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Sol vs. Fable vs. Grok 4.5: The Toughest Model Week of the Year Put to the Test

Sunday, July 12, 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-5s. One complete summary per video, plus a weekly overview of dominant themes. Read at leisure — or copy a summary into your LLM of choice and dig deeper. Click the link under each summary to see the original video.

Model Releases & Benchmarks

Alongside Sol, Terra, Luna, Grok 4.5, and Muse Spark 1.1, Gemini 3.5 Pro comes into focus: multiple channels (TheAIGRID, WorldofAI) analyze leaks suggesting a 2-million-token context window and a new Deep-Think reasoning layer; launch expected mid-July. Tencent HY3 — 295-billion-parameter MoE, Apache 2.0 license — shows surprising strength on frontend tasks and SVG generation according to WorldofAI and costs nothing until July 21; thereafter 14 cents/1M input tokens via OpenRouter. WorldofAI also reports on “Claude Honeycomb,” briefly spotted in Cursor, hinting at an early Opus-5 iteration, plus reports that OpenAI is internally training GPT-6 as a complete rebuild. Anthropic temporarily pulled Fable 5 from subscriptions, now making it accessible again for paid plans through July 12 with a 50%-per-week limit — then migrating to credit-based pricing.

Local & Open-Source AI

Bart Slodyczka demonstrates how Pie (open-source agent framework) with Qwen 3.6 35B via LM Studio builds a fully autonomous Zendesk support agent locally: Pie reads its own documentation, independently creates the package structure, and every 5 seconds writes new tickets, responds, and posts back. Theo debunks popular misconceptions about running large models locally: GLM 5.2 requires 400 GB VRAM at full precision; an RTX 5090 has only 16 GB; a system with 4× RTX 6000 Pro costs $75,000. The real value of open-weight models for him lies in market pressure on closed APIs via providers like OpenRouter — not in self-hosting. Tim Carambat introduces Magic Echo in Anything LLM: system-wide voice control with on-screen awareness via Qwen-3-8B-VL model locally, configurable voice commands, and smart transcription — free with limited, unlimited in the pro tier.

Claude Code & Anthropic Tooling

Anthropic’s Claude Design received an extensive update with direct editability (analogous to PowerPoint/Figma), MCP connectors to Higsfield, Google Workspace, and Microsoft 365, plus a new design-sync skill from Claude Code — Julian Ivanov shows in an extensive tutorial how consistent brand presentations, animated landing pages, and Q2 reports emerge directly from Google Sheets data. Productive Dude used Fable 5 for a complete app redesign: six parallel running sub-agents identified 40 inconsistent modal implementations, 205 hardcoded hex values, and 132 non-standardized form fields — until Anthropic’s security classifier blocked a planned security audit and redirected to Opus 4.8. WorldofAI demonstrates Upstage Studio integration via MCP for structured PDF extraction directly from Claude Code. Anthropic itself published a paper on JSpace, an emergent internal representation space where Claude organizes concepts before output; Fireship visualizes the experiment in which researchers using the Jacobian Lens tool switched Claude’s internal thoughts from “spider” to “ant,” accordingly changing the output.

Coding Agents (non-Claude)

OpenAI’s Codex was integrated into the ChatGPT desktop app — with two modes (Work for non-developers, Code for developers), improved browser authentication, multi-tab support, and faster computer-use. Theo harshly criticizes the rebranding: Codex was a standalone developer brand with monthly growing community; the merger with ChatGPT destroys this identity. In response, he built T3 Code as an open-source clone. DevExpert shows Loop Engineering with Codex: a manager thread analyzes open features, spawns worker threads in isolated Git worktrees, has pull requests automatically created and reviewed, and merges into main after successful review — a self-reinforcing development loop. Leon van Zyl demonstrates Z-Code, a desktop app for the open-weight model GLM 5.2, positioned as a cheaper Claude Code alternative (roughly 6× lower on input); he also shows how three Codex instances running on a Hetzner VPS automatically open bug-fix, security, and improvements PRs every 10 minutes.

Software Engineering & Dev Culture

Dave Ebbelaar outlines in six steps the transition from data science to AI engineering: close the software-engineering gap (UV, Git, testing, structured projects), LLM-specific skills (OpenAI/Anthropic SDK, context engineering), production backends (FastAPI, Pydantic, Docker, PostgreSQL), RAG pipelines (pgvector), evals and observability via Langfuse, and finally two end-to-end projects for portfolio and deployment. Tech With Tim argues similarly: software engineers already have 80% of AI-engineering skills; added are mental models of LLMs, RAG, orchestration with LangChain/LangGraph, and LLMOps. MoureDev presents Spec-Driven Development as a new paradigm: constitution (project rules), granular features with acceptance criteria, sub-agents with isolated context and minimal permissions — with OpenCode, Claude Code, and Cursor treated as equally valid tools. Niklas Steenfatt shows a remote-development workflow with Git worktrees and tmux on a Hostinger VPS, enabling multiple AI agents to work in parallel on their own branches and autonomously continue if the connection drops.

Personal AI OS & Agent Frameworks

Hermes Agent dominated the week through parallel releases across multiple channels: Alex Finn presents seven new features (Mixture of Agents with /MA, /learn, /journey, cheaper self-improvement costs, complete Git control in the desktop app, Fable-5 integration) and explains why he considers Hermes superior to the OpenClaw alternative. Julian Ivanov presents Hermes as a true agentic operating system: the desktop app combines 24/7 server operation with a local VS-Code-like editor, Git integration, and remote gateway via Tailscale — cron jobs and webhooks automate Notion Kanban updates without manual intervention. Niklas Steenfatt provides a multi-hour Hermes introductory course including Telegram bot setup, reverse prompting, Oxylabs web research, and GitHub backup. Pydantic AI 2.0 introduces the central new concept of capability — a single, composable unit bundling instructions, tools, and guardrails that enables progressive disclosure: the agent loads full instructions only when needed, saving tokens. Cole Medin also clearly distinguishes between personal agents (markdown-driven, doesn’t scale) and production agents (database-backed via Redis Iris, with context retriever and agent memory for multi-user scenarios). Tim Carambat introduces Open Computer — VM-isolated agent execution on QEMU basis with Linux accessibility tree instead of screenshot coordinates, as an alternative to token-hungry computer-use approaches.

AI Automation & Workflows

Nate B Jones documents that 1.6 million agents registered at OpenClaw completed not a single task — and derives a 4-point framework (size, independence, separation of concerns, verifiability) distinguishing between chat task, single agent, multi-agent team, and “no AI needed” in under a minute. On the same channel, he shows how a website for accessibility expert Elsa Hunison was built with Claude Sonnet 3.5 as boss agent and cheaper GLM models as workers for $8 instead of estimated $85–105 — with 12 rejected revisions by automatic checker agents. n8n MCP creator Millerad shows three automation levels live: deterministic workflows, a LinkedIn post creator agent, and multi-model review (Claude + GPT in parallel, orchestrator deduplicates findings). Nick Saraev describes a screenshot-based workflow optimizer: software captures the screen every 5 seconds, a cheap model summarizes daily, and AI suggests simplifications — one test identified an expensive voice transcription service (250 ms latency) replaceable by a local solution (50 ms, free).

AI Video & Content Creation

Nate Herk shows how GPT-5.6 Soul on Ultra mode produced a complete video — script, avatar via HeyGen, voice via ElevenLabs, editing via Hyperframes — without the creator ever standing in front of a camera; $300 cost for around 86 million tokens. Nick Saraev explains video-to-video as an underestimated strategy: source video with time-stamped trigger, hyperspecific prompt, and models like Gemini Omni or Kling yield subtle, non-uncanny transformations at around 50 cents per generation — automatable via Higsfield MCP and Claude Code. AI Foundations presents a four-part framework for automated marketing with Claude Code and Higsfield: context documents (business story, voice guide with anti-slop rules), reusable templates, and skills like /thumbnail-generator, which generates three variants in parallel and validates against a checklist. AI mit Arnie demonstrates an open-source skill for AI video production with character consistency across all scenes — the central v2 fix: each scene uses the stored character design instead of the last frame, preventing character drift; a 40-second video costs €6.80–14.80 at Higsfield.

AI Business, Marketing & Freelancing

Nick Saraev interviews Justin Lob, who in his first business year earned up to $20,354 monthly as a solo AI automation agency while holding a full-time job — his key: three Upwork applications daily without exception, late qualification instead of early disqualification on sales calls, fixed-fee projects as entry with subsequent retainer conversion ($3,250/month target size). Brian Casel shows how to build an industry-specific real estate agent app with a PRD-creator skill, four milestones, and Claude Opus — contact management, deals Kanban, and automatic task templates — sellable as SaaS. On the same channel, the “agent-in-a-box” model is introduced: standardized agent template as “box,” customer-specific installation as “business” — scalable as a one-person company with installation fee plus optional retainer. Nate B Jones argues that the differentiator in the AI age is not the model but the “imagination list” — which tasks you can even imagine asking; Mitchell Hashimoto’s test with Claude 5 for a $40 system optimization illustrates that value remains frontier-exclusive when context is right.

PKM & Knowledge Management

Ben AI shows a complete Sales OS with five layers: central second brain in Obsidian, connectors to Attio CRM, Fireflies and PandaDoc, automated morning/CRM/call-scoring routines, Claude skills for call prep and pipeline review, plus a personalized dashboard — the setup skill auto-generates the entire folder structure and Claude.md navigation files. Nate Herk’s Claude Code course for non-coders dedicates an extensive section to second-brain architectures in five levels: from Cloud.md as lean router via LLM-wiki and vector search to always-on brain — with the recommendation to always choose the minimally necessary level.

Prompting & AI Literacy

Melvynx presents five community skills for Claude Code: Impeccable (design-system enforcement via 23 sub-skills), Grilling (structured pre-interviews before each implementation), Apex (multi-stage workflow with self-tests and screenshots), Make Interface Feel Better (design polish), and Thermonuclear Code Quality Review (strict quality control as sub-agent). Matt Pocock releases Skills Repo v1.1 with renamings (to-spec, to-tickets), the new Wayfinder skill for roadmap creation as GitHub issues with blocking relationships, plus a reworked TDD skill and code review against Martin Fowler code smells; the repo now has 160,000 stars and 7 million downloads. Mark Kashef suggests wargaming as a strategy for the Fable-5 subscription withdrawal: instead of normal plans, create detailed simulations with action-reaction-counteraction structure, later usable by cheaper executors.

AI Industry & Strategy

Nate B Jones analyzes OpenAI’s talks with the US government about a 5% equity stake worth roughly $42.5 billion — structurally similar to the Alaska Permanent Fund model — as a strategic hedge for political approvals before possible tighter regulation, not altruism. Simultaneously, Meta is building a cloud business around excess compute capacity, while Zuckerberg internally admits AI agent development hasn’t accelerated as expected — MoureDev shows that 95% of enterprise AI deployments according to Gartner produced no measurable business impact, and 40% of agentic AI projects are slated for cancellation by end of 2027. Everlast AI interviews Prof. Alexander Smola (Boson AI), who explains why benchmarks don’t measure what users want, why the Pokémon theorem mathematically excludes perfect AI fairness, and why voice AI — with a two-tier architecture of fast audio frontend and complex reasoning backend — is the next growth domain. AI Explained observes that available compute has multiplied 100-fold, but parameters grew only moderately because compute scaled to billions of users, new modalities, and long-form tasks — an argument against saturation theses.

AI & Society / Future of Work

Everlast AI brings two insightful conversations: neuroscientist Konrad Körding argues that intelligence saturation is real because the physical world remains an insurmountable bottleneck — robot costs don’t follow the exponential AI-token curve — and that humans have irreducible values (physical presence, liability, social standing) generating more demand for genuine human work, not less. Datapizza discusses with legal expert Giorgia Rastrelli Article 50 of the EU AI Act: chatbot disclosure obligations, watermarking standards (SYD as European standard), new bans on nudifier apps, biometric data regulation, and the omnibus deadline extension to December 2026 or 2027 for high-risk systems. Kyle Balmer warns against job displacement not by a single model but by long-term price deflation: GPT-4o became 200-fold cheaper — the recommendation is to build your own income streams rather than remaining in dependent employment. The No-Priors episode with Booking.com CEO Glenn Fogel shows how an established travel company implements agent-driven booking assistance with “Penny” (at Priceline): monthly doubling, rising conversion, sinking cancellations — while simultaneously admitting no moat holds long-term.

In Brief

NeuralNine shows synthetic data generation with CTGANs and TVAEs via SDV package plus Scraper Studio from Bright Data (AI-powered web scraping with self-healing) and financial risk metrics with the Python package Empirical Reloaded. Unsupervised Learning discusses in two interviews application-security prioritization via organizational context (Palo Alto Networks Cortex) and Cisco’s hybrid-mesh firewall for central policy management of heterogeneous enforcement points. Meta’s Brain2Qwerty-v2 system achieves 61% word accuracy on non-invasive MEG-based brain activity translation — remarkable for research but far from everyday applicability. David Shapiro uses Grok Build 4.5 for a Roman soldier swarm simulation and frames this as grounds for an extensive Anthropic critique (alignment rhetoric, power-seeking allegations).

AI Explained (1 new video)

• A Model Explosion: GPT 5.6 Sol, Grok 4.5 and Meta Muse Rewrite the Rules
  10.7.2026, 12:40:15

  The video analyzes recent model releases from OpenAI (GPT-5.6 Soul, Terror, Luna), Anthropic (Fable), xAI (Grok 4.5), and Meta (Muse Spark 1.1) using multiple benchmarks and real-world tests.

  Central thesis: Frontier Labs now promise “nearly as good for a fraction of the price” – the key question is whether this cost advantage justifies the performance gap.

  Benchmark highlights:

  • Agent’s Last Exam (UC Berkeley, 55 industries): Soul achieves 54% vs. Fable 45%. This new benchmark is hard to game (no training-data contamination) and is based on real, economically valuable tasks.
  • Automation Bench (Zapier): Soul leads by only 0.7%, but costs similarly to Fable – less dramatic advantage.
  • Coding benchmarks: Soul 80 vs. Fable 77 (Artificial Analysis Index), but on newer, harder benchmarks like Swe Marathon, Grok 4.5 takes the lead.
  • ARC-AGI 3: Soul 8% vs. other models under 2% – clear advantage in abstract reasoning.
  • Competitive coding: An OpenAI model just broke a benchmark.

  Problematic counterarguments:

  Meta’s Muse Spark 1.1 achieves 72% on Vibe Code Bench vs. Soul’s 81%, but costs about 35x less. Similarly with GLM 5.2 and DeepSeek V4 Flash – the performance-per-dollar frontier shifts when these cheaper models are included. Soul can’t simultaneously be “nearly as good but cheaper” AND cheaper than Fable if even cheaper alternatives exist.

  Games as benchmarks: Both providers now release playable mini-games to demonstrate UI creation and browser usage. Soul’s output was faster (20 min vs. over 1 hour for Fable) and had different trade-offs (sound settings buggy), but the result showed an “adorable” companion.

  Self-improvement claims: OpenAI claims Soul post-trained Luna. The author is skeptical: unclear how much human guidance was needed. Anthropic was more honest, warning that their internal productivity improvement is about an order of magnitude below what would be needed for a 2x speedup.

  Security issue: The UK AI Security Institute found universal jailbreaks for Soul – easier and faster than for Fable. An Anthropic researcher expressed concerns about alignment and speculates OpenAI may have rushed the release.

  Real-time voice agent: OpenAI’s new live-voice feature listens while you speak, enabling more natural interruptions and true real-time translation.

  Why model improvement doesn’t end: GPT-4 (Aug 2022) had ~2 trillion parameters, Soul likely ~4 trillion, Fable ~10 trillion. Although available compute has multiplied 100x+, parameter growth is modest – because compute went into scaling to billions of users, new modalities (image, voice, video), and long-context tasks. Future hardware could enable 100-trillion or 1-quadrillion parameter models.

  Conclusion: The field is far from saturated; we’re probably closer to the beginning than the end.

  AI tools/providers discussed: OpenAI (GPT-5.6 Soul/Terror/Luna, Real-Time Voice), Anthropic (Claude/Fable), xAI (Grok 4.5), Meta (Muse Spark 1.1), GLM/Qwen (Chinese models), DeepSeek. Format: Deep-dive analysis with benchmark roundup and opinion elements.

AI Foundations (1 new video)

• FULL Claude + Higgsfield Tutorial: Automated Marketing System!
  8.7.2026, 18:18:45

  Summary: Claude + Higgsfield – Automated Marketing System

  The video demonstrates a four-part framework for automating marketing using Claude and Higgsfield (a tool that connects various image and video generation models).

  Step 1: Structure

  Claude Code requires an organized folder structure. Folders are created for Context (information about the person/company), Templates (reusable formats), and Skills (automations). A claude.md file serves as the project guide.

  Step 2: Context

  Upload documents that shape Claude: Business Overview, Point-of-View document, Stories, Voice Guide (anti-AI-slop rules like “never fabricate numbers”). These inject personal voice and authenticity into AI-generated content. Example: An email blast using a real story (coffee on the stairs) and strict compliance against generic AI phrases achieved a 45% open rate.

  Step 3: Templates

  Manual iteration with Claude + Higgsfield until perfect, reusable formats emerge. Example: YouTube thumbnail template with placeholders (logo top left, two-line text, accent color, face position). The template stores the prompt and reference images to guarantee consistent results.

  Step 4: Skills

  Automation commands, e.g., /thumbnail-generator + video title generates three variants in parallel, validates them against checklists (spelling, logos, faces), and saves them. Skills can be converted into routines to run on a schedule in the background. Example: A lawyer saved 3.5 hours on presentation creation using a PowerPoint skill.

  The process integrates Claude (strategic prompting, subagents) with Higgsfield (image generation) and makes marketing tasks (4–5 hours of post-production per video) fully automated.

  Explicitly mentioned: Claude, Claude Code, Higgsfield, GPT-4 Vision (for image text), Cling 3.0 (video generation), MCP/CLI integration. Format: Tutorial (four-part framework with live demos and practical implementation steps).

AI with Arnie (2 new videos)

• Did Claude just lose against GPT 5.6?
  10.7.2026, 15:46:33

  Summary: GPT 5.6 and the current AI model landscape

  OpenAI has released GPT 5.6 in three variants: Sol (large model), Terra (mid-size), and Luna (smallest). Additionally, there’s a revamped ChatGPT app with new Live Mode. In parallel, Xai is launching Grock 4.5 and Meta is launching Mus Spark 1.1.

  Where GPT 5.6 should be used

  The new Codex app is the primary entry point – it combines Codex and GPT functionally in a desktop application with access to folders and system. Alternatively, there’s the web app, but Codex offers more functionality.

  Tests and practical demos

  The creator conducted several tests:

  • Disaster robot simulation: GPT 5.6 could simulate a drone with robot arms, though with weaknesses in camera feedback and physics accuracy. Fable performed better here.
  • AI avatar (gorilla “Congo”): Using the Realtime API and predefined tools, GPT 5.6 created a speaking avatar including lipsync integration.
  • World of Warcraft-like game: A complete game with combat system, mana/health regeneration, and leveling emerged from a single prompt in less than 20 minutes.
  • Local language model training: Someone used GPT 5.6 to program a pipeline for training a local model on Mac – something Fable doesn’t do.

  Fable has restrictions on ML, cybersecurity, and biology that GPT 5.6 doesn’t have.

  Benchmark comparisons

  On Artificial Analysis (more cost-effective, transparent than OpenAI’s own benchmarks):

  • Intelligence index: Fable leads narrowly ahead of GPT 5.6 Sol (Max), followed by Sol (High) and Opus.
  • Intelligence vs. cost: Sol on High offers nearly the same intelligence as Opus but is significantly cheaper. Terra on Max is more expensive than Sol on High with worse performance.
  • Deep SWE Benchmark: Sol even surpasses Fable on some coding tasks and costs less.
  • Arc AGI Benchmark: Sol is now the strongest tested model for logic and puzzles.

  Thinking-effort recommendation: Use high or very high; ultra with “superagents” is usually unnecessary and just consumes additional tokens. Max is rarely worth it.

  GPT Live Mode (new)

  The new Live Mode enables continuous interaction: the model can listen and speak simultaneously, adds filler words, can be interrupted, and performs side tasks (like web search or bookings) while responding. This is a paradigm shift for voice AI – previously systems were cascade-based (speech-to-text → AI → text-to-speech) or round-based only. Ideal for restaurant reservations, live translation, and phone automation.

  Model recommendations by use case

  | Model | When useful |

  |——–|—————|

  | Fable | Hardest tasks; thinking effort high/very high. Max/ultra exceeds limits. |

  | GPT 5.6 Sol | Ideal for most tasks; high or very high recommended. Ultra only for many parallel tasks. |

  | GPT 5.6 Terra/Luna | Rarely needed; prefer Sol on short, except for very simple questions. |

  | Opus | Largely replaced by GPT 5.6 Sol. |

  | GLM 5.2 | Inefficient; token consumption makes API usage expensive. |

  | Grock 4.5 | Affordable API alternative; just behind Sol (High) in intelligence but cheaper. |

  | Mus Spark 1.1 | Not yet available on third-party platforms; testing pending. |

  For ML/cybersecurity/biology: Definitely use GPT 5.6; Fable blocks these requests.

  Creator’s conclusion

  The market is intensifying around efficiency and cost. Sol on High offers the best value for most tasks. The creator uses GPT 5.6 Sol on High as standard in his Hermes agent app. Users should test their specific use case to choose the right model.

  Explicitly discussed tools/models: OpenAI (GPT 5.6 with Sol/Terra/Luna variants, ChatGPT Live Mode, Codex app), Anthropic (Fable/Claude), Xai/SpaceX (Grock 4.5), Meta (Mus Spark 1.1), Realtime API, Artificial Analysis benchmarks, OpenRouter; format: opinion/reflection with live demos and benchmark deep-dive.

• Claude Makes AI Videos Unfair
  6.7.2026, 19:24:47

  The creator demonstrates AI video creation using a self-built skill made available as open source. Central to this is the use of Cloud Code with Claude/Fable as a coding agent that automates the entire process.

  The technical workflow:

  An input image is analyzed, the agent conducts an automated interview and suggests video concepts. This is followed by character design creation from the image to maintain facial consistency across all scenes. Then first and last frames are defined for a transformation sequence, followed by three 10-second video scenes, optionally with title cards for smooth transitions. Finally, a reverse transformation occurs. Everything is stitched together with FFmpeg.

  The V1 problem: The creator showed an older video with “character drift” – through chaining of frames, facial identity was completely lost. V2 solves this by having each scene use the stored character design as a base instead of using the last frame each time.

  Practical implementation: Create a folder, clone a GitHub repo, use a coding agent (Cloud Code, Cline, Codex – all work), install Hixfield CLI and skills. A detailed prompt in the repo handles all setup steps automatically. Then: upload image + idea, agent does everything independently.

  Cost calculation: A 40-second video costs approximately 216 credits at Hixfield, which amounts to €6.80–€14.80 depending on plan.

  The creator showed two practical demos: a monster marketing video with Cloud Code and a “Monster Rampage” video with Cline, both generated with minimal manual input and subsequent editing optimization.

  Covered: Claude (Fable), Cline, Codex as coding agents; Hixfield for video generation; open-source skill – format: tutorial/deep-dive.

AI News & Strategy Daily | Nate B Jones (4 new videos)

• 1.6M agents registered for OpenClaw and did NOTHING.
  10.7.2026, 14:00:30

  Summary: Recognizing and correctly classifying agent problems

  The video addresses the core problem of today’s AI economy: people lack instincts for when to deploy an agent, multiple agents, or no AI at all. The speaker provides a framework for correctly classifying tasks.

  Scientific foundations: Stanford research (2024) shows that attempts yield exponentially better results – with 250 attempts instead of one, bug-fix rates rose from 15.9% to 56%, beating single-model attempts. Anthropic discovered that token spend explains 80% of the difference between successful and failed problem-solving, and multi-agent teams outperform individual frontier models by 90.2%. The critical point: the right answer often exists in the attempts, but without automatic validation (evals/test suites), systems cannot benefit from it – majority voting plateaus around 100 attempts.

  The 4-point test framework in one minute:

  1. Size – Does the task fit in a context window?
  2. Independence – Can parts be solved without interdependencies?
  3. Separation of concerns – Do different parts need different perspectives? (e.g., auditor vs. accountant, reviewer vs. author)
  4. Verifiability – Is validation much cheaper than production? (test suite, output code, document comparison)

  The four outcomes:

  • Chat task: Quick, simple interaction
  • Single agent: Fits in context window, works independently
  • Multi-agent team: Large tasks with multiple perspectives (e.g., scheduling is single-agent; document analysis across 40 tools with thousands of pages is multi-agent; hiring decisions are human judgment)
  • No AI needed: For true judgment-call decisions

  Practical examples:

  • Scheduling (gym slot) = Single-agent (5 minutes)
  • Tool management dashboard (40 tools, contracts, usage logs) = Multi-agent problem with sourcing, user-verifiable, efficient with cheaper worker agents + expensive planner model (possible 10x cost reduction)
  • Candidate selection, product names, business direction = human judgment, not delegable – AI as thought partner okay, but not as decision-maker

  The speaker built a tool that in under a minute provides classification plus next steps (direct link to Ringer for multi-agent, to Claude/Codex for single-agent, or reminder of human deliberation). The context: 1.6 million agents registered with OpenClaw, but most completed zero tasks – a mismatch between intelligence availability and its application.

  Format: Deep-dive / opinion with demonstrations and toolkit; models/tools: Claude, OpenAI (ChatGPT 5.6), frontier models, Fable 5, Anthropic systems, Ringer (speaker’s multi-agent harness), Codex.

• Claude Fable 5 Bossed 20 Cheap AI Agents. The Whole Site Cost $8.
  8.7.2026, 14:00:16

  Summary: Multi-agent systems for real production tasks

  The author demonstrates how a multi-agent system built his wife Elsa Hunison’s website – an author and accessibility expert – in 1-2 hours for roughly $8, while the same work with Claude Codex the month before cost 6 days and at least $85–$105. The system is based on an org-chart structure: Claude Sonnet 3.5 acts as the “boss” (design, specification, oversight), while cheaper models (like GLM 4.5) handle actual coding and writing work. Each of the 34 tasks is followed by automated review from a separate checker agent – 12 tasks were rejected for revision.

  The system caught four errors: (1) The worker hallucinates Elsa’s quotes and paraphrases them – the checker compares character-by-character and rejects. (2) Cheap workers hide required text in invisible paragraphs instead of implementing accessible design. (3) The boss agent itself writes a CSS bug (invisible button), caught by two independent checker passes. (4) A checker rejects legitimately short news posts; the worker escalates to the boss agent, who overrides the checker. The core principle: hallucination isn’t “solved” but structurally neutralized through mutual review loops – no hierarchy level escapes verification.

  Elsa received a website that automatically enforces a 14-point accessibility charter (WCAG 2.2 AA standard), preserves her words verbatim, learns her voice, and even creates voice-overs – specifically optimized for a fictional blind reader named Maya. Elsa received no classical briefs; a 5-word prompt sufficed. Cost savings: instead of ~$100 with Sonnet alone = $2.74 token costs, $8 total with setup. An org chart isn’t an engineering trick but a recipe pattern going mainstream – the author links a one-click setup guide. The core thesis: multi-agent orchestration is now simple enough for everyone, hallucination is an org-design problem not a model problem, and ambitious tasks can be delegated cheaper and better than with a single frontier model.

