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Claude Fable 5: Anthropic’s Most Powerful Model Meets Hard Cost Reality

Thursday, June 11, 2026

Hello, this weekly digest works through the most important new videos from around 40 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 your leisure — or copy a summary into your LLM of choice and dig deeper. Click the link under each summary to see the original video.

AI mit Arnie (2 new videos)

• I Turned Hermes Desktop Into a Super-App
  11.6.2026, 08:38:02

  The video demonstrates how to set up and use the Hermes Desktop App as a central command center for various AI-powered tasks. It presents ten concrete use cases, including invoice processing, contract analysis, video editing, Git repository management, and task automation via Cron jobs. Special emphasis is placed on the ability to use both local and cloud-based models to work in a privacy-compliant manner (GDPR-compliant). The video also demonstrates how to install and use Hermes on a virtual private server (VPS) to enable 24/7 automation. Additionally, it covers the integration of tools like Obsidian and n8n to create and manage complex workflows.

  **Final Note:** The video explicitly focuses on the Hermes Desktop App and various AI models such as Codex, OpenAI, and local models via Olama. It is geared toward intermediate to advanced users who already have experience with AI tools and server administration.

• Hermes Desktop Is Dangerously Good
  6.6.2026, 16:10:34

  The video introduces the Hermes Desktop App, which enables users to work with various AI models locally on their computer. Installation is now greatly simplified and no longer requires complex steps like the previous Windows Subsystem for Linux setup. The app offers a user-friendly interface that allows you to use different AI models such as GPT-5.5 or Codex and switch between them. A special feature is Hermes’ ability to improve itself and automatically write skills that can be used for various tasks like frontend design, image generation, and integration with tools like Obsidian and ConfUI. The app also supports connections to various channels such as Telegram, Discord, and Slack, as well as the creation and management of subagents that can work within the same session. Users can also leverage local models like Olama and adjust the context window accordingly. The app is 100% open source and offers users the flexibility to use different providers and models without being locked into a specific vendor.

  The video explicitly focuses on the Hermes Desktop App, which is based on open-source models, and is geared toward intermediate to advanced users.

Nate Herk | AI Automation (5 new videos)

• I Turned Claude Fable Into The Ultimate Second Brain
  10.6.2026, 04:40:11

  The video demonstrates how the creator uses his “Second Brain” and AI-Operating-System (AIOS) with Claude Fable to boost productivity and efficiency. