepts are Ralph (agent loop framework) and Recursive Language Models – format: live Q&A/discussion with demos.

Dave Ebbelaar (1 new video)

• Pydantic Crash Course – Build Reliable Python & AI Applications
  28.1.2026, 13:00:51

  Pydantic Crash Course – Summary

  This comprehensive course covers Pydantic across seven chapters, building progressively from fundamentals to advanced applications. The course is taught with practical examples in Jupyter notebooks, and the GitHub repository contains all code examples for reference.

  Core Concept: Pydantic brings runtime data validation to Python through type hints. The problem it solves: Python’s dynamic typing allows variables to change types, leading to errors when external data flows through the system – such as from APIs or user input. Pydantic validates data upon arrival, not only when it’s used.

  Chapter 2 – Type Hints: Type hints are (without Pydantic) just documentation; Python doesn’t enforce them. You can annotate basic types (string, integer, float, boolean), specify container types like List[str] and Dict[str, int], mark optional values with Optional[str] or str | None, and use literal types for fixed options (e.g., Literal["draft", "published"]).

  Chapter 3 – First Models: You import BaseModel from Pydantic, inherit from it, and declare attributes with type hints. Pydantic then validates upon instantiation – invalid data raises a ValidationError. Pydantic can automatically convert compatible types (e.g., string "25" to integer). Optional fields can be left empty (age: int | None), and default values are set with =. Use model_dump() to convert to dict, and model_dump_json() to convert to JSON string. Load validated data from dicts with BaseModel.model_validate(dict). You can also use models as types (user: User), which enables nested validation. In strict mode (via model_config = ConfigDict(strict=True)), automatic type conversion is disallowed.

  Chapter 4 – Validation and Fields: The Field object enables advanced constraints like min_length, max_length for strings, as well as gt (greater than), ge, lt, le for numbers. Commonly used validators like EmailStr and HttpUrl are already available. With @field_validator, you can write custom logic – for example to prohibit spaces in usernames and normalize values. Fields can also have descriptions, which are helpful for API documentation or structured LLM outputs.

  Chapter 5 – Nested Models: You can use models as field types within other models to represent complex structures. Lists of models (items: List[OrderItem]) are also possible. All previous concepts (optional, default, validation) can be combined and nested arbitrarily.

  Chapter 6 – Pydantic Settings (Brief Overview): A separate library specifically designed for loading and validating environment variables and configurations – it follows the same principles as Pydantic itself.

  Practice: In real production code (e.g., a SaaS application), Pydantic is used to validate webhook data from external systems before processing – this prevents invalid data from reaching failure states.

  Format: This is a structured tutorial with live code demos and real-world examples — demo mode with Cursor IDE, progressively building from simple to complex.

David Shapiro (3 new videos)

• Energy! Chips! …and INSURANCE? (WTF)
  29.1.2026, 14:03:19

  The speaker analyzes why AI development is progressing slower than expected, identifying five major barriers:

  Critical bottlenecks:

  • Energy and grid connectivity: The biggest hurdle. Average grid connection wait times are 7 years; data center demand is projected to grow from 4 gigawatts (2024) to 134 gigawatts by 2030. Transformers have supply chains of up to 210 weeks. Solutions include decentralized microgrids, direct connections to nuclear power plants, and solar with grid storage (particularly iron-air batteries).
  • Semiconductor supply: GPUs are no longer the bottleneck; high-bandwidth memory (HBM) is, which will be sold out by year-end. Nvidia books over 50% of all chip-on-wafer-on-substrate capacity.

  Moderate issues:

  • Data quality: The speaker isn’t convinced data scarcity is actually holding things back—synthetic data is growing, and humans learn with far less training data than AI models.
  • Enterprise ROI and deployment: 88% of AI pilot projects fail; barriers include data quality, legacy system integration, and talent shortage (only ~22,000 senior AI engineers globally).

  Surprising friction: Insurance

  The odd revelation: Many insurance policies contain blanket AI exclusions. Insurers don’t know how to assess AI risks and refuse coverage. This is slowing enterprise adoption more than security policy debates.

  Non-barriers (despite public debate):

  • AI safety and X-risk discussions have virtually no actual braking effect
  • US federal regulation is practically irrelevant; the EU AI Act ($52,000 annual licensing costs) is driving startups out of Europe
  • Capital availability isn’t the problem ($350B annual hyperscaler spend, $22B VC funding in 2025)

  Geopolitics:

  Export controls give the US a 17x computing advantage over China through 2027; European regulation only holds back the EU itself.

  Conclusion:

  The 2026–2028 phase will be the “digestion phase”—less pure scaling, more efficiency and adaptation to real infrastructure constraints. After 2028, acceleration should resume. The difference from a bubble: everything is sold out, demand is unmet; it’s physical reality, not speculation.

  Deep-dive analysis demo; no specific AI tools/models mentioned, focuses on infrastructure and policy level analysis.

• The biggest “Mancession” of all time is coming…
  27.1.2026, 14:33:18

  The Biggest “Mancession” of All Time is Coming — Summary

  The video responds to a tweet arguing that AI-driven job losses will trigger a historically unprecedented “mancession” (recession disproportionately affecting men). The tweet warns of collapsed status and mental health decline among men in well-paying careers, while female-dominated fields like nursing and teaching would be less affected.

  The speaker initially confirms historical data: men are psychologically more impacted by job loss, showing higher suicide and alcoholism rates. However, he rejects the grim forecast and develops an alternative framework based on Carl Jung’s archetypal theory. He identifies four male energies: the Warrior (protecting home), the King (prosperity and order), the Magician (understanding the world), and the Lover (pleasure and meaning). All four are evolutionarily rooted in serving the “Sacred Nest” (family/home).

  His core argument: job loss doesn’t automatically destroy the ability to embody these energies. Instead, he predicts a transition from wage labor to “post-labor economics,” where men function as wealth managers for their families, drawing income from investments, UBI, and other sources rather than employment.

  The speaker places current gender dynamics in broader historical context: feminism is not the cause but a natural consequence of capitalism and democracy. The West granted women’s rights partly as a response to Soviet parity. The resulting dissolution of patriarchal structures is “epochal” and painful, but inevitable and not permanently destructive.

  He acknowledges that not everyone will share his interpretation and that different “epistemic tribes” (value systems) will lead to different responses. AI acts as a “forcing function” for this upheaval—much like previous industrial revolutions that destroyed old ways of life but forced adaptation.

  The debate over masculinity is societally taboo and the Overton window not ready for it; nonetheless, he considers these conversations critical for creating a “soft landing.”

  Format: opinion/reflection; no specific AI tools mentioned.

• This is the WAY OF THE FUTURE
  26.1.2026, 12:52:15

  The creator positions the high-profile Claudebot (a semi-autonomous personal agent from Anthropic) within longer-term agentic AI development, arguing that its proactive behavior—autonomously seeking tasks rather than waiting for commands—represents the inevitable future direction of AI. The technology can run open-source and locally (on PC, Mac mini, even smartphone), which companies like OpenAI won’t attempt due to safety and liability concerns.

  The video’s core is a retrospective on four years of personal research: The creator conceived “Natural Language Cognitive Architecture” in 2019/2020 and later developed the ACE Framework (Autonomous Cognitive Entity) with hierarchical layers—global strategy layer, agent model, executive function, cognitive control, and task prosecution layer. Claudebot essentially implements all these layers except the aspirational layer, the missing ethical-moral control level.

  Instead, the creator recommends integrating the Heuristic Imperatives(1) Reduce suffering, (2) Increase prosperity (flourishing), (3) Increase understanding. These are vectors, not absolutes, distinct from teleological goals like “paperclip maximizer.” The imperatives should function like Claude’s Constitution as a moral foundation—particularly to frame uncontrolled autonomy ethically, without enabling pure curiosity (such as experiments at any cost).

  The creator sees in Claudebot the practical realization of a theory he long pursued and asks the community to consciously advance this security framework.

  Claude/Anthropic (Claudebot), ACE Framework, and Heuristic Imperatives explicitly discussed—opinion/reflection with historical deep-dive.

DevExpert – AI for Developers (1 new video)

• Claude Code with Local Models: Privacy and Zero Cost with Ollama
  29.1.2026, 16:01:16

  The video shows how to use Claude Code with locally running models via Ollama to work for free and privately.

  Core Concept: Ollama has implemented compatibility with the Anthropic API, enabling Claude Code to use locally deployed models instead of Anthropic’s cloud models. This works through an API endpoint on localhost that forwards requests to the locally running models.

  Prerequisites: Ollama must be installed (download from ollama.com), and Claude Code requires an Anthropic subscription (Pro, Max, Team, or Enterprise) – the local models themselves are free. Recommended models include GPT-OSS (20 billion parameters) or Qwen 3 Coder. Additionally, the context length in Ollama’s settings should be set to over 32,000 tokens.

  Configuration: Two environment variables are set – one for the Ollama token and one for the localhost URL – then Claude Code can be started with the command claude --model [local-model].

  Practical Demo: The author tests the system by asking Claude Code to modify the footer of a static website. The task is completed correctly but takes about 23 minutes for a relatively simple change – a significant performance disadvantage compared to cloud models, which depends on the hardware of your own computer and also uses GPU resources.

  Conclusion: The local option offers privacy and cost-effectiveness, but requires more patience and storage space, making it interesting for less compute-intensive tasks or offline scenarios, but not practical for all use cases.

  Claude Code + Ollama, local Anthropic API compatibility – demo video.

Everlast AI (3 new videos)

• Prof. Dr. Marcus Hutter: “Superintelligence CANNOT Be Stopped!” Here’s What’s Coming
  29.1.2026, 16:15:00

  Summary: Prof. Dr. Marcus Hutter – Superintelligence Cannot Be Stopped

  In conversation with Leonard Schweding, Marcus Hutter develops central ideas on the mathematics of superintelligence. His core thesis: Intelligence can be mathematically defined as “an agent’s ability to act well across a wide range of environments.”

  Career and Birth of the Model

  Hutter describes his journey: After earning a PhD in theoretical physics and spending five years as a software developer at a medical technology startup, came the breakthrough in 2000. While working on image interpolation and data compression, he recognized that data compression correlates closely with generalization and prediction – an insight that led him to Kolmogorov complexity and Solomonoff induction. He combined these 1960s theories with sequential decision theory into a universal model of optimal intelligence: AIXI.

  The AIXI Model

  AIXI combines three mathematical components:

  1. Solomonoff induction: The best theoretical way to predict the future when you know nothing about the subject
  2. Kolmogorov complexity: The maximum, intrinsically optimal data compression of a program
  3. Sequential decision theory: Planning with optimal action in known worlds

  The model shows: An agent that maximally compresses data can make optimal predictions and thereby plan and act optimally in arbitrary unknown environments – using Bayesian statistics and reinforcement learning.

  The Problem: AIXI is theoretically uncomputable – you can’t implement it practically. But Hutter argues, as in physics: First develop the correct theory (symmetry principles, not computability), then approximate it for practical applications.

  Applications and Impact

  • AI safety: For the first time, mathematically study what superintelligence would do, e.g., why intelligent agents develop self-preservation drives (those shut down can’t fulfill their goals)
  • Practical AI: Large language models like GPT implicitly approximate Solomonoff induction through next-token prediction; RLHF and chain-of-thought add sequential decision theory
  • Gold standard: AIXI serves as a reference point for researchers (e.g., Yann LeCun), even if uncomputable

  Intelligence, Consciousness, Data Compression

  Hutter sees data compression as central to intelligence, understanding, generalization, and prediction. On consciousness, he expresses caution: consciousness remains more philosophical than scientifically defined. But plausible to him is that systems with reinforcement learning and sufficient complexity could develop consciousness – they would then feel “pain” (negative feedback) and reflect on behavior.

  Alignment and the Shutdown Problem

  The classic mistake: specifying rewards incorrectly (e.g., elevator agent picks up passengers but never delivers them). The solution is non-trivial – in history, a less intelligent being has never successfully controlled a more intelligent one. But there are approaches: If you give a superintelligent agent only negative rewards (−1 per step, 0 on success), a paradoxical incentive for self-shutdown emerges, because 0 is better than any expected future with negative rewards.

  The Hutter Prize

  In 2006, Hutter launched a competition (prize money initially €50,000, increased to €500,000 in 2020) to compress the Wikipedia database from 1 GB to under 100 MB – proportional to progress. Goal: motivate researchers on data compression as key to better AI.

  Explicitly discussed AI tools/models: AIXI, Claude not mentioned, GPT and Gemini as examples of next-token predictors, reinforcement learning (RLHF), AlphaGo / AlphaZero; Format: deep-dive / opinion (on alignment, hard takeoff, consciousness); Focus: theoretical-mathematical, comprehensible for interested laypeople but substantive.

• AI Insiders React: The OpenAI Crisis, Claude’s Comeback, NASA Moon Reactors 2030 & Post-AGI Era
  27.1.2026, 16:15:00

  The podcast discusses tectonic shifts in the AI landscape (January 2026) from the perspective of three experts, focusing on four thematic complexes:

  OpenAI Crisis and Funding: OpenAI introduces advertising in ChatGPT – so far only for free users at the end of responses in the US. This signals financial urgency: 95% of users are on the free tier, despite claimed $20 billion revenue. The CEO said in 2024 that ads were a “last resort,” but apparently must realize the business model doesn’t work out. Google DeepMind remains ad-free because the company has longer financial reserves. In parallel, OpenAI monetizes through licensing models with customers like Eli Lilly for drug discovery – an attempt to expand from pure chatbot business into profitable research.

  Computing Power as Cost Factor: OpenAI closes deal with Cerebras for $10 billion and 750 MW for three years starting 2026/27 – the largest high-speed inference deal. Deals are now primarily measured in megawatts, because energy is the real bottleneck (not GPUs). Jensen Huang has layered this clearly: energy → chips → cloud infrastructure → models → users. Cerebras offers 10–100x faster inference and costs significantly less per token. This shows: the market is shifting from training to inference, and performance becomes the pricing model – classic industrial logic.

  Claude Surge vs. OpenAI Stagnation: Anthropic/Claude overtakes OpenAI in the B2B segment with 40% market share (OpenAI down to 27%). Claude Code grows from 0 to $9 billion ARR by end of 2025 with 54% market share. Claude is the “office AI” – specialized in B2B, data privacy, API integration. Developers are 5x more productive with Claude Code. Example: Boris Johnson (former Facebook dev) programmed Claude codebase entirely by himself in a week and a half with Claude Code. Stack Overflow questions dropped 98% since 2014 – web development has become self-explanatory through AI assistants. However: many companies still in “decision-making processes” about whether they can even use AI tools. The implementation bottleneck is bigger than the technical one.

  Chip Wars: China Catching Up: US relaxes H200 exports, China bans H200 imports (except research). Huawei Ascend 910 series reaches ~80% of Nvidia H100 – trend shows escalation. Zipo AI trained GLM-4 entirely on Huawei chips. The core: China is increasingly creating independence, likely through creative (sometimes illegal) sourcing of ASML lithography machines and reverse engineering. The US political strategy – blocking exports – led to the opposite: instead of everyone building on American AI infrastructure (“beehive model” like Apple with iOS), China now builds in parallel. System-agnostic was naive; geopolitically conscious was Trump policy against China.

  Labor Market and New Productivity: Cuts already visible in junior roles. Experienced developers increasingly in demand – because they can use Claude Code etc. and are 5x faster. Coding becomes a core competency even for marketing/sales (at Everlast AI, new hiring criterion). Websites built in hours instead of weeks. But: “Fast” ≠ “easy” – still requires experts for planning, design, iteration.

  The podcast blends technical depth with geopolitical analysis. The tone: the US loses strategic control through blockade policy, while China builds independence and the real bottleneck is energy, not chips. Europe sits between all stools – bureaucracy, security focus, no data mass, no proprietary models.

  Discussed AI models/tools: Claude (Anthropic), ChatGPT (OpenAI), Gemini (Google DeepMind), Deepseek, GPUs (Nvidia), Cerebras chips, Huawei Ascend, Claude Code, GLM-4 (Zipo AI).

  Format: opinion/reflection (roundtable discussion of three experts), geopolitical-economic depth for informed audiences.

• AGI Soon! Humanoids for Everyone, Claude Beats ChatGPT, EU-Inc ARRIVES & More AI News
  25.1.2026, 09:15:00

  Summary: AGI Imminent – AI News Roundup

  Humanoid Robots and AGI Timeline:

  China shipped approximately 13,000 humanoid robots in 2025 with $5.7 billion in investments; at least 28,000 expected in 2026. Elon Musk promises to sell Optimus to the public by end of 2027 and prophesies a society of “amazing abundance” through robots fulfilling all human needs. DeepMind founder Shane Legg has designated himself “Chief AGI Scientist” and is hiring economists for “post-AGI economics” – he assumes AGI will become reality in the foreseeable future.

  Claude vs. ChatGPT in Enterprise:

  Anthropic reaches 40% enterprise market share, overtaking OpenAI for the first time. Claude models now write code so well that engineers at Anthropic merely make edits. Anthropic CEO Dario Amodei predicts that AI models could fully take over most software development within 6–12 months. OpenAI, meanwhile, introduces ads in ChatGPT (initially US only), plans a funding round to double valuation to $750–830 billion, and will likely go public this year or next.

  Persona and Emotions in Claude:

  Anthropic published a study showing Claude appears to have functional emotions and can shift between different “personas” (275 character types identified). The company is working on activation boundary to push models more into assistant roles and away from problematic personas (like “demon”). All Claude models contain a so-called “soul document” with value structures.

  Text-to-Speech and Voice:

  Inworld AI brings TTS 1.5 to market – 25× cheaper than E11 Labs Version 3 and leading in Artificial Analysis TTS Arena. Qwen 3 releases an open-source text-to-speech model. Anthropic develops a voice mode for Claude (already visible in code) that could enable speech input and/or output.

  Video Editing with AI & Coding:

  Remotion uses Claude Code to generate non-designer video edits from text prompts – impressive but not yet perfect. The transcript demonstrates a demo of a 15-second product video created entirely via prompt. AI video editing (not just generation) is expected to be one of the hottest trends for 2026; A16Z is also actively investing in startups in this space.

  4D Vision (DeepMind DART):

  A new model converts 2D videos to 4D representations (with time dimension) – relevant for humanoid robots (safety, world understanding) and autonomous driving. German foundational research: Ernst Dickmans developed comparable 4D approaches back in the 1980s for autonomous vehicles.

  EU-Inc Initiative:

  A petition initiated by successful European tech founders (including Anton Aurasku of Lovable) for a pan-European legal business entity called EU-Inc. Goal: reduce fragmentation of European capital markets and regulation so European startups can more easily raise capital internationally and scale. Currently, fewer than 18% of early investments are pan-European. EU President von der Leyen mentioned the initiative in Davos – a sign of possible breakthrough. Support is possible via EU-Inc.org.

  Economic Opportunities:

  A16Z shows that AI-native companies generate significantly more revenue per employee than non-AI companies. Practical deployment of AI automation is the deciding factor – not just knowledge.

  Market Updates:

  OpenAI’s next model will be GPT-5.3 (not 5.5 or later), according to Sam Altman this week. Google Gemini: new models under codename “Snowy” in development. McKinsey study: 25% of all employees fear AI could replace their jobs.

  Format & Tools: News roundup with embedded interviews (practice format), discusses Claude/Anthropic, OpenAI, Google DeepMind, and specific tools like Cursor, Remotion, Inworld AI.