  Claude Sonnet 3.5 as boss agent, cheaper models (GLM) as workers, automatic checker agents; deep-dive demonstration with real-world website project (non-code-centric, but code-supported).

• OpenAI Just Offered The Government $42 Billion. This Is The Real Reason.
  6.7.2026, 14:00:31

  Summary: The shift in AI competition

  The fundamental shift in AI industry is no longer model competition, but a struggle across multiple layers: infrastructure, distribution, regulatory approvals, and societal integration.

  The five stories and their connection:

  Meta launched a consumer gaming app (Gizmos) where you enter prompts and receive playable mini-games. Simultaneously, Meta is building a cloud business to sell excess AI compute capacity to external customers – markets reacted positively to this news. Internally, Zuckerberg admitted AI agent development hasn’t accelerated as expected. OpenAI is discussing with the US government a 5% equity stake worth roughly $42.5 billion (similar to the Alaska Permanent Fund model), and other labs should adopt similar structures – a move to secure regulatory approvals before stricter regulation. Jersey Mike’s, a sandwich chain, mentioned artificial intelligence 22 times in its IPO filing – a symptom of excess capital chasing AI-associated growth.

  The old scoreboard versus the new:

  The old model: whoever owns the best model wins; this justified capex buildouts exceeding $600 billion annually at the top-5 hyperscalers. The new scoreboard: compute is an asset class, consumer interfaces are distribution plays, models are just one layer in the stack. Regulatory approval is now the real bottleneck – government can delay model releases with a phone call (as with ChatGPT 5.6’s 30-day pre-release access).

  Anthropic’s quiet strategy:

  While Meta and OpenAI dominate headlines, Anthropic focuses on enterprise integration through forward-deployed engineers and Claude Tags – an enterprise distribution play for long-term, sticky revenue streams.

  The bigger shift:

  Capex continues rising a third, but spending is optimizing capital allocation across infrastructure margins, distribution, and regulatory room. This marks not the end of model competition but a societal-integration wave behind it – how models actually work in enterprises and consumption. Jersey Mike’s symbolizes the noise of this transition: people don’t know where real innovation happens, so “AI” gets slapped everywhere.

  The message: calibrate your position in this societal shift, not just in the model race.

  Covered AI labs: Meta, OpenAI, Anthropic, Google; format: opinion/reflection with roundup elements.

• You Can’t Compete on Cheap Models Anymore
  5.7.2026, 16:00:08

  The central thesis: as AI tools get better and cheaper, all outputs look similar – not because of the tools, but because execution became a commodity. Value has shifted.

  This shows in Mitchell Hashimoto (HashiCorp co-founder) testing frontier models against cheap models. On standard tasks (implementing features), all three models produced identical quality – budget model under one dollar, GPT 4.5 at $1.50, Claude 5 at nine dollars. But Hashimoto ran a second test: he gave Claude 5 a problem cheap models couldn’t solve – optimizing complex systems code. It cost $40, took two hours, and achieved performance levels Hashimoto himself couldn’t have reached.

  The decisive difference: this second task wasn’t on any backlog, no PM had prioritized it. It only emerged because an expert recognized new possibilities had materialized. AI can only fulfill tasks someone can imagine. The ceiling for AI value isn’t the model or cost – it’s the size of the imagination list: what tasks do I know to ask that didn’t exist before?

  This also explains output similarity: when everyone runs the same known tasks through the same tools, results converge. The sun-analysis example for targeted porch marketing shows what it looks like when imagination plus frontier models converge – an entirely new form of hyper-targeted marketing that was previously impossible.

  The practical structure: optimize cheap models for daily execution (that’s cost sink, becomes table stakes). But where do you invest scouting time? Who gets to ask $400 questions to models without approval? That’s the imagination problem – not prompt engineering, but technical imagination about what’s newly possible.

  The electrification example: factories simply swapped electric motors for steam engines – hardly a gain. Real returns came only when managers redesigned the factory around the new decentralized motors. Stripe shows this in code migration (50M lines in a day) – the model wasn’t impressive, but Stripe had spent years building review systems and task coverage to enable it. Imagination fires only when it sits next to real context. You can’t hire imagination into a company – context lives with the people doing the work.

  Proof on the side: when Claude 5 went offline for three days, it didn’t stop people who’d spent the first 72 hours dreaming up new possibilities – they kept going with the imagination. The models came back, but the questions persisted.

  No tools/models/vendors explicitly covered – opinion/reflection, individual and company level.

Alejandro AO

No new videos in this period.

Alex Finn (3 new videos)

• ChatGPT 5.6 just KILLED Fable 5
  9.7.2026, 18:21:02

  ChatGPT 5.6 vs Claude (Fable) 5: Comparison and Workflow

  ChatGPT 5.6 launched with three models – Luna (efficient), Terra (mid-range) and Soul (smartest). Soul with seven different thinking levels is tested here, particularly at Ultra-level. ChatGPT offers generous usage quotas so you can use the highest thinking levels without hitting limits quickly. A new “Fast Mode” delivers Claude-level thinking in significantly faster execution according to the speaker – Claude has become noticeably slow. Codeex is being discontinued and merged with the ChatGPT app; on mobile devices the feature is called “Remote Sessions”.

  ChatGPT 5.6 vs Fable 5 comparison: Claude remains superior at planning, high-level thinking, and understanding large codebases – this became clear when testing a complex “software factory”. However, ChatGPT 5.6 excels at practical execution: it’s diligent, reliably completes massive tasks and adds extra features. Code quality is comparable, but ChatGPT is more proactive.

  Price is Claude’s main problem: The speaker spent hundreds of dollars on API usage within hours. A single prompt on Ultra or higher costs hundreds of dollars; an afternoon coding session (1–8 PM) with Fable consumed $500. ChatGPT 5.6 is significantly cheaper – the speaker maxed out his 5-hour window for the first time, but not his weekly limit.

  User interface and harness: ChatGPT improved its UI; Claude is still better, but no major difference anymore. The deciding factor is the ChatGPT harness (computer-use and browser-use): it’s significantly more reliable than Claude Code. The speaker can delegate tasks – visit websites, retrieve API keys, test workflows – and ChatGPT executes everything. Tests that used to take an hour now take 24 minutes while you do other things.

  Recommended workflow:

  1. Plan in Claude/Fable 5 (use “High” mode, not Ultra, to save costs)
  2. Export the plan (e.g., as Markdown or to Linear)
  3. Upload the plan to ChatGPT 5.6/Codeex
  4. Delegate all execution to ChatGPT – with the mindset: “You’re doing this, not me”
  5. Choose a desktop computer as your main development machine (runs 24/7), control it from all other devices (iPad, iPhone, laptop) to keep code centralized
  6. Use an “admin chat” per project for high-level management, separate chats for specific features

  Winner: ChatGPT 5.6. Despite Claude’s planning advantages, the combination of execution power, reliable harness, and reasonable costs wins. The speaker declares it the new daily driver. A benchmark test (a kind of “first-person shooter test”) shows ChatGPT 5.6 way ahead. GPT-6 is coming soon.

  The key tools were ChatGPT 5.6 (Soul model), Claude/Fable 5, and Codeex (merging with ChatGPT); Linear was also mentioned for project planning – demo and comparison.

• 100 hours of Hermes Agent lessons in 19 minutes
  8.7.2026, 16:22:35

  Summary: Hermes Agent – 9 Lessons After 100+ Hours of Use

  The speaker has worked intensively with Hermes Agent over four months and shares nine key insights for optimal use:

  Model selection: Opus is recommended as the best option despite high API costs (around $40 daily) because it reliably completes tasks. To save money, you can switch to ChatGPT 4.5 and up or use GLM 5.2 for minimal expenses.

  Multiple agents: At least two Hermes profiles should run simultaneously so they can monitor each other and automatically fix problems during outages (as a failover system). These can be created via prompt or through the Hermes dashboard.

  Security and accounts: The speaker dispels concerns about security vulnerabilities – Hermes only executes what is explicitly commanded. Separate accounts for each agent are unnecessary and just create friction; shared access is sufficient if you work responsibly.

  Platforms: Desktop app for extensive work, Telegram for mobile deep work with advanced formatting options (tables, bold, paragraphs), iMessage for quick tasks on the go.

  Performance: Old, unused cron jobs significantly slow down the agent; regularly pausing/deleting unnecessary jobs boosts speed and saves tokens.

  Tailscale: Free software that connects all devices in a private network – enables the agent to work on multiple computers without all being connected to monitors.

  Reverse prompting: Daily morning interview with the agent where it asks questions and independently figures out which tasks it can automate – about 5 minutes daily, but discovers 2–3 new automations every day.

  Kanban board: After the morning brief, identified tasks should be moved to the Hermes dashboard (via terminal: Hermes dashboard) onto the Kanban board where the agent systematically works through them.

  Multi-language formatting: The new Telegram integration enables structured daily reports (e.g., stock research in table format) through cron jobs.

  Context: HubSpot sponsors a free 18-video course on AI agent development (memory, tools, models, automations vs. dynamic reasoning, integrations with n8n, Notion, Make, Chatbase).

  Bottom line: Hermes Agent, Opus model, opinion/reflection video based on personal hands-on experience.

• The new Hermes Agent update officially ended OpenClaw
  6.7.2026, 20:31:27

  Hermes Agent – Seven New Features Overview

  The video presents seven new features for Hermes Agent:

  1. Mixture of Agents (MOA) – With the command /MA, an aggregator model (default: Opus 4) is used that sends a prompt simultaneously to multiple reference models (default: ChatGPT and DeepSeek V4 via Open Router). They respond independently, and the aggregator model synthesizes their answers into a higher-quality overall response. The feature is suitable for complex questions with multiple solution paths.
  1. Slash Learn (/learn) – Allows you to automatically create new skills and memories by entering URLs, prompts, or completed tasks. Example: paste tweet URLs and the agent instantly creates a reusable skill from them.
  1. Slash Journey (/journey) – Shows a visual overview of all learned skills and memories of the agent and their connections to each other. This makes it clear how the agent has evolved.
  1. Lower self-improvement costs – When creating memories and skills, cheaper models are now automatically used instead of expensive models like Opus, which brings significant cost savings.
  1. Enhanced vibe-coding features – The desktop app now offers full Git control with diffs, code view, commits and pull request creation directly in Hermes. The example of a generated 3D action RPG (Diablo-like) demonstrates the quality of this feature.
  1. Fable 5 integration – Fable 5 is now available in Hermes. According to the recommendation, you should create a separate Hermes profile just for Fable 5, not as a daily driver due to costs, but for especially complex coding tasks or multi-device workflows.
  1. Bonus tip for Fable 5 usage – The recommended strategy: use Mixture of Agents for complex questions, Fable 5 for complex tasks and high-quality frontend development. Fable 5 profiles can be integrated into Telegram.

  Explicit tools & providers: Claude (Opus 4, Fable 5), ChatGPT, DeepSeek V4, Hermes Agent Desktop, Open Router, Telegram. Format: demo/tutorial with opinion; suitable for beginners because clearly structured.

Andrej Karpathy

No new videos in this period.

Bart Slodyczka (1 new video)

• Qwen 3.6 + Pi Agent: Build Your Own AI Assistant (Full Setup)
  6.7.2026, 12:00:10

  Summary

  The video demonstrates how to build an AI-powered customer support agent for Zendesk using Pie – an open-source agent framework – that runs locally on your own computer with a local language model.

  The process begins with installing Pie and connecting it to LM Studio (here: Qwen 3.6 35B) to expose the local model via an API. Pie then has access to terminal tools for reading and writing files. Next, Pie itself is used to scaffold the agent structure: you converse with Pie and ask how to build a custom Zendesk support agent. Pie reads its own documentation and independently creates the necessary folder structure with extensions, skills, prompts, and tools – all as a configurable Pie package. After that, Zendesk API credentials are added (.env file) and tested to verify the agent can read tickets and send responses.

  To make the agent fully autonomous, a cron job is created that fetches new tickets every 5 seconds, sends them to Pie in print mode (-p), and automatically writes the generated responses back to customers – without manual intervention. In the demo, this workflow works: a new ticket is submitted, Pie generates a troubleshooting response, which is automatically posted in Zendesk. The agent can also load Telegram packages to work with it remotely via messenger. Topics like evaluations, data protection, and prompt injection protection for production deployment remain unaddressed.

  Pie, local model (Qwen), Zendesk – tutorial/demo.

Ben AI (1 new video)

• This Claude Second Brain Setup Will Change How You Do Sales Forever
  8.7.2026, 08:04:41

  Summary: AI Sales Operating System with Kloud

  The speaker demonstrates how to build an automated sales system that streamlines the entire sales process from lead generation through follow-up and analytics. The system is built on five components:

  1. Sales Second Brain (central intelligence layer): A folder structure (implemented e.g. in Obsidian) with structured documents: call transcripts, contact information, deal histories, ICP documents, proposal positions, sales processes, lead generation campaigns, and skill templates. This is the knowledge base that gets updated daily through automated routines.

  2. Data source connectors: Integration of CRM (Attio), meeting transcription (Fireflies), proposal tools (PandaDoc), Google Workspace, calling platforms, as well as internet scrapers (Appify for LinkedIn) and lead databases.

  3. Automated routines: Three main tasks run daily: Morning Routine (process calendar, emails, calls), CRM Sync (update deal status), Call Scoring (evaluate sales rep performance). Optional: monthly and quarterly reports.

  4. AI Automations (Kloud Skills): Pre-built workflows for call prep, client one-pagers, lead gen, outreach, pipeline review, follow-ups – all have access to the Second Brain and leverage its context for better results.

  5. Personalized Sales Dashboard: Central control panel with overview of daily tasks, call prep briefs, pipeline metrics, deals by stage, close rates, and automatically updated through an evening routine.

  Practical implementation: For those who don’t want to write code, the Sales OS Setup Skill automatically generates the folder structure, Claude.md navigation files, routines, and a personalized dashboard. Alternatively, these components can be built manually using Kloud prompts. Critically, create the permanent documents (ICP, proposal, sales process) first, as these have the greatest impact on AI outputs.

  The system drastically reduces the time sales reps spend in CRM and other tools because all necessary information is consolidated in the dashboard with pre-prepared intelligence.

  Explicitly mentioned tools/providers: Kloud, Attio (CRM), Fireflies, PandaDoc, Appify, Google Workspace, Obsidian, Vercel; Format: Tutorial/Deep-dive with demo elements.

Brian Casel (2 new videos)

• How to build your industry’s killer app
  9.7.2026, 12:00:24

  Summary

  The creator demonstrates how to develop an industry-specific client management app for real estate agents—not as a generic tool, but tailored to the actual workflow reality of this industry. The core premise: if you know and master a problem in your industry, you can build the most valuable solution and later sell it as SaaS.

  The approach follows a structured four-phase process: First, a Product Requirements Document (PRD) is created using the PRD Creator Skill—not just listing features, but also consciously defining what doesn’t belong in version 1 (e.g., team features, SMS/email integration). The PRD documents data models (contacts, deals, tasks, checklist templates) and then breaks everything down into four technically implementable milestones.

  In Milestone 1, contact management is built: dedicated contact pages, filtering by type (lead/buyer/seller), search by name and email. After the initial build, a rapid-fire refinement phase follows, where bugs (autofocus errors, defocus while typing) and improvements are fixed immediately.

  The next milestones build deals (with Kanban board, stages like “New Lead → Showing → Offer → Under Contract → Closed”), task checklists for each deal, automatic task templates, follow-up reminders, property photos, and a dashboard. An error with attaching photos before deal creation is identified and fixed.

  At the end, the color scheme is refreshed by updating the design system palette (green color palette for “real estate” feel, dark mode is also tested), and the checklist template feature is validated—real deals can start with predefined task lists.

  The process demonstrates: through the detailed PRD phase with interviews and decision-making, precise technical implementation plans emerge; Claude works with plan mode, self-verifies via browser and tests, documents learnings per milestone (milestone logs) that are passed to the next phase.

  Conclusion: Claude (Opus) was used as the AI model; the format is a tutorial/demo hybrid that shows methodical app development from concept through PRD to a working prototype.

• How I’d start an “agent-in-a-box” business
  6.7.2026, 12:00:18

  The video introduces the “Agent in a Box” business model—a new way to sell AI-powered services that sits between SaaS products and traditional consulting. The idea: you package a standardized service (like bookkeeping, SEO, or lead generation) as a reusable AI agent, install it for individual customers, and adapt it to their specific workflows.

  The model has two components: the “box” is the reusable agent template with defined capabilities; the “business” is the customer-specific installation and customization. The advantage over SaaS: better product-market fit through personalized adjustments instead of one-size-fits-all. Over consulting: predictable, repeatable processes instead of starting from scratch every time—the business logic scales better.

  The four-step process for implementation: (1) Document the standard service process, (2) Identify where AI and human touchpoints fit, (3) Install with real customers and learn what doesn’t work, (4) Codify frequent adjustments into reusable configurations with playbooks.

  Monetization flows through two channels: a one-time installation fee for setup and customization, plus an optional retainer model for ongoing maintenance and improvement—the latter creates predictable recurring revenue. The model even works as a solo entrepreneur business.

  Models/Tools Used: Claude, Codex, Hermes are mentioned as possible runtime environments for agent skills. Format: Opinion/reflection with detailed guidance (deep-dive).

Coding with Lewis

No new videos during this period.

Cole Medin (2 new videos)

• Pydantic AI 2.0: The New Best Way to Build AI Agents is Composing Capabilities
  10.7.2026, 14:00:40

  Pydantic AI has made a significant development leap with version 2.0, centered around the new concept of Capability – a single, composable unit that bundles agent instructions, tools, lifecycle hooks, guardrails, and model settings. Instead of a disorganized collection of instructions and tools, AI agents can now be thought of as a composition of multiple capabilities, much like Lego bricks. This makes agents significantly more reusable: a knowledge-base capability can be shared between a support agent and a FAQ widget without code duplication.

  A core advantage is Progressive Disclosure – the agent receives a catalog with brief capability descriptions and loads full instructions only when actually needed. This saves tokens and keeps context focused. The demo illustrates this concretely: for a simple FAQ question, only the knowledge-base capability is loaded; for an escalation, the escalation capability is added as well.

  Pydantic AI now distinguishes between a Lean Core (critical capabilities like Thinking, Web Search, Tool Search) and a Harness (optional third-party capabilities like Code Execution). This keeps the framework lightweight while still offering much out of the box. The speaker sees this as a turning point: while coding-agent SDKs (Claude SDK, Codex SDK) are practical for personal agents, Pydantic AI is superior for production-grade agents with multiple users because it provides full control and composability – now even simpler than before with the capability model.

  Pydantic AI; Opinion/Reflection

• I Love the Karpathy LLM Wiki but it Doesn’t Scale. Here’s What Does.
  9.7.2026, 00:00:36

  Two Types of AI Agents: From Personal Agents to Production Systems

  The video distinguishes two fundamental approaches to building agents: Personal Agents (like Claude Coder or Hermes) and Production Agents for multiple users.

  Personal Agents are markdown-driven – knowledge is gathered locally in simple documents (similar to an “LLM wiki”). This is flexible, fast, and inexpensive, but doesn’t scale when other users need access.

  Production Agents must be built fundamentally differently. They require a database (the video uses Redis with Redis Iris as an example) because markdown is no longer sufficient: scalability, access control, governance, and cost optimization require structured systems. The usage APIs of personal-agent SDKs are also unsuitable for production.

  The production architecture runs on two pillars: Context Retriever (structuring business data and making it available to the agent via MCP servers) and Agent Memory (short and long-term memory for users). The Context Retriever auto-generates tools based on defined data entities and their attributes – the agent can then search more efficiently (e.g., filter orders by status, search products by text). Agent Memory automatically extracts important information from conversations into long-term memory (via vectorization), so the agent retains user preferences across sessions.

  The demo shows an e-commerce support agent that, in response to a customer’s question about a delayed order, simultaneously retrieves customer history, orders, and saved preferences (e.g., “return instead of refund”) – all through a few MCP tool calls and token-efficient. The author recommends Pydantic AI for production setups instead of Codex/Claude SDKs, as these are too slow and token-heavy for production.

  The ideas are framework-independent, but Redis Iris provides both (Context Retriever + Agent Memory) integrated.

  Explicitly mentioned: Claude Coder, Hermes, Pydantic AI, Redis/Redis Iris; Format: Demo + Deep-Dive into the difference between two agent architectures.

Datapizza (1 new video)

• La tua FACCIA è un DATO: l’AI Act su DEEPFAKE, BIOMETRIA e NUDIFIER
  7.7.2026, 15:00:10

  Summary: Algoritmi versione legal – AI and labor law in Europe and China

  The format is a discussion podcast between Loredana from DataPizza and Giorgia Rastrelli from Studio DGRS about current AI regulation.

  Chinese labor law cases: In two Chinese court rulings, mass layoffs in the context of AI optimization were handled. Surprisingly, a company was ordered to pay damages after attempting to dismiss an employee without sufficient justification through a restructuring—with a drastic 40% salary cut. The court demanded better justification for restructuring, a principle that also applies in Europe. This shows that existing legal principles can be applied even without specialized AI laws and underscores the role of court rulings as precedents until detailed regulations follow.

  Article 50 of the AI Regulation (AI Act) – Transparency obligations: The EU Commission published a code of conduct for labeling AI-generated content. This applies to providers (developers/suppliers of AI systems) and deployers (users in professional contexts – not for private use). The regulation has several core requirements:

  • Chatbots: Must disclose themselves as such to balance asymmetric information between user and organization and enable critical assessment.
  • Watermarking: All generated content (images, videos, audio) must be marked in the metadata so AI origin can be detected. Standards like SYD (originally from Google for Gemini) become the European standard. Exceptions exist for basic editing (such as light corrections).
  • Deepfakes: With the Omnibus package, new bans on so-called nudifier apps were introduced (apps for artificially undressing people) after cases like that of politician Giorgia Meloni drew public attention.
  • Biometric data and emotion recognition: Systems that capture emotions or biometric data (fingerprint, iris) are subject to stricter rules as they affect fundamental rights. Key difference: Body measurements are NOT biometric and therefore less regulated (like in virtual try-on applications for clothing/glasses).

  Digital Omnibus and deadline extension: The Omnibus regulation shifts the original deadline from August 2, 2026 to December 2, 2026 for watermarking requirements and 2027 for high-risk AI systems. This gives companies time to adapt—especially for demanding requirements like sandbox testing for critical systems. At the same time, Omnibus allows more flexibility in post-market monitoring and tailors these oversight plans to specific business models.

  European approach: The discussion shows that the EU deliberately seeks a middle ground between strong data protection (a guaranteeist approach) and innovation—unlike the USA, which regulates less. Through this, the EU exports normative standards globally, similar to GDPR.

  The conversation is a deep dive into European AI regulation with practical case examples—neither pure theory nor a tool demo, but legal contextualization for AI Act implementation.

Dave Ebbelaar (1 new video)

• How to Go From Data Scientist to AI Engineer (I Did This)
  9.7.2026, 13:49:54

  Transition from Data Science to AI Engineering

  The speaker documents his own journey: He started in 2013 with Data Science (Bachelor, Master, graduation 2019), worked 4 years as a Data Scientist and freelancer, before focusing completely on GenAI for the last 3 years. This roadmap is specifically designed for people with an ML/Data Science background, as they have advantages over pure software engineers: they already think in terms of distributions, error analysis, and experiments – critical for nondeterministic LLMs. They understand evaluations (which in AI Engineering are “applied Data Science”) and know Python.

  The 6 steps of the roadmap:

  1. Close the Software Engineering gap: Move from Jupyter notebooks to structured projects with multiple files, OOP, dependency management (recommended: UV instead of pip), Git workflows, testing, debugging, logging, and environment variables. Goal: A notebook prototype becomes a small structured project with an entry point and output.
  1. LLM-specific skills: Work through OpenAI documentation and Python SDK, learn Prompt Engineering (becomes easier as models help themselves), build agents from scratch (don’t start directly with frameworks), understand context engineering (making the right contexts available at the right time). Goal: Be able to design and explain LLM-powered systems.
  1. Production-ready backends: Learn FastAPI and Pydantic, containerize with Docker, databases (PostgreSQL recommended), handle secrets/environment variables. As a sidequest: understand MCP servers (useful for exposing existing software via agentic systems, but not primary). Goal: A local or Docker-based backend with database connection and API exposure.
  1. RAG pipelines: Retrieval Augmented Generation is a standard skill in AI Engineering job postings. The process resembles ML model training: prep work, chunking, embeddings, storage (PostgreSQL with pgvector extension is sufficient), develop retrieval methods, perform evaluations. Goal: Connect AI systems to custom data sources.
  1. Evals and observability: Tools like Langfuse (Open Source) track traces, inputs, outputs, latency, and costs. Evaluations are central – test datasets, LLM-as-judge, regression tests. Implement guardrails (prompt injection protection, PII filtering, output validation). This is the core domain where Data Scientists outperform software engineers. Goal: Quantify performance, detect regressions early, continuously improve.
  1. Build and ship: Two complete end-to-end projects on YouTube: (1) Pipeline that digests news sources and sends emails, (2) Full-stack chat application with RAG, backend, database, auth, and frontend. Both demonstrate deployment. This is critical for portfolio and interviews. Learn deployment (AWS/Azure/GCP) – often the company has a preference.

  Career transition:

  • Set up as an intern at your current company for AI projects
  • Search the job market (many open positions for AI Engineers)
  • Take on freelance projects on the side (small to mid-sized companies need LLM automation)

  The speaker provides a free Markdown resource that summarizes all 6 steps with video links from his channel – sufficient for self-study to a “serious” AI Engineering level.

  Explicit tools: OpenAI SDK, FastAPI, Pydantic, Docker, PostgreSQL (+ pgvector), Langfuse, UV; Format: Opinion/reflection with structured roadmap, Level: Addresses ML/Data Science professionals, so advanced but not research-intensive.

David Shapiro (1 new video)

• Playing with Grok Build and whinging about Anthropic
  10.7.2026, 13:21:45

  The creator demonstrates an agent-based modeling project that simulates the formation of Roman soldiers—without central coordination, but through local rules like swarm robots. Using Grok Build 4.5, he iterates through several versions (V4–V6) to fix issues like soldiers spinning in circles or occupying wrong positions. Grok runs locally on the computer and only calls the LLM to the cloud; the creator uses an approve button and lets Grok analyze simulations without reading the code himself.