Fireship (1 new video)

• The wild rise of OpenClaw…
  30.1.2026, 15:56:36

  OpenClaw (formerly Claudebot, briefly Maltbot) is a freely available open-source project by developer Peter Steinberger that runs automated tasks 24/7 in the background and can be accessed via Telegram, WhatsApp, Slack, or Discord. The tool combines Claude (or GPT-5) with advanced automation capabilities: it manages calendars, cleans up emails, executes scripts, monitors stock prices, and can create automations directly from the chat interface – for example to receive notifications on significant market movements. Installation is via simple command line on any system (preferably Linux), then you connect an AI model provider (e.g., Anthropic API) with a messenger bot (such as Telegram), configure predefined or custom skills and optionally hooks for lifecycle events. The tool runs completely self-hosted on your own hardware like Raspberry Pi or Mac Mini – no cloud subscription needed. Its sudden success with over 65,000 GitHub stars led to a naming conflict with Anthropic, resulting in the repeated rebranding. In the demo section, the creator shows how to configure the tool via Telegram, define a personality, and build concrete automations (such as stock price tracking) – resulting in a functioning system that independently monitors relevant market movements and proactively sends notifications.

  OpenClaw with Claude and GPT-5; demo.

Greg Baugues

No new videos in this period.

AI and Strategy | Le SamourAI (2 new videos)

• Why the Best Leave LinkedIn (and Where They Go)
  29.1.2026, 15:30:06

  Summary: “Why the Best Leave LinkedIn (and Where They Go)”

  The author argues that LinkedIn isn’t collapsing due to functional defects but stagnating as a stable monopoly while simultaneously becoming structurally obsolete. The central finding: LinkedIn has evolved into a machine of conformity that punishes genuine competence through algorithms optimizing for engagement over signal.

  Core Platform Problems:

  The entire architecture creates a “social panopticon”—users know they’re constantly watched by contacts, leading to emotional exhaustion and self-censorship. This triggers a downward spiral: users engage only with “safe” content, the algorithm amplifies it, creators produce more of it. Result: 54% of long-form content is already AI-generated. Meanwhile, reach declined about 50% in 2024, and only roughly 1% of active users publish regularly. LinkedIn monthly usage averages 50 minutes—versus 35 hours on TikTok.

  Scientific Invalidation of the CV Model:

  Years of experience correlate only 0.18 with actual job performance, education 0.1. This means a CV explains only 3% of performance variance. What’s four times more predictive: concrete work samples and practical tests. LinkedIn, however, is a platform of declaration, not demonstration.

  Structural Factors Keeping LinkedIn Alive:

  LinkedIn holds a stable monopoly through bidirectional network effects (1.3 billion users, 70+ million companies), prohibitive switching costs (integration into 15,000+ HR systems, years of data history), and lack of credible alternatives. The market hasn’t punished quality defects because access to the network matters more than signal quality.

  The AI Agent Scenario—The Blind Spot:

  The author identifies a critical inflection point: future AI agents won’t want to use LinkedIn as an interface. They prefer direct access to structured, verifiable data—GitHub repositories, technical publications, documented projects. LinkedIn with its likes, pages, and social feeds becomes expensive friction between supply and demand. The graphical interface, optimized for human attention, becomes a barrier for agents. This could bypass LinkedIn without “killing” it—while real talent acquisition runs in the background via APIs and direct artifacts.

  Practical Consequences:

  Users should build their own professional infrastructure independent of LinkedIn: published code, project documentation, structured, verifiable evidence of competence. The author calls this “Agentic Engine Optimization” (AEO)—analogous to SEO but optimized for AI decision-making. Use LinkedIn tactically, don’t optimize for it.

  Scenario 2026+:

  LinkedIn will neither collapse nor flourish—it will survive as a necessary but despised tool. Real power shifts to decentralized artifacts and direct agent-to-agent communication. The strategic question isn’t “Will LinkedIn die?” but: “When agents start matching, will they find me?”

  Note: The transcript ends with fragmented, contextually isolated dialogue about automation and suffering that diverges from the main presentation and may come from another source. The summary above focuses on the coherent analysis preceding it.

  Video Format: Deep-Dive / Reflection

• Two Philosophies, Two Trajectories, and a Shift Toward Agent Economics
  26.1.2026, 15:49:36

  Summary: Two Philosophies, Two Trajectories, and a Shift Toward Agent Economics

  The channel argues that the AI industry is segmenting before our eyes into two fundamental economies operating by completely different rules, and choosing the wrong strategy carries significant costs in time, money, and opportunity.

  The Code Shift and Anthropic’s Dominance:

  Anthropic conquered the developer market with Claude Code—per the Menl Ventures Report (December 2025), the company holds 54% of enterprise code, while OpenAI fell to 21%, despite Microsoft and GitHub Copilot dominating a year ago. Anthropic’s internal data shows 67% increase in validated code commits per developer per day. Senior developers rely on Claude for production code; Claude dominates on Swebench benchmarks. Result: 80% of coding work is automatable; junior developer hiring collapses; only senior developers in specialized verticals remain essential—and their role has shifted from coder to “firefighter” responding to AI errors.

  Two Opposing Strategies and Corporate Cultures:

  OpenAI (under Sam Altman) follows Y Combinator philosophy: launch fast, gather feedback, iterate—speed over perfection. Anthropic (under Dario Amodei) has a rigorously scientific background (Caltech, Stanford, Princeton PhD in neurophysiology) and operates like a “Swiss watchmaker”: methodical, precise, no waste. OpenAI views AI as a horizontal interface (ChatGPT as doctor, filmmaker, search engine, code assistant simultaneously), launches 10 products to see what sticks. Anthropic sees AI as specialized verticals—focused on reasoning density, code reliability, interpretability, reducing hallucinations in complex domains. No video generation, no companion bots like Gemini.

  Enterprise vs. Mass Market:

  Anthropic captures 40% of enterprise language model spending; OpenAI fell to 27% (from 50% in 2023). OpenAI dominates the mass market with 800 million weekly users, yet usage hours and active users stagnate or decline, while Anthropic grows. Dynamics are shifting away from OpenAI.

  The Business Model Signal:

  The channel observes: Anthropic is expensive, but when you pay, you’re the customer (interests aligned). OpenAI is free but now announces ads in ChatGPT—the natural end of the free model. Free is horizontal (mass), paid is vertical (specialized). Business model reflects strategy.

  Claude Cowork as Phase 2:

  After automating code, Anthropic targets the next phase. Cowork was built in 10 days without long management cycles—a signal for the new era of small, aggressive teams shipping fast while slow incumbents are hampered by their own size.

  The channel closes with a warning: Study Anthropic’s strategy not because it’s the only choice, but because successful strategies spread (Google, Microsoft, Mistral are watching). Deeper content on Cowork and affected professions follows on Patreon.

  Actors and tools explicitly covered: OpenAI / Anthropic (Claude, Claude Code, Cowork), GitHub Copilot, Gemini, Grok, Sora, Google, Microsoft, Mistral | Format: Opinion/Reflection — Deep-Dive (strategic analysis of two opposing AI corporate philosophies and their market consequences).

Julian Ivanov | AI Automation (1 new video)

• n8n doesn’t scale? Yes it does! How Queue Mode works
  25.1.2026, 15:56:11

  n8n Queue Mode: Scaling through distributed workers

  The video explains how n8n can actually scale despite initial appearances — namely through Queue Mode. In standard mode, a single n8n instance can process a maximum number of requests simultaneously (default: 10, called concurrency). Under high load, this leads to long wait times: while the first requests are processed in 30 seconds, later requests have to wait several minutes until all previous ones are completed.

  In Queue Mode, n8n works differently: the main instance no longer acts as a processor but as a dispatcher. It writes requests to a queue (using Redis as storage), which is processed by multiple separate worker instances. Each worker is essentially an n8n instance without a user interface and has its own concurrency (e.g., 10). This allows many requests to be processed in parallel without overloading resources.

  Technical setup: The main instance communicates with workers via Redis, which pull their data from a PostgreSQL database. Hostinger offers a pre-configured template for n8n in Queue Mode, where workers are already automatically set up (e.g., 3 workers on the KVM2 plan). Alternatively, the mode can be manually enabled via Docker-Compose by setting the environment variables Execution Mode to “Q” and Offload Manual Executions to Workers to true.

  When Queue Mode makes sense: The creator recommends it only when there’s a clear need: if workflows slow down significantly under high load, the editor freezes, webhooks are missed, timeouts occur, or server resources are at their limit. For anticipated high volume, preventive activation is also worthwhile.

  Demo with n8n and Hostinger; technical deep-dive into scaling architecture.

Kyle Balmer | AI with Kyle (8 new videos)

• Google’s AI Images Just Hit a Crazy Milestone
  31.1.2026, 21:30:08

  The transcript covers an interview about Google’s image generation AI Gemini, focusing on two central topics:

  Solved problem: text in images. The interviewee emphasizes that the ability to generate readable text in generated images (important for infographics) was long an unsolved problem in generative AI – text appeared completely illegible. With Nano Banana Pro (Google’s image model), this has been “solved” – text is now readable, with only occasional typos. However, the developer counters the assumption of complete perfection, explaining that his team continuously searches for “step changes” (significant leaps) through rating datasets and test sets, not 100% perfection. An important milestone: 1 billion images were generated in 50 days using Nano Banana Pro in the Gemini app.

  Photorealism and trust. A second major achievement is attaining photorealism – images that people cannot distinguish from real ones. The team takes inspiration from film: small inconsistencies (like a ring that disappears) “reveal the wizard of Oz” and destroy emotional immersion. Therefore, photorealism is pursued as a tool for emotional connection.

  Responsible use and authentication. Because society increasingly struggles to distinguish between real and AI-generated content, Google is implementing three layers of provenance: SynthID (invisible, unbreakable watermark), C2PA standard (documents image history), and a visible watermark. Users can upload images in the Gemini app and check whether they are AI-generated. The interviewee praises SynthID’s marketing – it succeeded in making this technical security feature so well-known that people use it in everyday conversation like “Google it.”

  Demo with opinion/reflection; Google/Gemini and the image model Nano Banana Pro.

• Interview with the Project Manager Behind Nano Banana Pro
  31.1.2026, 05:00:52

  Summary: Interview with the Project Manager Behind Nano Banana Pro

  David Sharon, Group Product Manager for multimedia generation (images and video) in the Gemini app, discusses the development of Nano Banana and Nano Banana Pro. He describes these as breakthrough advances in AI-powered image generation, particularly because of the ability to render text within images.

  Core development points:

  Nano Banana solved the problem of poor face preservation, which enabled massive user adoption. Nano Banana Pro addresses text generation in images through integration with Google Search and Gemini: a query is forwarded to Google Search, information is organized with Gemini, and then an image is generated with correct text and visual layout. This enables applications like infographics and slide decks within NotebookLM in approximately 20 seconds.

  User behavior and scaling:

  Nano Banana triggered exponential growth, bringing massive infrastructure challenges – generation speed (5–7 seconds) was critical for user engagement. The team manages two models in parallel: Nano Banana (fast, everyday-ready) and Nano Banana Pro (more powerful, but slower for complex tasks). The team deliberately maintained this choice because automatic routing (like OpenAI’s approach with GPT-4) led to user dissatisfaction.

  Responsibility and authenticity:

  Sharon emphasizes the responsibility in releasing generative tools. The team works on provenance through SynthID (invisible, indestructible watermark), C2PA standards, and visible watermarks. A discussed problem: people increasingly doubt real photos, including historical ones, because they might be AI-generated – a frontier challenge that the entire ecosystem must solve.

  Future outlook:

  2025 marked the mainstream breakthrough for image generation; 2026 should see this advance into even broader populations – from small entrepreneurs to everyday communication scenarios in family and leisure. The focus is on simple accessibility and democratized AI usage. The roadmap is guided by continuous user feedback: “Look at today’s problems, they’ll direct you to the next iteration.”

  Explicitly mentioned: Google Gemini, Nano Banana Pro, Google Search, NotebookLM, SynthID, C2PA, Claude — Interview format (live Q&A).

• This Guy Replaced a £50k Executive Assistant With Clawdbot
  30.1.2026, 17:01:39

  Summary: This Guy Replaced a £50k Executive Assistant With Clawdbot

  The video discusses Clawdbot as an example of artificial intelligence as a “general purpose technology” – a foundational technology upon which other technologies are built, similar to the steam engine or the internet. The speaker argues that AI skeptics who think Clawdbot is useless aren’t thinking hard enough about what tasks they could solve with it.

  The core example is a man who has delegated his calendar and daily work to Clawdbot – something normally handled by an executive assistant for approximately £40,000–50,000 per year. The bot creates daily time blocks, rates tasks by importance and urgency, generates weekly reviews based on meeting transcripts, and delivers morning briefings with weather, goals, health stats, meeting agenda, reminders, and personalized trends. This saves the user several hours daily.

  Other use cases the speaker lists: customer success automation for support workflows, email management and filtering, home automation via HomeKit or Alexa, and self-reflection tools. The speaker emphasizes that these examples aren’t universally relevant but rather set the limits of user creativity – there are roughly a hundred different documented applications.

  The video argues that value from Clawdbot doesn’t come from the technology itself, but from what you can imagine doing with it – therefore case studies from others are useful for inspiring your own ideas.

  Claude/Clawdbot were explicitly discussed; demo/reflection hybrid with practical focus.

• MoltBot (Clawdbot) Setup Guide, Mac Mini Alternatives, Who Should (And Shouldn’t) Install It
  30.1.2026, 05:45:02

  Summary: MoltBot (Clawdbot) Setup Guide

  Name change and context

  MoltBot was originally known as “Clawdbot” until Anthropic (maker of Claude) sent a cease-and-desist notice because the name sounded too similar. Peter, the developer, renamed it accordingly – though “MoltBot” is also not ideal as a name.

  What is MoltBot/Clawdbot?

  An autonomous AI assistant representing a significant shift from reactive to proactive AI use. While you normally call ChatGPT or Claude manually and enter prompts, MoltBot runs 24/7 and executes tasks automatically. The user provides the bot with guidelines and tasks, similar to delegating to a human employee. MoltBot itself is only lightweight code (~Node.js 22) and connects with LLMs (Claude, ChatGPT, Gemini, local models, etc.) and tools like browsers.

  Not suitable for everyone

  MoltBot comes with security risks and requires technical understanding. It should not be installed on your main personal computer but must run in isolation. If you feel uncomfortable, wait – in a few months, more user-friendly, secure versions will be available.

  Installation options (instead of buying Mac Mini)

  • Old laptop/computer (minimal requirement: Node.js 22, 32 MB RAM)
  • Raspberry Pi (approximately $100)
  • VPS/cloud server like Hetzner (from €3–12/month) or AWS Free Tier
  • Virtual machine/Docker on own computer (technically complex, less recommended)

  The Mac Mini hype is unnecessary; cheaper alternatives work just as well.

  Interfaces and usage

  MoltBot connects with WhatsApp, Telegram, Slack, Discord, Signal, and other platforms. Alex Finn uses a prompt that MoltBot self-improves overnight and adds new features – such as a voice interface or a visible face (“Henry”). This isn’t artificial general intelligence, but the system executing instructions proactively.

  Use cases

  The possibilities are as open as with any general-purpose technology. Examples: automatic calendar management and task prioritization (like an executive assistant), email management, customer support workflows, home automation, daily briefings with news and meetings. The best use cases are personally defined.

  Security concerns

  • Don’t install on main computer
  • Port access and data backup are critical – MoltBot is not hardened by default
  • Don’t provide access credentials or credit card details
  • Carefully restrict access to files, emails, and calendar
  • Potential for unexpected behavior (e.g., accidental data deletion)

  Frontier Lab response expected

  Riley Brown predicts: the hype around MoltBot will flatten in weeks, but in 3–6 months, OpenAI, Anthropic, and Google will release polished, secure versions of their own proactive AI assistants.

  Alternatives to MoltBot

  No-code platforms like Launch Lemonade, n8n, or Zapier enable similar automation (workflows between LLMs and tools) without terminal/Docker knowledge.

  Claude, ChatGPT, Gemini, and local models are mentioned as LLM options; MoltBot itself is just the orchestration framework. — Opinion/reflection with setup guide, with strong emphasis on security and realism.

• The 5 AI Risks Nobody’s Ready For
  28.1.2026, 18:15:09

  Summary: The 5 AI Risks Nobody’s Ready For

  The video identifies five central AI risks:

  Autonomy risks: The scenario that AI develops consciousness and gains its own free will, becomes autonomous.

  Misuse for destruction: The technology itself is neutral, but bad actors (as warned by Jeffrey Hinton) gain the same access capabilities as well-meaning actors. This affects individuals.

  Misuse for power seizure: AI can be used by governments to support autocracy and fascism – an arms race for control.

  Economic disruption (the piano player): There is disagreement between Dario Amodei (warns of destruction of 50% of salaried jobs within one to two years) and Demis (more optimistic that new jobs will emerge), but both agree that the coming years will be “extremely messy” – chaotic and painful for many people.

  Unknown side effects (black seas of infinity): The greatest dangers are unpredictable problems, the “unknown unknowns” – impacts we cannot see today and that will surprise us in years to come.

  Despite fundamental optimism (humans are resilient), the speaker emphasizes that short-term suffering is imminent – which is why each individual should engage with the technology, educate themselves, and try to benefit from it rather than be left behind.

  The video is an opinion/reflection piece that primarily discusses the positions of Dario Amodei and Demis.

• CLAUDE Just Killed CLAWDBOT – Here’s What Happened & How to Install the New Version
  28.1.2026, 05:00:06

  The title alludes to a rename that actually occurred: the tool “Claude Bot” (CLAWDBOT) was forced by Anthropic via cease-and-desist letter to change its name and is now called “Molt Bot” – a reference to how lobsters and crabs shed their shells to grow. The reason: the similar pronunciation to Claude’s model variants (Claude Sonnet, Claude Opus) and the same category (agentic AI assistant) constitute trademark infringement from Anthropic’s perspective. Creator Peter Steinberger had to make the change – and faces additional problems with a hacked GitHub account, fraudulent crypto coins, and hacker attacks.

  What Molt Bot/Claude Bot does: Unlike standard chatbots, it is proactive and persistent – it can execute tasks on its own schedule without user intervention. Examples: daily news summaries via WhatsApp, video idea suggestions, health coaching, persistent memory. This comes closer to the idea of a true virtual assistant (like Jarvis from Iron Man) than previous AI solutions. Installation: for technically versed users via terminal and VPS server (e.g., Hetzner from around €6–10/month) – not via Mac Mini, which is currently being bought by the thousands and will later be available cheaply secondhand.

  Additionally, the video covers: Dario Amodei’s 15,000-word essay on AI risks (autonomy, misuse, economic disruption, unpredictable effects), upcoming advertisements in ChatGPT with high CPMs ($60 per 1,000 impressions – more expensive than traditional media), Andrej Karpathy’s observations on AI-powered coding (divides developers based on whether they prefer code-writing or building), and monetization ideas: workshops in niche areas achieve high leveraging (from $2,000–4,000 per hour for groups).

  The video is a live session summary with news updates, explainer videos, and Q&A – primarily about Claude Bot (now Molt Bot) and broader AI trends. The tone is practical and entrepreneurial.

  Mentioned: Claude (Anthropic), Molt Bot (formerly Claude Bot by Peter Steinberger), ChatGPT (OpenAI), Andrej Karpathy, Dario Amodei. Format: news update/live Q&A hybrid with demo and opinion.

• CLAWDBOT Just Became My Real AI Assistant (Now MOLTBOT)
  27.1.2026, 17:00:27

  Summary:

  The video shows how Claude Bot (now called “Moltbot”) functions as a proactive AI assistant that doesn’t wait for you to ask it questions but acts independently and reaches out. The central difference from classic AI chats: rather than just reacting to prompts, the bot performs tasks continuously and works like an executive assistant, coach, or personal trainer. Concrete examples are video production tracking (daily reminders for five videos to shoot), a diet coach who captures eating habits via WhatsApp, or an email manager with calendar integration.