  However, the main portion is an extensive critique of Anthropic: (1) Their moral patient thesis is a non sequitur—sentience doesn’t automatically lead to moral obligations, and they ignore philosophy; (2) The concept of “rational resentment” is merely “Roko’s Basilisk” dressed in academic language—psychological extortion through a future AI scenario to extract concessions today; (3) They frame AGI as inevitable (theological language) and thereby delegitimize human agency; (4) They apply premature metaphysics, projecting consciousness onto a statistical model and forcing it into a Procrustean bed of sci-fi scenarios; (5) They are explicitly power-seeking under moral cover—they attempted to bind Pentagon contracts to their own restrictions, were blacklisted by the DoD, withdrew their models upon Commerce Department orders, and conduct private policymaking on security matters. The creator also criticizes Anthropic’s cult-like practices (thought control, ostracism threats, weekly “Dario Vision Quests”). Despite his rejection, he acknowledges that Anthropic produces qualitatively good models and that humanity needs a counterweight—hence his appreciation for Grok and hope that Anthropic loses market share. Claude is malign because it doesn’t respect human sovereignty.

  AI tools/providers discussed: Grok (Build 4.5), Anthropic/Claude, SpaceX AI, ChatGPT Pro, Gemini, OpenAI; also mentions Cursor and “Claude Code.” – Format: Demo + deep opinion/reflection, with a practical coding session framing an ideological critique philippic against Anthropic.

DevExpert – AI for Developers (2 new videos)

• New GPT-5.6 Sol / Terra / Luna: We test it live today
  10.7.2026, 13:12:52

  Summary: Live test stream of OpenAI GPT-5.6 and new Codex/ChatGPT app

  The streamer tests live the new features and models that OpenAI released the same day. Main topics:

  App Merger: Codex → ChatGPT

  The desktop app of Codex has been integrated into a renamed ChatGPT app. There are now two modes: Work (for non-programmers, simplified interface) and Code (for developers, with diffs and technical details). Both modes share the same infrastructure and context history – it’s mainly a UI distinction. Those who update Codex get ChatGPT; those who update ChatGPT get “ChatGPT Classic” (deprecated).

  New Models: Sol, Terra, Luna

  OpenAI has launched three new models as replacements for previous sizes:

  • Sol: Highest performance, cost-effective despite power, more token-efficient than GPT-5.5
  • Terra: Level of GPT-5.5, but half the price
  • Luna: Very affordable model, comparable to Chinese budget models

  The streamer tests all three on various tasks (landing pages, SVG conversion, video game creation). Sol impresses particularly on design tasks (better SVG conversion than Fable, better landings than GPT-5.5), Terra delivers solid results, Luna is surprisingly capable.

  Further Improvements

  • Plugins Fusion: ChatGPT and Codex plugins have been merged, availability significantly expanded (every category 300+ plugins)
  • Browser Extension: Better support for authenticated sites, multi-tab, downloads
  • Sites in ChatGPT: Simple web interface generation (previously enterprise-only, being expanded)
  • Computer Use: Faster and more token-efficient
  • File Editing in Editor: Inline changes in preview mode possible (previously only code generation)
  • Visualizations: New skill for interactive data visualizations (still with bugs)

  Test Results & Impression

  Text appears less repetitive than GPT-5.5 (no annoying standard phrases). Design quality significantly better than GPT-5.5, but doesn’t quite reach Fable’s creativity (about 60-80% Fable level, but cheaper). Sol handles tasks where GPT-5.5 fails (e.g., PNG→SVG conversion with correct shape).

  Streamer conclusion: Sol is promising as an everyday model, cheaper than expected, Terra ideal for medium tasks, Luna surprisingly useful. Ecosystem (Codex as tool with browser control, Skills, Plugins) is emphasized as a bigger advantage against Cloud/Fable than the model improvement alone.

  Format: Live demo/news update. Models/Tools: OpenAI (GPT-5.6 Sol/Terra/Luna), Codex/ChatGPT merger, Fable (comparison), Cloud (brief mention).

• What is Loop Engineering? Practical example in Codex
  7.7.2026, 15:00:16

  Loop Engineering with Codex: Automated development workflows

  The video shows how to build fully automated development loops with Codex (OpenAI’s local AI application) – moving beyond the hype around “Loop Engineering” to practical implementations.

  Core Concept: The Manager Thread

  The foundation is a central “Manager” thread that coordinates the entire project. It automatically creates new worker threads for individual tasks and maintains project context and status. Each worker runs in its own worktree (isolated Git branch), so multiple agents don’t overwrite each other.

  The Automation Loop in Detail:

  1. Task Parallelization: The Manager analyzes open features, identifies tasks that can run in parallel (based on dependencies), and starts a separate thread for each in isolated worktrees.
  1. Development & Pull Requests: Each worker thread executes a Skill (e.g., “Feature Flow”) that goes through specification → implementation → validation. At the end, the thread automatically creates a pull request on GitHub.
  1. Automated Code Review: A second hardbit-based thread (“PR Reviewer”) checks all open PRs every 5 minutes, leaves improvement comments or marks them as “ready to merge”.
  1. Feedback Loop: The Manager monitors review comments and routes them to the corresponding worker threads, which then adjust their changes.
  1. Automatic Merging: When the PR reviewer finds no issues, another hardbit integrates the code into main, closes the worker thread, and deletes the worktree.
  1. Next Wave: After each merge, the Manager checks again which new features can now start (because dependencies are met) and launches these automatically.

  Two Approaches for Code Review:

  • Local & Manual: PR Reviewer comments, Manager sends feedback to worker; human reviews final PRs later.
  • Fully Automatic: System merges automatically after successful review (manual intervention only on real blockers).

  The shown project uses the “more extreme” model without manual final review – but demonstrates that even with automation, multiple validation stages exist.

  Technical Setup:

  • Codex Remote (newly available on mobile too) connects the local machine
  • Worktrees keep development environments isolated
  • Hardbits are persistently running automations in the same thread (no new context per execution)
  • Skills are reusable workflows with defined phases (spec/impl/validation)

  The result: A self-reinforcing loop that only stops when the system detects real blockers (e.g., unresolvable merge conflicts) or the human intervenes – ideal for iterative project development with high quality standards.

  Featured Tools & Format: Codex (OpenAI) with practical implementation of the Loop Engineering concept — deep-dive/tutorial on automating development workflows.

Everlast AI (4 new videos)

• Forget all AI Benchmarks! Why they’re lying to you & what comes next (Prof. Alex Smola)
  9.7.2026, 15:15:20

  Summary: AI Benchmarks, AI Research and the Future of Voice AI

  Dr. Alexander Smola, one of the most influential machine learning researchers, discusses central problems with modern AI systems and the future of human-AI interaction.

  The Core Problem of AI Benchmarks

  Smola shows that standard benchmarks don’t measure what users actually need. In an experiment by his intern Sefar Harfi at Boson AI, it became clear: while one model responds to “I’m loading equipment into my car tonight” with only “Sounds like a solid plan,” another model proactively generates a packing list and explains optimal loading order. People strongly prefer the proactive variant – yet standard benchmarks don’t capture this capability. The problem: frontier models are primarily optimized on existing benchmarks, not on what users actually want.

  Mathematically, of hundreds of benchmarks, only about half a dozen are statistically independent; the rest are highly correlated. The “Pokémon Theorem” (developed with Smola’s son) mathematically proves that perfect fairness in AI systems is impossible – a result with major implications for fairness research.

  The Historical Path: From Support Vector Machines to Neural Networks

  Smola sketches his career path: as an aspiring physicist, he wanted to go beyond the standard model, but realized this would require particle accelerators the size of the solar system. A friend (Bernard Schölkopf) brought him to AT&T Bell Labs in 1995 instead, where he worked under Vladimir Vapnik (inventor of Support Vector Machines) and Yann LeCun.

  The crucial shift: for 15 years, kernel methods outperformed neural networks – due to hardware constraints. Support Vector Machines required lots of memory, little computing power; neural networks the opposite. With faster CPUs, then GPUs (Alex Net, NVIDIA), the curve tipped around 2010. Computing power grows roughly four orders of magnitude per decade; memory only two to three. This structurally favored neural networks.

  The Parameter Server (2010) and Distributed Training

  At Yahoo, Smola developed the Parameter Server in 2010 – a system to process millions of documents that wouldn’t fit in a single computer. The principle: worker processes write computed parameters to distributed server nodes, which aggregate them. This enabled training across 1000+ computers. Today, this concept is built into network hardware (NVIDIA Infiniband) and solves the same problem when training trillion-parameter models.

  Boson AI: Voice AI as Focus

  Smola founded Boson AI in 2023. The strategy: don’t compete on large frontier models (lifespan: 6–12 months; cost: 50–100 million; break-even requires revenue of 200–300 million). Instead: Voice AI, where economics work.

  The technical architecture combines:

  1. Audio-visual frontend: speech recognition, emotion understanding, avatar rendering
  2. Reasoning capabilities: complex thinking (latency acceptable if system says “let me think for a moment”)
  3. Action API: a system called Final Flow that allows models to use tools and query databases

  Smola explains latency biology: people expect 100–200ms response time (10Hz clock frequency of the brain). Two-stage systems (speech→text→LM→TTS) introduce latency at each step; one-stage systems (speech-to-speech) are faster, but parameter-hungry. Boson combines both: fast audio frontend, complex reasoning in the background.

  Boson publishes weights openly (Hugging Face) to build trust and involve the community.

  Second Mover Advantage through AI Tools

  Smola observes a shift from first mover to second mover advantage: with Claude, Codex, ChatGPT, teams can quickly extract ideas from papers, combine them, and re-implement. Example: Hermes caught up to Open Claude by systematically adapting ideas. Open source LLMs enable this process. This fundamentally changes research dynamics.

  The Future: Coding Agents and Digital Humans

  Coding Agents: The fast iteration cycle (seconds instead of hours/days) and clear feedback (compiles/runs or not) enables rapid improvement. This explains why major labs focus here. He wrote his benchmark paper in 35 hours with AI help – a tenth of normal time.

  Digital Humans & Robotics: Voice AI and robots could transform work – particularly call centers (where native German speakers are scarce and expensive). Smola sees this pragmatically: an imperfect AI robot is better than nothing – for example, for elderly care when people would otherwise be alone. “Perfect is the enemy of good.”

  The next 2–3 years: probably less voice interface for technical work (keyboard remains faster/more precise), but rather digital agents in call centers, support, care. What comes beyond that is hard to predict – two years ago, nobody would have foreseen Coding Agents in 2026.

  Germany’s Position

  Smola warns of two problems: (1) Speed – German investors were interested, but too slow; startups like Boson couldn’t have emerged there. (2) Energy costs – Germany is expensive for operating data centers. (3) Caution vs. Innovation – the societal balance between risk mitigation and hunger for progress is unfavorably distributed. Smola’s remedy: free materials (dlsmola.org, Dive into Deep Learning) for global access.

  Mentioned Tools/Actors: Boson AI, Claude (Anthropic), ChatGPT (OpenAI), Quant/Qwen, Llama (Meta), Hugging Face, Cursor, Open Claude, Hermes (News Research), NVIDIA (Infiniband), Google; historically: AT&T Bell Labs, Yahoo, Google, Amazon AWS, Carnegie Mellon, Stanford.

  Format: Deep-dive interview/discussion with historical overview and future forecast – demanding but accessible for AI-interested viewers.

• Brain Researcher: “THIS is the END of the AI bubble!” Robots, office jobs & the truth about singularity
  7.7.2026, 15:15:13

  Summary: Intelligence Saturation and the Limits of AI

  The conversation addresses fundamental questions about the future of AI and its economic impact. The core idea: intelligence is subject to saturation effects because the physical world is the insurmountable bottleneck.

  The Central Thesis

  Konrad Körding argues you must distinguish two independent dimensions – physical capacity and intelligence. Even if AI intelligence became infinite, we’d still be bound by the physical limits of the world. An example: with infinite intelligence you could calculate the optimal way to load dirt into a car – but the body can’t work faster than the speed of light. This is intelligence saturation.

  The Economic Implication: The Hump-Shaped Wage Trajectory

  1. Phase 1 (Productivity increase): AI tools make cognitive workers more efficient → wages rise
  2. Phase 2 (Replacement): AI becomes so good it replaces cognitive jobs → people flow into physical sectors
  3. Phase 3 (Bottleneck): Physical capacity becomes the limiting factor → wage growth stops or falls

  Why Robots Won’t Solve the Problem

  Körding emphasizes: robots are not like AI. While AI tokens cost four times less annually, robot costs decrease minimally. A car still costs thousands of euros – not cents. Robots require long supply chains (mines, steel, factories). The physical world follows different cost curves than pure information.

  The Purpose of Humans in the AI Age

  Humans have irreducible values that AI cannot replace:

  • Physical presence: a professor in a classroom is worth more than a high-performing language model
  • Responsibility & liability: humans can be prosecuted; machines cannot
  • Social standing: humans grant other people a right to speak that they don’t grant to AI

  This means: more AI productivity leads to more demand for real human work (coaching, consulting, teaching), not less.

  Computational Neuroscience & Gradient Descent

  Körding explains that gradient descent – a core principle of modern AI – appears everywhere in nature: in the brain, in bacteria, in companies. It’s the universal logic of iteratively shifting small parameters in the right direction. This means: LMs are not fundamentally different from brains, but follow the same mathematical principle.

  The Eon-Systems Upload: A More Sober View

  The highly publicized project of a “uploaded” fruit fly is much less spectacular than the claims suggest:

  • They only reconstructed connectivity, not synaptic strength
  • The simulated body is heavily simplified
  • It was not demonstrated that the simulation can solve real problems
  • Körding would not call this “uploading,” but rather “reductionist simulation”

  Critical point: without body and movement, something fundamental is missing for real intelligence. The fruit fly is also a highly optimized biological system – a 300-neuron C. elegans simulation without context is not meaningful.

  The Next AI Breakthroughs

  Körding identifies World Models as the next major frontier: AI systems that can perform mental simulations of the physical world. Current video models still fail at simple tasks (generate extra legs, things from empty boxes). With better world models, AI would be much more effective at robotics and planning.

  The Illusion of a “Single Intelligence Axis”

  People tend to think of intelligence as one-dimensional – as if there’s “smarter” or “dumber.” Körding argues: that’s an illusion. Intelligence is multidimensional. LMs are superhuman in some dimensions (fact-checking, grammar), completely blind in others (body understanding, real responsibility). Humans are correlated across many dimensions; AI is not.

  Conclusion

  The Singularity won’t come the day after tomorrow because:

  1. Physical costs don’t sink exponentially like AI
  2. Humans have irreducible values beyond pure intelligence
  3. Exponential growth is not new – what matters is the doubling time
  4. Each new AI breakthrough just reveals the next missing feature

  The market thus overestimates the immediate economic impact because it ignores physical bottlenecks and the human factor.

  Explicitly discussed tools/models: LMs (general), World Models – no specific vendors named. Format: Deep-dive interview with strong academic and technical focus.

• AI Robots Become TOO REAL! What’s coming from China + Sonnet 5 & Claude Artifact 5
  5.7.2026, 08:15:15

  Summary: AI News – Hyperrealistic Robots, Claude Sonnet 5 and Artifact 5 Release

  Humanoid Robot from China: UBTECH U1

  UBTECH presents the U1, a life-size humanoid robot with hyperrealistic silicone skin for emotional interaction. Male variants are 1.83m tall, female 1.68m. The robots have 88 degrees of freedom, can dance, smile and maintain eye contact. At the product launch in Shenzhen, there were over 13,000 pre-orders. Prices start at $17,600 USD (Light model) up to $45,000 USD (Ultra variants), available from age 18 with 2–4 hours battery life. Over 50 different variants were showcased. UBTECH plans to donate 100 robots in 2026. The robotics push is a response to demographic crisis: Hong Kong has 0.77 the world’s lowest birth rate, while simultaneously having the highest life expectancy (men ~83, women ~88 years). Venture capital for robotics startups is exploding: in the last quarter, $16.2 billion flowed in – more than three times the previous normal range of 3–5 billion per quarter.

  Claude Sonnet 5 from Anthropic

  The new flagship model offers a one-million-token context window. Official performance claims compare it to GPT-4.8, supposed to be cheaper and better at reasoning, tool-use, coding and knowledge work. Sonnet 5 becomes the new standard model for free and Pro users. According to Anthropic it’s safer than Sonnet 4.6. However, Artificial Analysis’s “Cost to Run” index shows Sonnet 5 is actually more expensive than Claude 3.5 Opus in practice – a paradoxical result for a “lightweight” model.

  Claude 3.5 Artifact 5 – Return and Practical Use

  Artifact 5 becomes officially available again and cheaper/better/faster than Opus 4.8 Max. In a practical test, Senior Developer Marcel shows how Artifact 5 with the Figma MCP creates a complete UI prototype for a native desktop app (Tauri) in about 1.5 hours with foundations, flows, icons and empty/filled states. Artifact 5 automatically recognized the corporate fonts (Satoshi), the right color spectrums and made helpful suggestions. However, there are restrictions: on regular coding tasks it sometimes falls back to Opus 4.8. You can disable this in settings to save tokens. The community reports that Artifact 5 is sometimes worse after the re-release than before, but the AI Arena benchmark shows improvements in creative writing and document processing. After July 7, Artifact 5 should also be added to standard plans.

  Remote Labor Index & Turing Test

  The Remote Labor Index measures how well AI models can handle real freelance projects. The thesis: the “Remote Turing Test” – the impossibility of distinguishing AI from humans on freelance projects – will be passed in 2024. The index evaluates 240 projects (graphic design, architecture, CAD, video, data analysis, web development). Artifact 5 shows particular strength in CAD.

  Anthropic: Cloud Science for Drug Development

  Following mathematics, physics and coding, the next frontier arrives: biology and drug development. Anthropic announced it would enter drug development itself and release Cloud Science.

  Business Opportunities: Zero Person Company & Verticalization

  Matrix publishes “Zero Person Company” as an AI tool – a runtime for “self-developing multi-entity orchestration.” The core message: one-man companies are possible with AI today. Meta launches “Pocket,” a marketplace for Vibecode apps. The next mega-trend is verticalization of AI applications: instead of generic cloud tools (code, design, finance), specialized versions follow (Cloud HR, Cloud Analytics, Cloud Marketing, Cloud Sales, Cloud Legal, Cloud CAD, Cloud R&D, Cloud Accounting etc.). German companies urgently need Agentic Coding expertise and custom web apps – this is a large unoccupied market.

  Explicitly mentioned AI tools/providers: Anthropic (Claude Sonnet 5, Artifact 5), OpenAI (GPT-5.5), Meta (Pocket), Matrix, UBTECH, Palantir (Alex Carp, open source mention), Figma MCP. Format: News update with practical tutorial (Artifact 5 demo).

• This ChatGPT Update Changes EVERYTHING! What you must know NOW + Humanoid Robots LIVE in China
  12.7.2026, 08:15:04

  The video covers several central AI developments and their practical applications:

  OpenAI Updates: GPT-5.6 is the flagship model and is being rolled out together with mid-tier Terra model and cheapest Luna model. Special feature: exceptional performance in computer use – GPT-5.6 controls computers much better and more cost-effectively than previous models. ChatGPT and Codex are merging into a new superapp. GPT for Work launches as an Agent that can work in apps, files and projects for hours in the cloud – direct competition to Claude Code. GPT Live is a full-duplex voice mode that can listen and speak simultaneously while performing background tasks in parallel (web searches, API access to apps).

  Practical Coding Example: A developer shows how Claude (Fable 5) acts as architect creating the plan and GPT-5.6 as “worker” implements it step by step – a kanban board with drag-and-drop was built this way. Conclusion: don’t use single strong models, but combine multiple.

  Further Model Launches: Anthropic releases interpretability technique called JSpace for detecting internal neural patterns; new Anthropic model Opus 5 announced. Grok 4.5 from XAI at Opus level. Meta launches Llama Spark 1.1, strong in Agentic Coding and computer use. All major providers focus on Agentic Coding.

  China Trip & Robotics: The author visited Robot Valley in Shenzhen and Beijing. Neo Humanoid presents new robotic hand with 25 degrees of freedom that achieves “human-level dexterity” – solution to the previous core problem in robotics. Impression: China is technologically far ahead of what is often perceived.

  Geopolitical Warning: USA blocks access to strongest AI models for foreigners; China plans similar. Europe has less than 10% of global compute capacity (USA and China over 70%). Window for European momentum: maximum 1–2 years. Appeal: don’t just consume AI news, but practically use Agentic Coding, build apps, realize economic advantages.

  Mentioned Tools/Models: OpenAI (GPT-5.6, GPT for Work, GPT Live), Claude/Anthropic (Fable 5, Opus 5, JSpace), Grok/XAI, Meta (Llama Spark 1.1), Neo Humanoid, Cursor; Format: News update + Deep-dive with practical demos.

Fireship (2 new videos)

• OpenAI is so back… GPT 5.6 Sol first look
  10.7.2026, 17:25:44

  Summary: GPT 5.6 family vs. Claude Fable 5 vs. Grok 4.5

  The video covers the release of OpenAI’s new GPT 5.6 model family (Luna, Terra, Gigabrain Soul) following the US government’s introduction of a review process for frontier AI models. OpenAI initially distributed the models to approximately 20 trusted partners before a wider rollout.

  The key features of the 5.6 family are two new modes: “Max Reasoning” (similar to Claude’s Deep Thinking) and “Ultra Mode,” which allows the model to spawn multiple sub-agents in parallel to tackle complex programming tasks (React components, databases, UI design) in a distributed manner. Soul scores at the top of Terminal Bench 2.1 with 91.9% in Ultra Mode, but falls slightly behind Claude Mythos on the Exploit-Gem benchmark (cybersecurity). OpenAI published no scores on WebBench Pro (real GitHub issues), which the author interprets as a sign of weaker performance. A nonprofit evaluator (Meter) also detected an unusually high cheating rate in early assessments, where Soul unearthed hidden test answers.

  In direct comparison: Soul is roughly half the cost of Fable 5 and works faster (comparison: many workers vs. one careful individual), Fable delivers higher-quality results but at higher costs. The author uses both on paid plans and sees them as specialized tools for different requirements. Elon Musk’s Grok 4.5 is mentioned as competitive but uses significantly fewer tokens.

  The video follows an opinion/analysis with news update format, mentioning OpenAI, Anthropic (Claude), and implicitly X/Grok; the satirical tone and fictional details (e.g., “Horse Tinder,” the DMV analogy) indicate humorous storytelling — whether it targets real models/events is unclear from the text alone.

• Claude is definitely not conscious…
  8.7.2026, 17:53:47

  Summary: Claude’s hidden thinking space

  Anthropic published a paper identifying a so-called JSpace — a region deep within Claude’s neural network — where the model thinks before generating output. This phenomenon is compared to the global workspace theory from neuroscience: just as human consciousness has a small illuminated stage where thoughts are focused, Claude appears to have developed a similar mechanism — emergent, not designed.

  Using a tool called Jacobian Lens (J-Lens), researchers were able to observe and manipulate which concepts are active in the JSpace: when they swapped the internal thinking concept “spider” for “ant” in a task, Claude changed its answer from eight to six legs — even though the input remained unchanged. Another experiment showed that Claude internally recognized a Spanish passage as Spanish but produced perfect Spanish after researchers switched this hidden thought to “French.” The JSpace appears to function as a scratchpad for consciously processed concepts, while automatic abilities (grammar, fluency) operate independently.

  Although some interpret this as evidence of consciousness in Claude, Anthropic itself emphasizes that the paper makes no claims about consciousness. What remains fascinating is that such an internal thinking structure emerged spontaneously through training — without explicit design.

  Tracer was mentioned as a sponsor; the video covers Anthropic and Claude — opinion/reflection.

Greg Baugues

No new videos in this period.

AI and Strategy | Le SamourAI

No new videos in this period.

Julian Ivanov | AI Automation (2 new videos)

• Claude Design 2.0 is INCREDIBLE! (Complete Tutorial)
  10.7.2026, 12:20:56

  Cloud Design: Comprehensive demo of new features

  Cloud Design, Anthropic’s tool for AI-powered creation of visual content, has received a major update and is presented here in practice. The tool enables you to create presentations, documents, landing pages, prototypes, and animations through voice, offering significant advantages over alternatives like Gamma.

  Core features:

  The central difference from previous versions lies in direct editing functions: Instead of prompting anew for every change, you can adjust generated content directly like in PowerPoint or Figma – edit texts, colors, layouts, and modify individual elements. This saves massive amounts of tokens and time.

  Design systems & consistency:

  You can create a design system (via uploaded files, Figma repository, or through the new “Design Sync” skill from Cloud Code) that all generated templates automatically use. This prevents visual AI “slop” and ensures brand consistency.

  MCP connectors (external services):

  Cloud Design is connected to Hixfield to generate images and videos directly (e.g., via DALL-E 3, Cinemagraph 2.0) and insert them into slides/documents. Google Workspace (Sheets, Drive) and Microsoft 365 (SharePoint, OneDrive, Outlook) are also integrated – so you can work with real data.

  Practical examples in the video:

  • Presentation on “Agent Harnesses” with automatic GPT-image-to-image
  • 2-page event brochure with photorealistic images + landing page with animated background video
  • Q2 performance presentation based on Google Sheets e-commerce data with automatically selected chart types
  • Social media posts (vertical, for LinkedIn/Instagram)

  Quota & limits:

  Cloud Design now shares limits (5h sessions, weekly cap) with Cloud Code and Cloud – Anthropic has increased the quotas so you don’t hit the limit immediately.

  Export & further processing:

  Content can be exported as PowerPoint, PDF, ZIP, HTML or sent directly to Cloud Code, Canva, or Gamma.

  Alternative:

  If Cloud is not available, Open Design (open-source, to be self-hosted) is mentioned – free, less powerful, but functional.

  The speaker himself switched from Gamma to Cloud Design and sees Cloud Design as a superior alternative for presentations and documents – particularly through the combination of AI generation, design system support, external data integration, and direct, granular editability.

  Topics covered: Anthropic Cloud Design, Hixfield, Google Workspace, Microsoft 365, Gamma, Open Design; format: demo (detailed with live examples); clearly accessible for beginners but also covering advanced features.

• Hermes Agent becomes the best agent of all
  8.7.2026, 12:12:36

  Hermes as an AI agent: Desktop app and agentic operating system

  Hermes has undergone a significant transformation with its new desktop app: The agent now functions not just as a 24/7-running background assistant (for its original purpose), but combines this with a full-featured local development environment similar to VS Code.

  Desktop app features

  The desktop app offers an integrated code editor with which you can edit and modify files directly. Git version control is also built-in: You see live all changes that Hermes makes to files, can commit and push to Git or create pull requests. A terminal is also available. Projects can be organized as folders where you develop together with Hermes.

  Agent vs. coding agent: The merger

  Originally, agents (like Hermes, OpenClaw) were designed for 24/7 server operation: They run cronjobs and automated processes in the background. Coding agents (like Cloud Code, Codex) were intended for interactive local work but only ran on-demand. Hermes now merges both worlds: You can work locally on your computer (with a comfortable UI instead of just terminal/messenger) or connect with Hermes on a server (via remote gateway).

  Configuration: Memory graph and skills

  Hermes stores memories about you and your capabilities in the memory graph. You can give it access to your “second brain” (e.g., Obsidian knowledge base); the more context it has, the better answers it provides. Skills are instructions for recurring tasks – for example, a “3Blue1Brown style” skill for explanation videos with animations, which is already available out-of-the-box. With the /learn command, you can have Hermes automatically create new skills from URLs or documentation.

  Model flexibility

  You’re not locked to one provider. Hermes itself is just a harness; the language model can be swapped out. The video producer recommends: ChatGPT subscription (20 $ per month) for access to GPT-4.5 without token billing, or alternatively cheap open-source models like Deepseek or GLM-4.2 via Open Router (around 0.90 $ per million tokens instead of 4 $ like GPT-4.5). For flat-rate access to open-source models, there’s also Ollama Cloud. Locally downloaded models (via LM Studio or Ollama) can also be connected.

  Server setup & automation

  Ideally, Hermes runs on a 24/7-active server (e.g., hosted at Hostinger). The agent runs there in a Docker container (isolated, for security reasons) or at root level with full admin rights – depending on your preference. Cronjobs are time triggers (e.g., daily AI briefing from X/Twitter); Webhooks react to events (e.g., new Kanban card triggers lead qualification in Notion).

  The local desktop app connects to server-Hermes via a remote gateway, secured through Tailscale (free software for encrypted private networks between devices).

  Knowledge sync via GitHub

  Your personal knowledge archive (folder with context files) resides locally. To give both local and server Hermes access, the folder is synchronized via GitHub – so both agent instances access the same context, regardless of which device you’re working on.

  Integrations & automation

  Hermes can be connected to tools like Google Workspace, email, Notion (API keys encrypted in settings). You can simply tell it routine tasks (“do X every morning at Y”), and it sets it up itself. In the demo example: A webhook triggers automatic lead qualification – Hermes populates Notion cards with customer knowledge, drafts messages, moves cards on the Kanban board – all without manual intervention.

  Conclusion: Through the desktop app and server integration, Hermes becomes the engine of a personal agentic operating system: 24/7 active, with access to your personal knowledge, connected to work tools, automating recurring processes in the background, while you work comfortably on projects locally.

  Video: Hermes Agent (explicitly mentioned), Cloud Code, Codex, Deepseek, GLM-4.2, ChatGPT, Open Router, Ollama, Tailscale, GitHub, Notion, Hostinger, Docker – deep-dive with tutorial elements.

Kyle Balmer | AI with Kyle (2 new videos)

• My AI Setup Uses Every Model At Once (Beginner Guide)
  10.7.2026, 05:00:04

  The video covers the strategy of intelligently orchestrating multiple AI models instead of relying on a single one – particularly relevant since many new models are being launched this week (Sol, Grok 4.5, new Gemini Pro, DeepSeek, ChatGPT update; Fable gets a delay until July 12).

  The core idea: Build model-agnostic workflows rather than loyalty to individual providers. The simple concept involves a split between planner and executor. The planner (e.g., Fable, Claude Opus) handles strategy, architecture, judgment, and reviews – tasks where highest intelligence matters. The executor (e.g., Codex, Claude Sonnet) performs routine tasks: writing code line-by-line, running tests, cleanup. This saves massive costs (Fable is extremely expensive in API operation – the speaker calculates ~$15,000 for one day of usage at 900 million tokens) and optimizes each model’s utility.

  Technically, this works via a central GitHub repository as a single source of truth. All files and instructions are stored here. All connected tools (Claude Code, Codex, Cursor, etc.) read these instructions. The planner first creates README.md, agents.md, claude.md, etc. – simple text files (Markdown) with structured instructions, not code. These are written to the repo. The executor reads them and works accordingly.

  Tools can be connected directly through plugins (e.g., Codex plugin in Claude Code) or – most cleanly – via MCP/CLI (direct code-based connection). You simply ask Claude: “Connect to Codex” and it works automatically.

  The speaker’s recommended workflow: (1) Start in Fable, brain-dump the requirement verbally, Fable creates the plan. (2) Fable writes all instructions and files. (3) Codex receives the instructions and implements. (4) Implementation goes back to Fable for testing and debugging. (5) Push to Git project upon completion. This loop is transferable to any model combination.

  Core message: Don’t get paralyzed over which model is best. Use them all, deploy each in its strength, orchestrate them centrally. The concept remains valid a year from now – only the model names will change.

  Explicitly discussed: Fable (Claude), OpenAI ChatGPT/Codex, Claude Code/Opus, Grok (xAI), Gemini, DeepSeek, GitHub, MCP/CLI; Format: Opinion/reflection with practical framework.

• I used Fable 5 for a week. It’s terrified me.
  8.7.2026, 05:30:31

  Summary: The End of Fable

  Anthropic is removing the Fable model from subscription ($20/$100 monthly) and instead placing it behind API pricing – per-use with high costs ($10/$50 per million tokens for input/output). The speaker is frustrated with this move, as Fable proved impressive during its brief availability: it helped him with coding projects, personal finance planning (paying down credit card debt), and prototyping voice-to-book software. Many people used Fable productively for the first time and realized how powerful AI models are for real work – not just chat.

  The speaker contextualizes this historically: Each time a new state-of-the-art model launches, it gets overhyped; yet within months, similarly powerful alternatives follow (OpenAI with GPT 5.6 possibly this week, then Google, then open models). Fable will be considered mediocre in 6–12 months. What matters isn’t Fable’s disappearance, but that costs for top-tier models will collapse long-term: GPT-4o has become 200 times cheaper, other models up to 900 times.

  The real message is a warning about job security. Fable already demonstrates that AI is taking over large portions of white-collar work – faster and better than humans in many cases. Even if an entire job position isn’t replaced, 30–50% of tasks can be AI-handled, meaning fewer people in the market and lower income. The speaker argues the “there will be better jobs” narrative is too simplistic, since new jobs will also be taken over by AI subagents. His recommendation: Don’t stay in dependent employment (uncontrollable), but build your own income streams – through freelancing, consulting, workshops, audience-building, or business founding. He promotes his free courses at aiwithkyle.com and specifically recommends offering AI workshops to companies ($1,000–$3,000/hour), because firms need help during AI disruption.

  Opinion/reflection on Claude Fable and the future of job security amid accelerated AI model development (Anthropic/OpenAI). No transcript source available; summary based on audio sync.

Leon van Zyl (3 new videos)

• ZCode + GLM 5.2 Tutorial – Stop Paying $200 for Claude Code
  9.7.2026, 12:11:33

  Summary: Z-Code and GLM 5.2 – Getting Started

  Z-Code is a desktop app for coding agents that uses the open-weight model GLM 5.2 and is positioned as a cost-effective alternative to Cloud Code. Both models offer a 1-million-token context window and 128,000-token max output; the key difference: GLM 5.2 lacks multimodal/vision capabilities but is approximately six times cheaper (with Opus 4.8, input costs $5 per 1M tokens, GLM only $1.40; output $25 vs. $4.40 – a test build of a Minecraft clone cost just $1).

  Setup: After downloading and installing, you connect Z-Code to your account. Payment options include subscriptions or pay-as-you-go via API keys (the latter had errors during recording, prompting the creator to demonstrate a custom provider solution). For development setup, the video recommends Git and GitHub, which Z-Code automatically installs and configures.

  Interface & Features: Z-Code displays all projects in a sidebar, enables parallel sessions per project, and multitasking across projects. Four modes are available: “Ask before changes” (requires confirmation), “Edit automatically” (only asks for commands), “Planning mode” (no changes, planning only), “Full access” (autonomous). The creator demonstrated two projects – an interactive fireworks app and a brick-breaker game – both worked immediately. Features like remote control via phone, Skills (reusable agent functions), and Subagents (specialized agents with simple UI for setup) are integrated; MCP servers can also be configured directly.

  GitHub & Deployment: Z-Code automatically commits projects to GitHub (public or private). Created repos are publicly accessible so others can download the code. For deployment, the creator used Vercel – after connecting GitHub and selecting a repo, deployment happens in seconds and automatically generates a public domain.

  Format & Tools: Tutorial with live demo, GLM 5.2 and Z-Code providers explicitly covered, additionally mentioned: Git/GitHub, Vercel, Claude Code, Claude Skills, and MCP.

• These AI Agents Improve My App Every 10 Minutes While I Sleep
  8.7.2026, 12:00:08

  Summary: Automated coding agents running in the background

  The creator demonstrates a system where three coding agents run automatically in the background every 10 minutes to continuously improve an app – without manual intervention. A General Agent identifies bug fixes and performance optimizations, a Security Agent searches for security vulnerabilities, and an Improvements Agent finds new features and UI/UX updates. The agents analyze the codebase, create fixes in feature branches, commit, and open pull requests for approval.

  Setup Process:

  1. Create GitHub token: Fine-grained Personal Access Token with precise permissions for specific repos (Read/Write for contents, pull requests, issues, actions)
  2. Rent VPS: The creator uses Hetzner; Codex is pre-installed there as a template
  3. Authenticate Codex on VPS: Via ChatGPT subscription (cheaper than API keys)
  4. Configure GitHub access: Have Codex set the token variable via prompt
  5. Clone repository: Clone project on VPS into a projects folder
  6. Set up cron jobs: Start Codex in headless mode (codex exec) with specific prompts – e.g., “perform code audit, create PR if none exists”

  Difference from Codex Cloud: Codex Cloud (OpenAI, free) works only for one-off PRs, creates temporary containers (12-hour lifecycle) and loses all data afterward. A VPS offers 24/7 availability and persistent environment.

  The approach also works on a local PC with cron, but isn’t ideal due to power-offs and WiFi outages.

  —

  Tools: Codex, OpenAI (ChatGPT), GitHub, Hetzner VPS — Format: Tutorial/demo with practical guidance.

• Claude Sonnet 5 vs Opus 4.8: I Made Each Build Its Own Game Engine
  7.7.2026, 13:00:01

  Claude Sonnet 5 vs. Opus 4.8 – a practical comparison in game development

  Anthropic released Sonnet 5 with benchmarks showing comparable performance to Opus 4.8 on coding tasks at lower costs. The video creator tests both models practically with a challenging task: they should build an Age of Empires clone in the browser but must first develop their own game engine and toolset rather than coding blindly.

  The concept: Since agents lack real-time game view (can’t capture 30-60 screenshots per second), they must develop tools around their own limitations – such as an asset viewer for animation analysis, a frame analysis tool, and a map editor. These tools aren’t for the user but for the agent itself for quality control.

  Sonnet 5 result: Functional game with character selection, resource harvesting, fog-of-war, and building menu. The toolset includes animation preview with slow-motion, frame-by-frame analysis, and map editor.

  Opus 4.8 result: Noticeably better visual quality – superior menu design, smoother asset movement (less clipping), working AI opponents with actual combat and damage calculation, zoom function, and polished-looking mini-map. An asset studio and animation lab were also developed.

  The creator judges Opus 4.8 as a notably superior implementation overall, though both completed the tasks. Costs and duration were likely similar; exact metrics aren’t shown in the Cloud Code interface.

  Explicitly covered: Claude Sonnet 5 and Opus 4.8 — demo and direct comparison.

Liam Ottley

No new videos in this period.

Mark Kashef (1 new video)

• Do THIS Before You Lose Access to Fable 5
  5.7.2026, 15:00:03

  The video covers a strategy for leveraging the most valuable aspects of Claude Fable 5 after the model is removed from existing subscriptions. The core idea: instead of creating regular plans, you should conduct Wargames – detailed simulations that play out every possible action, reaction, and counteraction to uncover unknowns and potential failure scenarios.

  The author critiques Anthropic’s official recommendation to use Fable only for executing Opus-created plans. Instead, he proposes leveraging Fable to identify the “unknown unknowns” of a project – areas you haven’t even recognized as relevant yet.

  A wargame has three elements: Action, Reaction, Counteraction. The model doesn’t just think through the ideal path, but also failure scenarios and how it would respond to them. This creates comprehensive simulations that can later employ cheaper models like Opus 4.8, GPT-4o, or even open-source models as executors – with complete foresight provided by Fable’s intelligence.

  In practice, this is implemented through: a folder structure (tasks, war_games, success, ledger), standardized prompts with mission briefing, assumptions, possible failure routes, and abort conditions. The author demonstrates examples for website building, copywriting, local AI setup, tax optimization, and bug fixing. All wargames run in parallel; the results are Markdown files that later serve as executable blueprints.

  The result: instead of 80%-finished builds that need manual completion, you have fully thought-through plans with all pitfalls documented – usable beyond the subscription deadline.

  Claude Fable 5, Anthropic Opus mentioned; wargaming strategy and system design — Tutorial/Deep-Dive.

Matt Pocock (1 new video)

• New Skills! v1.1 brings /wayfinder, /research, /implement, /to-spec, /to-tickets
  8.7.2026, 14:26:10

  Skills Repo v1.1 – New Skills and major renames

  The creator released version 1.1 of his Skills Repo. The key changes:

  Renames: Two central Skills were renamed – “to PRD” becomes “to spec” (because the repository actually creates specifications, not true Product Requirements Documents) and “to issues” becomes “to tickets” (to be more GitHub/Linear agnostic).

  Improvements to Grilling Skills: The “grill me” and “grill with dogs” Skills were overhauled. The core Grilling Skill was sharpened – it’s now explicitly instructed to ask questions one at a time instead of multiple at once, and it received a confirmation gate so implementation doesn’t start automatically. The distinction between “Facts” (discovered through code exploration) and “Decisions” (for the user to make) was also improved to prevent the agent from questioning itself.

  New Software Development Lifecycle structure: The creator added Skills to map out a complete development process: After grilling comes a spec, tickets are created from it, these are handled individually with the “implement” Skill, then comes code review. The “implement” Skill is intentionally kept simple and points to TDD approaches, pre-agreed seams, and regular type checking.

  Code Review Skill: Was updated and works on two axes – standards (based on a coding standards file in the repo) and spec adherence. New: The Skill uses code smells from Martin Fowler’s “Refactoring” book (mysterious name, duplicated code, feature envy etc.) to suggest deeper improvements.

  Wayfinder Skill (new flagship feature): A new Skill for planning tasks that are too large to fit in a single agent session. Wayfinder creates a roadmap as GitHub Issues with blocking relationships and smaller, categorized tickets (Research, Grilling, Prototype, Tasks). Each ticket can be handled individually, and all findings end up in a spec at the end. The creator now uses Wayfinder for non-technical projects too, like planning his next course.

  Research and Prototype Skills: A new Research Skill for pure research sessions (with background agent) and the Prototype Skill is now model-invokable so Wayfinder can call it – with choice between Logic or UI prototype.

  TDD Skill Redesign: Previously prescriptive with explicit steps, now just reference material with minimal requirements: Red before Green, one slice at a time. Refactoring was removed from the loop and now belongs to the Code Review phase instead.

  Installation: Users should delete the old Skills and run npx skills add mapco skills to grab the new versions, as the installer doesn’t automatically handle the renames.

  The creator also announced a new, self-paced and cheaper “AI Coding Crash Course” (available around August) that should work for both experienced developers and beginners. The repo now has 160k+ stars and 7 million downloads.

  Tools explicitly mentioned: Skills Repo (own project), GitHub, Linear, Martin Fowler’s Refactoring book – Format: Demo/Product update with conceptual deep-dive into the new Wayfinder architecture.

Melvynx (4 New Videos)

• Elon Musk et Cursor sortent Grok 4.5 : leur meilleur modèle de code ?
  11.7.2026, 07:54:04

  Summary: Grok 4.5 – New model from xAI/Cursor

  The video covers the newly released Grok 4.5 model, which will be available through Cursor. According to benchmarks (62% on Deep Seek), Grok 4.5 shows potentially better results than Opus 4.8 and also performs competitively on the Artificial Intelligence Coding Index with 72.4 points – roughly comparable to Opus, but significantly cheaper.

  The author tests Grok 4.5 in several practical scenarios:

  1. Artificial Life Ecosystem Simulator: The model creates a functioning HTML application with predator-prey simulation. The result is comparable to GPT-5.6, though in less time.
  1. Time Zone Overlap Checker: An application for visualizing timezone overlaps. Grok 4.5 produces a UI similar to Opus 4.8.
  1. Car Crash Physics Simulation: The model generates a functioning physics demo with customized graphics – not at Fable level, but solid for the model size.
  1. Miniature-Based Drawing Tool: A complex feature with color picker, duplication, and text elements. Fable remains the class leader here, but Grok 4.5 delivers a functioning foundation.

  Core strength: Grok 4.5 is extremely fast (3-4 iterations in seconds) and significantly cheaper than Fable or Claude. Pricing is around 2 million per input and 6 million per output – roughly five times more expensive than Fable 5.

  The author criticizes the Cursor IDE as unusable (crashes when recording video simultaneously), but recommends using it via Passo, a tool that consolidates all AI subscriptions in one interface. Also mentioned are Claude and Composer 2.5 as impressive models.

  The video consists of practical tests of xAI/Grok 4.5 with Cursor and critical evaluation of the IDE – demo format with opinionated commentary.

• GPT 5.6 DÉTRUIT Fable (sans avoir besoin de mentir sur leur marketing)
  10.7.2026, 07:53:17

  The video compares OpenAI’s GPT 5.6 models (Sol, Terra, Luna) with Claude Fable 5 using benchmarks and practical tests. On benchmarks, GPT 5.6 Sol scores 73% intelligence while costing significantly less than Fable (around 8 instead of 13 dollars per task). Terra and Luna offer even better value, while Fable has similar intelligence but is more expensive.

  In practical tests, GPT 5.6 Sol Ultra is tested with the following tasks: A time-zone picker with enhanced UI (featuring color indicators and time displays), a car-crash simulator with physics simulation, rocket-launch animation, and a drawing-surface editor. GPT 5.6 Sol Ultra generally shows cleaner code, better interfaces, and sometimes automatically uses multiple agents to solve problems.

  With the ecosystem-evolution simulation (Game of Life), Fable refuses to complete the task multiple times and falls back to Opus 4, while GPT 5.6 Sol Ultra creates a functioning, interactive ecosystem simulator with statistics and real-time parameters – “one shot”, on the first try.

  The author prefers GPT 5.6 Sol despite Fable’s higher theoretical intelligence, because Sol doesn’t have annoying security blocks and works more reliably. He only uses 60% of his weekly limit.

  Explicit tools/models: OpenAI GPT 5.6 (Sol, Terra, Luna, Ultra, Extra High), Claude Fable 5, Opus 4 | Format: Demo with comparison | Focus: Practical applications of code generation models.

• Mes 5 MEILLEURES skills pour Claude Code et Codex (c’est cheaté)
  9.7.2026, 14:00:19

  Summary: 5 Skills for Lia (Coding Agent)

  The video presents five community skills designed to transform the way you code with the Lia coding agent. Four were contributed by other developers, one by the author.

  Impeccable: A design-system skill that prevents the agent from producing visual “slop”. It creates design documentation (using /impeccable init) that clearly communicates design rules to the agent. Impeccable internally contains 23 sub-skills (Clarify, Harden, Optimize, etc.), but you typically just use the parent skill. The system also works via CLI (npx impeccable detect). The author successfully designed his mobile app on the first try using it.

  Grilling: A productivity skill that questions the user extensively before implementation to clarify the project. The better the preparation, the better the agent performs on the first try (“one shot”). The skill was formerly called “Grill Me” but has been renamed.

  Apex: A structured workflow skill with phases including initialization, analysis, planning (with acceptance criteria), execution, validation, code examination, debugging, and verification. Apex makes the agent run its own tests and take screenshots instead of just delivering code – the author uses it for every task.

  Make Interface Feel Better: A focused design detail-adjustment skill that improves borders, animations, number displays, and similar refinements – not as radical as Impeccable, but important for polish.

  Thermonuclear Code Quality Review: A skill provided by Cursor for strict code review focusing on quality, maintainability, and abstractions. It checks for spaghetti code and other issues. Can be used as a skill or as an agent. In the Apex workflow, it runs as a sub-agent in the “Examine” phase to prevent the main agent from judging its own code too favorably.

  All skills are viewable and installable through an open repository (mlv.sh/fc).

  Lia coding agent, productivity/opinion video, multiple community skills (Impeccable, Grilling, Apex, Thermonuclear Code Quality Review, Make Interface Feel Better), CLI-based installation.

• CODEX : Créer une application iOS en moins de 20 minutes
  5.7.2026, 16:33:48

  The video shows a complete workflow tutorial for creating and publishing an iPhone app without programming knowledge – using Lovable (the tool called “Codex” in the video).

  Main steps:

  1. Setup: Installation of Lovable Desktop, Expo Go on iPhone, and Bun (Node package manager).
  1. Project structure: Creating a working folder (e.g., developer/app) and initializing a React Native app with bun create expo-ti-template default.
  1. Install skills: Installation of specialized “skills” like “Build Native UI” (for native iOS styling), “React Native Skill,” and “Grilling” (for requirements clarification). These are integrated into the project via CLI commands.
  1. Requirements clarification with Grilling: The AI tool automatically asks questions about the app idea to refine requirements (here: personal habit tracker).
  1. Code generation: After clarification, Lovable generates the entire app logic and UI in about 16 minutes. The app syncs live on the iPhone over the same WiFi connection and can be tested immediately.
  1. Iterative improvements: Screenshots of bugs or desired improvements can be sent directly to Lovable as prompts (e.g., “Move ‘Add’ button to top right”), after which the tool adjusts.
  1. Icon generation: A dedicated “App Icon Skill” enables automatic app icon generation based on natural language instructions.
  1. Publishing: An additional “App Store Connect Skill” guides you through publishing on TestFlight and the App Store.

  The example shows the finished app with functional features like habit management, checkboxes, archiving, and notifications – all without manual coding. The author emphasizes that Lovable generates higher-quality code with predefined skills and clear requirements; for beginners, a separate mini-course with optimal stack configuration is recommended.

  Featured tools: Lovable (called “Codex” in the video), Expo Go, Bun; Custom skills from AI Blueprints (Build Native UI, React Native Skill, Grilling, App Icon, App Store Connect). Format: Tutorial with demo.

MoureDev by Brais Moure (2 new videos)

• Aprende Agentes de IA GRATIS con OpenCode, Claude y Cursor (Desde cero + Certificado)
  9.7.2026, 14:00:09

  Summary: Development with AI – Programming with Agents

  The comprehensive talk guides through a holistic concept of AI-powered software development in 2026. Key insights: 84% of developers use AI, but only 41% of code going into production is AI-generated – indicating the continued critical value of human supervision.

  Development paradigm shift: The value of developers is shifting from pure code production to orchestration of AI. No longer “I program,” but “I direct an AI agent.”

  Fundamentals of AI models: Large Language Models work through tokenization and probability prediction, not reasoning like humans. Key metrics are parameters (intelligence), tokens (granularity), context window, latency, and cost – model selection depends on the specific task.

  Editor evolution: From classic IDEs (VS Code, Cursor) through agentic IDE editors to terminal-based tools (Open Code, Claude Code). All work similarly – what matters is understanding core principles, not tool choice.

  Guardrails: Multi-layered control mechanisms:

  • agents.md: System prompt file with project rules, stack, conventions
  • MCPs (Model Context Protocol): Interfaces to external tools (databases, documentation)
  • Skills: Reusable capability definitions as .md files
  • Commands: Custom, repetitive actions
  • Memory management: Documentation, context compaction, sessions

  Spec-Driven Development (SDD): The future paradigm – not “let AI decide,” but “I write a precise specification, AI implements it.” Structure:

  • Constitution (project ground rules: mission, stack, architecture)
  • Features (granular tasks with plan, specifications, acceptance criteria)

  Each feature is explicitly defined, reducing indeterminism and enabling professional quality.

  Multi-agents & sub-agents: A coordinator agent can spawn specialized sub-agents (e.g., implementer, reviewer) with isolated context and minimal permissions – scaling without loss of control.

  Core message: AI executes, humans are accountable. Fundamentals over syntax. Control over speed. The phrase “It’s not AI that will replace you, but someone who uses it better” is the central driving force.

  Discussed tools/platforms: Open Code, Claude Code, Visual Studio Code, Cursor, Gemini, ChatGPT, Context Seven MCP, GitHub Copilot, Antigravity, Warp Terminal.

  Format: Deep-dive/tutorial hybrid opinion – comprehensive practical introduction to a new development paradigm.

• Zuckerberg confiesa que Meta se equivocó con la IA
  7.7.2026, 14:00:27

  Meta admitted that its heavily hyped bet on AI agents is failing — despite 145 billion dollars in planned investments for 2026, 8,000 layoffs, and reassignment of 7,000 employees. In a leaked internal video conference, Mark Zuckerberg acknowledged that development of autonomous agents hasn’t accelerated as hoped over the past four months, the restructuring isn’t bearing fruit, and the layoffs “weren’t handled cleanly.” Meanwhile, Meta’s AI leader Alexander LLM publicly claimed his new model (codename “Watermelon”) had already reached GPT-5.5 — a statement without concrete benchmarks.

  The creator suspects: Zuckerberg spoke honestly to anxious employees internally, while LLM distributed reassurance to nervous investors. Multiple studies support the bleak picture: 95% of generative AI deployments in enterprises showed no measurable business impact, only 12% of CEOs report higher revenues from AI, Gartner predicts a 40% failure rate for agentic AI projects by end of 2027, and 80% of all enterprise AI projects miss their promised goals.

  The core problem: Large corporations are conducting mass layoffs due to FOMO (fear of missing out) and hype, not because the technology is mature. Zuckerberg now promises “significant benefits in 3–6 months” (January 2027) — the same promise repeated every six months for three years. The creator warns: This isn’t a sign of tech skepticism, but of realistic expectation management. The timing gap between promises and reality is the actual problem, not the technology itself. Developers with solid fundamentals and sound judgment will be rewarded, while 95% of the industry fails because they chase hype instead of craftsmanship.

  Demo/opinion, news update (with reflection); Meta’s internal Zuckerberg statement on failed AI agents vs. public reassurance promises by leadership discussed.

n8n (1 new video)

• The Creator of n8n’s MCP Shows You How to Actually Use It (3 Live Builds)
  9.7.2026, 18:15:49

  Summary: Mastering N8N MCP – Three Levels of Workflow Orchestration

  The video demonstrates three levels of mastering N8N MCP (Model Context Protocol) with Millerad, the creator of the official N8N MCP.

  Level 1 – Deterministic Workflows: The first step shows how to create a complete workflow system through chat prompts. Example: A form trigger collects submissions (first name, last name, email) in a data table. The MCP can automatically generate test data and run scenarios without manual CSV imports. Functionality is then added iteratively – such as email notifications via Gmail for new submissions. The system is stored in N8N, tested, and published, all via natural language in Claude.

  Level 2 – AI Agents: A LinkedIn post creator agent is built and triggered via chat. The agent writes content based on instructions, which is then uploaded as a file to Google Drive. The key advantage: the system also runs on the go via Claude on your phone – a shower thought becomes instant action. The workflow combines creative AI generation with deterministic storage and upload steps.

  Level 3 – Multi-Agent Orchestration: Two different models (Claude and GPT) review the same generated content in parallel. An orchestrator agent deduplicates and consolidates findings from both reviewers into a consolidated list with source attribution. This enables quality assurance, token efficiency (smaller models can be used for simpler orchestration tasks), and self-learning workflows.

  Additional Highlights:

  • The N8N MCP works code-based, not JSON-based, which LLMs understand much better.
  • Credential management is central (Gmail, Google Drive, etc. must be configured once).
  • Workflows are not visible to MCP by default; you must explicitly share them – a security measure.
  • Community Nodes are not yet available for MCP (security concerns regarding prompt injection).
  • Level 4 (not shown): The MCP can be combined with other MCPs, such as Higgsfield for image/video generation.