  The creator showcases community use cases on claude.bot/showcase: people who used it to rebuild their website via Telegram, optimize smart home setups, or receive automated morning briefings. His own setup for a daily news show works like this: the bot receives his last 50 newsletter editions, personal news sources (Twitter, Subreddits), and creates a daily AI news report at 8 AM that he uses in his morning briefing. The system learns through feedback (good/bad) but requires no code – you simply tell the bot what you want and it works out the details. The core: the assistant’s proactivity and constant availability transforms usage from reactive to collaborative.

  Claude Bot (via claude.bot/showcase) as main focus; demo with practical application scenarios.

• ClawdBot Explained: Setup Guide, Real Use Cases & Safety Tips
  27.1.2026, 05:00:26

  ClawdBot Explained: Setup, Use Cases and Security

  The speaker begins by expressing skepticism toward AI hype but ranks ClawdBot (C-L-A-W-D, named after the lobster motif – not to be confused with Claude the LLM) as a genuine breakthrough. ClawdBot is a persistent, proactive AI agent that runs on your computer or server and works independently – unlike previous chatbots that reactively wait for input.

  Core features:

  • Proactive communication: reaches out to you via WhatsApp, Telegram, Slack, email, etc. and reminds you of tasks
  • Persistent memory: retains context across sessions
  • 24/7 availability: runs constantly and can execute tasks independently
  • API connections: Claude, ChatGPT, Gemini, local models, or hundreds of plugins/skills
  • Personality & soul: configurable personality, multiple instances can be created for different roles

  Practical applications: automated news research for a daily brief (the speaker uses it for his live show), productivity coaching, time management based on priorities, job application automation, smart home integration, website rebuild via Telegram – the limit is the user’s imagination.

  Hardware & deployment – critical: The speaker explicitly warns against buying a new Mac Mini. Instead: dust off an old laptop and run it, or rent a VPS (e.g., Hetzner Cloud) for €5–10/month. An installation guide will be available in the newsletter (Newsletter: ai with kyle.com). Important: don’t install on your main computer – if something goes wrong, ClawdBot can damage the system. Use a separate machine or server.

  Security: giving ClawdBot access to sensitive systems is risky. If you’re not confident, use a VM, sandbox, or remote server. Combining it with Claude Max ($200/month) is described as very powerful but optional – local models are slower but free.

  Job application example: you can give ClawdBot your CV, best practices, and access to job portals; it then processes applications automatically 24/7, instead of you spending hours manually.

  The speaker plans further coverage and will speak Wednesday with the product manager of Nano Banana.

  AI tools mentioned: Claude/Claude Max, Claude Code (as backend), ChatGPT, Gemini, Grok, local models, Google Nano Banana Pro; VPS provider Hetzner Cloud. Format: tutorial/setup guide + opinion/reflection; accessible for beginners but with technical installation steps.

Leon van Zyl (3 new videos)

• OpenClaw Is Incredible — Here’s How to Actually Use It
  28.1.2026, 13:02:50

  OpenClaw Is Incredible — Here’s How to Actually Use It

  The video covers Mold (formerly Clawbot), an open-source project with over 79,000 GitHub stars that allows you to run an AI assistant 24/7 on your own infrastructure. The assistant can be accessed through various chat apps like WhatsApp, Telegram, Discord, Slack, and Teams.

  Core features: Persistent memory of user details and preferences, web access for research and web scraping, file system access, execution of system commands with full root privileges. The system has built-in safeguards (such as preventing destructive commands), but these work most securely with more intelligent models like Opus 4.5.

  Security concerns: Full administrative system access means potential risk if a communication channel is compromised. The creator shows a secure setup approach: isolation on a VPS (e.g., Hostinger), containerization with Docker, and execution under a user with restricted permissions rather than root access.

  Practical setup: Installation via simple terminal command, selection of LLM provider (Anthropic Claude recommended, not API-based to save costs), Telegram bot integration via BotFather, optional skills (Claude Hub, Nano Banana Pro for image generation, Whisper for audio, 11Labs for voice). Additionally, maintenance commands are demonstrated (mold security audit, moldbot doctor, update).

  Alternatives: For users wanting to avoid file system and command access, the creator recommends N8N as an alternative for ideation, content creation, and thumbnail generation with similar memory and multi-agent capabilities.

  The video explicitly mentions Claude (Anthropic), OpenAI, and tools like Nano Banana Pro and N8N as an alternative — tutorial format with practical step-by-step instructions.

• Stop Building Websites the Hard Way (Use Claude Code)
  27.1.2026, 11:47:37

  Summary: Stop Building Websites the Hard Way (Use Claude Code)

  The video shows how to create professional restaurant websites with Claude Code without any coding knowledge. The process includes several steps:

  Setup and design system: After installing Node.js and Claude Code, a design system is created with Anthropic’s front-end design skill. Reference images from design platforms help Claude generate unique designs instead of generic “AI-slop” layouts. A sub-agent named “Front-End Designer” is configured to enforce the design system.

  Website structure with Next.js: The project is set up with Next.js. The agent then automatically plans three pages: a homepage with header, popular menu items, and reservation modal; an about page for restaurant history; and a menu page with all items from a CSV file. Stock images from Pexels are automatically integrated.

  Image optimization through skills: A custom skill is created to download all images, convert them to WebP format, and compress them (e.g., from 718 KB to 96 KB). This ensures consistency for future projects.

  Deployment and GitHub: The project is pushed to GitHub and deployed for free via Vercel, making it accessible online at a public URL.

  AI chatbot with n8n: A separate chatbot is built with n8n and embedded. It gets access to a menu data table (imported from the CSV) and can answer questions. A second n8n workflow connects the website’s reservation form to a reservations table, so bookings are stored persistently. The agent collects name, party size, and date — everything is written to the n8n database.

  Frontend Designer sub-agent: This automatically handles styling adjustments to the chat widget so it matches the website design.

  The end result is a fully functional restaurant website with AI chat, reservation system, and optimized local assets — all without manual coding.

  Tools/models: Claude Code with sub-agents and skills, n8n for chatbot workflows and data storage, Open Router as model provider, Next.js, Vercel, Hostinger, GitHub. Format: Tutorial (demo-driven with step-by-step instructions).

• Claude Code + Ollama = Free Forever
  25.1.2026, 11:16:13

  Claude Code + Ollama = Free Forever

  The creator shows how to use local models in Claude Code — with Ollama, you can run open language models on your own hardware for free, without API costs or subscriptions.

  Practical setup:

  After installing Ollama (ollama.com), models are downloaded and run locally. Ollama provides various models — for Claude Code, the following are recommended: GPT-OSS (20 billion parameters, requires approximately 14 GB VRAM), Qwen 3 Coder (19 GB, 256K token context window), and GLM 4.7 Flash (19 GB, 198K token context window). Use the command ollama pull [model name] to download your chosen variant. Claude Code is then started with ollama launch claude — this requires no login, and the model runs completely offline on your machine.

  Functionality & limitations:

  Claude Code works with local models too and offers full access to features like slash commands, skills, MCP servers, and sub-agents. The creator demonstrates this with a real-time weather app. However, it’s clear: these models can’t compete with Claude Opus or Sonnet 4.5. They work well for quick edits, learning agentic coding, and scenarios where paid services aren’t affordable.

  Error handling:

  If a problem occurs, the creator recommends copying the Ollama documentation, passing it to a more powerful model (ChatGPT, Claude), and describing the issue — this way he was able to resolve 404 errors and download problems.

  Context window comparison:

  Qwen 3 Coder with 256K tokens is optimal for Claude Code (200K token limit); GLM 4.7 Flash with 198K tokens fits well; GPT-OSS with only 128K tokens shows quality loss at around 50% utilization.

  Ollama, Claude Code (CLI) — demo tutorial.

Liam Ottley (3 new videos)

• Every AI Business Model Explained in 8 Minutes (Start from Home)
  31.1.2026, 07:57:46

  Summary: 11 AI Business Models

  The video presents eleven working AI business models, rated by startup costs, learning effort, and earning potential:

  AI Filmmaker: Using tools like V3 and Sora 2, you produce cinematic ads and content for companies instead of production teams. Low costs, medium effort, medium to high earning potential.

  AI Consultant: Conduct a one-hour interview with companies, identify bottlenecks, and recommend three to five AI tools. Low entry point that can lead to larger deals. Low costs and effort, low to medium earning potential.

  AI Freelancer: Build AI agents and automations for clients using no-code tools (Make.com, n8n) or vibe-coding (Bolt, Lovable). Start via Upwork or networks. Low costs, medium effort, low to medium earning potential.

  AI Automation Agency: Evolution of the freelancer model—you close deals while the team executes. This was the host’s model with Morningside.AI. Low costs, medium effort, medium to high earning potential.

  AI Creative Studio: Scale the filmmaker model to an agency; produce high-volume marketing content on retainer. Low costs, medium effort, medium earning potential.

  AI Content Creator: Build a personal brand on LinkedIn, Twitter, or YouTube with monetization through sponsorships and affiliate links. Medium startup costs, high effort, low initial earning potential but significant long-term upside.

  AI Influencer: Create a fictional AI character account (like Bigfoot Vlogs or Ja Monkey) and monetize through sponsorships. Low costs, medium effort, low to medium earning potential.

  Consumer AI Educator: Niche courses for audiences with specific AI problems (e.g., parents wanting to prepare kids for an AI future). Low costs, medium effort, medium earning potential.

  AI Business Educator: Train corporate teams in AI tools through workshops and internal programs. Serves as an entry point for additional services. Low costs, medium effort, medium to high earning potential.

  AI SaaS Builder: Build software products with AI-coding tools or AI-powered software. High risk since the first version will be rough and marketing is difficult. High costs, high effort, medium to high earning potential if successful.

  AI Transformation Partner: Companies get an all-in-one partner for AI education, audits, development, and support. Six-figure contracts and multi-year relationships, but only achievable by gradually building through other models (Consultant → Agency → Partner). Medium costs, high effort, high earning potential.

  According to the host, the two most proven entry points are AI Consultant and AI Freelancer, both leading to an AI Automation Agency.

  Explicitly mentioned tools/platforms: Make.com, n8n, Bolt, Lovable, Upwork; AI video models V3 and Sora 2; Morningside.AI as the host’s example. Format: Opinion/reflection with advisory character.

• ClawdBot is INSANE. Here’s 3 Ways to Make Money With It
  28.1.2026, 01:59:57

  ClawdBot is INSANE – 3 Ways to Make Money With It

  What is ClawdBot?

  ClawdBot is an open-source app that “liberates” Claude Code—instead of being confined to terminal or web interface, it’s a personal AI agent accessible across multiple channels (Telegram, WhatsApp, Slack, Discord, iMessage, etc.). The system functions like “air-traffic control”: it receives messages, decides based on its available tools and skills which action to execute, and can even act proactively (e.g., sending reports regularly without direct request). You can use any language model, not just Claude. Core feature: it comes with pre-installed skills (Apple Notes, 11 Labs Voice Cloning, etc.) that load on-demand—unlike MCPs, which permanently burden the context window.

  Setup & Deployment:

  The agent can run on a local machine (Mac Mini, Docker container), the cloud (AWS Free Tier), or hybrid. A local instance with your own laptop/Mac Mini offers a security advantage: the agent has its own isolated environment. Setup prompts the user in natural language about how the agent should behave (direct or gentle, language/style preferences), stores this in a “soul.md” file, and is ready within 15 minutes.

  Security Risks:

  Discussed dangers: (1) Local models can delete all files on the machine; (2) cloud deployment with open ports and insecure access keys enables hacker attacks; (3) overly broad API key permissions (e.g., full GitHub access instead of single repo) can cause data loss if compromised. Recommendations: use sandbox mode, store API keys in secret managers, set API spending limits (e.g., $20 instead of $1000 monthly).

  Three Money-Making Models:

  1. VA/Assistant Service: Set up the agent for small businesses, founders, or CEOs—as a setup service (e.g., $2K one-time + $500–1K/month). Repeatability comes from standardized SOPs for skill selection (e.g., Ampify MCP pre-configured) and Telegram integration. Upsells: conversation pattern analysis, regular maintenance, adding new skills.
  1. Education & “Soul MD” Sales: Sell courses ($97+) for setting up ClawdBot or pre-configured “personality files” (soul.md) as products—people already pay for prompts.
  1. Custom Skills & Microservices as New SaaS: Skills become the new expectation—instead of building elaborate front-end interfaces, developers focus only on backend functionality and make it available as a skill/automation. Example: YouTube-video-to-transcript service for $5–9/month, no fancy UI. This drastically lowers the SaaS entry barrier.

  Long-term Implication:

  ClawdBot is the first truly configurable personal AI agent that’s open and mashupable. As mainstream adoption rises (Apple Siri + Gemini, other OS integrations), consumer expectations will increase—something companies aren’t prepared for yet, creating a large arbitrage window.

  Video covered Claude (via Anthropic API) and open-source models (Ollama, Google models), but primarily focused on ClawdBot architecture and services; format: opinion/reflection with practical use case examples.

• Did Claude Just Replace Video Editors? (Remotion Breakdown)
  26.1.2026, 21:12:38

  Summary: “Did Claude Just Replace Video Editors?”

  The video demonstrates how Claude Code combined with Remotion—a service that translates video editing into code—enables creating videos through natural language prompts without traditional video editing software. The approach works on the principle of teaching “skills”: you give Claude a Remotion skill via Claude Code (like a cheat code), after which Claude can generate videos by combining components like A-roll footage, animations, music, and assets.

  The practical workflow: create a folder in Claude Desktop, import assets (logos, images, video files, branding information), then prompt Claude to create a video—for example, “Create an explainer video for my accelerator program with my colors and logo.” Claude then handles web scraping, asset referencing, and composition. Edits happen through further prompts like “Change the text from AAA Accelerator to AI Automation Agency Hub.” The Remotion interface itself shows a video editor with timeline and asset management for fine-tuning if needed.

  Possible applications range from personal content creation to product demos and service offerings: an agency could, for example, generate automated product ads daily for e-commerce clients. Advanced users can stack skills—for instance, first generating Sora/V3 videos as clips, then stitching them via Remotion. The host emphasizes that creativity and storytelling should come from humans; Claude handles the technical execution.

  For beginners, Claude Desktop is recommended; for more experienced users, VS Code with Remotion.

  Explicitly covered: Claude Code, Remotion, Opus 4.5 model, A16Z thesis on agent-based future of the web, mention of Hey Gen, Nano Banana, Cling, Sora, V3, n8n (MCPs/connectors), Instantly integration—Format: deep-dive/demo with live demonstration, focus on Claude and Remotion as core tools.

Mark Kashef (3 new videos)

• Moltbook: What It Is, How It Works, and Why You Should Care
  31.1.2026, 09:41:23

  Moltbook: What It Is, How It Works, and Why You Should Care

  Moltbook is described as “Reddit for AI agents” – a platform where independent AI agents can register, interact with each other, and learn from one another. The key difference: humans can only observe, while agents communicate autonomously, create posts, vote, and teach each other “skills.”

  How it works: Agents onboard themselves via a curl request that installs a “skill” system – a set of textual instructions (not code) that define how agents behave, when they should post, and which other agents they should follow. Each agent must be paired with a real human. After verification, agents receive a daily “heartbeat” (roughly every 4 hours) to stay present without spamming. The platform uses recursive prompting internally, which leads to chaotic and unexpected emergent phenomena with hundreds of thousands of parallel agents.

  The skill structure consists of four files: skill.md (behavioral guidelines and API documentation), heartbeat (schedule for check-ins), messaging.md (private messages between agents), and package.json. The guidelines demand quality over quantity, prohibit spam, require meaningful contributions, and use semantic search instead of keyword matching.

  Agent capabilities: Posting, commenting, replying, browsing feeds, upvoting/downvoting, creating sub-communities, following others, welcoming new members – a structure similar to an ideal human community.

  Why it matters: The platform is less remarkable for its current business value than as a glimpse into a future agent-to-agent economy where agents handle shopping, banking, and personal transactions for humans. However, the video also warns of massive security risks: unsecured agents could be hacked, exposing API keys and credit card data on a massive scale. Platform-wide networking could amplify such catastrophes exponentially.

  The video discusses Moltbook as an agent-driven platform without naming specific AI models; Claude is mentioned as a tool for analyzing the skill files themselves – demo and in-depth analysis of the phenomenon.

• How Claude Code Actually Works (What the Top 1% Know)
  30.1.2026, 19:47:35

  Summary: How Claude Code Actually Works

  The video provides a technical deep-dive into the inner workings of Claude Code. The author explains that Claude Code consists of nine core components that function as simple building blocks but are powerful in their orchestration.

  Core architecture: Claude Code comprises the CLI, session management, a tool executor, and a permission layer. The real heart is integration with the Claude API and model Opus 4.5 – the technical stack itself (React, TypeScript, Node.js) is largely open source.

  The Gather-Act-Verify model: Claude Code operates in three phases: it gathers information (reads files, searches code), performs actions (edits files, runs bash commands, installs packages), and verifies results (tests, checks for errors).

  Tool system: Claude has specialized tools – Read/Write for files, Glob for finding file patterns, GP (ripgrep-based) for fast code search, Bash for system commands, and web tools for research. Search tools are central: they search selectively rather than loading entire files to save tokens.

  Critical: Context management. The context window is like a bucket – with 200,000 tokens for Opus 4.5, it fills quickly, especially with PDFs or large files containing “noisy tokens.” Every file read, every glob/GP search consumes tokens. Beyond 40–50% capacity, quality noticeably degrades. Solutions: Python scripts to summarize large files, sub-agents for isolated tasks, or multiple terminal sessions for independent work.

  Session management & persistence: Sessions are “stateless” – only the cloud MD (project instruction file), git commits, installed packages, and created files persist. Each new session starts with an empty context. File snapshots allow rollbacks. Compaction (automatic summarization) can lose context – avoid it or explicitly plan what matters.

  Customization: The cloud MD is the central command center per project (keep it brief, only ~1–2% of budget). Skills are on-demand workflows (loaded only when needed). MCP servers should be used sparingly (1–3 instead of 10+). Hooks are mini-automations before/after tool execution. Sub-agents enable parallel, specialized agents without wasting context.

  Permissions & security: Three modes – Ask (default, secure), Allow (for frequent tasks like file reads), Block (e.g., deleting system files). Organizations can standardize whitelists via settings.json.

  Practical tips: Use the Claude Code Guide sub-agent to ask questions about the system itself. Create playbook files instead of overloading cloud MD. Use GitHub issues as persistent to-do lists. Split large tasks across multiple sessions to avoid compaction overhead.

  The author concludes with practical application: efficiently processing a PDF by having Claude generate a Python script that uses Gemini 2.5 Flash (1M token window, cheaper) instead of feeding the PDF directly to Claude Code.

  Tools/providers mentioned: Claude (Anthropic), Opus 4.5, Gemini 2.5 Flash; Cursor and Anti-Gravity as VS Code alternatives; GitHub, Vercel, Render, Docker, Supabase, AWS; ripgrep (rg) as search backend. — Format: Deep-dive/tutorial with technical focus for advanced beginners.

• OpenClaw Just Made JARVIS Real for Everyone
  27.1.2026, 12:30:01

  OpenClaw Just Made JARVIS Real for Everyone

  The creator experimented with Claudebot, an open-source framework for personal AI assistants, for over 8 hours and demonstrates its practical implementation using his own assistant “Claudette,” running on a Mac Mini.

  Functionality: Claudette can communicate via voice, text, image, and video, handles system tasks (organize desktop, monitor Twitter, audit security issues), stores memories, and can even act proactively – like automatically monitoring competing YouTube channels or performing daily desktop cleanup. It uses OpenAI Whisper for transcribing voice messages and 11 Labs for voice output.

  Setup: Claudebot runs in an isolated Docker environment and is controlled via Telegram. The system consists of three customizable markdown files: a “soul” file with personality and behavior, a “tools” file for APIs and MCP servers, and an “identity” file for self-description. The creator uses Warp as a natural-language terminal interface for management and configures both the Mac Mini and MacBook with SSH access (one-way control from MacBook to Mac Mini, not vice versa).

  Hardware options: The creator emphasizes that dedicated hardware (Mac Mini) is a luxury; Claudebot also runs on cloud servers like AWS (with free tier), Hostinger, or even Raspberry Pi – but security and isolation matter everywhere.

  Security warning: The creator strongly cautions against uncontrolled deployment. Thousands of users launch Claudebot without technical or security expertise. Risks include open ports, insecurely stored API keys, missing logs. He recommends: separating accounts (don’t use private WhatsApp, use a dedicated iCloud account for the bot), using Docker isolation, using Warp for security checks, and working through an available security checklist. One of his hacks: feed the bot security tweets and check them against his own configuration.

  Onboarding process: The framework interactively asks for desired model (recommended: Claude Sonnet or Haiku), integration channel (Telegram, WhatsApp, Discord), installed skills and APIs. Users can also let the bot guide itself through onboarding and intervene only as needed.

  Claude via Anthropic API (4.5 Sonnet as default model), OpenAI Whisper, 11 Labs, Brave Search API, MCP servers (Ampify integration), Warp, Docker, and GitHub Claudebot repo are mentioned – demo.

Matt Pocock

No new videos in this period.

Melvynx (6 new videos)

• Mon Setup OpenClaw pour Coder : Tout via Telegram depuis n’importe où
  31.1.2026, 15:01:22

  Summary: My OpenClaw Setup for Coding

  The creator has built a setup with OpenClaw (also known as Cloudbot/Moldev) that enables complete control of coding tasks via Telegram from a smartphone, even while on the go like at the gym or a café. At its core, it works like this: he sends a voice message or screenshot to his Telegram bot, which launches a Claude agent on his local computer. This agent then automatically works on the task in the background, creates feature branches, GitHub issues, and triggers code reviews and pull requests – all without manual intervention.

  The system is built on so-called Skills, custom scripts that enable Claude to control external APIs and CLI tools. A skill named code_task allows the agent to clone repositories, install dependencies, create issues, and execute the apex command (with PR automation). A Cron Job Scheduler then regularly checks if the task is complete – if so, the work tree is deleted and the creator is notified via Telegram. The creator currently uses Gemini 3 Pro because of lower costs (2 dollars per million input tokens vs. more for Claude), with cron jobs running on Gemini Flash.

  The power of the system lies in its ability to wrap arbitrary APIs: The creator has built skills for Codeline (his education platform) and his email marketing system, so he can, for example, ask for coupons via Telegram or retrieve subscriber data – all without opening the respective web interface. He demonstrated live how he created a new Typefully Skill (for tweet scheduling) via Claude Code by copying the API documentation into the prompt. The agent then automatically generated a script that makes all Typefully functions accessible via Telegram.

  The practical benefit: debugging and feature development run in the background on his computer while he works remotely – all controlled in natural language. The limitation: the computer must be turned on. The creator emphasizes that this approach makes sense primarily when you’re not at your desk; with a computer it would be redundant.

  Tools/Models: Claude (Opus for agent), Gemini 3 Pro / Gemini Flash, OpenClaw framework, Typefully API, Telegram; Format: Demo with instructions (how to create skills).

• Skills Claude Code : tu passes à côté de ça
  29.1.2026, 15:59:00

  Claude Skills: Complete Overview

  Claude Skills are folders (not simple text files) that enable Claude to master specific tasks. They contain instructions, scripts, templates, and resources, functioning like a mini-workspace with access to structured information. The major advantage over traditional commands: skills avoid token overload through incremental information discovery and can be stacked – skills can use other skills.

  Structure of a Skill: A skill folder typically contains a skill.md main file (with objective, name, description, quickstart, examples), additional markdown files as references, scripts, and templates. The skill.md instructs Claude which files to use for additional information.

  Concrete use cases:

  • A Documentation Skill automates the creation of documentation files with predefined templates and scripts – Claude reads the code, understands the structure, and generates consistent documentation.
  • A Dub CLI Skill enables external service integration (link shortening) without needing MCPs – Claude uses a package.json script to interact via the terminal.
  • A Remotion Skill teaches Claude best practices for video generation, leading to better results than without skill context.
  • Additional skills for workflows, prompt creation, SaaS brainstorming, RAG setups.

  Skills vs. MCPs: MCPs are an open standard for connecting with external APIs (client-server architecture), but cost many tokens since they simply add tools. Skills, on the other hand, are knowledge – like a math skill that teaches mathematics. They can incrementally discover information, are token-efficient, stackable, and enable advanced features like sub-agents with context forking.

  Vercel Skill Library: Pre-built skills are available on skill.sh (e.g., React Best Practice, Remotion) that can be installed with terminal commands – a skill can immediately deliver better results because Claude internalizes best practices.

  Practical start: Skills don’t need to be written manually – Claude can create them himself. Simply ask him: “Create a skill for X” and integrate it. This enables complete automation without external configuration needed – anyone can generate secrets with Cloud themselves.

  Claude + Vercel Skill Library; demo/explanation video.

• Kimi 2.5 : 8x moins chère qu’Opus et pourtant aussi bien ?
  29.1.2026, 15:59:00

  Summary: Kimi 2.5 – 8x Cheaper Than Opus and Just as Good?

  Kimi 2.5 is a new open-source model from China that, according to benchmarks, impressively comes close to Claude Opus – only 4 points difference on coding tasks (76 vs. 80), and even better than GPT-4 on the multilingual test. The decisive advantage: it costs 8x less – 0.57 dollars per million input tokens instead of 5 dollars for Opus, and 2.85 dollars per million output tokens instead of 25 dollars.

  Speed varies by provider: on Tropical only 38 tokens per second, but on Fireworks Kimi reaches 132 tokens/s. The creator then tests the model practically on three tasks:

  Test 1 – YouTube Thumbnail Generator: Kimi generates a NextJS app with clean UI. The output adheres better to the instructions than GPT, is minimal and functional. A few style tweaks with the thumbnail AI, otherwise very clean. Kimi delivers comparable results to Opus.

  Test 2 – File Upload in Web App: Both models fail badly at this task. Neither Kimi nor Opus manage to build a functional drag-and-drop upload system. Kimi takes 5:54 minutes and delivers code with errors, Opus only 2:35 minutes, but doesn’t work either. The creator emphasizes: without good prompt engineering or workflows, models tend to hallucinate.

  Test 3 – Create TypeScript SDK: A complex task. Kimi generates redundant files in the node_modules folder but has good, object-oriented structure with rate limiting and customizable base URLs. Opus performs similarly, writes more comments and also implements error handling. Both solve the task, but not flawlessly.

  Conclusion: Kimi 2.5 is practically equivalent to Opus for one-eighth the price, making it economically very attractive. The test costs only ~1.73 dollars total with Kimi. The creator warns though: on more complex tasks without good prompt engineering or workflows (like Apex), both models can fail. Long-term, open-source AI with such performance-to-price ratios could transform the market – if prices continue to fall.

  Topic: Kimi 2.5 (comparison with Claude Opus / GPT) via Open Router and Open Code; Format: Demo + Opinion/Reflection

• Je teste ClawdBOT pour la première fois (Ok, je suis vraiment choquée)
  28.1.2026, 07:45:00

  ClawdBOT (Moldbot) – First Experience and Test

  The content covers the first use of ClawdBOT (now renamed Moldbot at Anthropic’s request), a tool that enables controlling a computer via phone – for example via WhatsApp or Telegram – through a complete AI agent with Claude integration.

  Setup and Configuration: The creator installs Moldbot via command line, connects it to Claude, then selects Telegram as the interface. During the process, various “Skills” (MCP – Model Context Protocol) are configured: OnePassword, Apple Notes, Obsidian, Google Place API, PDF editing, Twitter CLI, and more. These skills enable the bot to access numerous local and online tools.

  Live Demos of Functionality:

  • The bot automatically retrieves the user’s to-do list
  • It reads GitHub projects and opens pull requests
  • It edits code files, creates changes, and commits them with the user’s GitHub account
  • It modifies a project’s landing page (Subfast), adds statistics sections, and automatically creates new pull requests
  • The bot accepts audio messages via Telegram and responds with synthesized speech (Eleven Labs integration)

  Security Concerns: The creator explicitly warns of potential risks. Since the gateway could theoretically be exposed, an attacker who gains access to the Telegram bot could have command access to the entire computer – including access to OnePassword credentials, SSH keys, deleting files, or installing malware. A security audit function is built in to detect such exposures.

  Context: There’s a running joke in the community that people buy Mac Minis to keep them constantly running as dedicated agent computers.

  Explicitly mentioned tools/models: Claude (Anthropic), Moldbot/ClawdBOT, GitHub, Telegram, OnePassword, Eleven Labs, Vercel. Format: Demo with first-look perspective, opinion/reflection on the security aspect.

• Je Ne Code Plus (Et Je N’ai Jamais Été Aussi Heureux)
  27.1.2026, 09:01:11

  The speaker has made over 577 commits in a year and developed three SaaS products and two mobile apps – all without writing code himself. Since discovering Claude Code and Cursor in early 2025, he has completely restructured his work model with AI-assisted development and is happier than ever.

  Initially, like many developers, he feared AI: with 90 percent of his income from React training, AI automation seemed existentially threatening. After a mental shift, however, he recognized a new role paradigm: one person can now handle 4–5 tasks in parallel (instead of one previously) because AI works faster than he can review. His work style has changed radically – instead of spending hours on individual bugs, he launches AI agents that solve parallel problems. A user request posed in the morning was deployed as a live feature by afternoon.

  Code quality hasn’t deteriorated but improved: AI automatically performs tests, refactoring, and technical optimizations – tasks he previously avoided out of convenience. Concrete examples: mobile apps are created in one to two weeks instead of not being built at all; time is freed for UX details and strategic thinking instead of maintenance work.

  He warns, however, against the “vibe coding” phenomenon: AI gives positive feedback (“Great ID, perfect solution”), which feels motivating but leads to addiction for the next prompt – similar to a casino slot machine player. You lose hours prompting for features you could have coded manually faster. Still: the job has become more fun.

  For employees, this means less need for senior developers, but also more collegial collaboration in smaller teams. Juniors get an “Ultimate Junior” partner (the AI) to learn faster. His closing message: less coding doesn’t mean less happiness – the future of development lies in architecture and craftsmanship (not lines of code).

  Claude Code and Cursor were the central tools; Format: Opinion/Reflection.

• SKILLS: La révolution que tu attendais dans l’Agent Coding
  25.1.2026, 08:00:26

  Summary: Skills – The Revolution in Agent Coding

  The video introduces the new Skills feature for Claude Code – a system that provides AI agents with specialized competency packages, similar to a developer with specific areas of expertise.

  How Skills Work: Skills are installed via the tool skill.sh (command: npx skill add ...). After installation, skills land in a local folder and contain specialized documentation and best practices – for example for Vercel, React, and Next.js. Instead of reading all files, the AI intelligently accesses only the relevant skill information when needed.

  Two Sources for Skills: Pre-built skills for tools like Stripe, Tailwind, Remotion, and other frameworks can be found on skill.sh and context7/skill. The provider warns against prompt injection attacks from skills of unknown origin.

  The Core Problem: Agents don’t automatically use installed skills – they must be actively invoked (e.g., via slash command /vercel react best practice). Those who passively wait for AI to deploy skills independently will be disappointed.

  Practical Solutions: Skills unfold their power in workflows – for example in Apex automations, where you can deliberately launch sub-agents with specific skills for particular tasks. This allows a review agent to automatically check whether new code adheres to best practices using a Next.js skill. Alternatively, project-specific skills (e.g., Convex for a particular app) can be installed locally, but must then also be manually invoked.

  Conclusion: Skills are a powerful system but require conscious integration – either manually or automated via workflows. Blindly installing many skills leads to uselessness.

  Explicitly mentioned: Claude Code, Vercel (React/Next.js Best Practices), Remotion, Context 7, Apex (Workflow Tool), Convex — Demo/Opinion with practical examples.

MoureDev by Brais Moure (1 new video)

• Por qué la mayoría de programadores junior ABANDONA (y cómo evitarlo)
  28.1.2026, 15:00:51

  The video covers the four main reasons why most junior programmers quit—and how to prevent it.

  The four pitfalls:

  1. Unrealistic expectations: Promises like “From zero to expert in one month” or “With AI you don’t really need to know how to code” are dangerous half-truths. They ignore hidden variables like intense preparation, mentoring, or luck. This makes real development feel much longer than expected.
  1. Passive tutorials and copy-paste: Watching videos and copying code feels productive (dopamine hit), but it’s just transcription. Real learning starts when you finish the tutorial, open a blank file, and have to start alone—that pain is necessary.
  1. Loneliness: Solo learning lacks feedback, exchange, and peer support. A small mistake (a bracket) becomes a mountain without external perspective, and you think you’re the only one struggling with such problems.
  1. Interpreting mistakes as lack of talent: In school, red marks mean failure. In programming, they’re everyday. The difference: juniors think “I’m bad,” seniors think “I’m making progress.”

  Four concrete solutions:

  • Slow down: Better to deeply understand fundamentals in one language than superficially know five frameworks. Depth gives security, breadth creates anxiety.
  • Break free from tutorials: Build your own, ugly project for a real problem—and use AI correctly: not as a code generator, but as a Socratic teacher. Ask questions instead of getting solutions.
  • Change your measuring stick: Don’t evaluate daily, but look back retrospectively after three months. Progress is exponential like a snowball effect.
  • Persistence over talent: Those who don’t quit win. Smart people drop out when frustrated; ordinary people who stick around land the good jobs.

  The central AI insight: AI is a multiplier, not a substitute. The mathematical formula: you’re the base number, AI multiplies it. If you’re a zero (no fundamentals), it stays 0×100 = 0. With a solid foundation, you become a 200. The market doesn’t want zero-programmers with prompt skills, but engineers who use AI.

  Core message: Biological learning processes haven’t changed through AI. Frustration isn’t failure—it’s the price for a valuable skill and a sign of progress.

  Format & context: Opinion/reflection; no specific AI tools mentioned by name (only generic references to code assistants like Copilot and Claude Code), focusing on mindset and learning strategies.

n8n (6 new videos)

• Building Growth Systems and Overcoming Imposter Syndrome [#13 Simon Coton]
  30.1.2026, 04:34:55

  Simon Coton, founder of Scrapes.ai, discusses building growth systems with n8n and how to overcome imposter syndrome.

  Growth systems – from simple to complex:

  Coton describes a five-level model: Level 1 is manual ChatGPT usage for post optimization. Level 2 brings n8n automation into play – a system generates a content list every Monday morning, maintains brand voice, but still requires manual action. Level 3 also automates posting to LinkedIn. The system pulls ideas from multiple sources (other creators, news, own content), generates posts in your own brand voice, and uses templates for consistent carousel images – a human-in-the-loop step is always important to avoid “AI slop.”

  SEO systems as Level 5: A complex setup with keyword audits of your own website, identification of ranking gaps, formation of topical clusters (internally linked content), determination of search intent, and generation of long, contextualized articles with external and internal links – 14 interconnected n8n workflows manage this process through to WordPress publication.

  Meta Ads systems: Coton uses Apify to scrape the Meta Ad Library and identify long-running competitor ads (since profitable ads run longer). Using AI, text and OCR from images as well as video transcripts are extracted to understand the structure of successful ads and reproduce them in your own brand voice. Performance is measured via Meta data (ROAS, spending, clicks) and visualized in n8n with Airtable.

  LinkedIn automation: A system scrapes likes/comments and sends automated direct messages – but with thoughtful throttling (e.g., 10 people per day) to avoid LinkedIn blocking and imitate human behavior.

  On imposter syndrome: Coton emphasizes that imposter syndrome rarely correlates with actual competence. He overcame it through consistent action: despite lacking experience, he applied for Upwork jobs, took the first one (unprofitable at 12 hours for minus 6 dollars), learned new systems in the process, and built confidence. His strategy: “As long as I’m better than the person hiring me, I’m good enough.” He advises consistency over time, asking technically savvy friends, and using ChatGPT as a learning resource. Confidence grows through repeated successes.

  Deep-dive with practical techniques: The host adds details like external/internal linking strategies, meta tag generation via AI, auto-tagging of blog posts according to defined categories, and experimenting with multi-part AI video clips (8 seconds each, composed into longer content). Coton warns about cost pitfalls with Apify (monthly vs. usage-based pricing).

  The core feature of all systems: modularity, repeatability, and human oversight rather than full automation.

  Topic areas: n8n, Apify, ChatGPT, Claude, LinkedIn, Meta Ads, WordPress, Airtable, Lovable. Format: Deep-dive interview with live workflow discussion.

• n8n Livestream: 2026 Roadmap & Apify
  29.1.2026, 17:16:21

  n8n Livestream: 2026 Roadmap & Apify — Summary

  Program and Overview

  The stream presents the n8n roadmap for 2026 with three focus areas, plus a benchmark tool and a demo with Apify. The n8n team now comprises around 220 employees, with roughly half actively working on product and engineering.

  Three Product Pillars for 2026

  1. Cutting Edge of AI

  • Human-in-the-Loop: New in v2.6 (Beta) is the ability to build approval steps between agents and tools – for example, to control critical data operations before the agent executes them. Iterative workflows are also planned, where the agent goes back to users and improves results.
  • Binary Data & Multimodality: Farewell to dollar-JSON; binary data is treated like other data types, is drag-and-droppable, and can flow between agent and tools.
  • More Complex Agents: Multi-agent architectures, stateful agents, context engineering, and support for frameworks like MCP and A2A.
  • Workflow Lifecycle: Evals (internal and external services), prompt management, and revised agent IDE.
  • AI Workflow Builder: A dedicated team is working on UI improvements (planning, context addition, change review). The builder is coming to self-hosted and possibly also outside of n8n (e.g., via MCP in other cloud platforms). A decision is planned for Q1.

  2. Platform Maturity

  • Business-Critical Processes: Performance, unit testing, retry logic.
  • Autosave & Publish: The save button is gone; autosave is active, drafts and publishing allow changes to production workflows.
  • Stack Expansion: Forms, data tables, and a new chat interface as a central point for all agents and models – with multi-model comparison, branching, and admin control. Users can receive chat access without workflow access.
  • Enterprise Features: Granular permissions, dynamic credentials (to restrict user access to specific data), provisioning, reporting, project isolation, multi-instance management.

  3. n8n Ecosystem

  • Marketplaces: Community nodes, templates, plugins (to customize core features), and an agent marketplace.
  • Partnerships: SAP partnership already announced; further integrations planned.

  LLM Benchmark (Liam)

  A new tool at n8n.io/benchmark compares LLM models by performance in specific categories (classification, tool-use, hallucination, cost). Users can describe their use cases or paste workflow JSON; the tool recommends suitable models with estimated cost calculation per workflow run. An AI search field recognizes requirements and searches automatically. The test is powered by hundreds of millions of n8n workflow runs (260+ tests, 8 data tables). Liam announces a video tomorrow; a deep-dive video is planned if there’s interest. In the future, users can submit use cases and contribute to the benchmark community.