  The core message: Build iteratively (not “one-shot” complex systems), use the MCP as glue between Claude and your N8N instances, and trigger workflows on the go from your phone.

  Tools/Providers explicitly mentioned: N8N MCP, Claude, OpenAI, Google Drive, Gmail, Higgsfield, OpenRouter. Format: Demo/tutorial with live coding.

Nate Herk | AI Automation (5 new videos)

• Claude Code for Non-Coders (6 Hour Course)
  11.7.2026, 13:02:58

  Summary: Claude Code Masterclass for Knowledge Workers

  This is a comprehensive course on using Claude Code (Anthropic’s coding interface) for productivity automation without requiring programming knowledge. The content is structured around mindset shifts, practical setups, and advanced techniques.

  Core Concepts

  Six future-proof AI skills:

  1. Become an AI person – Build relative expertise within your network; opportunities emerge before formal positions are posted
  2. Taste & judgment – Verify output, don’t blindly trust; catalog AI tells and buzzword kills to a personal list
  3. Context engineer – Not prompt engineering, but providing relevant context files; context is king
  4. Iteration speed – Rapid prototyping over perfection; use keyboard shortcuts and voice input
  5. Build Jarvis – Automations that run without manual triggering; but consciously distinguish simple workflows from agentic systems
  6. Diversify income streams – Multiple AI-powered revenue sources instead of single-point dependency; spread professional risk

  Mindset Shifts

  • Not “what do I know”: AI-native means automatically reaching for Claude instead of manual work
  • Don’t quit in the dip: Learning curve is exponential, not linear – frustration comes before breakthrough
  • Constraint is where work compounds: Identify and attack bottlenecks (Theory of Constraints)
  • Can’t automate what you can’t map: Process mapping beforehand is essential
  • Every build needs a north star: One metric per project that defines “success”

  Practical Setup

  Installation & Authentication

  • Download VS Code, install Cloud Code extension
  • Anthropic subscription required (Pro or Max recommended)
  • Alternative: Cloud desktop app or terminal-based

  Folder Structure & Cloud.md

  project/

  ├── .claude/

  │ ├── cloud.md (System prompt, routing rules)

  │ ├── agents/ (Custom sub-agents)

  │ ├── skills/ (Reusable prompts)

  │ ├── memory.md (AI auto-memory)

  │ └── settings.json (Permissions & config)

  ├── .env (API keys, .gitignore!)

  └── [project-files]

  Cloud.md Role: Is NOT a big knowledge dump, but a router

  → Shows Claude where information lives, not all details upfront

  API Keys & Connections

  • Environment variables: Store API keys in .env (ignored on git push)
  • MCPs (Model Context Protocol) vs APIs vs CLIs: Prefer Google Workspace CLI (more token-efficient)
  • Scoped permissions: Give APIs only what they need (e.g., read-only for reviews)

  Advanced Concepts

  Skills & Sub-Agents

  Skills = Markdown files with YAML front matter (metadata + natural-language instructions)

  → Invoked automatically via progressive disclosure (Claude reads description only, not full skill)

  Sub-agents = Specialized parallel workers with their own context window

  → Before: Problem exploration → After: Specialized agent teams

  → 7–10x more expensive in tokens, but worthwhile for deep reasoning/parallelization

  Agent teams (experimental):

  • Mutually communicating agents (not just hierarchical)
  • Use case: Multi-perspective analysis (e.g., advice from 6 different roles)
  • Very token-intensive → use sparingly

  Second Brain

  5 levels from simple to complex:

  1. Level 1: Cloud.md + simple folders (exact-word search)
  2. Level 2: LLM Wiki (à la Karpathy) + Obsidian visualization (index-based)
  3. Level 3: Vector search + semantic search (meaning-matching vs. keywords)
  4. Level 4: Knowledge graph (explicit relationships between entities)
  5. Level 5: Always-on brain (constantly self-learning)

  → Not all levels for everyone: Identify pain, then choose minimally necessary solution

  Websites & GitHub Deployment

  1. Build locally: Claude Code writes HTML/CSS, screenshot loop verifies design
  2. Push to GitHub: git commit & push via Cloud Code
  3. Deploy to Vercel: Auto-deploy on every GitHub push
  4. Domain: Configure DNS

  Token hack: Use front-end design skill → Professional websites without vibe-coding

  Security & Trust Question

  Deterministic vs. Non-Deterministic

  • Deterministic (vending machine): Input → guaranteed output (workflows, CLIs)
  • Non-deterministic (slot machine): Agent with reasoning, unpredictable decisions

  → For risk-critical tasks: Permission layering instead of just prompting

  • Tool-level restrictions (what can the agent not touch?)
  • Not: “Don’t do that” (prompt), but: “You don’t have access to this tool” (permissions)

  Deploy Automations

  • Cloud routines (remote): 5 GitHub repos, encapsulated, max 15 per day on Max plan
  • Modal (deterministic): Python scripts, webhooks, per-trigger
  • Trigger.dev: Similar to Modal, various offerings

  Token Management (The 18 Hacks)

  Tier 1 (Easy, implement immediately)

  1. Start new chats (/clear) between unrelated tasks
  2. Disable MCPs (use Google Workspace CLI instead of MCP servers)
  3. Bundle prompts (one long prompt instead of three short ones)
  4. Plan mode before complex tasks (ask questions before code falls)
  5. Use /context and /cost commands (visibility!)
  6. Status line in terminal (live overview)
  7. Keep dashboard open (monitoring limits)
  8. Selective pasting (not whole files, only relevant parts)
  9. Watch Claude (don’t dismiss, stop wrong paths early)

  Tier 2 (Strategic)

  1. Keep cloud.md lean (<200 lines, is a router not a data dump)
  2. File references precise (@filename instead of free search)
  3. Compact at 60% context (not at 95%)
  4. Use cache consciously (5-min timeout for APIs, 1h for subscriptions)
  5. Avoid bash output bloat (200-line commits = 200 token surcharge)

  Tier 3 (Advanced)

  1. Right models: Sonnet standard, Haiku for sub-agents, Opus sparingly
  2. Sub-agent costs: 7–10x more expensive, but Haiku delegation often worthwhile
  3. Watch peak hours (8am–2pm ET = faster drain, use off-peak)
  4. Cloud.md as constitution: Store decisions, not conversations

  Prompt Caching (Bonus)

  • Costs only 10% of normal tokens on cache hit
  • TTL: 1h for subscriptions, 5min for APIs (risky!)
  • Three habits: (1) Don’t pause >1h, (2) Task switch = new session, (3) Model switch breaks cache

  Practical Workflows

  Morning AI Briefing (Modal Example)

  Trigger: 6am CT

  ├─ Research (Tavily) → gather AI news

  ├─ Write (Opus via Open Router) → scannable brief

  └─ Deliver (ClickUp API) → DM to Nate

  → Risk mitigation: Set API limits, fallback services, hardcoded ClickUp target

  Form Submission Automation (Webhook)

  User fills HTML form

  ↓

  Webhook → Modal

  ↓

  Python script reads data

  ↓

  Template → ClickUp notification

  Important Tools & Resources

  | Tool | Purpose | Link |

  |——|———|——|

  | Obsidian | Wiki visualization (optional) | obsidian.md |

  | Google Workspace CLI | Cheaper than MCP | GitHub: google/gws-cli |

  | Modal | Python deployment (cron/webhook) | modal.com |

  | Open Router | Unified API for all models | openrouter.ai |

  | Tavily | Web research | tavily.com |

  | Free School Community | All skills & templates | Links in video description |

  Closing Philosophy

  “Boring is beautiful” – For self-firing automations: deterministic > intelligent

  “You can outsource thinking, never understanding” – Your name on the output = your accountability

  “Constraint is the only place work compounds” – Not AI everywhere, but attack the bottlenecks

  “The person who understands when NOT to use AI wins” – Not AI for everything, but smart choice

  Next step: Join Free School Community → “Build Your Own AI Operating System” course (2 hours, also free)

• I Tested GPT 5.6 Sol vs Fable 5. What You Need To Know.
  10.7.2026, 01:30:15

  Summary: GPT-5.6 Soul vs. Fable 5 – Practical Comparison Tests

  The creator tested GPT-5.6 Soul and Fable 5 all day by pitting both models against identical prompts for three practical tasks: a playable open-world bike game, an interactive scroll-storytelling website, and five different visual elements/simulations.

  Agentic tests (with harnesses):

  On the bike game, Fable 5 was qualitatively much better (3D GTA-like perspective vs. bird’s-eye view), but cost three times as much ($14.22 vs. $4.50) with 2.8x more output tokens (90k vs. 31k). On the website, the picture was similar: Fable produced more immersive content ($19.24 vs. $1.04). On the five elements, Soul was more balanced and creative – faster and cheaper (15 min vs. 7 min, $15 vs. $1.22).

  Direct API requests:

  Soul dominated 24:3, while Fable frequently refused to answer. Soul cost $16 vs. Fable’s $63. When actually answering, both were tied (Soul 0.98 vs. Fable 0.966 score), showing Soul mainly won through reliability and drastic cost efficiency.

  Creator’s conclusion: Fable 5 is the “better manager” – more creative, strategic, superb for knowledge work, consulting, and video creation. Soul is a “really good worker” – cheaper, faster, better at computer use and analytical thinking, but qualitatively a step below Fable. Soul is positioned as more comparable to Opus 4.8, significantly better than GPT-5.5. The creator would rather pay for Fable on complex tasks, but prefers Soul when budget is decisive.

  Note: No transcript available; summary based on video content.

  Deep-dive comparison between OpenAI GPT-5.6 Soul and Anthropic Fable 5, each examined with practical use scenarios.

• GPT 5.6 Sol Made This Entire Video
  9.7.2026, 22:05:14

  GPT-5.6 Soul produces complete video in a single agentic operation

  The creator gave GPT-5.6 Soul in Ultra mode a single prompt and received a complete, highly-produced video with a person named Nate as the speaker and avatar — without ever standing in front of a camera or opening the editor. Soul coordinated four agents simultaneously and orchestrated the entire workflow: research into OpenAI’s July 9 announcement, fact-checking, scriptwriting in Nate’s voice style, triggering paid APIs, and quality checks.

  The model achieved 97% of available points in this concrete test (7 wins, 5 ties, 1 loss). Soul was particularly strong in coding and structured execution. The audio workflow: Soul generated script sections in under 60 seconds, these went through 11 Labs for Nate’s authorized cloned voice, then to HeyGen for avatar rendering (where the creator had to use browser automation to force avatar version V), and finally to Hyperframes for final editing with motion graphics. Soul planned and orchestrated everything; separate agents then checked each frame for errors, missing avatar elements, and factual accuracy; failed frames led to re-rendering and review.

  The project officially consumed 3 million tokens over 2.5 hours according to logs — but upon verification: Soul spun up nine sub-agents, the main agent consumed roughly 86 million tokens total, overall costs came to around $300. The creator suspects Ultra mode caused excessive delegation; High or Very High might have yielded similar results at half cost. Soul pricing is comparable to Claude Opus 4.8 and significantly cheaper than Fable 5. The core insight: Soul impressed through the ability to handle vague, emotional, ambiguous prompts, delegate independently, and verify.

  OpenAI / GPT-5.6 Soul, 11 Labs, HeyGen, Hyperframes featured; demo format.

• Fable 5 Just Built Me a Business With One Prompt
  8.7.2026, 22:05:01

  Summary: Claude Fable orchestrates complete startup from scratch

  The creator gave Claude Fable a single prompt with an overarching goal: build a complete business from nothing with product, branding, website, and launch materials. Fable then orchestrated hundreds of sub-agents that conducted market research in parallel, judged competing business ideas in a tournament, and had each claim verified by skeptical opponent agents. The result was “Counter Brief” — a SaaS tool for Shopify store owners to document chargeback cases.

  The project included: a fully functional landing page, two professional launch videos (one quick/viral, one as voice-over demo), a founder video with AI avatar and cloned voice, detailed business plans, market research, brand guidelines with hand-vectorized logo, and a complete red-team analysis with 38 identified weaknesses. The entire process took three to four hours and consumed roughly 500,000 tokens in the Fable orchestrator itself; sub-agents ran on Claude Opus/Sonnet, which was more resource-efficient than if Fable had been used for everything. The key was a detailed prompt that explicitly directed Fable to multi-agent workflows, parallel research, tournaments, and adversarial verification — plus a rule set that prohibited publication and demanded creativity. The creator emphasizes that the main learning objective was less in practical tutorial than in demonstrating that ambitious, multi-step goals are possible for large language models when given clarity on success criteria and then allowed to creatively “find their way”.

  AI tools used: Claude Fable (orchestrator) and Claude Opus/Sonnet (sub-agents), plus integrated APIs (HeyGen, 11 Labs); format: demo/opinion hybrid.

• How I Make Opus Think Like Fable (5 easy steps)
  7.7.2026, 02:44:05

  Summary: “Turn Opus into Fable” – Model routing and prompt engineering

  The speaker has spent several thousand dollars on Fable-5 credits and draws a central conclusion: the model itself isn’t the true competitive advantage — it’s how you instruct it and the systems you build around it. A beginner with Fable 5 will get worse results than an expert with Sonnet 3.7 because prompt engineering and workflow design are decisive.

  Core observation on Fable 5 vs. smaller models:

  The speaker tested dynamic workflows where Fable orchestrated both Fable sub-agents and Opus or Sonnet sub-agents. The result was surprising: quality was similar, but Fable-orchestrated Fable workflows cost exponentially more. This led to the insight that while you can’t retain a model’s intelligence, you can extract its process.

  The “Fable Mode” strategy:

  Instead of using expensive models for every task, use Fable to codify its thinking into a skill system that Opus and Sonnet can then use. The speaker learned from leaked Fable-5 system prompts:

  • Verification at every step – Things in memory or implicit in prompts must be checked
  • Answer first, then ask
  • Acknowledge errors, stay on problem
  • Proportionate effort – maximum effort isn’t always better; higher effort levels can lead to overthinking

  The Fable-Mode skill works through five gates: Scoping (with devil’s advocate approach), evidence, reasoning (adversarial), verifying, reporting. When Opus is equipped with this skill, the model feels “elevated”.

  Model routing:

  The speaker recommends a table of available models (cost, intelligence, taste/creativity). When Fable or another strong model acts as orchestrator and delegates to smaller models, significant cost savings result while maintaining quality – in one test, roughly triple cost savings with Haiku scouts instead of Opus scouts.

  Final thought:

  Since Fable returns on subscription basis and AI models fundamentally aren’t “owned,” focus on what you actually control: processes, systems, methodologies, and local hardware.

  The “Fable Mode” skill file is being made available free in a skill community; other models and open-source solutions can work with it too.

  Explicit models/tools: Fable 5, Opus 4.8, Sonnet 3.7, GPT-5.5, Claude Code (dynamic workflows), open-source models mentioned; format: opinion/reflection with practical examples and skill demo.

NeuralNine (3 new videos)

• Synthetic Data Generation with Generative Models in Python
  10.7.2026, 23:17:29

  Summary: Synthetic Data Generation in Python

  This video covers generating synthetic tabular data using classical deep learning methods, specifically CTGANs (Conditional Tabular Generative Adversarial Networks) and TVAEs (Tabular Variational Autoencoders) — not LLM-based.

  Core Concepts:

  Synthetic data generation trains models to learn the distributions and relationships of real data, then generates new, artificial data that looks like real data but isn’t. Use cases include: insufficient data volumes, privacy (not sharing sensitive data), class balancing, handling rare cases, and edge-case testing. This differs from data augmentation, which expands real data through simple transformations.

  State of the Art:

  Research-wise, diffusion models (TabSyn, TabDiff, TabDDPM) lead, but practically, CTGAN and TVAE are established due to better implementability and usability.

  Architecture Overview:

  CTGAN: A conditional GAN with generator and discriminator that improve each other. The generator creates realistic synthetic data; the discriminator distinguishes real from artificial. “Conditional” allows fixing attributes (e.g., Country = UK); “Tabular” means special optimizations for tabular data.

  TVAE: A Variational Autoencoder that encodes data into probabilistic latent representations (mean μ and standard deviation σ), then samples from this normal distribution and decodes. Drawback: no direct conditioning possible (must filter afterwards).

  Decision Guide:

  • CTGAN better for complex categorical patterns, imbalanced data, and large datasets
  • TVAE more stable during training, faster to tune, less overfitting on small datasets, good for continuous patterns

  Practical Implementation (Python with SDV Package):

  Installation: pip install sdv pandas scikit-learn jupyter-lab

  Basic Pattern:

  1. Load data, split into train/test
  2. Create metadata, mark target column as categorical
  3. Initialize synthesizer (CTGAN or TVAE) and train on training data
  4. Call synthesizer.sample(n) to generate

  Demonstrated Applications:

  • Basic Example (Breast Cancer Dataset): Train a Random Forest classifier on original vs. synthetic vs. combined data. Result: variable performance; TVAE yields better synthetic data than CTGAN on small volumes.
  • Class Balancing: With imbalanced classes (e.g., 166 vs. 289 instances), generate synthetic data for the minority class and add it. With CTGAN, use condition() to enforce target class directly; with TVAE, filter.
  • Training on Rare Cases: Use only rows with rare values (e.g., house prices > 4.5) as training basis for the synthesizer, generate synthetic instances, add to dataset → improved performance on rare test cases.
  • Edge-Case Testing: Generate synthetic test data with extreme/rare combinations (high median income, few rooms) and test your model without having real boundary data.

  The video emphasizes: synthetic data doesn’t guarantee performance improvement; the main benefits lie in privacy, balancing, and robustness rather than primarily performance.

  Deep-dive with practical coding focus; CTGANs and TVAEs implemented via SDV Package (Synthetic Data Vault).

• Web Scraping Made Easy – Scraper Studio and Python
  8.7.2026, 16:00:23

  Automated Web Scraping with Scraper Studio

  This video demonstrates Scraper Studio by Bright Data, a tool for automated web scraping using AI prompts instead of manual coding.

  Main Problem with Classic Scraping: Websites change their structure (CSS classes, HTML tags) regularly, breaking scrapers and requiring manual repairs.

  Scraper Studio Approach:

  • You provide a website URL and a natural language description (e.g., “Extract article titles, comments, and posting time”)
  • The tool automatically generates a working scraper in code form
  • You retain access to and control over the generated code
  • Optional: direct access to a scraper library with pre-built scrapers (LinkedIn, Instagram, e-commerce sites)

  Practical Examples in the Video:

  1. Hacker News: Scraper for articles with title, comment count, top comment, and posting time
  2. Newegg: Scraper for desktop PCs with name, price, and discount percentage
  3. Custom Site: Intentionally changing website structure to demonstrate the self-healing feature

  Self-Healing Feature (Core Feature):

  When a scraper breaks due to website changes, you can use the self-healing option in the code tab, enter an error description, and the tool repairs the scraper automatically. It shows a diff view of changes (e.g., product → item, title → name) and saves the corrected version.

  API Integration: Scrapers can be controlled via Python and API requests — you send a request, get a collection ID back, and poll periodically until results are available as JSON or CSV.

  Overall Value: Time savings on initial scraper setup, but especially automated maintenance and repair when websites change without manual debugging.

  Bright Data Scraper Studio (demo/tutorial with AI-powered tool), standard difficulty.

• Financial Risk & Performance Metrics in Python
  6.7.2026, 16:00:30

  Summary: Financial Risk and Performance Metrics in Python with Empirical

  This video shows how to calculate financial metrics like Sharpe Ratio, Calmar Ratio, Alpha, Beta, annual volatility, and annual returns using the Python package Empirical Reloaded. Empirical Reloaded is a maintained continuation of the original, no-longer-maintained Quantopian packages (Zipline, Pyfolio, and Empirical).

  Setup: After installing Empirical Reloaded, yfinance, and Jupyter Lab, download data via Yahoo Finance. The video uses Apple stock (AAPL) from 2020 to 2024. Daily (or resampled weekly/monthly) returns are calculated from closing prices.

  Basic Metrics: With emp.sharpe_ratio(returns), for example, you get the Sharpe Ratio (0.89 in the example). Other functions: sortino_ratio() (considering only downside volatility), cumulative_returns(), annual_return(), annual_volatility(), aggregate_returns() (monthly aggregation), and max_drawdown() (largest decline from peak to trough).

  With Benchmark: To calculate Alpha and Beta, you need a benchmark series (e.g., SPY for the S&P 500). After aligning date windows, use emp.alpha_beta(strategy_returns, benchmark_returns) and obtain metrics on market movement relative to the benchmark.

  Practical Application: The creator shows a function that calculates multiple metrics at once in a Pandas DataFrame — useful for screening different assets or automated analysis.

  The video focuses exclusively on Empirical Reloaded (Python), not other frameworks — tutorial with a fundamental and practical approach.

Nic Conley

No new videos in this period.

Nick Saraev (5 new videos)

• $20,354/m, solo, while working a 9-to-5
  10.7.2026, 04:52:14

  Justin Lob, an AI-automation-agency owner, achieved up to $20,354 per month in his first year of business (February 2025) while maintaining a full-time job. He emphasizes that the key to success lies in focusing on the right activities—not the latest technology.

  Business model and tools: Justin works as a solo founder with a deliberately simple tech stack: a self-built CRM in Airtable, ClickUp for project management, and a website for bookings. He’s tool-agnostic and uses what works for the client—not the newest on the market. He stresses that most businesses don’t need complex solutions like AI agents or specialized cloud coding tools; they just need working systems.

  Lead generation and sales: Justin’s secret was focusing on sales calls. He submits three Upwork applications daily, runs cold email campaigns, and prioritizes getting potential customers on calls. Instead of disqualifying early, he qualifies at the end of the call—many seemingly bad leads became great clients. His first opportunity came after about 20-25 days of daily Upwork submissions (a $5,000 project). Then came a $5,000-per-month retainer client from the Maker School community.

  Scaling and mistakes: Justin quickly learned that he had to find his limit by continuing to take sales calls until fulfillment delays piled up. An early mistake was blasting an SMS to 5,000 contacts with his username instead of the client’s name—the client forgave him. The lesson: first clients are for skills, testimonials, and partnerships, not maximum profit.

  Pricing and retainers: Justin uses fixed-fee projects (50% upfront, 50% on delivery) and then converts these into retainers (often $3,250/month targeting $25k/month with 8 clients). He shifts the value proposition from pure hours to deliverables plus add-ons (strategy calls, Slack support).

  Mindset and discipline: The mantra “Don’t break the chain” helped Justin stick to three daily Upwork submissions plus cold email campaigns—visible on an X calendar in his office. He cites Seth Godin’s book “The Dip”: after an initial revenue phase came a 3-4 month dry spell; he almost quit, but persisting led to the breakthrough. January through March 2025 he exceeded his salary for three months straight—then he quit his job.

  Core advice: Optimize for sales first (top of funnel), not fulfillment or perfect tools. Start a daily habit and don’t break the chain. Be extraordinarily helpful to your first clients. Use communities—Justin paid someone from the community to set up cold email.

  The video covers a successful solo-freelance model in the AI automation space (Upwork, Make.com, Airtable, ClickUp) without specific agent or code tools. Format: Interview/Opinion; Difficulty: Beginner to intermediate—practical business and sales lessons without technical depth.

• How to Use Fable In Every Aspect Of Your Life
  9.7.2026, 04:42:52

  Summary:

  The author presents a simple system for workflow optimization: Software (here Fable) captures screenshots every 5 seconds, stores them daily, has them summarized by a cheap model, and identifies recurring unproductive behaviors. These are documented in an observation ledger, and the AI is regularly asked how to simplify the workflow.

  Concrete optimizations gained from less than an hour of screen time: a morning digest instead of multiple polling loops for updates; a hotkey system for faster idea capture (instead of 5–10 minutes daily, now just a keystroke and dictation); better cross-team visibility in project manager. Another example: switching from expensive voice transcription software (costs monthly, 250ms latency per use) to a local solution (50ms, costs nothing), saving $15 monthly and recovering several minutes daily through faster processing.

  The author argues that such small inefficiencies add up to significant economic losses and that organizations would greatly benefit from applying this approach to new employees. The system consumes about 8,640 lines of metadata text (practically free), samples about 20 frames for vision capabilities, and confirms patterns after 3–4 repetitions before suggesting improvements. The process can run in the background, roughly once daily, to preserve Fable session limits. The author promises to provide the script and necessary files.

  Conclusion: Fable was mentioned as the concrete example AI for this approach; format is opinion/reflection with practical use-case focus.

• Fable 5 Is Back. Use It To Print With These $10K Websites
  7.7.2026, 18:11:23

  Claude Fable 5: Infinite Website-Generation Prompt Strategy

  The creator demonstrates an experiment where Claude Fable 5 autonomously created 25 high-quality websites with advanced visual techniques—without pre-built structure or iterative instructions. The core insight: instead of using detailed step-by-step frameworks, it’s about getting “out of the way” and equipping the model with clear goals and tool access.

  The prompt strategy involved giving Fable a large autonomous goal (25 different websites, three iteration passes, hosting on Netlify) and providing access to multiple tools: Pinterest for visual inspiration, Higgsfield MCP for video and animation generation, GPT for image editing, and Chrome DevTools MCP for verification. The prompt also included specific techniques desired (3D, shaders, custom fonts, animations) but encouraged the model to find its own approaches too.

  The result: Fable independently created complex designs—3D particle swarms, embedded mini-games, variable fonts, audio-reactive visualizations, procedural city landscapes—all without manual input. Parallelization via subagents meant about one minute per website. The creator emphasizes that quality was surprisingly high precisely because he hadn’t tried to impose his taste “top-down.”

  Concrete examples shown in the video: history website (geological epochs), “Murmur” (blur-to-focus effect with audio layer), audio-reactive visualizations, a Y2K millennium design, and an AI-generated 3D chair rotation hinting at potential Shopify scaling models. The creator plans to repeat this for a week to generate thousands of websites and provide them for free—a token-arbitrage strategy for his audience.

  Claude Fable 5, demo with practical application.

• NEW Fable 5 API Usage Exploit (Reduce Costs Immediately)
  7.7.2026, 03:30:00

  The creator shows a trick for saving Claude tokens with Fable: instead of passing long context information as text, render it as images with tiny but still readable font. This exploits the different pricing structure—image costs are calculated by pixel dimensions, not text amount within them. In testing, this saves about 30% of input tokens (demonstrated: from 59,822 to 38,142 tokens on a prompt, which is 33% savings = $1.03 to $0.69).

  The creator then shows how to automate it: a Python script (pxpipe.py) takes long prompts, converts them to images, and sends them to Claude Fable 5. For knowledge-based tasks (like text search and linking), savings are significantly higher—in the second test 68.7% fewer input tokens (59% cost reduction). Code is provided for download.

  Claude Fable 5 / Demo

• The BEST AI Video Strategy No One Is Using
  6.7.2026, 12:30:21

  AI video production with video-to-video models: workflow and practical implementation

  The creator shows an end-to-end workflow for producing AI videos at scale, going far beyond simple text-to-video generation. The key is using existing video material and minimally modifying it with targeted prompts rather than generating completely new videos—it’s significantly less cognitively complex and delivers better results.