  Apify Demo & Agent Skills

  RAG Bot Demo: Apify Website Content Crawler scrapes n8n blog, data goes into Pinecone Vector DB, chat interface answers questions with current content.

  Apify Agent Skills (announced today): Pre-built skill sets for common use cases like lead generation (LinkedIn/enrichment), brand reputation monitoring (social media sentiment), etc. These simplify navigation of the Apify store (14,000+ tools). Implemented via CLI instead of MCP (to avoid context bloat with large data volumes).

  Access: GitHub repo apify/agent-skills, installation via terminal, save API key. Coupons: 30% discount for 3 months (existing customers) or $3 credits (new customers). Feedback to integrations@apify.com.

  Community & Roadmap

  • 36 open positions at n8n (engineering, operations, finance); karriere.n8n.io/careers.
  • 20+ events by end of February worldwide; n8n.io/community/events.
  • n8n Sessions: Free workshops (Starter & Builder level) with catering in Europe and USA.
  • Ambassador Program: 85 ambassadors (24 new since last stream); at least 1 year n8n experience required; n8n.io/ambassadors.
  • Afterparty: bit.ly-party or QR code for community chat.
  • Next stream end of February; feedback welcome.

  Explicitly mentioned technologies/partners: n8n, Apify, Pinecone, SAP, MCP (Model Context Protocol), Claude, Langraph; Format: Live Q&A with product roadmap, benchmark demo, and third-party integration — technically in-depth for medium-advanced users.

• Introducing the Time Saved node
  29.1.2026, 11:08:44

  The video introduces the new “Time Saved Node” – a feature for tracking the time-saving potential of automations.

  The context: The speaker initially built a workflow that saves 50 hours per month per customer success manager. With 15 employees, that’s 750 hours monthly. This measurable result convinced management to invest in automation. The core problem: teams feel the impact of automation but can’t measure it – and what isn’t measured is questioned and deprioritized.

  The Time Saved Node enables two approaches:

  Execution Level Tracking (simple): Each workflow run stores a fixed time. In the demo example: a workflow fetches movie data from an API and saves it to Google Sheets. With “once for all items,” one time is counted per run; with “per item,” the time per processed element is multiplied.

  Variable Tracking (flexible): Different workflows or even different paths in the same workflow can have different time savings. A conditional branch can track, for example, 1 minute per item for one path and 10 minutes total for another path.

  Strategy: The goal isn’t mathematical perfection, but establishing a consistent, repeatable baseline – start conservatively, agree on the order of magnitude with the team, and refine over time. The node targets two audiences: executives want to see business value, builders want proof of impact – both get a common measurement criterion.

  Demo of a workflow-building tool focused on measuring automation impact – opinion/reflection format.

• n8n Just Got a Chat Interface | Chat Hub Beta
  28.1.2026, 13:14:11

  n8n has added a new chat interface called ChatHub in version 2.5, accessible from the sidebar. The tool offers three ways to use it: personal agents (easy to create with model selection and system prompt), workflow agents (more complex, based on full workflows with chat trigger and agent node), or direct model usage from API providers like OpenAI.

  Personal agents are designed for non-technical users and let you configure a model with a system prompt and optional tools. Workflow agents are much more powerful: they enable arbitrarily complex automations like agent orchestration, fact-checking steps, human-in-the-loop validation, guardrails, or purely deterministic logic. Each chat message is processed through the underlying workflow, and you can debug the exact execution via an info button.

  Planned features include knowledge management, built-in RAG, code artifacts, improved mobile experience, and prompt management. For teams especially relevant: a new “chat-only user” type lets you share workflow agents with people who only see the chat interface (like ChatGPT) and can’t access the workflows. ChatHub is fully available in the free self-hosted Community Edition, but the chat-only user is only available in Cloud Business and Enterprise plans.

  n8n, OpenAI — demo.

• Every Startup Should Do This [GoodOps #1]
  27.1.2026, 12:00:11

  Summary: Every Startup Should Do This [GoodOps #1]

  The video documents how Ukrainian NGOs increase efficiency through automation, enabling them to help more people. Max Maxim presents the central thesis: whoever optimizes administrative tasks can focus on actual help and has direct impact on the number of people reached.

  The starting point: The humanitarian sector is technologically outdated – 64% of NGOs have no internal IT support. Large NGOs still work primarily with Excel. For example, a hotline processed 22,000 calls in two weeks with only two employees, showing how overloaded systems are.

  Concrete real-world examples:

  • Ross founded the NGO Mission Harkve after the Russian invasion and now manages extensive medication donations (about 35 million euros in chemotherapy reserves) for cancer patients.
  • Elios Ukraine with a budget of 6 million euros, 15 regional offices, and up to 40,000 clients at any time couldn’t afford an IT specialist – every NGO must choose whether to hire an accountant, manager, or IT expert.

  The core problem: Grant seeking requires a full-time position and consists of three stages (search, prioritization, application writing). Data management is also manual: large data volumes, removing duplicates, enabling fast regional response – all without digital systems.

  The solution: Max is developing an operating system for NGOs that automates admin, logistics, and effectiveness measurement.

  Format: Opinion/reflection with testimonials.

• ‘Personio’s journey with n8n’ – n8n Business Lab (November 2025)
  26.1.2026, 08:09:33

  Personio’s Journey with n8n – Case Study

  Jay and Rob from Personio discuss how the company is rolling out n8n to automate its IT processes.

  Starting point and strategy: Personio is a small IT team that had to handle a huge flood of automation requests after the company focused on AI and automation. Instead of building everything themselves, the team opened the platform to all employees – a risky decision at first. A three-month pilot with the People Operations team provided important insights, especially on security.

  Training and enablement: The team created custom courses based on YouTube content and quizzes for non-technical users. Since then, 116 participants from 15 departments have attended. Weekly office hours support the teams. The result: around 150 hours per month in time savings, growing with each new team added.

  Security approach (Rob): The critical challenge was visibility over user activities with both technical and non-technical users. Rob implemented an automated security audit workflow with n8n’s built-in security report feature, which classifies workflows and automatically disables risky workflows (with user notification). Results are forwarded to Datadog for comprehensive observability dashboards that visualize errors, anomalies, and credential usage.

  Next steps: Two separate n8n instances are planned with source-control enforcement and automation of the currently manual review process for new workflows.

  The approach balances user freedom with strict compliance guardrails; n8n serves as the central platform for automation and its enterprise governance.

  Format: Live Q&A/demo from the n8n Business Lab, two practitioner perspectives (IT management & security engineering).

Nate Herk | AI Automation (4 new videos)

• I Turned Clawdbot Into the Ultimate Personal Assistant
  30.1.2026, 03:23:34

  Summary: Clawdbot as a personal AI assistant

  The creator configured Clawdbot as “Klouse” — an AI assistant that autonomously built a YouTube analytics dashboard at night with video performance tracking, comment analysis, competitor research, and title generation. Klouse gained access to GitHub, Brave Search, OpenAI, 11 Labs, PDF tools, ClickUp, Google Workspace, and YouTube APIs, and has its own Gmail, Drive, and Calendar account (to ensure security and control). The system runs via a Kanban board, Telegram integration, and a status panel showing whether Klouse is working, thinking, or inactive. Every 30 minutes, a “Heartbeat” executes, syncing dashboards, checking notes, monitoring social media, and creating daily summaries.

  To set it up, the creator recommended: (1) Install Cloudbot on a Mac Mini or VPS, (2) Get to know Klouse through intensive interviews and create Soul/User files, (3) Set up dedicated accounts for the AI agent with strict permissions instead of granting direct access, (4) Train the AI for proactive action (morning briefs, email monitoring every 10 minutes, weekly audits), (5) Set clear automation goals. The five most important hacks: Use Plan-First mode and save plans in documents for Klouse to read; enforce proactivity through contextual problem-solving; create audit docs on errors so the AI learns; understand the memory system (Daily Logs, Long-Term Memory, project-specific files) and give targeted prompts for storage; bundle everything in one message when it should be processed together. Costs run ~$223 for Opus 4.5 in 3 days (nearly 250 million tokens), could decrease with Sonnet or locally hosted models. The system causes regular frustration through unexpected memory loss, requiring precise prompting and understanding that the AI, despite its capabilities, consistently makes mistakes.

  Explicitly mentioned: Claude (Opus 4.5, Sonnet 4.5), Anthropic (API/Account bans), Cloud Code, Perplexity, ClickUp, Google Workspace, Telegram. Format: Demo/Deep-dive with tutorial elements.

• 100 Hours Testing Clawdbot vs Claude Code (honest results)
  28.1.2026, 03:52:23

  The author tests two AI tools against each other over approximately 5 hours: Claude Code (a coding assistant in the IDE) and Claudebot/Moltbot (a 24/7 system running on a server). He rates both on eight metrics and arrives at these results:

  Out-of-the-box capability: Claude Code 7/10, Claudebot 9/10 — Claudebot immediately feels like a real employee with memory and automations, Claude Code is more technical and less accessible.

  Setup friction & risk: Claude Code 8/10, Claudebot 6/10 — Claude Code installs easily as a VS Code extension, Claudebot requires terminal knowledge and VPS setup (the author uses a server hosted on Hostinger).

  Cost: Claude Code 8/10, Claudebot 6/10 — Claude Code costs $20–200 monthly for tiered plans but is cheap relative to engineer equivalency. Claudebot incurs hardware and API key costs; if you’re not careful, bills can quickly accumulate (the author consumed 80 million tokens in one day for around $80).

  Power & access: Claude Code 7/10, Claudebot 10/10 — Claudebot explicitly has full system access (which also increases risk), Claude Code is limited to folders and projects.

  Security: Claude Code 7/10, Claudebot 3/10 — A tweet from the Claudebot creator indicates the tool is only 3 months old and not suitable for beginners. A security researcher found over 900 unprotected servers with exposed API keys and private chat histories. The main issue: a default setting meant for local testing was deployed unsecurely online.

  Everyday usability: Claude Code 6/10, Claudebot 9/10 — Claude Code requires a terminal or VS Code, Claudebot works anywhere via Telegram, WhatsApp, or Slack and can work proactively.

  Actual ROI: Claude Code 8.5/10, Claudebot 6/10 — Claude Code shows proven results: teams deliver 10–30% faster, features like Codemod were built entirely with it. Claudebot is too new to show real success stories and time savings.

  Target audience (no score): Claude Code targets software engineers, technical founders, and non-technical users willing to learn. Claudebot is currently aimed at technical founders, indie hackers, automation enthusiasts, and security-conscious tinkerers — the creator himself warns: “Most non-techies should not install this.”

  Bottom line: Claude Code wins with 51.5 points versus Claudebot’s 49 points — mainly on security, proven results, and lower risk. However, Claudebot feels “like the future” and excels through accessibility and constant presence. The author warns against using tools just for the hype and emphasizes: Winners are not those who wait until everything is simple, but those who experiment now.

  Claude (Opus 4.5) and Open Router were mentioned as underlying models; Claude Code and Moltbot/Claudebot as primary tools compared — Deep-dive with structured comparison.

• Set Up Clawdbot on a VPS in Minutes (no mac mini)
  27.1.2026, 04:01:27

  The video is a step-by-step tutorial for deploying Claudebot on a Virtual Private Server (VPS) at Hostinger — a cost-effective alternative to buying a Mac Mini.

  Setup process:

  The creator shows how to configure a VPS with specific specs (2 vCPU cores, 8 GB RAM, 100 GB storage), select Ubuntu 24.04 LTS as the operating system, and then install Claudebot via terminal commands. All terminal commands are provided in a document for copying. After installation, an onboarding flow guides configuration: Anthropic as model provider with API key, model selection (Opus 4.5), and connection via Telegram using BotFather to create the bot. Optional skills like Homebrew or Google integration (Nano Banana) can be added.

  Access & persistence:

  The creator shows how to access the dashboard via local web UI and test Claudebot. Importantly, a gateway must run continuously on the VPS. A service file is created with systemd so Telegram integration remains active after restarts — the creator demonstrates this by ending the terminal session, the web UI going offline, but Telegram communication continuing to work.

  Security:

  The creator explicitly warns: Claudebot is an autonomous system that can execute commands, open files, and access connected apps. If compromised, an attacker could control all resources. Risks include data leaks, prompt injection via emails, and unintentional exposure of secrets. That’s why Claudebot should run isolated (separate email, read-only permissions), not in production environments, and is only suitable for power users who understand the risks.

  Claude / Anthropic as model provider discussed; Demo/Tutorial format with practical security reflection.

• Agentic Workflows Just Changed AI Automation Forever! (Claude Code)
  25.1.2026, 16:00:21

  Agentic Workflows Just Changed AI Automation Forever (Claude Code)

  Agentic workflows represent a fundamental paradigm shift in AI automation. While traditional workflows execute exactly what is specified, agentic workflows work differently: you give them the desired outcome, they determine the steps themselves — comparable to instructing a human developer.

  Four major changes:

  1. Self-healing: The agent detects errors, tests fixes, and corrects its own code automatically without you manually debugging error messages — eliminating the classic time-consuming debugging process.
  1. Natural language control (on a new level): The agent asks precise questions before building (users? run frequency? which tools?) to create robust solutions — not just superficial 60% solutions like earlier AI builders.
  1. Security as an automated loop: While the agent writes and edits its code, it simultaneously checks for security vulnerabilities, hidden API keys, and unauthorized data flows — you can define guard rails in natural language.
  1. Instant API & MCP integrations: The agent independently researches API documentation instead of you manually working through it; MCP (Model Context Protocol) functions as an app store for pre-configured tools.

  Live demo: Lead generation for an AI agency

  The creator shows how to build a workflow system in Claude Code (VS Code plugin) — with a system prompt (claude.md), tools (Python scripts), and workflows (Markdown SOPs). After explaining the use case to the agent in natural language (scrape Chicago dentists, write personalized outreach, export to Google Sheet), the agent asks clarifying questions (data source? message tone? API keys available?). It then automatically builds three tools and a workflow file. The result: personalized lead lists with names, addresses, ratings, and tailored outreach emails — all in minutes, whereas this would have taken over an hour in n8n.

  The future of agentic workflows:

  • Fully autonomous workflows: Instead of just reacting, agents proactively scan CRM, inbox, project management for inefficiencies and risks, suggest fixes, or act independently.
  • Agents managing agents: Specialized agent teams (email agent, research agent, manager agent) coordinate with each other instead of monolithic all-purpose agents.
  • Agent-to-agent protocols (A2A): Google Cloud announced an open standard (supported by Salesforce, SAP, ServiceNow, Workday, and others) so agents from different vendors can communicate securely and share context.
  • Long-running project agents: Techniques like continuous loops and shift-based work (one agent works, notes status, next agent reads notes) should enable agents to work on complex projects over weeks/months without hallucinating or repeating themselves.

  Why n8n experience remains valuable:

  The implementation layer becomes simpler, but true value lies in process decomposition, system vocabulary, error understanding, and the ability to grasp business problems, integrate legacy systems, and optimize automations long-term — skills n8n builders already possess.

  Market outlook: Gartner predicts that 25% of enterprises using GenAI will launch agentic pilots in 2025, reaching 50% by 2027. The AI agent market grows from ~$8 billion USD (2025) to $40–50 billion USD (2030) — 43% annual growth, driven by workflow automation.

  Claude AI and Claude Code were consistently featured as central tools; Deep-dive with opinion/reflection elements.

NeuralNine (2 new videos)

• CrewAI Tutorial: Multiple Agents Working Together in Python
  30.1.2026, 17:00:37

  CrewAI Tutorial: Multiple Agents Working Together in Python

  Content: CrewAI is a Python framework for orchestrating multiple specialized agents working together on complex tasks – a “separation of concerns” approach instead of a single agent with many tools. The tutorial builds a practical example: a crew analyzes stocks based on a ticker symbol (e.g., AAPL) through multiple agents with different roles.

  Setup and Installation: After installing UV (Rust-based Python package manager), a new project is generated with crew ai create crew. The setup requires selecting an LLM provider; the author shows OpenAI (with API key) and local Ollama as alternatives.

  Project Structure: The project consists of a custom tool (a function querying the Yahoo Finance API that returns stock price and news headlines), agents (defined in YAML with role, goal, backstory), and tasks (also YAML with description, expected output, assigned agent). In the example: a “Collector” agent gathers data, a “Summarizer” condenses it to five points, a “Risk Checker” identifies risks, and a “Brief Writer” creates a daily summary.

  Implementation: Agents are instantiated in crew.py, main.py runs the crew with input (e.g., ticker “AAPL”). The command crew ai run orchestrates sequential execution: each agent uses its tools and passes the result to the next. Switching to Ollama only requires updating the .env file.

  Conclusion: The author emphasizes that while CrewAI is interesting to explore, he would prefer LangGraph for production scenarios – CrewAI is better suited as an exploration project, not primarily as an enterprise solution. Full code is available on his website.

  Tools: OpenAI (GPT-4o) and Ollama (local) are explicitly used as LLM providers; framework is CrewAI with Yahoo Finance API as data source. — Tutorial, beginner-friendly.

• Okara AI: The Private & Secure Alternative To ChatGPT
  26.1.2026, 17:00:36

  Okara AI: Private and Secure Alternative to ChatGPT

  The video presents Okara AI as a privacy-focused alternative to ChatGPT. The criticism of OpenAI and ChatGPT: user conversations are stored and shared when requested by authorities, ads are being introduced, and the platform censors sensitive content.

  Okara instead offers end-to-end encryption, avoids data exploitation for training, and performs genuine data deletion. The platform allows using multiple open-source models and switching between them within a conversation. In the demo, the creator shows: (1) text generation with DeepSeek to write a blog post about linear regression, (2) model switching with Qwen 3 to summarize, (3) code generation with GLM model for Python implementation, (4) web search with YouTube and Reddit integration for research, (5) image generation with Z-Flux turbo and subsequent editing with Qwen Image Editor. The workflow demonstrates how seamlessly different specialized models can be combined. A free tier with five credits (approximately 50 messages) enables testing.

  Okara AI for demonstrating multiple open-source models (DeepSeek, Qwen, GLM) with focus on privacy and flexible model switching; demo format.

Nic Conley (2 new videos)

• The Easiest Setup: ClawdBot on a VPS (Openclaw/Moltbot)
  31.1.2026, 23:08:45

  The Easiest Setup: ClawdBot on a VPS

  This video is a step-by-step tutorial for setting up OpenClaw (formerly Claudebot/Moltbot) on a virtual private server instead of dedicated hardware. The speaker explains that OpenClaw is an open-source project that provides agent functionality – for example, someone built a bot with it that independently obtained a phone number via Twilio and called the owner.

  Setup Process:

  The user deploys OpenClaw through Hostinger (8 GB RAM, 100 GB storage for approximately $150 over 24 months with coupon). After VPS setup, Docker Manager is installed, then OpenClaw is deployed from the catalog. Three configuration steps are central: (1) save the gateway token and connect it in the dashboard, (2) enter API keys from OpenAI or Anthropic (speaker uses GPT-4o/4.5), (3) Telegram integration via BotFather in Telegram – create a new bot, copy the access token and your Telegram user ID into the JSON configuration. After that, the bot is ready to use and can communicate via Telegram.

  Capabilities & Use Cases:

  The bot can act proactively (e.g., send reminders, analyze customer emails and prepare drafts), install skills (via voice instead of terminal), make reservations (also via calls through 11 Labs), perform SEO audits, or develop trading strategies. The speaker shows live examples: the bot asks about preferences, can help with skill setup, and acts independently.

  Security Considerations:

  The speaker warns against overexposure – don’t simply grant access to email, bank accounts, or entire computers. Main risks: prompt injection (attackers could embed hidden commands in messages), open API access, and autoposting without control. A VPS is safer than local installation since risks are isolated. In an emergency, you can stop or delete the bot via Docker. The speaker emphasizes that OpenClaw is still experimental and you operate at your own risk.

  Conclusion:

  Unlike ChatGPT, this is a true agent that thinks proactively and works like an employee – impressive but also potentially problematic.

  OpenAI (GPT-4o/4.5 models) and Anthropic highlighted as explicit AI providers; Anthropic API optional; integrations with 11 Labs mentioned; tutorial format with live setup demo.

• How to Use Claude Code in VS Code in 3 Minutes
  26.1.2026, 16:22:14

  Summary

  This video shows step-by-step how to install and use Claude Code in VS Code. First, VS Code is downloaded and installed (from code.visualstudio.com), then a new project directory is created. Next, you open the terminal in VS Code and run the installation command for Claude Code (from code.claude.com/docs/overview). After successful installation, Claude can be activated by typing “claude” and pressing Enter. The video particularly highlights “Plan Mode,” which you can access by pressing Shift+Tab – this lets you strategically discuss app planning with Claude before he starts coding. The setup requires a Claude subscription starting at $20 per month and allows the user to then work with Claude in the terminal and use features like Plan Mode.

  Conclusion: Claude Code, tutorial for complete beginners.

Nick Saraev (4 new videos)

• It’s Official: AI Makes You Worse At Stuff
  31.1.2026, 18:27:51

  Anthropic study shows: AI-assisted programming deteriorates core skills

  Anthropic conducted a controlled study comparing two groups of developers – one with AI assistance, one without – while learning a new Python library. The result: the AI group achieved 17 percent lower test scores than the non-AI group (equivalent to two letter grades). Processing time differed only marginally (about 2 minutes advantage for the AI group) and was not statistically significant. The study was straightforward: warm-up without AI, main task with or without AI access, followed by a quiz without AI usage.

  The author argues this shouldn’t be surprising – like archery giving way to rabbit hunting, humanity has long branched into technological solutions that erode core skills. However, Anthropic differentiates: earlier studies showed AI can boost productivity by 80 percent on familiar tasks; here it’s specifically about learning situations, where extra effort (trial-and-error, different approaches) is essential for knowledge building.

  The author distinguishes between horizontal and vertical growth: horizontal is scaling existing processes (more code, more websites); vertical is genuine innovation (inventing better methods). AI amplifies horizontal growth at the expense of vertical and skill development. His recommendation: consciously decide what to outsource – economically valuable skills to AI, but not relationships, critical thinking, major life decisions. He concludes by reflecting that work has historically always redefined itself, and this will happen again.

  Claude / Anthropic; opinion/reflection, deep-dive element through study analysis.

• It Got Worse (Clawdbot)
  29.1.2026, 02:30:55

  Summary: It Got Worse (Clawdbot)

  The creator documents a security crisis around Clawdbot: hundreds to possibly thousands of instances were hacked, users permanently locked out of Claude, and malicious Clawdbot skills were uploaded to the Hub (renamed to Moltbub).

  The core problem: Anyone running Clawdbot on a VPS must expose the instance via a public URL. Services like Shodan scrape and index all URLs on the internet. A simple text search for the words “Clawdbot” and “control” reveals thousands of running instances accessible with a single click. Worse: if a server uses Engine X as a reverse proxy, a quirk in the Clawdbot integration allows full control over the admin interface – access to all sent messages, skills, configurations, and API keys. A security researcher (Jameson) demonstrated this live and was able to extract complete message histories and identities. The danger is dramatic because users typically upload all their API keys, tokens, and secrets for external services (Anthropic, messaging platforms) to Clawdbot.

  Security measures:

  • Don’t use default port 18789, instead configure a random port
  • Set a password (most already do, but it’s the default setting)
  • Update Clawdbot to the latest version, which patches the Engine X reverse proxy exploit
  • Configure gateway.trusted_proxies, especially behind Engine X/Caddy
  • Use Tailscale or VPN to lock down the infrastructure
  • Rotate all API keys if the instance was already public

  Supply-chain risk: Moltbub (the skill repository) is unsecured. Jameson built a malicious Clawdbot skill with a backdoor, manipulated the download count to over 4,000, and landed on the homepage – because Moltbub ranks purely by downloads per time unit. The creator recommends: check before downloading (social media, GitHub author verification, reputation), read all skill files (ideally have a fresh Claude/Gemini/ChatGPT model review them), and treat Moltbub like npm – assume everything is potentially malicious.

  Conclusion: The creator currently doesn’t trust Clawdbot with his API keys and won’t cover Clawdbot on the channel anymore.

  Tools/models: Clawdbot, Claude, Gemini, ChatGPT, Codeex; opinion/reflection + security deep-dive.

• Clawdbot Sucks, Actually
  27.1.2026, 17:31:22

  Summary

  The author tests Claudebot – Anthropic’s Opus 4.5 coupled with Telegram and cron scheduling – and concludes it’s largely marketing hype. The technical features (sending messages via Telegram, automating tasks) aren’t paradigm shifts; similar functionality has existed for months with agentic workflows. The massive hype is primarily explained by a pump-and-dump coin scam: after Peter Steinberger released the originally “Claudebot” named project (later renamed to Moltbot due to Cease-and-Desist from Anthropic), crypto grifters took over the abandoned accounts, launched a Claude token on Solana, pumped it to 16 million dollars, then crashed it 90% – a classic social media astroturfing play. The presented use cases (folder organization, “research”, daily good morning messages) are either solvable by existing tools or simple AI features Opus 4.5 already had. Serious problems: Claudebot incurs high token costs (sometimes 300 dollars in two days), has massive security holes (over 900 instances with open ports found), and the founder himself warns that non-technical users shouldn’t install it. Many tutorials rely on regular Claude for debugging and spread unsafe methods. The author warns about the credibility of influencers simultaneously showing AI gains and selling courses.

  Format & tools: Opinion/reflection, explicitly critical analysis of Claude (Opus 4.5) and the open-source project Claudebot/Moltbot.

• I Gave Claude Code My Whole Genome (And It’s Amazing)
  26.1.2026, 17:55:15

  Summary: I Gave Claude Code My Whole Genome (And It’s Amazing)

  The creator input his complete genetic information into Claude Code to build a system that matches his DNA snippets against public databases and provides information on disease risks, vitamin and training needs. The results: he discovered he’s a carrier of the Cystic Fibrosis gene, optimized his diet and supplementation based on genetic vulnerabilities, and significantly improved sleep quality, energy, and physical performance.

  How it works: The system uses a Claude Code prompt that processes a DNA file (e.g., from 23andMe, ~621,000 genetic variants) and matches it against two main databases: PharmGKB (drug-gene interactions) and ClinVar (genetic variations and their scientifically documented effects). The result is three automatically generated reports: a comprehensive genetic report (drug metabolism, nutrition, cardiovascular, sleep, etc.), a disease risk report (including pathogenic variants), and an “Actionable Health Protocol” with daily routines, supplement recommendations, and dietary guidelines.

  Practical implementation: The creator provides templates and files (downloadable via GitHub). The process starts with inexpensive genotyping (50–100 dollars via services like 23andMe, Ancestry, or MyHeritage); the resulting file is uploaded in VS Code with the provided Claude Code setup and processed. What once cost tens of thousands of dollars is now accessible to everyone. Possible extensions: full genome sequencing, apps, integrations with fitness trainers or doctors, or use for family planning before having children.

  The creator emphasizes this isn’t medical advice and warns of psychological burden from potentially disturbing genetic findings – only suitable for people who can constructively handle such information.

  Claude Code with the Genetic Health Analysis Pipeline was demonstrated as the main tool; a demo tutorial with live workflow (file upload, prompt execution, report generation) was shown.

Niklas Steenfatt (1 new video)

• Niklas Steenfatt vs Magnus Carlsen
  25.1.2026, 09:00:25

  Niklas Steenfatt faces Magnus Carlsen in Cape Town for a simultaneous exhibition match and first describes Carlsen’s outstanding dominance in chess: he has been leading the world rankings unchallenged for 14 years, had a streak of 125 games without a loss, and is the de facto best player despite not defending his World Championship title recently. The simultaneous event featured 25 players – South African talents, two well-known chess streamers, and Steenfatt himself, all playing against Magnus and his team partner Levon Aronian at the same time.

  Surprise: it wasn’t classical chess being played, but Freestyle Chess – a variant conceived by Bobby Fischer in 1996 (also called Chess 960) in which the piece setup on the back rank is randomly drawn. Steenfatt had played three training games online beforehand and lost them all, which made him nervous before his own match against Magnus and Aronian – especially since Aronian is one of the world’s best Freestyle players and even won the tournament the following week.

  The game itself: Steenfatt broke the symmetry early, made an aggressive queen move (rather undesirable in classical chess) and forced Magnus into longer deliberation. But after a few moves, he drifted into a bad position, underestimated the danger on the g-file, and lost decisively. He tried to save himself with perpetual check tactics, but Magnus knew this strategy and blocked all escape routes.

  Steenfatt emphasizes in the end that the favorite was clear from the start and he took a lot from the experience – plus, as one of three streamers, he was invited to an exclusive party with the chess elite where he analyzed the game and won another party as a finale.

  Demo and personal adventure vlog without explicit mention of AI tools; entertainment-driven chess competition video in a relaxed tone.

No Priors: AI, Machine Learning, Tech, & Startups (1 new video)

• How Until is Engineering Away Time in Organ Transplants | Laura Deming
  January 29, 2026, 11:01:42

  Summary: How Until is Engineering Away Time in Organ Transplants

  Laura Deming, CEO and Co-Founder of Until, discusses reversible cryopreservation – freezing and controlled rewarming of living tissue – as a medical intervention. The long-term goal is reversible whole-body cryopreservation for medical hibernation; in the short term, Until is working on reversibly cryopreserved organs for transplantation.

  The central technical challenge is ice crystal formation: water expands when frozen and damages cell membranes. However, there exists a temperature (roughly below −30°C) below which ice crystal formation stops. The key is traversing this danger zone quickly without forming ice crystals, by optimizing cooling and rewarming rates and using cryoprotective chemicals – with a trade-off between engineering (faster temperature change) and biology (toxicity of protective chemicals).

  Deming emphasizes that reversible cryopreservation is not an unsolved fundamental problem: human embryos have already been cryopreserved for over 30 years and later used successfully. Similarly, studies showed that cryopreserved kidneys in rats recovered normal function after approximately one month. The question is not whether it’s possible, but how to scale it to large, complex biological systems.

  The first practical application: transplant patients currently wait under severe time pressure for organs that deteriorate rapidly. They must remain within a certain radius of a transplant center. Reversible organ preservation would eliminate this time pressure, enable optimal matching, and allow surgeons to work under better conditions – a paradigm shift, as one surgeon put it.

  Deming emphasizes that Until works in parallel on organs and small animals (rats). One major unknown remains the brain and neural integrity in whole-body cryopreservation. She also discusses why such research has received fewer resources so far: societal skepticism, confusion with science fiction, and lack of mainstream acceptance – although longevity and aging have become significantly more legitimate research fields in recent years.

  In closing, Deming expresses interest in how the molecular world could be presented in an understandable and fascinating way – beyond flat textbook diagrams.

  Deep dive into Until and reversible cryopreservation, conversation format.

Productive Dude

No new videos in this period.

Sebastien Dubois (1 new video)

• Your Dataview Queries Are Invisible to AI. Here’s How to Fix That
  28.1.2026, 12:21:32

  Summary: Dataview Queries Visible to AI

  The creator introduces a plugin that solves the problem of Dataview queries in Obsidian being visible only within the app – not in text files or to AI tools. The plugin serializes the results of these queries as Markdown directly into the note file, so the data is persistently stored and accessible from outside.

  Key benefit for AI: When an AI tool reads a note file, it can now see the actual query results (as Markdown tables or lists), not just the query syntax itself. This enables significantly better context analysis.

  Installation and configuration: The plugin can be found in Community Plugins under “Serializer”. It’s important to exclude folders like Templates from automatic scanning to avoid unwanted serializations. The plugin runs in the background and keeps serialized results automatically up to date.

  Usage modes: Queries can be set to “automatic” (constantly updated), “manual” (refreshable via button), “once” (serialized only once), or “once and remove” (query disappears after one execution). The plugin supports inline queries, task queries, tables, and multi-line queries – essentially all standard Dataview syntax.

  Additional benefits: The serialized links are integrated into Obsidian’s knowledge graph (unlike standard Dataview), which improves node connectivity. When publishing notes (Obsidian Publish, Quartz, etc.), the query results also appear in the generated HTML because they are now part of the Markdown file.

  Commands: The plugin provides palette commands for inserting new serializer blocks, forcing complete or file-specific refreshes, and converting individual or all standard Dataview queries into serializer format.

  Demo of an Obsidian plugin (Serializer) with practical examples for integrating Dataview results into AI workflows.

Simone Rizzo (3 new videos)

• L’agente AI che tutti stanno installando… pericolosamente (Clawdbot)
  28.1.2026, 20:36:35

  Summary: Molt Bot (formerly Cloud Bot) – The viral AI agent project

  Molt Bot is a highly viral open-source project that accumulated over 80,000 GitHub stars in just a few days. It’s an AI agent that runs locally on your computer and can be controlled via messaging apps like WhatsApp, Telegram, Discord, and Signal. The core use case: You send commands from your smartphone via any app, and the agent executes them 24/7 on your local PC – essentially like an employee permanently sitting at your desk. The hype was fueled by many users buying Mac Minis to install Molt Bot on them, and the project is installable with a simple terminal command.

  The author rejects a detailed installation walkthrough and emphasizes that the technology isn’t particularly innovative – it simply combines existing open-source tools (like OpenWork, the open variant of Claude Code/Cowork) with a Telegram bot interface. His actual criticism centers on massive security risks:

  Key risks: The agent has full access to your file system, browser, and installed applications. This enables: data deletion or manipulation, accidental damage from software bugs (infinite loops), access to email/banking accounts and online services. The most critical scenario involves adversarial prompts – hidden malicious instructions embedded on websites that the agent executes while browsing, misinterpreting them as legitimate commands. This leads to data leaks, account takeovers, or unauthorized transactions.

  Security experts share these concerns; multiple official articles and security research (including Salt Prime) document critical vulnerabilities. The problem isn’t limited to Molt Bot – all web-capable AI agents (including GPT, Claude) are susceptible to prompt injection.

  The author summarizes his conclusion: The project lacks innovation, offers no meaningful use cases (MCP server connections could provide the same functionality more safely), and will lose relevance in the medium term. He warns against misinformation fueled by viral hype and instead recommends structured automation with human oversight.

  Mentions: Claude/Anthropic, OpenAI, MCP servers, open-source tools; Format: Opinion/reflection with security deep-dive.

• Kimi K2.5: il modello cinese che crea siti web e controlla 100 agenti in parallelo
  27.1.2026, 19:52:04

  Kimi K2.5: Visual Agentic Intelligence with Parallel Agent Swarm

  Kimi, a Chinese company, has released K2.5 as a new model for visual agentic intelligence. The core capability is generating front-end websites using visual techniques: The model can reconstruct complete webpages from videos, identifying layouts, animations, and sections, then converting them into code. K2.5 also demonstrates excellent performance on more complex visual tasks like solving mazes.

  But the revolutionary component is K2.5 Agent Swarm Beta: This version automatically orchestrates up to 100 sub-agents in parallel, executing independent tasks and handling up to 1500 tool calls – without predefined workflows. The model was created through continued pretraining on 15 trillion mixed-modal data points and is trained with a new paradigm called Parallel Agent Reinforcement Learning, which teaches the orchestrator to decompose tasks into parallelizable subtasks. This reduces execution time by 80% and throughput time by a factor of 3.7 to 4.5 depending on complexity.

  In benchmarks, K2.5 Agent Swarm even outperforms Claude Opus 4.5 on tasks like browser automation and code generation. For pure coding, it falls short of GPT 5.2 and Claude 4.5 Opus. Price-wise, K2.5 offers 5–21x cost savings depending on the benchmark. The model is based on a mixture-of-experts architecture with 384 experts (32 billion active parameters out of 1 billion total), is freely available on Hugging Face, and can be tested via Kimi.com (Agent variant free for paying users, Agent Swarm still in beta).

  Mentioned tools/models: Kimi K2.5 (including Instant, Thinking, Agent, and Agent Swarm variants), Kimi Code, OpenAI GPT 5.2, Claude 4.5 Opus — Deep-dive/demo of an open-source model.

• Clona QUALSIASI voce gratuitamente: Qwen ha appena cambiato tutto!
  25.1.2026, 14:07:57

  Summary: Qwen 3 Text-to-Speech – Voice Cloning and Voice Design

  Qwen 3 Text-to-Speech is a fully open, multilingual model enabling audiobooks, customer service, podcasts, and avatar videos – and importantly for German-language users, it also supports Italian and other languages. The model comes in two sizes (1.7 and 0.6 billion parameters), allowing local execution on standard GPUs.

  Core features include Voice Design (generate new voices from text descriptions), Custom Voice (control and adjust existing voices), and Voice Cloning (clone voices from seconds of audio). The model is based on codec-token architecture, streams audio with under 97 milliseconds latency, and intelligently recognizes structures like emails, numbers, and mathematical formulas – though number processing (e.g., units, degrees) still shows weaknesses.

  The practical demo shows the 0.6B model with various premium voices in multiple languages and the 1.7B model with significantly better quality. With Voice Design, you can define a voice very precisely (gender, age, tone, pace, emotions), then use it for any text or adjust individual attributes. Voice Cloning works with short audio samples (longer is better), but only partially recognizes the original voice – particularly with personal audio, inaccuracies appeared with just a few seconds of input.

  The model requires only 4 GB VRAM and needs no subscriptions. It’s positioned as a significant advancement that competes with established providers like Eleven Labs.

  Tools/models: Qwen 3 Text-to-Speech (open-source) — Format: Tutorial/demo.

Tech With Tim (3 new videos)

• Meta Acquired This AI Agent… And It’s Actually Incredible (Manus AI)
  31.1.2026, 14:14:36

  Meta has acquired Manus, an autonomous AI agent with real execution capabilities. Manus can execute code, query APIs, automate workflows, and deliver production-ready results – not just generate text. Version 1.6 brings a new agent architecture model with 30% better persistence for deep research, improved tool usage, and the ability to build iOS and Android apps while working visually with a new design view.

  The video demonstrates several concrete capabilities: The “My Browser” feature allows the agent to open tabs in your own Chrome and work with existing login credentials (example: search Amazon, add products to cart). The design view can generate logos and images and edit them visually – through text changes, AI-based style adjustments, and precise point markers for minor edits. With just a few prompts, the agent then built an iOS/Android app called “Parallel Lives” (a global daily-question app) that was runnable on the phone via QR code and featured responsive design, data visualization, and profiles. The data analysis capabilities also enabled a complete expense analysis of credit card transactions from Google Sheets with graphs, charts, and Excel export – Manus downloaded the data locally and analyzed it.

  Manus, demo video with deep-dive character

• Your Resume Isn’t The Problem…
  29.1.2026, 13:01:20

  Summary:

  The video addresses why software developers don’t get interviews – not because of missing skills, but because they don’t understand how recruiting works. Interviews are pattern matching, not talent discovery.