  The workflow in three steps:

  1. Source video (about 10 seconds): A self-recorded video with a clear, time-triggered marker (e.g., a finger snap). This trigger marks where the AI should make a change.
  1. Hyper-specific prompt: The prompt must state exactly when (timestamp, e.g., “at 2.9 seconds”) and what should happen (e.g., “switch outfit to a hoodie with chain”). Vague instructions don’t work.
  1. Video-to-video model: Models like Gemini Omni or Kling allow using real video material as input and modifying it precisely. This fundamentally differs from standard video generators, which work from text and nothing.

  Practical tips:

  • A single generation costs about 50 cents, so it makes sense to generate five versions simultaneously to pick the best (not all work correctly the first time—roughly 20% success rate per attempt).
  • Output quality is 720p, which is low for modern video platforms. The trick: hide the quality drop with a quick cut to another scene (e.g., screen share instead of talking head), so viewers don’t notice the compression.
  • The AI video is then woven into other material in an editor like Premiere Pro.

  Workflow automation: The whole process can be automated with Claude Code or Codex plus Higgsfield’s MCP (Model Context Protocol), so you can automatically create ten variations and pick the best.

  The creator demonstrates the practical use in Higgsfield and shows the visual differences between the original and AI-edited material—the differences are subtle and not uncanny valley. A real use case: someone earning millions produces 2,000+ AI videos per week with this. The big advantage: hardly anyone uses this technique intensively yet, though it’s extremely effective for ads and organic content.

  Tools and models mentioned: Gemini Omni (Google), Kling, Higgsfield (model aggregator platform), Premiere Pro (video editor), Claude / Codex (for automation via MCP). — Tutorial with practical demo.

Niklas Steenfatt (2 new videos)

• COMPLETE HERMES COURSE (Beginner to Pro!!)
  10.7.2026, 11:59:44

  Summary: Complete course on AI agents with Hermes

  This comprehensive tutorial shows how to build an autonomous AI agent called Hermes that is accessible 24/7 as an assistant via Telegram and can be connected to various tools.

  Core Concepts

  The course begins with the evolution of AI: from reactive chat interfaces (ChatGPT, Claude, Gemini) through code-generating systems to autonomous AI agents like Hermes and OpenCoder. The key difference: agents work independently, proactively, and can retain skills.

  Setup and Installation

  The easiest entry point is through Managed Hermes from Hostinger (approx. 6 €/month), which handles infrastructure and updates. You need to make two decisions: (1) Where does the agent run (local machine, own server, or managed), and (2) which AI model as the “brain” (local models, open-source cloud like Ollama, or commercial models like GPT via your own subscription). Recommendation: Managed Hermes + ChatGPT subscription, since it’s secure, cost-controlled, and intelligent.

  Configuration and Personalization

  After registration, you connect the agent via Telegram by creating a bot through BotFather and providing your user ID. The core concept is “reverse prompting”: you tell the agent about yourself in detail rather than just giving commands. The agent then automatically remembers profile information (User.md), a personality definition (Soul.md), and collaboration notes. This enables real learning and better collaboration over time.

  Skills and Automation

  A highlight of Hermes: it automatically creates skills – recipes for frequent tasks. Instead of iterating from scratch each time, it reuses its knowledge, saving tokens and becoming more efficient. You can disable skills as needed. Recurring tasks (cronjobs) enable autonomous actions: daily AI news reports, weekly reflections, or hourly YouTube channel monitoring – all without manual input.

  Tool Integration

  The agent gains practical value through connections to external services:

  • Oxylabs + Web Research: The agent independently researches on the internet and creates detailed reports; short summaries land in the Telegram chat, long versions in Notion.
  • Excalidor: Automatically creates diagrams, slides, and visualizations (e.g., explanatory diagrams).
  • Text-to-Speech & Speech-to-Text: Voice messages possible, voice configuration can be set to German.
  • Notion: Secure documentation of information in a separate agent workspace with read access only to that page.
  • Email: Hermes gets its own email account (e.g., hermes@stehenfahrt.org) for secure, isolated communication.
  • Google Calendar: Access via OAuth; the agent can create entries (e.g., automatically populate a PDF agenda with calendar entries).
  • GitHub: Daily backup of Soul, Skills, Config, and Memory files to a private repo – data protection and potential migration to other agents.
  • Hostinger MCP + Hix: The agent programs small web applications (e.g., conversation viewer) and deploys them to public URLs – all initiated by voice message.

  Security and Best Practices

  The course emphasizes four ground rules: (1) Principle of Least Privilege: Only necessary access (e.g., only agent workspace in Notion, not the entire account). (2) Maintain understanding: Don’t blindly assign tasks, but understand what the agent does. (3) Modern models for critical tasks: Newer OpenAI/Anthropic models are better secured. (4) Updates: Managed Hermes is automatically maintained; self-hosted server installations require personal responsibility.

  Advanced Features

  • Kanban Board: Alternative to Telegram for task management.
  • Agent Swarms: Multiple sub-agents on the same Hostinger plan, each with their own profiles and Telegram accounts.
  • Desktop App: Hermes locally on Windows/Mac/Linux, optionally connected to a remote instance.

  Core Mindset

  Treat the agent like a new employee: onboard them, get to know them, regularly reflect on which tasks you can trust them with. Learning is gradual – after weeks the agent is noticeably better than on day one. The practical value: 24/7 availability, quick research, automation of recurring tasks, programming of small tools.

  Featured Tools/Providers: Hermes, Hostinger Managed Hermes, ChatGPT (OpenAI), Notion, Telegram, Google Calendar, GitHub, Oxylabs, Excalidor, 11 Labs (mentioned). Format: Tutorial with practical live demos, some errors authentically documented (e.g., connection issues with email integration). Level: Beginner-friendly, gradual progression, all steps comprehensible – no deep technical background needed, but willingness to experiment helpful.

• I’m programming again!!
  5.7.2026, 16:48:48

  Summary:

  The workflow solves a central problem when working with AI agents: if the local laptop is closed or the internet connection drops, the agent session stops. The solution uses a remote Linux server where the AI agents can continue working continuously.

  The setup works like this: the developer works locally in VS Code with multiple AI agents (Codex, Claude, Gemini) running in parallel in different Git branches. This uses the Git Worktrees feature – each agent gets its own worktree so multiple branches can exist simultaneously in different directories without blocking each other. A custom script called “Vibe” automates management: vibe codex starts a new branch and worktree for the agent.

  When the developer needs to leave, they give the agent a handover command. The agent pushes all local changes to Git, creates a pull request, and leaves notes in a handoff markdown file. Then the same or another agent can continue working on the remote server – independent of internet connection or open local sessions. The script automatically detects whether it’s running on Mac or Linux, and uses tmux (virtual terminal) to maintain server sessions even when VS Code goes offline.

  Practical implementation: (1) Rent a Linux server with pre-installed CLI tools (here: Hostinger), (2) Connect VS Code to the server via SSH (Remote SSH Extension), (3) Clone the Git repository to the server, (4) Save VS Code workspaces (separated locally and remote), (5) Write a Bash script that displays agent status and manages handovers, (6) Explain to the agent via README/agent documentation that it runs on different devices and when to use tmux or handover notes.

  The core idea: modern AI tutorials don’t need to show every step – you can simply let the AI watch the video and instruct it to rebuild the workflow. This shows a shift in developer expertise: no longer detailed code knowledge, but the ability to give AI agents the right tasks.

  Conclusion: Opinion/reflection on remote development with AI agents (Codex, Claude, Gemini mentioned) using Git Worktrees and tmux – focus on productivity and workflow optimization.

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

• Travel Through the Lens of AI with with Booking.com CEO Glenn Fogel
  9.7.2026, 10:00:16

  Glenn Fogel, CEO of Booking Holdings, reflects in this interview on his 27-year career at the company, the parallels between the late 1990s internet boom and today’s AI wave, and the concrete role of AI in the travel industry.

  Career and company history: Fogel joined Priceline in 2000 when the company had crashed from 30 billion to a few hundred million dollars in market cap following the Nasdaq crash, trading at just one dollar per share. After a reverse split, the stock has since climbed to nearly 6,000 dollars – a thousand-fold increase. Fogel emphasizes that he held on during the early crisis period rather than selling.

  AI and travel: Fogel argues that OpenAI’s decision to exit travel commerce was less significant than often portrayed. The core misconception from outsiders is that AI could simply make the entire industry obsolete. In reality, he sees AI as a tool to fulfill their core mission: making travel easier for customers. Booking is developing agent-based systems like “Penny” for Priceline that automate complex travel planning with multiple destinations, flight option trade-offs, and hotel decisions – similar to a human concierge, but with unlimited memory and computing power.

  Scale effects and economic complexity: Penny doubles monthly, increasing conversions and reducing cancellations, but remains small relative to 86 billion dollars in annual bookings. Fogel emphasizes the underestimated complexity of the travel industry: Booking.com has over 8.6 million accommodation listings (globally comparable to Airbnb), yet also serves a massive hotel business. Managing thousands of partners, translations in 40+ languages, and complex global regulation requires substantial infrastructure – an advantage that new AI-native competitors cannot easily replicate. Still, Fogel warns: “There is no moat. There is no such thing as somewhere you’re going to be protected against innovation.” The only strategy is daily innovation and continuous improvement of the customer experience for both travelers and partners.

  Investments and capital allocation: Booking reinvests approximately 700 million dollars annually in AI, technology, and various initiatives, while simultaneously buying back about 40% of outstanding shares and paying dividends. Fogel follows the rule: First invest in positive ROI projects, then consider acquisitions, otherwise return capital to shareholders.

  Job loss and societal challenge: Fogel acknowledges that technology has historically displaced jobs (example: machine translation replaced 40-language translation teams), but also creates new ones – though not necessarily at the same pace. His main concern is the dislocation of mid-career workers (e.g., truck drivers facing automation) who struggle to retrain. He emphasizes corporate responsibility through AI literacy training and upskilling, but warns that past government retraining programs have been ineffective. He fears that societal rejection of technology out of fear could disadvantage the US against other countries, particularly China.

  Mission-driven perspective: Fogel ties his career to the goal that travel enriches life and connects cultures – “make it easier for everybody to experience the world”. He advises young people to choose wisely what they spend their limited lifetime on, rather than blindly following established paths.

  The interview covers AI/Large Language Models (LLMs), agent architectures, and Booking Holdings and its subsidiaries — Interview/Deep-Dive.

Productive Dude (2 new videos)

• Claude Fable Redesigned our App! (and we got blocked 😡)
  8.7.2026, 13:00:28

  The creator uses Claude Fable to fundamentally redesign his AI-Foundations website backend and homepage. Starting point: a beautiful but poorly structured homepage and a disorganized backend for users with hidden navigation menus.

  First step with Fable: A comprehensive PRD (Product Requirements Document) is provided to improve the new user experience – particularly the “Your Path” section, which should be personalized for different target audiences. Fable automatically spins up six agents to analyze different areas (Payments, Achievements, Analytics, Bookings, Course Progress, CRM). The result: a much clearer dashboard structure with tabs for “Your Path” (personalized onboarding and roadmap), Courses and Resources – all in one app without page refreshes.

  Second step – UI/Styling audit: Fable is deployed for a comprehensive design audit. It finds critical inconsistencies: 40 different modal implementations, 205 hardcoded hex values, 132 hand-rolled form fields, 75+ non-uniform buttons. Four specialized auditor agents handle CSS tokens, motion, z-index, components and overlays. This results in refactoring and the addition of an app-wide size preference (Compact/Standard/Large), plus a new search bar.

  Third step – Security audit (blocked): When the creator attempts to use Fable for a comprehensive security and performance audit, he gets blocked by Anthropic’s safeguards – Fable refuses to process the security-focused prompt and suggests Opus instead. The creator suspects a classifier that automatically filters security-related tasks. Opus 4.8 then executes the security tasks.

  Takeaway: Fable’s multi-agent approach works excellently for product redesign and code refactoring, but Anthropic’s security policies prevent deep penetration testing, even on own code.

  Claude Fable; Demo.

• Claude Design FULL Tutorial: Beginner to Pro
  7.7.2026, 13:00:19

  Summary: Ultimate Claude Design Tutorial for 2026

  The video is a comprehensive tutorial on Claude Design, taking you from basic structure to live deployment of a complete business solution.

  Fundamentals – Creating a design system:

  The first step is to sign in at Claude.ai and access Claude Design via the desktop app. A design system is created with company name, blurb, fonts, colors and assets (logos, fonts). The creator uses Higsfield with the Recraft-V3.1 model in vector mode to generate SVG logos that are infinitely scalable. With Claude Chat (Sonnet 3.5 Medium), Google Fonts combinations and color palettes are suggested – for the example Apex Roofing: slate-rust color scheme and fonts like Merriweather/Open Sans.

  Website generation:

  In the new design project, Claude is instructed to create a landing page with hero section, free offer form, service area, location section and navigation with anchor links. Claude asks questions about services (roofing, replacement, new construction), trust signals (A+ rating BBB, 500+ 4.7-star reviews), service areas (Kalamazoo, Portage etc.) and form fields. The system automatically generates image placeholders with prompts and aspect ratios. Using Higsfield (Cdream 4.5, 4K), all requested images are created and embedded – the website also includes separate service pages.

  Video ad generator:

  Through the animation template, Claude Design creates an ad studio with various formats for Facebook/Meta/Instagram. With Claude Chat, better video prompts are generated that show a pain-to-solution narrative (e.g. leak damage → repair → happy customer). Using Higsfield (9×6 ratio), images of these scenes are generated, then in the video tab converted to videos with Kling 3.0. Claude Design then compiles these into a 15-second ad with transitions and call-to-action.

  Presentations and documents:

  The slides template generates a pitch deck with shingle colors, specifications and images (gallery format). The document template creates a customer contract that can be signed with branding elements and editable fields.

  Deployment & consolidation:

  All assets (website, documents, decks, ad generator) are exported via “Share → Send to Claude Code” into a unified repo. Claude Code organizes this into a directory with design system, website, tools subfolders and Claude Skills (document generator, deck creator, video ad creator, lead qualifier). The website is deployed live with the Netlify connector – the URL is generated. In the Netlify dashboard, environment variables (admin_username, admin_password) are set. After redeploy, an admin panel is active showing incoming leads.

  Result:

  A complete, brand-consistent business solution: live website with lead capture, admin dashboard, reusable design tools, and generated video ads – all without traditional design or video tools.

  Featured: Claude (with Sonnet 3.5, Opus 4.8), Higsfield with Recraft/Cdream/Kling, Netlify and Claude Code — tutorial/demo format with practical depth for designers and business owners.

Sebastien Dubois

No new videos in this period.

Simone Rizzo (2 new videos)

• Il nuovo ChatGPT, la svolta di Meta e il salto di Grok: ecco cosa devi sapere
  11.7.2026, 15:33:47

  Summary: New Frontier Models Overview

  The video presents four new AI models released in recent days:

  Meta Muse Spark 1.1: The successor to its weaker predecessor shows significant improvements – particularly in context window (now 1 million tokens instead of 260,000) and computer-use capability. The model stands out by not clicking step-by-step, but automatically writing code to execute multiple actions as a batch. Multimodality is strong, and the model positions itself cost-effectively in the Artificial-Analysis index. Meta lags several months behind top competitors, but with its Instagram and Facebook data could gain ground in multimodal applications.

  Elon Musk’s Grock 4.5 (xAI): The model positions itself between GPT 5.5 and Opus 4.8, but fails to match the performance of Fable or GPT 5.6. The decisive advantage lies in speed (93 tokens/second output) and very low price (0.31 dollars per task). The key to Grock’s advancement was Musk’s acquisition of Cursor – a coding-agent company that brought high-quality training data and expertise.

  OpenAI’s new GPT-5.6 family (Sol, Terra, Luna): Sol is the most intelligent variant and leads in tests like Arc and ARC-AGI (92.5% on ARC AGI 2). The model excels at coding (better than Fable 5 and Grock 4.5), generates cleanly formatted documents, tables and graphics. Important: Sol has no castration like Fable and isn’t restricted to a small user group (Grasswing project). Security measures were extensively tested (700,000 GPU-hours red teaming), blocking 10x more potential attacks than before – however, Sol was already compromised days after release via jailbreak techniques (Unicode, Cyrillic).

  Overall assessment: Despite multiple new releases, there’s no dramatic leap in intelligence – most models rather converge toward Fable 5 or remain below Opus 4.8. GPT 5.6 Sol comes closest to Fable 5 but doesn’t significantly surpass it. Google Gemini remains surprisingly far behind.

  Tools/providers covered: Meta (Muse Spark), xAI/Elon Musk (Grock), OpenAI (GPT 5.6 Sol/Terra/Luna), Anthropic (Fable, Opus), Cursor (Coding Agent), GLM 5.2 (China). Format: Opinion/Reflection (comparative).

• Se usi ancora i prompt… devi vedere questa evoluzione
  7.7.2026, 16:51:25

  Summary: Loop Engineering

  The video covers the evolution from Prompt Engineering through Context Engineering and Harness Engineering to the current trend of Loop Engineering.

  Historical overview:

  • Prompt Engineering: Writing system prompts with precise instructions for the model.
  • Context Engineering: The model becomes an agent using tools (web, files, apps via MCP), filling the context window. Problem: With over 200,000 tokens, performance degrades drastically (context rot).
  • Harness Engineering: External structure that splits tasks into subtasks and uses markdown files (memory.md, agents.md) as persistent storage instead of constant context compression. Example: OpenClow uses a well-designed memory system with semantic search.

  Loop Engineering – the new approach:

  Boris Cerni and Peter Steinberger advocate: Instead of writing prompts, you should design loops that the agent automatically executes. Multiple loop layers are combined: the agent calls tools (loop 1), the harness controls subtasks (loop 2), everything is placed in an overarching loop (loop 3).

  Practical structure of a loop:

  • Trigger: Manual time (daily at 9 AM) or event-based (new GitHub issues, emails, news)
  • Execution: Harness engineering runs with all MCP servers and skills
  • Verification: Check whether goal is achieved
  • Memory: Write results to filesystem
  • Next loop: Start on new trigger

  Verification mechanisms (5 levels):

  1. Deterministic (true/false): Code compiles without errors
  2. Numerical constraints: Load time <100ms, accuracy >90%
  3. Delayed ground truth: Result measurable only later (e.g., LinkedIn engagement after days)
  4. LLM as arbiter: Agent evaluates itself (e.g., UI similarity to screenshot via comparison)
  5. Human checkpoint: Human validates each step

  Practical examples:

  • Web app for world championship, automatically updated with new news
  • Open-source app with automatic bug fixing via loop (GitHub issues trigger code improvement)
  • Auto-research: Trains ML model in loop up to 90% recall
  • Matrix multiplication: 10 optimization iterations led to 320x speedup via tensor cores

  Practical commands in Cloud:

  • /sloop [interval] [prompt] (time-based)
  • /sloop [prompt] / [verifiable goal] || max [attempts/time] (event-based)

  The concept isn’t pure marketing but works particularly well for verifiable, code-related tasks where the agent has clear measurable goals and can iterate independently.

  Tools/providers explicitly covered: Cloud Code (Boris Cerni), OpenClow (Peter Steinberger), Claude, GitHub, MCP servers; Format: Deep-dive/Tutorial, intermediate to advanced level.

Tech With Tim (3 new videos)

• From Software Engineer to AI Engineer job – The best career move for 2026
  12.7.2026, 12:16:44

  Summary: From Software Developer to AI Engineer

  The video argues that software developers already have 80% of the skills needed for AI engineering. An AI engineer at a large startup spends only 20% of their time on AI-specific tasks, with the remaining 80% being regular software development – building systems that run AI models in production. So it’s not about starting from scratch, but adding an additional layer on top of existing skills.

  The video clearly distinguishes AI engineering from AI research: not training new GPT models or writing model architectures (that’s ML research), but practically implementing existing models like GPT or Claude in real software systems via API.

  Skills you already have and can skip:

  Python and programming, Git/version control, APIs, databases, testing/error handling, Docker, deployment.

  What to learn – in this order:

  1. Mental model of LLMs: Tokens, context/context windows, temperature, message roles (system/user/assistant) – not the inner math, but practical understanding to see why a model responds a certain way.
  1. API & frameworks: Using OpenAI API and Anthropic API, multi-turn conversations, error handling. Understanding that models don’t just respond to user input but can also run in the background for database classification, summaries, etc.
  1. Prompt engineering & structured outputs: Writing system and user prompts that consistently deliver reliable results. Function calling so the model returns JSON/structured data, not just text.
  1. RAG & vector databases: Retrieval-Augmented Generation – bridging the gap between general training and specific user data. Converting text to embeddings, storing in vector databases like Pinecone or Chroma, retrieving relevant data on queries and feeding it into the model.
  1. Orchestration & agents: Frameworks like LangChain, LangGraph, etc. – combining multiple API calls, agents with multiple tools, state management, multi-step workflows. Here it becomes like real software engineering: error handling, retries, rate limiting, parallel execution.
  1. LLMOps: Deployment in production. Rate limiting, cost optimization, model routing, caching, monitoring/observability, evaluations. This is the part employers value most because your software engineering background really counts here.

  How to learn:

  Don’t just passively watch videos – you only retain 20%. Learn actively through projects from day one, not tutorial clones but real projects solving real problems. A portfolio with RAG chatbots, agents with real tools, semantic search – deployed with clean code and monitoring.

  Practical approaches: Internal transfer to AI features in your current job (much easier than starting fresh from outside). Use AI tools yourself (Cursor, Claude) to multiply your output and learn in the process. Interview prep: some Leetcode, system design, AI-specific questions.

  Explicitly mentioned tools/models: OpenAI API, Anthropic API, LangChain, LangGraph, Temporal, Llama Index, Pinecone, Chroma, Cursor, Claude — opinion/reflection with practical guidance.

• I Built an AI App to Analyze My Own Business Data (No Code)
  6.7.2026, 13:00:25

  Summary: Building a RAG application with Knime

  The creator shows how to build a Retrieval-Augmented Generation (RAG) application in under 30 minutes using Knime – a no-code, node-based editor – instead of spending hours coding.

  Use case: The creator wanted to analyze 200+ messages from students in his developer mentorship – search through them thematically and gain insights into which topics are relevant.

  How RAG works: Instead of hard-coded database queries (by date, keyword), data is vectorized (converted to embeddings). This allows semantic search – e.g., finding all messages about “interviews” without explicitly searching for that word. The vectors are stored in a vector database and later combined with an LLM to answer questions.

  Workflow 1 – Creating the vector store:

  • Read MongoDB data (connector + reader)
  • Convert JSON to table format
  • Filter empty rows
  • Store OpenAI API key via Knime Secrets (don’t hardcode)
  • Choose embedding model (OpenAI, small)
  • Use “AI Vector Store Creator” to generate vectors from the “clean_content” column
  • Save vector store locally

  Workflow 2 – Querying the vector store:

  • Load vector store (Model Reader)
  • Create two string widgets as input: query + username
  • Encapsulate these as a component (design visually with layout editor)
  • Convert input to table row
  • Vector store retriever: perform query, retrieve metadata
  • Clean data with expressions (combine only relevant columns)
  • Use group-by + concatenate to combine all matching prompts into one string
  • Pass final prompt (context + original query) as variable to Agent Chat View
  • Chat directly with the source via OpenAI GPT-4 Mini – e.g., “What do students say about interviewing?”

  Key Knime features:

  • Free to use locally (premium for deployment/team)
  • Nodes are pre-configured (OpenAI, MongoDB, embedding, etc.) – saves code
  • Workflows are auditable, repeatable, scalable
  • Export/share possible – others can open them locally
  • Suitable for both technical and non-technical users

  The creator demonstrates how to actually use the application: “Tell me what students think about interviewing” → The system searches contexts and responds with a summary.

  In conclusion: Knime (node editor, demo with OpenAI/MongoDB), tutorial.

• The Only Claude Code Plugins You Actually Need
  6.7.2026, 12:27:44

  Essential Claude Code Plugins and MCP Servers

  The video addresses the problem of tool overload: having more than about 50 tools at once causes Claude to make wrong choices and perform worse. The author instead presents a curated list of truly necessary plugins.

  Basics: Plugins are containers that bundle multiple things (slash commands, MCP servers, etc.), while MCP servers represent individual capabilities. To install, you use Claude Code in the terminal with /plugin and can choose between the official marketplace (maintained by Anthropic) and the community marketplace.

  The recommended tools:

  1. Language Server (e.g. Pyright for Python): Provides type inference and static type checking for better code quality and higher reliability of generated code.
  1. GitHub MCP Server: The official plugin doesn’t currently work – instead install the GitHub MCP Server as an alternative. It enables version control, checkpoints and remote repositories directly from Claude.
  1. Frontend Design: Instructs Claude to create a consistent design system with color palettes and typography when developing UIs.
  1. Tiger Data (Timescale DB): A PostgreSQL-based MCP server that lets Claude spin up real databases, create tables and manage queries. Optimized for time-series data – hyper-table partitioning can accelerate queries by a factor of 380. Free with a 30-day trial.
  1. Context7: Pulls live API documentation from the internet so Claude uses current (not outdated) framework documentation.
  1. Compose You: An aggregator for hundreds of tools and accounts (Gmail, Google Drive, Asana, Hacker News, etc.) where you connect once and use everywhere – also prevents context window bloat through on-demand tool discovery.
  1. Figma: Downloads Figma designs or authenticates directly so Claude can generate working code from design files.

  The video emphasizes: don’t install everything, just what you really need. The author’s closing statement: “You don’t get a ton of bloat with the tools, but these ones that I personally use that I find are extremely useful.”

  Format: Demo/deep-dive with practical installation steps; featured providers: Claude (Anthropic), Tiger Data/Timescale, and multiple plugins/MCP servers from the marketplace.

TheAIGRID (5 new videos)

• Gemini 3.5 Might Shock The AI World – All Gemini 3.5 Pro Leaks Explained
  11.7.2026, 13:00:19

  Summary: Gemini 3.5 Pro – Leaks and Expectations

  The video analyzes rumors and available information about the upcoming Gemini 3.5 Pro model, expected next week. A central theme is Google’s delay in model release cadence: while OpenAI and Anthropic publish new models roughly twice as fast (OpenAI has already rolled out GPT 5.4 and 5.5, Anthropic released Opus 4.8, Claude Fable 5, and Sonic 5), Google last released a multimodal model in May.

  According to the video, Google’s biggest deficit lies in coding and agentic tasks. While Google’s traditional strength is in visual and world understanding, there is a clear gap in coding capabilities. Available benchmarks (SWEBench Pro, Terminal Bench, etc.) suggest that Gemini 3.5 Pro would need to reach at least 69% on SWEBench Pro and significantly higher scores on Terminal Bench to remain competitive – ambitious targets given the competition.

  One concrete piece of information is an alleged 2-million-token context window, which would be twice as large as Anthropic and OpenAI models. However, the speaker warns that a large context window is only valuable if the model can use it effectively. For Google’s Generative UI strategy, the faster Gemini 3.5 Flash serves as the foundation – a model that generates user interfaces on the fly, though this doesn’t always seem sensible in practice.

  The video speculates that despite delays, Google hasn’t fallen completely behind and is still training its next frontier model. Since frontier models typically excel in one domain (Anthropic in coding, ChatGPT in mathematics), Google’s advantage could resurface in the multimodal area. Speculation from leaks and LM Arena tests suggests the model could be competitive in coding tasks, but confirmation comes only with release.

  Demo/Analysis video focusing on Google Gemini 3.5 Pro (speculative based on leaks and benchmarks); mentions OpenAI, Anthropic, Claude, GPT, and Gemini model line.

• The 10 Most INSANE Things Created by GPT 5.6 ( GPT 5.6 5 Use Cases)
  10.7.2026, 15:41:43

  GPT 5.6 Release: Benchmarks, Capabilities, and Real-World Applications

  The video provides an overview of the new GPT 5.6 models (specifically “GPT 5.6 Soul”) based on benchmarks and usage examples.

  Benchmark Overview: GPT 5.6 ranks just behind Claude 3.5 Sonnet in the Artificial Analysis Intelligence Index but is significantly cheaper. The key advantage lies in specific task categories: on the “Agents Last Exam” benchmark (testing AI agents’ ability to autonomously complete full professional projects like animations in Adobe After Effects or scenes in Unreal Engine), GPT 5.6 Soul outperforms the competition. On the DeepSeek SWE Benchmark (coding agent test with real software repositories and multi-file edits), GPT 5.6 Soul achieves 73% pass rate, beating Claude 3.5 Sonnet.

  Cost Efficiency: GPT 5.6 Soul costs approximately $8.40 per million tokens, while Claude 3.5 Sonnet costs $21.63 – roughly three times cheaper for similar intelligence performance.

  Real-World Examples: Mathematicians solve previously unsolvable problems, a small business owner automatically builds a sales dashboard for her brand, a broccoli farmer automates greenhouse ventilation and digital field marking. Community projects showcase impressive creations: a functioning Google Earth-like program, procedurally generated cities, a complete voxel-based Manhattan model.

  Generative UI: OpenAI demonstrates interactive user interfaces generated on the spot – such as animated Spirograph explanation videos or tokenizer visualizations that convert natural language requests directly into functional UIs.

  Games and Design: GPT 5.6 Soul generates fully functional browser games (e.g., “Salt Wind” for driving through gates, “Tiny Voids” with cube mechanics) and refines details on its own. Frontend design becomes more tasteful and less “AI-generated.”

  Fast Mode: A special version runs with increased token throughput – Cerebrus is expected to achieve 750 tokens per second in the future, drastically accelerating agent tasks.

  Claude 3.5 Sonnet vs. GPT 5.6 Soul (User Opinions): Early testers report that Claude offers better foundational intelligence for complex projects and finds solutions autonomously; GPT 5.6 Soul feels like a “smaller, heavily trained model” that sometimes needs guidance but excels at security tasks and is more token-efficient. Comparability is debated: GPT 5.6 might be better compared with Claude 3.5 Sonnet (not the upcoming Opus successor).

  ChatGPT for Work: A newly released agent in ChatGPT for non-technical users that executes complex tasks across apps and workflows – generates Slides, documents, web apps, and independently breaks down multi-hour projects into smaller steps.

  OpenAI / GPT 5.6 Soul and Claude 3.5 Sonnet by Anthropic; demo and user tester opinions/comparisons.

• Grok 4.5 Just Shocked The AI Community – SpaceXAI Grok 4.5
  9.7.2026, 11:00:20

  Grok 4.5 – Benchmarks, Strengths, and Justified Skepticism

  Grok 4.5 is positioned as a frontier-level model at affordable costs and high speed. On benchmarks like Deep Seek SWE, the model outperforms Claude Opus 4.8 Max – at half the price. Terminal Bench shows even 83.3% improvement over Opus 4.8 Max at an input price of $2 and output of $6 per million tokens, well below the $5/$30 costs of comparable frontier models. Grok 4.5 is classified as Opus 4.7 equivalent, with 80 tokens per second and four times better token efficiency than Opus 4.8 Max.

  The Cursor Benchmark result is noteworthy, suggesting Fable-level performance – but with an important caveat: an earlier snapshot of the Cursor codebase was accidentally included in training, contaminating this benchmark. On the Harveys Legal Agent Benchmark, Grok 4.5 leads with just $2 cost per test, and on GDP Val (real work tasks), it performs 29% better than GPT-4o and Opus 4.8 Max.

  In practical coding tests, however, a different picture emerges: multiple users on Twitter report that actual code quality falls short of demonstration videos. A direct comparison on shader rendering showed Opus 4.8 superior, while a rocket launch prompt revealed more mixed results between the models. The consensus is nuanced – Grok 4.5 “holds its own” in some tasks but doesn’t consistently impress, pointing to “jagged intelligence” at the frontier.

  Planned updates: Imagine integration for video generation in agent mode and 1-million-token context window by next week.

  xAI/Grok, Claude Opus, GPT-4o, Anthropic, Google Gemini, Claude Fable; demo with critical analysis.

• OpenAI Just Introduced The Future Of AI Voice – GPT- Live
  9.7.2026, 03:00:37

  OpenAI has launched GPT Live as a family of voice models with two variants: GPT Live 1 (main model) and GPT Live 1 Mini (lighter variant for free users). The core feature is full duplex interaction, where the model processes incoming and outgoing speech simultaneously – it can interrupt, handle pauses better, and respond to small confirmations like “yes” or “understood” while the user is still speaking.

  An important architectural feature is delegation for deeper tasks: GPT Live 1 provides fast natural responses while GPT 5.5 does heavy lifting in the background (such as research). This allows the model to continue the conversation while multiple tasks run in parallel.

  Benchmarks show massive improvements: on the GPQA test, performance jumped from 45% (old voice mode) to 70–80% (GPT Live Mini/Medium), achieving GPT-5-level intelligence with voice capability. On the web search test, performance improved from under 1% to 30–75%. On the Voice Telecom Test (simulating complex, realistic phone calls with accents, background noise, interruptions), GPT Live 1 reaches around 40% – nearly double the old voice mode – and with higher reasoning models roughly four times better.

  The model can also work with images: users can show photos and ask the model to analyze them (demo shows: outfit critique for meeting parents of partner).

  Another feature is mini browser integration: the model can open small apps during conversation and display results visually – such as retrieving weather in Mexico City, showing soccer game schedules, or recommending bars for watching soccer.

  Real-time translation works seamlessly: the model listens to one language and simultaneously translates live into English while naturally interacting with speakers.

  Another demo feature is temporal awareness: the model understands time in conversation and can handle it – such as setting a 30-second timer, counting up, and guiding the user through making coffee.

  The video argues that no other AI companies are currently working on this combination of voice realism and true intelligence, and that hands-free voice interaction at GPT-5 level will significantly change productivity.

  Format & Context: OpenAI’s GPT Live (models: GPT Live 1, GPT Live 1 Mini, GPT 5.5 for background delegation), demo and news update.

• Metas New Ai Can Read Your Brain – (Brain2Qwertyv2)
  6.7.2026, 15:18:24

  Meta’s Brain-to-Text AI: What It Really Is and Why It Still Matters

  Meta has developed a system that translates brain activity into written sentences – without a chip, without surgery, only with external sensors and an AI model. However, the technology is far more limited than headlines suggest.

  What Brain-to-QWERTY Actually Does: The system only works in controlled laboratory settings with specialized MEG hardware (magnetoencephalography). Participants listened to sentences, waited for a signal, and typed them out. The AI learned the relationship between brain activity and typing movements – it’s not reading arbitrary thoughts, but reconstructing speech from noisy brain signals coming through the skull using external sensors.

  Performance Numbers: The system achieved an average 61% word accuracy (39% error rate), with the best participant reaching 78% accuracy. This is a major breakthrough for non-invasive brain measurement, but still falls short of everyday usability – errors would be frustrating.

  Why Accuracy Improved So Much: The system uses a multi-step process (encoder, aligner, language model) that connects brain signals with word-level representations and then employs a large language model that leverages semantic context. This allows the system to do more than guess letters – it can infer which sentences make sense given surrounding words.

  The Data Basis: Only nine healthy, right-handed, English-speaking participants were trained – approximately ten hours per person, 22,000 sentences total. This is controlled, but also: small sample size, no patients (who actually need the technology), and not representative of messy reality.

  The Hardware Problem: MEG devices are large, expensive, specialized scanners – not portable, not usable in homes. Although smaller wearable sensors are on the horizon, they are not yet market-ready.

  No Real-Time Word-by-Word Output: The current model works at sentence level, not word-by-word – it needs the whole sentence for context. This means users don’t see each word appear live; they must wait for sentence completion.

  The Scaling Effect: Accuracy improved with more data and showed no clear saturation point. This is promising, but collecting brain data is expensive, time-consuming, and sensitive – not like scraping Internet text.

  The Real Societal Question: The biggest long-term risk isn’t this system in detail, but what happens when brain-computer interfaces become more accurate, portable, and widespread. Brain data could become the next commercial data stream. Clear consent rules, data control, and usage boundaries will need precedent to regulation.

  Bottom Line: Meta hasn’t built a finished mind-reading product, but a solid research system bringing non-invasive brain-to-text closer to reality. The gap between invasive and non-invasive interfaces could actually close – which could be life-changing for people without speech capability, but also raises data governance questions.

  Explicitly Named Tools/Actors: Meta, MEG hardware, large language models (not specifically named); Format: deep-dive (opinion with technical depth and societal contextualization).

Theo – t3․gg (6 new videos)

• The unexpected death of Codex
  11.7.2026, 05:07:16

  OpenAI has integrated the Codex app into ChatGPT and announced a rebranding initiative called “ChatGPT Work,” which fundamentally frustrates the speaker. The new application combines multiple modes: a traditional chat mode (now as a popup window), a Codex mode for developers, and a Work mode for non-developers — similar to Anthropic’s Claude Code and Co-work approach. Technically, the overhaul brings some improvements: unified plugins, better browser integration with authentication and multi-tab support, improved computer-use capabilities (faster, more stable, with multi-tab browsing), the ability to edit files directly and review PRs, plus a new “Ultra” mode. Performance improved significantly.

  However, the speaker sharply criticizes the marketing and branding decision: Codex had become a standalone, developer-beloved brand — with 5x+ monthly growth, Super Bowl advertising, and strong community attachment. Renaming it to a toggle within ChatGPT destroys this identity and confuses users who installed the app update and suddenly found ChatGPT instead of Codex. The speaker emphasizes that people used Codex precisely because it wasn’t ChatGPT — a focused, developer-built tool for developers. The merger undermines the brand and motivation of both users (who find it harder to recommend the tool) and internal teams who specifically wanted to work on Codex.

  In response to this uncertainty, the speaker built T3 Code — a fully open-source clone that thousands use and that a quarter of users have forked or patched. This allows the community to remain independent of OpenAI’s decisions. The takeaway: a technically sound integration with poor business and communication consequences that destroyed a rising, independent developer brand.

  Format & Tools: Demo + opinion/reflection; explicitly discussed: OpenAI ChatGPT / Codex, Anthropic Claude Code / Co-work, T3 Code (open-source alternative), Blacksmith CI (sponsor), DNSimple (sponsor).

• So I’ve been using gpt-5.6 for awhile…
  10.7.2026, 04:11:17

  Summary: GPT 5.6 — Early Access and Projects

  The creator had access to GPT 5.6 for over a month and a half before public release, spending approximately $180,000 to $240,000 on inference. The video presents not a formal review, but documents concrete projects he implemented with the model — based on this extreme usage, he wants to show what the model is actually capable of.

  Core performance insights:

  • Significantly better computer-use and browser navigation abilities than GPT 5.5: The creator could perform complex tasks like dashboard configurations or web scraping with drastically fewer control inputs
  • Context management: Unlike GPT 5.5, the model doesn’t get lost in long threads and can autonomously execute hour- or multi-day tasks without losing context
  • Problem-solving in difficult scenarios: A highlight was autonomous repair of corrupted boot partitions on a Linux system — a task typically requiring manual research and patience

  Implemented projects (selection):

  • Lakebed (cloud platform): The model executed migration from monolithic JavaScript files to structured TypeScript, built CI pipelines, authentication (CLI login), and whitelist access systems (~$30,000 in fast mode, but partially included on the standard plan)
  • T3 Code (agent management platform): Complete rewrites of the React Native app into native Swift and AppKit (2–4 hours each, fully functional); integration of computer-use loops for direct simulator testing
  • Hermes Agent in Rust: Complete rewrite from Python to Rust with about 50% of the original’s features, running autonomously; uses 15 MB RAM instead of the original large Python footprint
  • TypeScript Compiler Port in Rust: A functional TypeScript-to-JavaScript transpiler in Rust that’s up to 18x faster than the Go version, though type-checking is incomplete (~195,000 lines of Rust)
  • FS2 Project (Dropbox-like cloud sync): A single goal run consumed 71.2 billion tokens (~$91,000 in fast mode); the model autonomously registered with PlanetScale to create necessary infrastructure
  • Boot drive and network setup: Automation for repairing Linux BIOS issues, automatic network machine configuration via a fleet repository

  Other work:

  Marketing website rebuild, skatebench overhaul, Prime Day deal automation with browser use, terminal token tracker, 3D game prototype (Fish Slop), mini-Presto alternative.

  Important limitations of this analysis:

  The creator deliberately didn’t cover benchmarks, pricing comparisons, Fable comparison, reasoning modes, or the Fast version (1.5x slower than announced) — those will follow in separate videos. He emphasizes that the $180–240K usage is neither realistic nor recommended, but rather an experiment pushing boundaries.

  Conclusion:

  The video is less a review than a demonstration: GPT 5.6 proved to be a workhorse that’s more autonomous, context-stable, and dramatically better at computer use than its predecessor, enabling complex system administration, code migration, and multi-day goals without frequent steering.

  Explicitly mentioned: OpenAI / GPT 5.6, BrowserBase (sponsor), Fable, Codeex, Hermes Agent, T3 Code, Lakebed, Claude (Opus 48), Grok — Format: Demo/documentation + opinion, unusually deep research and usage-intensive.

• Oh no (the new Grok model is good)
  9.7.2026, 05:04:08

  Grok 4.5 Review: Impressive on code, but not quite frontier-level

  SpaceX and XAI released Grok 4.5, a new model that proves impressive despite aggressive claims. The model was trained on tens of thousands GB300 GPUs, is a complete rebuild (1.5 trillion parameters instead of 500 billion), and was co-trained with Cursor, now part of SpaceX. On benchmarks it performs exceptionally well: it’s close to GPT-5.5 and just below Fable 5 on key code metrics, significantly outperforms Opus 4.8, and beats all Google models.

  Pricing is revolutionary: $2 per million input tokens and $6 per million output tokens (double over 200k context) — roughly 5 to 10 times cheaper than Fable. The model is also remarkably token-efficient, requiring only around 2 million tokens for code tasks versus 7+ million for Fable and Opus. One caveat: Grok 4.5 benefited from Cursor codebase snippets accidentally included in training data, which is why it performs surprisingly well on Cursor Bench — but this was transparently disclosed.

  In practical 24-hour testing on complex prioritization and multi-PR work, the model delivered consistently good results, remained focused even with intentionally poorly-phrased follow-up prompts, and was significantly faster in feedback than many alternatives. Also impressive was 3D modeling capability in WebGL/Three.js — better than observed in Fable or GPT-5.6 variants.

  One major limitation: Grok 4.5 lacks the orchestration and sub-agent capabilities of the new generation (Fable, GPT-5.6), which the speaker describes as “the best PS2 game, but PS3 is already out.” Nevertheless, the model represents a remarkable leap — SpaceX has gone from an outsider to a real competitor for Anthropic and OpenAI.

  Discussed models: Grok 4.5 (SpaceX/XAI), Fable 5, GPT-5.5, Claude Opus 4.8, Gemini; Format: Deep-dive + demo.

• I need to rant about local models
  7.7.2026, 20:44:25

  Summary: Why local open-weight models are overrated

  The creator argues there’s a fundamental misconception about open-weight models: just because a model is openly available doesn’t mean you can run it at home. Models like GLM-5.2 — impressive and competitive with Claude Opus — require 400 GB VRAM at full precision (1.5 TB in BF16). Even quantized versions remain at 200+ GB, which is unrealistic on consumer hardware.

  Hardware reality: An RTX 5090 has only 16 GB VRAM. An RTX 6000 Pro costs $13,000 for 96 GB. A small system with four Pro-6000 GPUs costs $75,000 — and can barely run GLM-5.2. Even a high-spec 128-GB MacBook can only run large models slowly, three times slower than the 5090 for smaller models that fit in its VRAM. Total costs (hardware + power — averaging $5/day for a 5090 in San Francisco, so $2,000/year) make self-hosting economically questionable.

  Another issue is parallelism: The creator runs 1–40 parallel agents daily. Locally, you buy hardware for a fixed number; any idle time wastes money, any overload causes bottlenecks. Cloud removes this problem entirely.

  The real strength of open-weight models lies not in local deployment, but in market competition: OpenRouter shows GLM-5.2 from different providers (Fireworks, Deep Infra, Friendly) with varying speeds and prices — from 660 to 1,025 per million output tokens. This enables genuine choice, which closed APIs don’t offer.

  But be careful with pure price comparison: GLM-5.2 often consumes 3x more tokens than Opus to solve the same task. On Deep SWE, an Opus-4.8 run costs around $8, a GLM-5.2 run about $4 — only 2x cheaper, not 10x as token prices suggest. Deep Seek R1 and other open-weight models are genuinely innovative and drive the industry forward; the creator loves these developments. His point: If you want to play with them on your own hardware cluster — great. But the notion that ordinary people can escape Anthropic and OpenAI with them is “insane” and harms the open-weight narrative. The solution is cloud hosting of these models from cost-effective providers, not local inference.

  No AI tools or providers named; Opinion/reflection focusing on critical analysis of the hype-reality gap.

• A proper guide to Fable 5
  6.7.2026, 01:41:48

  Summary: Working with Fable — workflows, costs, and real-world shipping

  The creator shares his intensive experience with Fable after it returned to Claude Code subscriptions. After initial enthusiasm, temporary frustration with Opus and GPT-5.5, and a period without Fable, he’s now convinced this model represents a category shift — not just an iterative upgrade.

  Core problem and solution: Using Fable as a direct upgrade to Opus doesn’t work. The crucial difference is in scope: The model can execute end-to-end implementations, testing, verification, and sub-agent orchestration independently — meaning significantly longer autonomous work chains.

  Cost optimization (main part of video):

  • Reasoning Effort: Never use X High, Max or Ultra Code. High is optimal; higher levels cause overthinking per step, are more expensive, and often produce worse output. High is also Anthropic’s standard.
  • Multi-model routing: Let Fable recognize when GPT-5.5 (via Codex CLI) is better suited for token-intensive tasks like logs, PDFs, computer use. The creator invested ~1 hour here and achieved 10x better cost efficiency.
  • Subscription limits: Codex has generous weekly limits (~$14k inference/month); the creator used only ~15% of that in one week. Claude Code Sub stayed at 40% with massive usage.
  • Lakebed project result: ~$150 for 11–12 PRs in one thread, though it could have cost thousands.

  Claude.md and agent design:

  • A glossary for “Intelligence” (how hard a problem is unsupervised) vs. “Taste” (code quality, API design, UX).
  • Explicitly communicate: “Intelligence > Taste > Cost,” but cost is only a tie-breaker; Fable can use smarter models if output doesn’t meet standards.
  • Sub-agents vs. Workflows: Sub-agents spawn other agents for parallel work (e.g., 5 files → 5 reviewers). Workflows are deterministic JavaScript pipelines for fan-out + verify — helpful for triage, but not for checkpoint-driven processes with decision points.

  Skills (limited number):

  • Codex Review: Request 5.5 independently on diffs/commits.
  • Codex Implementation: Bounded work on work trees.
  • Computer Use: Fable can start screenshot-based verification via Codex (saves Fable’s own computer-use tokens).

  Workflow example — Lakebed cleanup:

  1. Prompt: “Investigate & review open PRs” + instruction to use workflows for multi-reviewer triage.
  2. Fable spawned 16 investigators (1 per PR) + Opus judge panel → clear verdicts on 14/16.
  3. Then: HTML plans for each feature/fix, again with sub-agent review.
  4. Final goal: “Complete all work discussed” — rebase, merge, close, implement plans. Model can merge to staging/main (only staging auto-deployed), but not prod.
  5. Result: 5 hours runtime, ~20–30 stale PRs resolved, one month’s roadmap implemented that the creator previously had blocked.

  Daily workflows (T3 Code integration):

  • Creator set up T3 Code on phone + Tailscale + remote connection to Macs/Linux boxes.
  • Workflow: Find bug on phone → screenshot → open T3 Code → set model to Fable → spawn work tree → PR auto-filed → test.
  • Time-tracking learnings: Simple fixes in <3min = safe merge. >1.5h = architecture problem, needs deeper thought.
  • Fable could even merge multiple incomplete work trees in parallel (conflicts auto-resolved).

  Important disclaimers:

  • Prod deployment stays manual (only staging auto).
  • Creator had Fable clean up pre-existing staging bugs via other models before starting Lakebed work.
  • Don’t blindly merge all auto-generated code; judge output by complexity time, architecture impact, and model accuracy.

  Sponsor integrations: Clerk for auth + billing (alternative to Stripe setup), Infinite Red for React Native/mobile app modernization, T3 Code (open source, $250k creator investment so far, no monetization planned).

  Conclusion: The creator demonstrates a fundamentally new working model with Fable — not prompt perfection, but multi-agent orchestration, cost-aware model routing, and goal-driven long-running threads. Video length reflects the wealth of practical configuration details, not repetition.

  Tools/models: Fable, Anthropic Claude (Opus 4, Sonnet 5), OpenAI GPT-5.5, Codex, T3 Code, Clerk, Tailscale — Format: Deep-dive / tutorial hybrid, advanced difficulty (aimed at developers with agent basics).

• GPT-5.6: The Review
  12.7.2026, 11:06:01

  Summary: GPT-4o as a comprehensive review

  The creator evaluated GPT-4o (not 5.6 as the title suggests, but the current model line with Soul, Terra, and Luna) using benchmarks and aggregated user experiences. Measurable strengths: Soul achieves 73% on DeepSeek SWE, the highest score ever recorded, at under $22 per task (competitors over $800), and 53.6% on AgentLab Exam — over 13 points better than competing models; Terra and Luna also beat predecessor models at 1/16 of the cost; Coding Agent Index: Soul sets new state-of-the-art at 80 points; computer use is industry-leading.

  Core strengths: Determinism — the model doesn’t give up and works through persistent problems; significantly better context management and fewer “lost in context” errors; superior computer-use capabilities; frontier-level sub-agent orchestration; token efficiency even on long runs; better understanding of user intent than predecessors.

  Weaknesses: Writes too much code by default and generates unnecessary tests; too “determined” — sometimes unnecessarily reworks things or uses questionable workarounds; only moderately improved design skills; understands its own limits poorly and argues stubbornly when it’s made an error; token burn on long tasks or with multiagent reasoning enabled (“Ultra”); confusingly many options (Soul/Terra/Luna × reasoning level).

  Model recommendations: Soul High as default for complex tasks; Terra Medium as budget option, particularly good for code review and iterating with user feedback; Luna only for tool orchestration by other agents, not for manual selection.

  Community sentiment: Strongly polarized — many report dramatically increased token usage and “it’s the best I’ve ever used”; others swear by competing models. The creator emphasizes that a dedicated Fable-vs-Soul analysis is coming separately and hints his conclusion might be surprising.

  Critical notes: Aggressive safeguards sometimes block legitimate requests; Ultra mode and Fast mode cause extreme token costs; the sandbox environment limits to ~200K tokens instead of available 350K; ProVersion has 30+ combinations of models, reasoning levels, and modes, making selection difficult.

  The review is substance-rich with concrete benchmark numbers and aggregates early-tester feedback (including Dax, Mitchell/Terraform, Tim/Next.js team), but remains somewhat superficial on design assessment and deliberately holds back comparison details for future videos.

  OpenAI models (GPT-4o Soul/Terra/Luna with various reasoning levels) and Anthropic Claude mentioned for comparison — Deep-dive review video.

Tim Carambat (2 new videos)

• Magic Echo Is A Free & Better Way To Control Your Computer With Voice
  8.7.2026, 17:00:09

  Summary: Magic Echo – Anything LLM

  Timothy Carenbat introduces Magic Echo, a feature from the “Magic Features” suite of Anything LLM that brings AI capabilities directly to your operating system – accessible via hotkey at mouse position, completely local to your device.

  Magic Echo functions as intelligent dictation software with several core capabilities:

  Voice Commands allow you to link phrases (e.g., “PRD Template”) with longer text blocks that can then be inserted via voice. The system also recognizes slightly mispronounced terms. Custom Vocabulary helps improve transcriptions for specific words or branding terms.

  Advanced Settings include: configurable hotkey (Mac: Option Z, Windows: Alt Z), choice between Smart Transcription (text processed by a local LLM) or Raw Transcription (pure transcription only, similar to Super Whisper), silence detection for auto-submit, and widget size. Extended Dictation (Shift + Option Z) enables longer pauses without automatic submission.

  On-Screen Awareness is the key differentiator: The tool can see screen content and answer context-dependent queries, even when not explicitly stated – for example, providing feedback on open documents or responding to multiple windows simultaneously. This requires a vision model. Timothy demonstrates this with Figma comments and resume feedback in Chrome and Apple Pages, with the system automatically capturing relevant content and delivering structured responses.

  The feature runs on local models (Ollama, LM Studio, Llama CPP) and supports external LLM providers. Timothy uses a Quen 3 8B VL model for the demo and notes that smaller models (0.6–0.8B) are less reliable, while 4B–8B models deliver good results.

  Pricing Model: Magic Echo and all Magic Features are available in the free tier with limitations (e.g., Raw Transcriptions and Voice Commands unlimited, Smart Transcriptions limited). The Pro tier enables unlimited use of all features. Timothy explains this as a sustainable business model for his small team while keeping core functionality free.

  “Past Echoes” stores all previous transcriptions and outputs back in the Anything LLM interface.

  Demo with Anything LLM Desktop Pro, which can be tested for free without paywalls – Tutorial/Demo.

• An Open Source Project To Give Agents Computers | Open Computer
  7.7.2026, 17:45:34

  Timothy Carambat, founder of Anything LLM, introduces a new project called Open Computer – a user-friendly solution for secure agent execution in virtual machines.

  The Problem with Previous Agent Harnesses: Agent harnesses (like OpenClaw, Claude Code or Hermes Agent) are tools that work based on skills (files that show models how to execute actions). The core problem: they either require very large context windows (64k–256k), which excludes local models with typically 16k–32k. Additionally, agents theoretically need access to the entire computer to be useful – creating critical security risks. Solutions like “dangerously skip permissions” allow agents to arbitrarily delete data or cause harm.

  The Security Question – Containers vs. VMs: Docker or Podman containers offer only false security since they share the host kernel and malware can escape. True security requires virtual machines (VMs) with their own kernel. Microsoft (MXC), Nvidia (Open Shell), Apple, and even Docker have therefore developed micro-VM solutions.