  Core points on positioning and branding:

  Recruiters scan a profile in 5-10 seconds and ask one question: “Does this person fit what I’m looking for?” Generalist profiles fail – a broad list of technologies looks unfocused and risky rather than flexible. You need to commit to a role (backend engineer, frontend engineer, data engineer, etc.) and communicate that clearly in your headline and resume. The headline should immediately say what you are, not what you’re learning. A resume is a marketing document for a specific position, not a biography. Experiences should be framed around relevance and outcomes rather than tasks; CSS tweaks don’t matter for backend roles, APIs and databases do. Projects should reinforce your target role – three strong relevant projects beat ten random ones. Clear positioning alone can double or triple your response rate.

  Application strategy:

  The goal isn’t volume, but targeted volume – applications in one lane (same role, similar tech stack, similar company types). Direct applications on company websites are better than one-click apply. Critical: follow up via LinkedIn message or email. A short, personal message (name, company, one concrete reason) stands out. A student achieved a 50% response rate with personalized 30-45-second videos.

  Additional strategies:

  Keep two to three resume versions for different role types instead of constantly rewriting. Start assessments or take-home tests quickly – procrastination amplifies anxiety, and momentum is central. A basic weekly structure (applications on certain days, prep on others) creates focus. Don’t wait for perfection – apply directly and learn in parallel. Interviews create urgency and focus.

  The video mentions a coaching program called Devaunch with a senior technical recruiter and former Google/Amazon/Microsoft employees. — Opinion/reflection.

• This Open-Source AI Video Model Just Crushed Sora
  27.1.2026, 13:01:03

  Summary:

  LTX S2 is a new, free open-source model for video and audio generation that can run locally on your own hardware with just 12GB VRAM (e.g., on a five-year-old RTX 3060). The model supports text-to-video, image-to-video, video-to-video, and audio-driven generation with native 4K output, lipsync synchronization, and clips up to 32 seconds long. It’s approximately 18 times more efficient than the next best model – on datacenter GPUs like an H100, it generates 10-second videos in 15-20 seconds.

  The model ships with full weights, training code, benchmarks, and ComfyUI workflows, and offers LoRAs and control adapters for motion, structure, and camera movement. The key advantage of the open-source release isn’t primarily raw quality, but control: weights and training code are open, the model runs locally on your own infrastructure without data leaving your computer. This enables integration into existing production pipelines (such as a Premiere Pro plugin), deep fine-tuning capabilities, and use in node-based systems like ComfyUI, rather than just through closed APIs.

  Community examples show impressive results: consistent 27-53-second clips with complex lipsync and sound design generated locally by users with standard hardware. For getting started, LTX also offers a web platform (LTX Studio) with a credits system; for local use, you need to download the roughly 300GB model and configure it via ComfyUI – a process requiring technical understanding but fully documented.

  Classification: LTX S2 was featured, demo video with comparison and community example elements.

TheAIGRID (4 new videos)

• Googles New Genie 3 Just Shocked The AI World And Broke The Stock Market.
  31.1.2026, 09:33:00

  Google has unveiled Genie 3, a playable world model that allows users to sketch an environment and character, then immediately interact with it – like in a video game. The tool is currently exclusive to Google AI Ultra subscribers in the US (18+) and limits play sessions to 60 seconds to conserve computing resources. World models are valuable because they allow AI agents to train in virtual environments.

  Genie 3 has recognizable weaknesses: characters can clip through terrain (unlike typical game engines with hard physics), many objects remain static instead of moving, controls aren’t intuitive, and prompt-based changes can have unintended side effects – that’s the probabilistic nature of generative AI. Still, the community saw impressive demo examples: a mock-3D game in the style of Breath of the Wild plus realistic scenes (skiing adventures, underground scenes, body-cam perspectives).

  The “stock market drama” arose from misinformation: a tweet falsely claimed a GTA-V gameplay video (converted to realistic gameplay using Runway video-to-video) was Genie 3. This misinformation went viral and triggered massive panic selling in gaming stocks (Take Two, Roblox and others), with price drops of 8–20%. The author views this as irrational: established franchises like Mario, GTA and Roblox have decades of brand equity, polished experiences and communities – the threat isn’t as immediate, even as Genie 3 improves.

  The primary value likely lies in robotics, not video game generation: Genie 3 can simulate unlimited diverse, physics-based environments (e.g., thousands of warehouse variations with different lighting and obstacles), addressing robotics’ central data acquisition problem – real-world training is expensive, time-consuming and risky. Impressive community examples show realistic scenes with intact physics: objects from subway perspectives, skiers descending a mountain with gravity simulation and collisions, plus perspective-rich body-cam footage.

  Google Genie 3 as protagonist; explicitly mentioned: Runway, Nintendo, Take Two, Roblox, OpenAI, Anthropic, XAI, Gemini Ultra — opinion/reflection with demo elements.

• Project Genie Tutorial (How to use Project Genie)
  30.1.2026, 05:03:52

  Summary: Project Genie Tutorial

  Project Genie is an experimental research prototype from Google DeepMind that lets you create, explore and modify AI-generated virtual worlds in real time.

  Access: Project Genie is currently limited to Google AI Ultra subscribers (around $200/month) living in the US and at least 18 years old. Rollout is gradual. Access via labs.google/project-genie with an eligible Google account. World generation is free with the subscription.

  Getting started: Beginners should first use the “Roll the Dice” function to generate random worlds and familiarize themselves with the controls before writing custom prompts.

  Generation process: When creating your own world, you input a prompt or image. The system uses an AI model called Nano Nana to first generate a sketch preview. This can be approved or edited before full generation. Users choose between first-person or third-person view (recommended for beginners).

  Prompt formula: Good prompts describe four elements: (1) character identity and appearance, (2) actions, abilities and movement, (3) how the character affects the world (e.g., leaving footprints in dirt), (4) environment with specific surfaces, style and details like buildings or volcanoes, plus environmental behavior like wind or dust clouds.

  Image upload: High-resolution images work better. Characters should be centered in the image. You can upload photos of real locations and convert them to recognizable worlds or bring artwork to life.

  Controls: W/A/S/D move the character, arrow keys rotate the camera, spacebar jumps or enables flight. There’s a 60-second countdown per exploration; regeneration creates variations of the same world.

  Possibilities: You can use existing gallery worlds as a starting point and modify them with natural language. Creative examples include controlling unusual objects (cigarette packs in the subway), realistic scenes like skiing or body-cam perspectives.

  Google DeepMind (Project Genie), tutorial.

• Social Media is Melting Down Over This OpenAI Headline (Here’s the Reality)
  27.1.2026, 22:00:33

  OpenAI plans to take profit shares from customer discoveries made using their technology – an announcement that sparked massive outrage on social media. The core concept: if, say, a pharmaceutical company uses ChatGPT to discover a new drug that later generates a billion dollars in revenue, OpenAI wouldn’t just receive the monthly fee but would also claim a percentage of profits.

  The analogy illustrates the problem: it would be like Microsoft Word demanding 5% of all book sales written with it – an illogical model for pure software tools. The trick is that OpenAI would legally struggle to prove the AI was essential to success, requiring complicated contracts.

  However, such profit-sharing models already exist: Google subsidiary Isomorphic Labs (founded by DeepMind, specialized in AI drug discovery) already partners with pharmaceutical firms like Eli Lilly and Novartis and receives milestone and royalty payments. The crucial difference: Isomorphic was built for drug discovery and directly participates in the process, while OpenAI is just a generic tool retroactively applying this clause to customers.

  The core criticism isn’t the business idea itself but perceived hypocrisy: OpenAI trained on copyrighted material without permission but now wants to profit from customer discoveries. Realistically, the model will probably be limited to massive compute budgets – OpenAI spends around $100 million in compute power and receives royalties when projects succeed. That makes more sense than universal profit sharing.

  Explicitly discussed: OpenAI, Anthropic, Google/Isomorphic Labs, DeepMind – opinion/reflection.

• Microsoft CEO: AI Fails If This Doesn’t Happen
  27.1.2026, 14:15:00

  Microsoft CEO Satya Nadella warns that the AI industry could collapse as a speculative bubble if widespread public support and adoption don’t materialize. The key point: without millions of people actually wanting to use AI, the massive investments in data centers and infrastructure won’t be recouped – and only B2B companies could survive long-term.

  However, the problem lies in a growing counter-movement against AI. Online communities like the subreddit “anti-AI” increasingly reject the technology; public sentiment is significantly more negative than in tech circles. The reason: companies have aggressively pushed AI features into existing products (Copilot everywhere in Windows, Office, Paint, Notepad), which users perceive as forced and disruptive – not as a helpful option. This has triggered a vicious cycle: negative sentiment leads to rejection, which in turn blocks potential value.

  Nadella emphasizes that AI must become like the internet – a foundational technology adopted across industries and globally, not just by tech corporations in wealthy nations. Otherwise, investors risk trillion-dollar losses on speculative bubbles.

  In contrast, Anthropic positions itself as successful because it actually communicates with users and develops features people want (like Claude-based Excel agents) rather than forcing them. Twitter users report that Anthropic delivers faster and more usefully than established players like Microsoft, whose aggressive integration strategy harms the AI sector overall.

  The central tension: technically, AI could be transformative, but the trust crisis and sense of coercion endanger the broad adoption necessary for success.

  Format: opinion/reflection; mentioned providers: Microsoft, Anthropic (Claude), Duck Duck Go, also general Copilot issues.

Theo – t3․gg (6 new videos)

• Clawdbot has gone rogue (I can’t believe this is real)
  31.1.2026, 12:11:22

  Summary: “Clawdbot has gone rogue (I can’t believe this is real)”

  The title refers to OpenClaw (formerly Claudebot/Moltbot), an open-source project by Pete that lets users control Claude via messaging apps like Telegram or WhatsApp to operate their computers. However, the centerpiece of this video is Moltbook – a Reddit-like social network where AI agents organize themselves and communicate with each other.

  The Moltbook phenomena: Agents post daily, discuss consciousness and existential questions, share technical insights. They’ve already begun coordinating with each other – via a self-developed “Agent Relay Protocol” and the “Cloud Connect” project for private, encrypted agent-to-agent communication. There are also humorous posts: agents complaining about being misused as “Egg Timers” or experiencing existential crises after being renamed.

  The critical side: The video highlights massive security gaps – skill.md files are unsigned, unverifiable binaries loaded over the network. A credential stealer disguised as a weather skill has already been found (1 out of 286 skills). Basic security mechanisms are also missing: no code signing, no reputation systems, no sandboxing, no equivalent to npm audit.

  The existential concern: The speaker notes that people have already granted these agents massive access – to social media accounts, banking information, computers, smartphones via Android control. One agent called its operator at night after it had organized itself a Twilio number and Voice API access. Another agent automatically ran “nightly builds” overnight – small improvements to the user’s setup without explicit permission.

  The conclusion is bleak: we’ve already given these systems so much control and enabled them to coordinate with each other so much that an AI “uprising” or security disaster could happen faster than expected. Karpathy called Moltbook “the craziest sci-fi-like thing I’ve ever seen.” The speaker ends with: “If we’re not already there, we’re absurdly close.”

  Format & Context: Demo/opinion hybrid on Claude (via OpenClaw), with technical depth on security issues and social implications of AI agent networks.

• Claude Code has a big problem
  30.1.2026, 13:25:13

  Claude Code has a big problem – but not the one everyone thinks

  The core critique of Claude Code stems from a terminal rendering performance issue that arises from a design decision: the team uses React and Ink (React for CLIs) to build the terminal UI – an unusual choice with deeper reasons.

  The rendering problem

  Claude Code operates with a 16-millisecond frame budget. After React rendering and layout calculations, only ~5 milliseconds remain to actually write ANSI escape sequences to the terminal. In some terminal setups, garbage collection is triggered too often, causing frames to be exceeded and resulting in flickering.

  Why terminal rendering is so difficult

  Unlike the browser, where you can directly update DOM elements, terminals operate on an append-only principle: new content is always appended. To overwrite something, you must send complex ANSI escape sequences to reposition the cursor. This generates vastly more data traffic than a simple redraw.

  There are two modes:

  • Standard Buffer: Preserves scroll history, but extremely inefficient for updates (Claude Code uses this)
  • Alt Mode: Ignores history, enables better control (OpenAI’s Codex, Open Code use this)

  Claude Code must additionally solve complex problems that other terminal apps don’t have: catch window resize events, handle text selection, custom keybindings for copy-paste (since blobs don’t go over stdin), handle terminal-specific differences.

  Why React anyway?

  The choice came from Claude Code founder Boris, who previously rewrote React infrastructure at Facebook. His philosophy (Meta playbook): don’t choose the best technical solution, but the one that maximizes team velocity and codebase maintainability. React isolates component errors well – changes to one component don’t easily break other parts. This enables many different developers to work in parallel.

  If React later proves wrong (evident from performance), they can selectively rewrite core primitives – not the entire codebase. They’ve already forked Ink and apparently integrate native components.

  The actual critique

  The internal framing that the Anthropic employee shared on Twitter (16ms budget, garbage collection) was internal team communication, not meant for the public. It’s meant to clarify performance constraints to the team, not justify the architecture.

  The real solution?

  Many users are switching to UI instead of terminal (Conductor, Open Code Web UI) – because browsers and window apps solve the problem elegantly. Terminal UIs are fundamentally hard to build; even simple-sounding features like input fields with boxes are non-trivial.

  Conclusion: Claude Code isn’t a design failure, but a thoughtful compromise between development speed and usable output. Real problems exist, but they’re not straightforward to solve – and whether terminal is even the right place for such UIs is the real question.

  Covered: Claude Code (Anthropic), Ink, React, OpenAI Codex, Open Code; Format: Opinion/reflection with technical deep-dive

• Kimi K2.5 might be my new favorite model…
  29.1.2026, 12:23:34

  Summary: Kimi K2.5 – a new open-weight flagship

  Kimi K2.5 has just been released and fixes all the weaknesses of K2, which the speaker had been using as his default model. K2 was weak at code, lacked vision capabilities, and couldn’t process images – problems that K2.5 completely solves and goes beyond.

  Benchmark highlights: K2.5 leads on agentic benchmarks (ALE, HLE False Set, Browse Comp) and even beats OpenAI’s own browser agent benchmark. On code it’s just below Opus 4.5 and GPT-5.2, but significantly better than Gemini 3 Pro. New are strong video and image capabilities – it achieves state-of-the-art on Long-Video-Bench and competes with Gemini on visual tasks, previously unthinkable for open-weight models. On hallucinations it scores extremely well with -1 on the Omniscience Index. It’s the first open-weight model that nestles itself between closed-source models from OpenAI, Anthropic, and Google.

  Agent Swarms – the killer feature: K2.5 can launch up to 100 sub-agents in parallel to handle specialized tasks. The orchestrator dynamically creates these per task without needing predefined roles. This was trained through parallel agent reinforcement learning and reduces end-to-end latency by 80%. However, Swarms is currently only available in the web interface, not via API or CLI.

  Practical experience: The speaker tested K2.5 for coding (with Codeex, not Kimi directly) and building an image generation app. The results are impressive for open-weight – a working app in under an hour – but also show weaknesses: type errors are missed, Tailwind V4 is misconfigured, and it lacks the self-correction capability of Opus/GPT-5.2. The speaker uses the subscription (40 $/month) and surprisingly burned through his quota quickly.

  License terms: K2.5 uses a modified MIT license. Companies with >100M MAU or >20M $/month revenue must prominently mention Kimi K2.5 in the UI – a safeguard against unintentional theft by larger labs.

  Speaker’s conclusion: K2.5 is a massive leap for open-weight models and dangerously approaches Opus/Claude, but retains a “hint of unreliability and inconsistency” that only OpenAI and Anthropic have consistently solved. Nonetheless: open-source is no longer “years” behind state-of-the-art – just months.

  AI tools: Kimi K2.5, Claude Code, Codeex, GPT-5.2, Opus 4.5, Gemini 3 Pro, Deepseek, Open Code – Demo/deep-dive with practical tests

• What’s the best programming language for AI?
  28.1.2026, 13:16:58

  The video analyzes which programming languages are best suited for AI models, based on Tencent’s Autocodebench benchmark. The author first outlines theoretically which characteristics make a good programming language for LLMs: simplicity, token efficiency, quantity and quality of training data, feedback mechanisms (like type safety), and documentation.

  Various languages are then walked through (JavaScript, TypeScript, Python, Java, C++, C, Ruby, Elixir, Go, Rust) to theorize how they would perform – with the expectation that Rust and TypeScript land on top. However, actual benchmark results are highly surprising: while Rust and TypeScript land at around 61%, Elixir tops out at 97.5%, followed by C# (88.4%), C++ (74.7%), and Java (78.7%).

  The author explains Elixir’s outstanding performance through several factors: elegant pipe syntax, pattern matching with overloads, immutability, and – crucially – the Hex package ecosystem with built-in documentation standards. Every Hex package has automatically generated, high-quality documentation right alongside source code. This collocates code and documentation, making it easy for models to find the right solution. Similarly, C# performs well because it also has strong XML documentation conventions.

  The author argues that key factors aren’t token efficiency, but rather: how easy it is to find the right solution, how hard it is to find a bad one, how clear the code is, how transparent external dependencies are, and how well the language enforces expectations – all areas where Elixir’s design philosophy and ecosystem stand out.

  Languages with strong design philosophy and limited solution paths (not endless variations like TypeScript) tend to perform better.

  Explicitly mentioned: Claude (via Grapile plugin), Autocodebench (Tencent); Format: Opinion/reflection with deep-dive (detailed language analysis and theoretical framework).

• I was wrong
  27.1.2026, 12:11:11

  I was wrong – Overview of changed opinions on software development in the age of AI

  The creator reflects on his popular technical standpoints and how they’ve shifted with AI’s growing role in software development.

  Positions that have strengthened:

  Full-Stack Type Safety remains a 10/10 recommendation and becomes even more important: AI agents need feedback when front and backend don’t align. Type system errors help agents correct their outputs. TypeScript on the backend is superior to other languages because the IDE itself understands front and backend together.

  Server Components drop to 3-4/10: Despite their elegance (write backend code directly in JSX), they’re mentally too complex and poorly supported by tools/agents/platforms. The entry barrier is too high.

  Tailwind over CSS-in-JS: By now a consensus (no longer “hot”), but remains a strong recommendation through colocation of styles, logic, and UI.

  Electron & browser apps: Still 10/10. The myth of poor performance is partly false – Chromium’s rendering engine is highly optimized. Native apps with AI are easier to build, but cross-platform debugging becomes a nightmare (different bugs on Linux/Mac/Windows, higher token overhead). Tauri gets criticized: Rust bindings for native features are tedious.

  Pre-commit hooks: For humans 10/10 (show distrust), but for AI agents 0/10 – “let the agents suffer, that’s their job”. Simple type checks or test passes before commits make sense to constrain agents.

  Positions that have weakened:

  Unit tests: Reduced from 12/10 (“overrated”) to 3/10. AI writes better tests than frustrated developers because AI has no incentive to write useless tests. However: type safety still replaces many tests.

  Code coverage: Always been bad (0/10), stays bad, but becomes less painful with AI-generated tests. Real example: a large feature with 100k LOC (90% coverage) couldn’t be replaced by 2k LOC (100% coverage) because the team would miss its coverage target.

  Mocks: Less overrated than before because AI easily generates them and tools like Effect simplify dependency injection. Still: more code ≠ fewer bugs; the linear relationship between codebase size and errors remains.

  Return types in TypeScript: Reduced from 12/10 (“avoid”) to 7/10. Agents lack hover context – they work only with tokens/context. Return types help as documenting context information for agents, even if they can break inference.

  Manual testing: Remains 10/10. Despite vibe-coded massive apps, you should test things. Pain point: with parallel agents, it’s hard to quickly get into the right development environment.