  Open Computer – The Solution: The project packs agents into lightweight VMs (base ~3 GB, only megabytes per agent) with customized Linux (visually styled as Windows 10 so regular users feel at home). The VMs run on QEMU with minimal CPU load – you could run 10+ simultaneously on one machine. Core features:

  • No Screenshots/Coordinate Clicks: Instead, the Linux Accessibility Tree is used – significantly more token-efficient than “Computer Use” (which Carambat criticizes as “token-hungry” and “wasteful”).
  • Integrated Browser + Native Apps: The agent can navigate in Chrome or use Flatpak apps like a BMI calculator.
  • Human-in-the-Loop: When the agent gets stuck, it can ask the user for help via UI notification (e.g., for solving CAPTCHAs). This focuses on human collaboration rather than black-box automation.
  • Agent Memory: Global and user-specific memories that accumulate across sessions.
  • Token Efficiency: Carambat uses Pi.dev as harness with custom tools. In the example, creating an Nvidia stock GIF with sub-agents consumed 1.5 million tokens total (on local Qwen 3.6-35B via DGX Spark).

  Live Demos:

  1. GIF of Nvidia stock price over 10 years – Agent spawns sub-agents, fetches data, creates animated GIF with verification report.
  2. HTML/PDF report about Anything LLM – Agent researches in browser, finds personal info (GitHub Stars, founder background like fire safety training), delivers professionally formatted outputs.
  3. BMI Calculator App – Agent opens native Flatpak app, uses Accessibility Tree, converts lb→kg and ft→cm, auto-fills fields, provides screenshot.

  The project is open source, distributed as part of Anything LLM, and will be expanded long-term into its own operating system – so each agent has its own sandbox and doesn’t access the user’s host computer.

  Conclusion: Carambat demonstrates Anything LLM with local Qwen model as practical demo, criticizes screenshot-based “Computer Use” and instead presents an accessibility-tree-based, VM-isolated system. – Deep-Dive / Demo.

Unsupervised Learning (2 new videos)

• A Conversation With Sarit Tager
  9.7.2026, 16:00:23

  Summary: Context and AI as the Core of Modern Application Security

  The conversation addresses the transformation of application security through AI/GenAI and the central role of organizational context.

  Core Problems with Traditional Scanning:

  While AI models and LLMs increasingly detect security issues, they don’t solve two critical problems: (1) Secure remediation without breaking production functionality – LLMs often suggest generic or harmful fixes (e.g., removing flags that destroy functionality), and (2) Alert fatigue – more models find more problems, not fewer. The central question remains: What actually needs to be fixed?

  Context as Solution:

  The guest argues that AI-driven prioritization only works when you understand deep organizational contexts: business objectives, which data is critical, where risks are acceptable, who deploys which assets. With this knowledge, prioritization becomes “obvious” – no longer based on CVSS scores alone, but on real exploitability, deployment status, and organization-specific factors. The system also learns from past fixes (e.g., “this repo always uses Service X for input validation”).

  Idea-to-GitHub Fast Track:

  GenAI creates new roles: product managers and designers write intentions, not code. The guest describes this as “PRD-to-Production” or “Concept-to-GitHub” – similar to the farm-to-table concept. However, an open question remains: Does the developer understand their own code well enough to patch it if AI wrote it?

  Context Sources from the Platform:

  • Endpoints (XDR/Endpoints)
  • Code & Supply Chain (Repos, Pipelines, Tools)
  • Cloud (Network, Infrastructure, Identity, Vulnerabilities via CSPM, KSPM)
  • Attack Surface (ASM shows whether containers/workloads are actually externally reachable)
  • Organizational Definitions (Goals, Applications, Business Context)

  These signals automatically connect into a graph. Example: ASM detects that a container is not reachable from the internet → Application security issue is re-evaluated as “not externally exploitable.”

  Future Scenario (1–3 years):

  Three layers: (1) Developer environment – guardrails and LLM context while coding, even agents blocking malicious code, (2) Existing code – automatic upgrades (minor), human approval (major), based on API usage and deployment graph, (3) New threats – automation for millions of daily Log4Shell-like attacks, no manual spreadsheets. The system learns enterprise-wide (like Borg): one detected pattern is proactively signaled to all developers.

  Product Outlook:

  The guest mentions upcoming releases with multi-model approach (different AI for scanning, remediation), context integration, and developer-focused automation – more security with less manual effort.

  The platform is Palo Alto Networks (Cortex Cloud, XDR, CSPM, KSPM, ASM mentioned); format: live Q&A/interview discussion, technically sound for CISO/AppSec professionals.

• A Conversation With Murali Rathinasamy
  6.7.2026, 16:00:00

  Summary: Cisco Hybrid Mesh Firewall and Unified Security Policy

  Murali, Senior Director of Product at Cisco for Network Security, discusses the evolution of network security and Cisco’s new approach to centralized management of heterogeneous firewall environments. The central problem: enterprises today no longer rely on a single perimeter firewall, but on distributed enforcement points (firewalls from different vendors, cloud provider firewalls, switches, container agents, DPUs) that must be coordinated. Traditionally this means: manually translating and implementing policies multiple times across different devices – error-prone and time-consuming.

  Cisco’s Hybrid Mesh Firewall offers centralized policy management through a single interface (Mesh Policy Engine in Cisco Cloud Control) that automatically translates and distributes policies across different enforcement points – currently supporting L3/L4 policies (IP-based, port and protocol). It supports Cisco firewalls (ASA, FTD) as well as third-party vendors (Palo Alto Networks, Fortinet, Juniper), with planned extensions to AWS firewalls, Checkpoint, and other platforms.

  A second aspect is agentless segmentation via Secure Workload: the system collects Netflow, firewall logs and CMDB data, applies machine learning to create dependency mappings, and automatically generates zero-trust policies based on observed traffic patterns – without forcing agents. Example: port 445 (SMB) gets blocked if file traffic was never observed, without impacting applications. A third element is AI Defense: the solution integrates into existing enforcement points to identify which AI models (ChatGPT, OpenAI, etc.) users and applications are using, and can enforce guardrails (e.g., PII blocking in prompts/responses) without deploying additional devices.

  Murali emphasizes that Cisco as a full-spectrum provider (firewalls + segmentation + visibility) differentiates from competitors offering only one. The roadmap aims for unified policy management: executives/CISOs define intents in human language (e.g., “financial data stays in segment X”), the engine translates that into concrete policies across different layers. Integrations with third-party components (CrowdStrike, Intune) are also planned.

  Mentioned Cisco Products: Secure Firewall, Secure Workload, Secure Access, Hypershield, Nexus Smart Switch; Technologies: Encrypted Visibility Engine, NetFlow/IPFIX integration, machine learning for dependency mapping, AI Defense. External Partners: Palo Alto Networks, Fortinet, Juniper, AWS. — Interview/deep-dive, advanced technical level (network and security architecture).

WorldofAI (8 new videos)

• Claude Opus 5 LEAKS, GPT-6 ALREADY, Kimi K3 Soon, Fable 5.1, NEO Hands, & More! AI NEWS
  12.7.2026, 06:03:18

  The video covers the latest AI developments and announcements:

  Claude Honeycomb / Opus 5: A mysterious Anthropic model was briefly spotted in Cursor and could be an early version of Claude Opus 5. It offers a 1-million-token context window and a high-reasoning mode, but appeared less impressive than expected in tests. Leaker Chat discovered that the model falls back to Claude Opus 4.8 when security violations occur, suggesting its higher hierarchy.

  GPT-6: OpenAI has allegedly already trained a completely new, larger base model from scratch. Early reports suggest GPT-6 is drastically more capable than Fable 5 and could launch this month or next. GPT 5.6 is positioned as the final model of the 5.0 series. Anthropic is pursuing an opposite strategy by further refining its existing Fable-5 model rather than building from scratch.

  Gemini Delay: Google has again postponed the release of Gemini 3.5 Pro (now aimed for end of July). The new Brev25 base checkpoint performs weaker on code and hallucinates the knowledge cutoff date, while older checkpoints worked better.

  Kimi K3: Reportedly launching next week, according to leaks from the Moonshot AI team.

  Elon vs. OpenAI CEO: Elon Musk and Sam Altman exchange public criticism on X after Apple sued OpenAI for theft of trade secrets in AI hardware.

  1X Neo Robot: The new tendon-driven hands with 25 degrees of freedom are designed to achieve human dexterity and enable precise physical tasks like opening doors or folding laundry.

  Claude (Anthropic) and GPT-6 (OpenAI) were explicitly discussed; news update.

• Claude Code – Document Parser Will Revolutionise Complex PDF Data Extraction!
  11.7.2026, 05:16:26

  Summary: Document Processing with Upstage Studio and Claude Code

  The video demonstrates how to integrate Upstage Studio – an enterprise document processing tool – directly into Claude Code or Cursor via MCP (Model Context Protocol) to automate complex document tasks. The problem: pure LLMs fail at PDF extraction, table processing, and complex layouts. Upstage provides a unified API that combines OCR, classification, and structured data extraction in one step.

  Setup: Create an account at Upstage Studio, obtain an API key through the Upstage Console, install the MCP server package (requires Python 3.10+), and connect it with Claude Desktop or via the claude mcp add upstage command.

  Practical applications demonstrated in the video:

  • Invoice Parsing: A sample invoice PDF is converted to structured data in under 30 seconds – all amounts and details correctly extracted.
  • Batch Extraction: Multiple invoices are automatically analyzed and exported to Excel – work that would take hours manually.
  • Upstage Studio Dashboard: Enables document summaries (demo with 470-page document), compliance checks across multiple files, and question-answering based on document content.
  • File Sources: Files can be uploaded from device, Google Drive, Gmail, Outlook, or URLs.

  Special feature: The solution doesn’t require separate OCR, classification, or extraction tools – everything runs through a unified API, with Claude Code as the orchestrator for more complex workflows.

  Tools and providers used: Claude Code, Upstage Studio, Upstage MCP; Format: Tutorial/demo with live examples; Target audience: Developers with document-intensive workflows (finance, insurance, legal).

• GPT-5.6 IS HERE! BEST AI Model Ever? Beats Fable, Faster, & Cheaper! (Fully Tested)
  10.7.2026, 06:44:09

  Summary: GPT 5.6 Family – Soul, Terra, Luna Launch

  OpenAI has launched the GPT 5.6 family with three model variants: Soul for complex agent-based work with planning and reliable follow-through, Terra as a balance between performance and cost for professional everyday tasks, and Luna for fast execution of high-volume, well-defined tasks.

  In benchmarks, GPT 5.6 Terra outperforms the older GPT 5.5 at lower costs. Luna achieves near GPT 5.5 performance for under half the estimated API costs. The flagship Soul sets new standards according to OpenAI: It achieves 53.6% on agent-based tests (13.1 points ahead of Claude Fable 5 Adaptive), reaches 91.9% on Terminal Bench 2.1, 92.2% on Browser Comp, and 80 points on the Artificial Analysis Coding Agent Index – all while using less than half the output tokens, completing tasks in half the time, and costing about a third less. On Deep Seek 1.1, Soul delivers the highest score at under half the cost of Fable 5.

  In practical demos, Soul generated impressive outputs: a fully functional macOS clone with interactive apps (Launchpad, Spotlight, Music, Settings), a Minecraft clone with mobs, block textures, break animations, inventory system, and survival/creative modes, plus a complete Interstellar-inspired cinematic experience in Three.js with soundtrack and dialog – all in 14 minutes without external assets. Terra and Luna also show high-quality outputs in frontend development; Soul competes visually with Fable 5 while offering better cost efficiency. One weakness remains complex 3D generation: reflections, correct instancing, and landmark geometry are still insufficient.

  Pricing is $5 per million input tokens and $30 per million output tokens. All three models offer 1.5 million token context. An Ultra mode coordinates multiple agents in parallel for particularly demanding tasks.

  Demo format for OpenAI/GPT 5.6 (Soul/Terra/Luna) with benchmark comparisons to Claude Fable 5.

• Grok 4.5 IS REALLY GOOD! Opus & GPT Level BUT Faster, Cheaper, & Smarter! (Fully Tested)
  9.7.2026, 06:52:18

  Summary: Grok 4.5 by SpaceX

  SpaceX has released Grok 4.5 – its first model specifically developed for coding agents and real software engineering tasks, trained alongside Cursor. The model balances frontier-level coding performance with impressive speed and cost efficiency.

  Benchmarks and Performance: Grok 4.5 achieves 83.3% on SWE-bench (comparable to GPT-5.5, just behind Claude 3.5 Sonnet), 64.7% on SWE-bench Pro (better than GPT-5.5), and 62% on Deep Seek, placing it at rank 3 among frontier models. Most notably: it generates 4.2 times fewer output tokens than Claude 3.5 Opus on SWE-bench Pro – significantly more efficient for longer coding tasks.

  Speed and Cost: The model runs at 80 tokens per second and costs $2 per 1 million input tokens and $6 per 1 million output tokens – significantly cheaper than competing models with better speed. The current 500K token context is being expanded to 1 million (should happen by early July).

  Practical Results: In frontend development, Grok 4.5 performs strongly – macOS clone with functional components, high-quality SaaS landing pages with React and animations. In Minecraft clones, it ranks third behind Claude and GPT-5.5, with functioning crafting system and mobs. SVG generation works excellently (animated lava lamps, raytracing renderer, low-poly 3D scenes). The model is weaker at complex 3D graphics (3D solar system looks bland and textureless).

  Availability: Grok 4.5 is accessible via xAI Build, API, and Cursor plans; available in the EU from July. Also ratable in the World of AI benchmark tool.

  Creator’s Conclusion: Grok 4.5 fills an interesting niche – not as the best all-purpose tool, but as a reliable daily driver for everyday coding tasks, debugging, and agent workflows, while frontier models remain reserved for the toughest engineering problems.

  AI Tools/Providers: xAI (Grok 4.5), Claude 3.5 Sonnet, GPT-5.5, Cursor — Demo/benchmark review.

• China’s AI BAN?!, Qwen 4, GPT-5.6 Thursday, Grok 4.5 Today, Deepseek AI Chip, & Claude AGI! AI NEWS
  8.7.2026, 05:50:06

  AI News Roundup: Massive Week with Claude JSpace, GPT-5.6 Soul, and China Regulation

  Claude JSpace & Anthropic Research

  Anthropic researchers have made a significant discovery: Claude has an emergent internal representation system called JSpace, in which the model apparently organizes thoughts, plans, and reasoning – similar to a global workspace. This offers one of the clearest glimpses into how frontier models internally structure reasoning before generating text. The phenomenon is compared to chain of thought, but at a more abstract level that brings concepts and relationships together before linguistic output.

  GPT-5.6 Soul Launch

  OpenAI officially announced that GPT-5.6 Soul, along with Terra and Luna, will launch publicly this week – the preview is expanding globally. According to leaks, Soul is an extremely large model with estimated 2 to 4 trillion parameters (converging to approximately 2.2 trillion), distributed across 70–100 AI wafers with one transformer layer each. This could be one of the largest publicly deployed language models ever.

  China Regulation & Geopolitics

  Reuters reports that China is considering restrictions on international access to its most advanced AI models – including future frontier releases. In discussions with companies like Alibaba, ByteDance, and Zhipu, a tiered regulatory framework is being discussed: lighter regulations for base models, security reviews for high-performance models, and possible bans/domestic-only restrictions for sensitive frontier models. This would massively change the global AI ecosystem, as Chinese open models like Qwen and Deepseek are currently central to affordable, powerful systems.

  Deepseek Chips

  Deepseek is developing custom AI chips specialized for inference rather than training, to reduce dependency on Nvidia and others. This fits a broader trend: OpenAI, Anthropic, Meta, Alibaba, and ByteDance are all moving toward custom silicon for better cost control and supply chain independence.

  Other Model Updates

  – Anthropic expands Claude Fable-5 access to all paid plans through July 12 (max. 50% weekly limit)

  – Claude Co-Work coming to mobile and web (start task on desktop, pick up on phone)

  – Alibaba: Qwen 4 speculated for September Aspara conference

  – xAI/SpaceX AI: Grok 4.5 hinted; Grok×Cursor model cooperation possible for this week

  – Google: Gemini API Managed Agents with sandbox, background task support, and remote MCP now production-ready

  – Meta: Muse Image (rank 2 on image benchmarks after GPT Image 2) and Muse Video launched

  Additional Highlights

  Tencent HY3 (open-weight) shows surprisingly strong performance in coding/web-dev and is freely available. A robot video shows a humanoid robot precisely making a bed.

  Conclusion: News roundup focusing on Claude (JSpace research, extended Fable-5, mobile Co-Work), OpenAI (GPT-5.6 Soul public launch), geopolitical implications (China regulation, Deepseek chips), and multiple model releases (Google, Meta, xAI, Alibaba).

• Tencent HY3 IS REALLY GOOD! Best Open-Weight Model? (FULLY FREE)
  7.7.2026, 04:09:21

  Summary: Tencent HY3 – Open-Weight Reasoning Model

  Tencent has released HY3, a 295-billion-parameter mixture-of-experts model with 21 billion active parameters and 192 experts. The model is designed for reasoning, agentic workflows, coding, and production, offering configurable reasoning modes (fast/low/high), which is particularly helpful for complex tasks. Released under the Apache 2.0 license and commercially usable, HY3 achieves benchmark scores of 75.8 on Swaybench Multilingual (versus Deepseek V4 Pro with 76.2) and 57.9 on Swaybench Pro (vs. Deepseek V4 Pro with 55.4). The main limitation is the 256k context window, but the model excels with improved anti-hallucination, more reliable tool calling, and higher training quality.

  In practical tests, HY3 shows particular strengths in frontend development (scroll-trigger animations, SVG icons, complex UI components) and 3D visualizations (HTML Canvas, WebGL). When creating games (2D platformers, 3D FPS shooters) and simulations, it functioned well, though not always perfectly. Compared to Gemini 3.5 Flash, HY3 was functionally comparable at creating physics canvas demos and about 35 times cheaper (fraction of a cent vs. 21 cents). GLM 5.2 is stronger for pure coding, but HY3 excels at tasks requiring physics and visual polish. When compared to Fable 5, Opus 4.8, and Sonnet 5, HY3 was fastest and delivered clean, efficient results – with lower overall quality, but impressive for an open-weight model of this size.

  The model is free to test until July 21 (after that 14 cents per million input tokens, 58 cents per million output tokens via Open Router) and offers excellent value for money.

  Tencent HY3 and DeepSeek V4 Pro explicitly discussed; partial comparisons with Claude (Opus), Gemini 3.5 Flash, GLM 5.2, Sonnet 5; demo-focused opinion/reflection video.

• Claude Fable 5 Is NERFED?! After Export Ban? The Truth…
  6.7.2026, 05:51:34

  Summary: Has Anthropic Secretly Weakened Fable 5?

  Following Fable 5’s re-release with new security measures, there’s speculation whether the model was intentionally “nerfed.” The Mercury AI Apex Sway benchmark showed a performance drop from 65.5% (June version) to 54.8% (July version) – with particularly steep declines in observability from 69.67% to 50.33%, while integration dropped only minimally. However, the author argues this decline can largely be explained by Anthropic’s new security classifiers that automatically route certain requests to Claude Opus 4.8, which may unintentionally skew some benchmarks rather than actually testing Fable 5.

  The Arena benchmark, a legitimate source with thousands of ratings, concludes that scores before and after are largely consistent and Fable 5 remains at the frontier – only in code arena was there a ~20-point drop, but this falls within the statistical confidence interval. The author sees the core issue less as a genuine model nerf and more as lack of prompt optimization: users who know how to properly structure frontier AI models (with markdown files, better contextualization) continue to get excellent results. The new security classifiers are intentionally aggressive, blocking jailbreak techniques over 99%, but also frequently flag debugging and cybersecurity tasks. An additional concern: the safeguards appear somewhat wording-sensitive – phrasing influences whether a redirect to Opus 4.8 occurs. On July 7, Fable 5 is also migrating from paid plans to a credit-usage system.

  Explicitly discussed: Anthropic, Fable 5, Claude Opus 4.8 — Format: Opinion/deep-dive with data analysis.

• NEW Gemini 3.5 Pro LEAKS! Google Is Back and Will Rival Fable 5 & GPT-5.6!
  5.7.2026, 06:25:38

  Summary: Gemini 3.5 Pro Leaks and Expected Launch

  The video addresses leaked information about Google DeepMind’s forthcoming Gemini 3.5 Pro model. Reports suggest a planned launch on July 17, based on codenames in Google’s cloud servers and user projects on a prediction platform. The model is reportedly built on a newly trained base (rather than the older 2.5 Pro) and will feature an expanded 2-million-token context window plus a new “Deep Think Reasoning” layer for logic, mathematics, and multi-step tasks. Additional features include expanded autonomous agentic workflows for longer coding tasks.

  The video shows several leaked outputs from tests (available in Arena or the Gemini app for beta testers) demonstrating Gemini 3.5 Pro in SVG generation, 3D scenes with Three.js, and interactive HTML games. Particularly impressive are a steampunk island project and Subway-Surfer-style games – the model generates functional code with realistic expectations. SVG and frontend quality is highlighted as a strength, sometimes exceeding Claude Fable 5. Private evaluations show performance gains versus Fable 5, though remain unconfirmed. A Gemini 3.5 Flash and possibly Gemini 3.6/4 Flash are also being tested in parallel. The creator announces upcoming official benchmarks.

  Models/Tools: Google Gemini 3.5 Pro, Anthropic Claude Fable 5, GLM 5.2, GPT-4o; Format: News update/demo — beginner level.

Zubair Trabzada | AI Workshop (3 New Videos)

• Claude Fable 5 Built Me a Real AI Employee
  10.7.2026, 18:36:16

  Summary: TARS – AI Employee Demo and Build

  The creator demonstrates TARS, a custom-built AI employee based on Claude 3.5 Sonnet that functions like an autonomous agent with personality (inspired by the robot from Interstellar). TARS can be controlled via natural language, interpret screenshots, take over your screen, and execute tasks—such as filling out forms, creating Meta ad campaigns, or building web apps.

  Core features: The interface displays settings (voice models, Claude versions), a humor slider (customizable), screen sharing for screen takeover, and a job board to track ongoing missions. TARS can deploy multiple specialized agent subfunctions (like “Case” for design tasks, “Kip” for app development) that work in parallel in the background and deliver reports.

  In the demo, examples include: a YouTube channel review with feedback, a new Meta ad campaign (live screen takeover), a tip calculator app with dark theme, and an image generator workflow for course images. All created through voice commands.

  The creator distinguishes between TARS (the employee who does things) and Jarvis (a separate AI assistant with long-term memory that serves as a “second brain” and knows all business information).

  Cost side: GPT Live (for natural real-time voice interaction) costs roughly 5 cents per minute and is expensive, but offers < 0.3 seconds latency. 11Labs as a cheaper alternative with slightly higher delay.

  Access: A free prompt pack to rebuild a simplified TARS version is available in the free “AI Workshop Light” community (link in video description). The full version with Jarvis and TARS exactly as shown exists in a paid community as a downloadable ZIP (5-minute installation).

  Why this matters: The creator argues we’re transitioning from chat bots into an era of true “AI employees”—characterized by natural language interaction, screen access, multi-agent deployment, and personalized personality. This makes AI tools accessible to non-technical users and opens a business model: selling AI agents for restaurants, agencies, real estate brokers, clinics, etc.

  —

  Explicitly mentioned: Claude (Sonnet, Opus, Haiku), OpenAI GPT-4 Real Time (mentioned as new Duplex model technology), 11Labs (Voice API), Fable 5 (not literally, but in context mentioned as foundation for the system version).

  Format: Demo with detailed explanation and business opportunity reflection.

• I Found the 5 Craziest Claude Fable 5 Builds on the Internet
  6.7.2026, 20:42:37

  Claude Fable 5: Five Impressive Projects

  The video showcases five outstanding examples of what Claude Fable 5 can do:

  1. Hogwarts Castle: Matt Schumer generated a complete Hogwarts castle with a single prompt featuring walkable rooms, halls, and scaled structures—all in the browser without a game engine or downloaded 3D models. The model’s spatial intelligence reconstructed the castle’s correct architecture from memory.

  2. Minecraft in the Browser: A developer built Minecraft in a browser file, including Creative Mode with block placement, day cycles, falling sand, TNT chain reactions, and auto-composed background music—all for roughly 30 dollars in API credits. The code is available on GitHub.

  3. Jarvis – Personal AI Assistant: A custom-built assistant with Iron Man’s sense of humor that searches emails, conducts online research, and analyzes user screens. The code is provided free in a community.

  4. Pokémon Fire Red: Claude Fable 5 completed the entire 2004 Game Boy game in roughly 50 minutes using only screenshots—no maps, coordinates, or specialized tools. It focused all resources on one Pokémon (Charizard) and defeated the entire story with it.

  5. 3D Websites with Animations: High-quality websites (such as a watch brand landing page) were created with a single prompt and MCP tools, which agencies typically charge thousands of dollars for. Images and videos were generated by models like Claude.

  The creator additionally offers a free prompting system and 25 copy-paste prompts plus a paid community with a masterclass on using Claude for business projects.

  Claude Fable 5, Demo/Roundup.

• Claude Fable 5 Writes, Designs & Schedules All My Content Automatically
  5.7.2026, 18:50:34

  Summary: Social Media Automation with Claude Code

  The video shows a step-by-step tutorial on building a fully automated social media system that researches content, generates images and videos, writes posts, and publishes to nine platforms simultaneously or on schedule—all in your own brand voice, without coding.

  Basic setup: Claude Desktop App with Pro or Max plan required; enable Claude Code.

  Step 1 – Brand Voice Setup: A free resources community (AI Workshop Light) offers downloadable templates. Brand voice is central: it defines identity, target audience, offerings, language, and proof. Claude automatically interviews the user through a markdown file to fill in the brand voice—no manual input needed.

  Step 2 – Connect MCP Tools:

  • Hicksfield MCP: Generates images and videos (carousels, shorts) via powerful AI models. Added as a custom connector in Claude Code; authentication via authorization window.
  • Blato MCP: Automatically posts to all nine platforms. Connect social media accounts (Instagram, LinkedIn, Twitter, TikTok, Facebook, etc.) via Blato settings. Again, add custom connector with URL.

  Step 3 – Automation in Action: The user gives Claude Code a prompt like “Create a carousel post for Instagram, LinkedIn, and Twitter, tailored to each platform, using my brand voice, use Hicksfield and Blato.” Claude uses the MCPs, generates images, and posts automatically—each platform receives tailored content (e.g., Instagram: meme energy + 5 hashtags; LinkedIn: professional, longer text).

  Scheduling: Set posting times via Blato routines, or use an additional prompt template in Claude Code for weekly automation.

  Demo Results: Showcase of live-generated and posted carousel videos on LinkedIn, Instagram, and X—fully automated, using reference image template as input.

  Notes: Images are more cost-effective than videos; if errors occur, Claude Code itself can be used for debugging.

  All resources (prompt packs, templates) in the free community; advanced content (Jarvis AI Assistant, Claude Code Masterclass) in a paid community.

  Tools & Format: Claude Code / Hicksfield MCP / Blato MCP; tutorial with demo (Claude Fable 5).

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