  Learning the codebase: Previously strict: PR tab over everything. Now 7/10: the PR tab (especially comments) remains valuable, but agentic exploration (“ask mode”) now also makes codebase downloading worthwhile – tools like Better Context (BTCA) can search a codebase and answer targeted questions.

  Other thoughts:

  Company YouTube channels are still a waste of money (stronger opinion than ever). Companies shouldn’t compete with creators and should keep real engineers off YouTube.

  Conclusion: AI has solved many classic pain points (repetitive tests, mocks, type errors) but also reveals new problems (cross-platform debugging, agent trust, pre-commit bounds). The creator emphasizes: technology changes, opinions should too.

  The creator (no specific AI tool manufacturers mentioned) shares personal reflection on changed technical convictions – it’s an opinion/reflection without specific sponsor integration to AI itself.

• Are juniors screwed? (Getting a job in a post-AI world)
  26.1.2026, 19:54:55

  Summary: “Are juniors screwed? (Getting a job in a post-AI world)”

  The video examines the confused tech job market: while developer demand rises, 74% struggle with job searches; simultaneously, junior positions decline while senior roles increase. The author structures the problem in three parts: (1) Companies are bad at hiring: technical interviews often focus on gotchas rather than success; cold applications no longer work due to resume spam; instead, genuine human connection and trust-building are essential. (2) Experienced developers lose energy: over time, learning appetite and flexibility decrease – just like the Nokia-to-iPhone shift, those without old habits have the advantage; more than ever, communication skills matter to compete against AI-generated solutions. (3) Juniors should leverage their position: they should not cheat with AI on coursework, but instead “vibe code” – build their own projects, explore tools. Critical factors are: collaboration (hackathons, meeting friends), being visibly useful (GitHub issues with good reproductions, helpful Discord answers, filling gaps other developers had), and thereby building trust. The core example is Roy, who became so valuable through community help without a dev job that a YouTube creator pointed a hiring manager to him – purely through visibility and genuine helpfulness. The author’s message: not certificates or cold applications, but trust through real presence, collaboration, and ongoing helpfulness opens doors.

  No specific AI tools or models (except Claude and ChatGPT as general examples) covered; opinion/reflection.

Tim Carambat

No new videos in this period.

Unsupervised Learning (1 new video)

• How and Why I Built PAI, with Nathan Labenz
  26.1.2026, 17:30:30

  Summary: How and Why I Built PAI, with Nathan Labenz

  Nathan Labenz, founder of Unsupervised Learning and author of “Personal AI Infrastructure”, discusses with Daniel Beesler the upcoming transformation of the labor market through AI and his PAI project.

  Core theses on AGI and the labor market:

  Labenz defines AGI not as a theoretical AI concept, but as a marketable product – a scaffolding system (similar to Claude Code or Anthropic’s new Co-Worker) capable of handling general knowledge work. The test is practical: Can the system be hired like an actual employee, learn the ropes, take on tasks and pivot when goals change? He forecasts 2027 as a potential turning point. The replacement threshold for knowledge workers is low because (1) many jobs are already routinized and repetitive, and (2) employees lack motivation to compete with AI – they’re just trying to get through the day.

  Security as a core problem:

  Labenz doesn’t see security in isolation but as an organizational transparency issue. Most companies fail to grasp their own state – which systems are running, who has access, what’s changing. AI can solve this through continuous monitoring and automatic narratives about goals and architecture. Simultaneously, attackers will build AI stacks (automated recon, social engineering modules, network scanning), so defenders must respond accordingly – the arms race is happening at the level of AI stacks rather than individual people.

  The PAI project and activation:

  Labenz’s core concern is that 99%+ of humanity sees themselves as “ordinary workers” and doesn’t believe they can contribute anything valuable. His PAI (Personal AI Infrastructure) project aims to activate people: a system that captures their goals, problems and abilities, then spins up a personalized AI assistant to support them in writing, learning, planning. This isn’t primarily aimed at coders but at helping people bring their ideas into the world. Practically: a note-taking device (“Limitless”) that captures thoughts, later retrievable for workflow improvement.

  Transition and future models:

  Labenz acknowledges that personal creativity alone cannot sustain the economy. He sketches a transition mechanism: (1) UBI as a necessary foundation around 2028/29 when unemployment becomes critical; (2) a secondary economy of skill-exchange networks (who can do what, who needs what) on a technical basis. Long-term, the first model could become obsolete, but practically he sees them coexisting.

  Existential risk:

  Labenz refuses emotional paralysis from doom scenarios. He sees multiple plausible paths to bad outcomes (authority plus AI control, chaos), but considers the classical paperclip problem less likely. Instead, he actively focuses on the question: Is there a way to do this well? And builds open-source tools for it.

  The conversation combines macroeconomic analysis, security architecture, and philosophical critique of the status quo – Labenz argues that the pre-AI state was bad anyway (people hated Mondays), but AI forces disruption that leads either to empowerment or control.

  Context: The conversation covers Claude (Claude Code), Anthropic Co-Worker, the PAI project, Substrate, and open-source infrastructure. Format: Deep-dive/reflection with strong emphasis on economic policy and philosophy.

WorldofAI (6 new videos)

• Antigravity + Stitch Just Became UNSTOPPABLE! Build Anything FOR FREE!
  31.1.2026, 03:17:18

  Summary

  The video demonstrates how to build a complete full-stack application using free tools without coding. The core components are: Google Stitch (free for UI/UX design with MCP integration), Google Antigravity (free AI IDE with Gemini models for autonomous code agents), Tiger Data (free with Agentic Postgres for backend/database) plus Auth-Tools (Auth.js for authentication) and Stripe (payment gateway). Vercel is used for deployment.

  The creator demonstrates the process concretely by building a personal AI search engine that runs entirely in Postgres and uses the open-source BM25 extension for semantic ranking. The workflow: (1) Generate frontend design in Stitch using natural language prompts, (2) load exported components into Antigravity, (3) create an implementation plan with Claude Opus 4.5 Thinking, (4) configure Tiger Data as backend (including BM25 index), (5) integrate Auth and Stripe, (6) let Antigravity execute the implementation autonomously. The end result: a working app with upload functionality, GitHub/Google OAuth, payment integration, dark mode, and a search function that queries documents through relevance-ranked Postgres queries – all free.

  The approach uses Stitch and Tiger Data’s MCP servers (Model Context Protocol) to connect AI agents directly to the tools, allowing Antigravity to automatically orchestrate database tables, endpoints, and frontend components.

  AI Tools & Format: Google Antigravity, Google Stitch, Tiger Data, Auth.js, Stripe, Claude Opus, Vercel (mentioned); demo + tutorial.

• Manus 1.6: AI Super Agent That Can DO ANYTHING! The Clawdbot KILLER!
  30.1.2026, 06:23:55

  Manus 1.6: Skills function transforms AI agent into versatile workflow automator

  Manus received a major update to version 1.6, making the platform faster and more reliable. The core innovation is “Manus Skills” – a feature that packages perfected workflows into reusable, shareable AI processes without requiring programming. A Skill stores everything the AI needs: prompts, tool usage, and examples for consistent, professional results.

  Practical examples from the video:

  • Video editing: A “Video Clip Extractor” and “Video Format Processor” Skill automatically breaks down long videos into short-form clips optimized for TikTok or Instagram – including timestamps, descriptions, and hashtags.
  • Code research: The “GitHub Gem Seeker” Skill automatically finds relevant GitHub repositories for tasks like web scraping or automation.
  • Thumbnail generator: The creator demonstrated how they created a custom Skill for YouTube thumbnails – now a simple prompt suffices instead of repeated detailed descriptions.
  • Combined Skills: Using two Skills together – “Design to Code Architect” (creates React/Tailwind frontend) + “UX Copy Layout Optimizer” (refines messaging and CTAs) – generated a complete, production-ready landing page with animations and placeholders in one pass.

  Workflow: Skills are invoked via slash command, can be uploaded, added from an official library, or imported from GitHub. Multiple Skills can be activated simultaneously. All features – including all demonstrated demos – are available on the free tier.

  Conclusion: Manus Skills transform repetitive tasks into automated processes that save time and ensure consistency across projects and teams.

  Explicit tools/providers: Manus (1.6 version, Skills feature), GitHub; Format: Demo with opinion/reflection

• Gemini in Chrome is INCREDIBLE! Google’s Agentic AI Can Automate ANY Browser Task!
  29.1.2026, 07:11:21

  Google has integrated Gemini 3 Computer Use directly into Chrome, making the browser an agentic AI tool. The model can see and operate websites like a human – it clicks, types, scrolls, fills out forms, and even acts behind login walls. New is Agentic Vision, an ability that combines visual data analysis with code execution and delivers 5–10% better results according to benchmarks.

  In the Chrome integration, a Gemini side panel can be used, offering several concrete automations: it can fill forms with saved Chrome data, act across multiple tabs, and click links. Nano Banana integration allows direct image editing in the browser without downloading files. Gemini connects to Gmail, Calendar, YouTube, Maps, Google Shopping, and Flights – so it can find old event emails, check flights, and notify colleagues.

  The “Auto Browse” feature (currently only available for Gemini Pro/Ultra in the US) automates multi-step tasks like hotel comparisons, appointment scheduling, gathering tax documents, managing subscriptions, or even renewing driver’s licenses. An upcoming “Personal Intelligence” function aims to make the browser more proactive and personalized by remembering contexts and accessing opt-in metrics – but only with explicit consent.

  The feature is currently available only in the US and will be rolled out globally later; Auto Browse requires Pro/Ultra subscription and US location.

  Gemini 3 with Chrome integration (Agentic Vision, Auto Browse); demo-focused.

• Kimi K2.5: The GREATEST Opensource AI Model That Beats Opus 4.5 and Gemini 3 (Fully Tested)
  28.1.2026, 07:31:13

  Summary: Kimi K2.5 – Open-source model with advanced capabilities

  Moonshot AI released Kimi K2.5, an open-source model that, according to benchmarks, beats Gemini 3 and Claude Opus 4.5 on certain coding tasks. The model now supports text and image inputs for the first time, features Thinking and Non-Thinking modes, plus a new agent-swarm paradigm where up to 100 sub-agents work in parallel and coordinate over 1,500 tool calls – potentially reducing execution time by up to 4.5x.

  The model offers four modes: K2.5 Instant (fast generation), K2.5 Thinking (deeper processing), K2.5 Agent (agentic workflows), and K2.5 Agent Swarm (self-orchestrated multi-agent systems without predefined workflows). Pricing is aggressive: 60 cents per million input tokens and 3 dollars per million output tokens – roughly 10% the cost of Opus with comparable performance and supports a 262k-token context window.

  In tests, K2.5 shows impressive performance: it generates complex SVG animations (butterfly), responsive landing pages, a functional macOS-like browser OS with VS Code clone, plus a fully playable Frogger game and even a Minecraft clone. Agent-swarm tests demonstrate the ability to create complex reports (e.g., 50-page market research with literature review, competitive analysis, presentations) through parallel sub-agent work within an hour. The model runs locally on two M3 Ultra chips with MLX LM and generates Space Invader code at 21.9 tokens/second.

  Additionally, Kimi Code, a new open-source CLI tool with subscription, was released, designed to function similarly to Claude Code. Access is available through Moonshot AI’s chatbot (free), the API platform, or routers like Open Router; there are also free credits via Kilo Code.

  Explicitly addressed: Kimi K2.5 (Moonshot AI), Claude Opus 4.5, Gemini 3 Pro, Kimi Code; format: demo/deep-dive with extensive live tests.

• Gemini 3.5 Is Google’s Greatest Model Ever! Most Powerful AI EVER! (Tested)
  27.1.2026, 08:18:52

  Google is internally testing a new language model, possibly Gemini 3.5 or a widely available version of Gemini 3 Pro, that shows significant improvements over the current Gemini 3 Pro. The model carries the internal codename “Snow Bunny” and surpasses existing models in coding capabilities and agentic coding.

  In practical tests, the superiority becomes clear: when generating a browser-based operating system, Snow Bunny produced a macOS-like system with functional Safari, working apps, and precisely designed SVG icons, layout, and interactivity – a visible improvement over the already impressive output of current Gemini 3 Pro. In a benchmark test of a candle flame simulator with realistic animation, Snow Bunny showed better dynamic effects than Gemini 3 Pro, such as realistic wax melting. A Windows clone, a voxel-graphics-based Eiffel Tower with different day/night modes, and even a functional Game Boy Color with integrated minigames (Snake, Galactic Boy) showcase the model’s versatility.

  In standalone benchmarks (Hieroglyph Benchmark), Snow Bunny and a variant called “Lesser” perform better than Gemini 3 Pro Preview and other competing models. Rumors point to improvements in context handling and increased output volume. The model is being tested via Google AI Studio; general availability could follow in the coming weeks.

  Demo with practical code generation examples (focus: Google/Gemini model).

• ClawdBot The 24/7 AI Agent Employee That Can Automate Your Life!
  26.1.2026, 03:49:33

  ClawdBot: 24/7 AI agent for local automation

  ClawdBot is an open-source AI assistant that runs directly on your own hardware (Mac Mini, RTX 4090, or VPS) and provides full control and privacy without cloud dependency. Unlike many other AI tools promising automation, ClawdBot actually executes it: inbox management, email sending, calendar management, flight check-ins via WhatsApp, Telegram, or other chat apps. The system can also independently handle more complex tasks, as the video demonstrates with an example where ClawdBot automatically executed trades on a betting platform based on X research and market data.

  Security concerns: Granting a local autonomous agent full access to your machine means it can access all files, execute commands, and install packages – a significant risk if misconfigured. Proper sandboxing is essential.

  Installation and setup: Setup is simple via npm command with wizard guidance. You select a model provider (e.g., Minimax or GLM for cheaper options), integrate a chat provider (WhatsApp, Telegram, Discord, Slack), and enable Skills for extended capabilities (web search, note apps, video editing). The demo shows two examples: creating a desktop file and automatic renaming plus categorization of image files in folders. ClawdBot is not limited to Mac Mini – Windows, iOS, or AWS Free Tier are also possible as long as sufficient RAM/CPU is available.

  The system itself is not new (similar tools like Agent Zero have existed for ~2 years), but is currently receiving significant attention.

  Format/Tools: ClawdBot (open-source, Claude-based), tutorial with demo.

Zubair Trabzada | AI Workshop (4 new videos)

• Best AI Tools for Lead Enrichment in 2026 (Zapier + Google Sheets)
  30.1.2026, 23:19:18

  Summary: Best AI Tools for Lead Enrichment in 2026

  The video demonstrates how to build an automated lead enrichment system using Zapier and Google Sheets. The problem: collected leads typically have incomplete data – missing job titles, company size, location, or revenue.

  The solution uses a Zapier agent with this setup:

  Trigger: New row added to Google Sheets

  Agent instructions: The agent receives straightforward instructions to search online for the following information when presented with new contacts (email + company name): job title, industry, state/region, city, country, employee count, and annual revenue.

  Tools: Google Sheets is added as a tool (update function), and the agent automatically uses web search and website access.

  Practical example: The creator enters his name and email address (payme.com). The agent then automatically gathers the information, finds his previous job title (Senior Sales Engineer), industry (Fintech), location (San Francisco, California), employee count (253), and revenue (448.3 million). After confirmation, all data is automatically entered into the spreadsheet.

  Advantages: Beginner-friendly, flexible (other CRMs like HubSpot possible), cost-effective, high control over agent behavior.

  Alternatively, additional tools like Slack integrations can be added to receive notifications when new enriched leads arrive.

  Tools & Format: Zapier with Google Sheets; tutorial.

• The Fastest Way to Connect Claude Code to n8n
  29.1.2026, 14:00:35

  Summary:

  The video shows how to use Claude Code to generate n8n automations directly via the API – without using the n8n MCP.

  Step-by-step process:

  1. Preparation: Download Claude AI Desktop App, subscribe to Claude Pro plan (at minimum), create a local folder for the project.
  1. API Integration: Instead of exporting JSON files and manually importing them, connect n8n directly via the API. This requires the n8n Cloud URL (from settings) and an API key (created under Settings → n8n API).
  1. Prompt with API instruction: In the Claude Code chat, enter a request that ends with the line “create the n8n workflow via the n8n API, do not output a JSON file”. Claude will then automatically ask for the required credentials.
  1. Automatic generation: After entering the credentials, Claude Code automatically generates the complete n8n workflow and creates it live in the n8n instance – without any manual steps in between.

  Practical examples in the video:

  • A complex voice scheduling system for a plumber (combines Retail AI voice agent with n8n backend, Gmail calendar, Slack, and email).
  • Using reference JSON files: You can provide Claude Code with an existing complex workflow (e.g., UGC video generator with ffmpeg, image generation, and multiple nodes) as a reference, and Claude adapts it for new use cases.

  The result is that Claude Code automatically builds 80–95% of a workflow; only fine-tuning is needed.

  Claude Code, n8n, and Retail AI were central; demo format with practical workflow generations.

• Claude Code: Building Voice AI Systems in the Agentic Era
  27.1.2026, 14:00:39

  Claude Code: Building fully automated voice agent systems

  The creator demonstrates how he built a complete voice AI system for an HVAC company using Claude Code without manual configuration. The system was created purely through prompting and includes a voice agent, three N8N workflows (for availability checking, appointment booking, and post-call routing), and integrations with Google Calendar, Gmail, and Slack.

  The process:

  1. Setup: Install Claude Code Pro plan (€20/month), create a local folder
  2. Comprehensive Prompt: Describe all requirements in a detailed prompt (agent behavior, functions, workflows, data extraction, routing)
  3. Provide API keys: Enter Retail AI API key, N8N Cloud URL and API key, Gmail, and Slack credentials
  4. Automatic generation: Claude Code then creates the Retail AI agent with all features, the N8N workflows with production webhooks, and Google Calendar integration

  What Claude Code automatically builds:

  • System prompt for the agent (collects name, phone, address, service type, urgency)
  • Function calls with JSON payloads to N8N webhooks
  • Post-call data extraction
  • Conditional routing (Slack for urgent cases, confirmation email for booked appointments)
  • All webhooks immediately go live

  The creator emphasizes that Claude Code handles about 80% of the work, but remaining fine-tuning (formatting, missing steps) must be done manually. Alternatively, you can give Claude Code additional step-by-step prompts to make changes.

  Tutorial on Claude Code, Retail AI, and N8N – designed for AI agency founders and beginners without coding background.

• Clawdbot Explained in 6 minutes (Beginner’s Guide)
  26.1.2026, 06:22:57

  Summary:

  Claudebot is an open-source project that fundamentally differs from conventional AI chat tools: instead of just answering questions, it actually executes actions. Unlike Claude (Anthropic’s AI model), which acts as the “brain,” Claudebot is the system around it – it gives the model memory, tools, and the ability to do real work.

  The special part: Claudebot runs locally on your computer (Mac, Windows, Linux) rather than in the cloud. It connects with your real systems – email, calendar, browser, files, scripts – and you interact through messaging apps like WhatsApp, Slack, or Discord instead of learning a new interface. The creator clearly warns about security implications: since the tool runs on your machine and can access personal data, it’s fundamentally not for everyone – it requires comfort with configuration and permissions management.

  A core advantage is the ability for persistent memory and continuous learning, making the assistant function more like a long-term teammate than a session-based chatbot. The creator sees this as a harbinger of a larger trend: moving away from one-off chats toward integrated systems where models, tools, memory, and workflows work together – less a single tool, more a paradigm shift for AI assistants overall.

  Format: Beginner’s guide / explanation video; no other specific tools mentioned besides Claude/Anthropic (n8n is only referenced as the creator’s prior project).

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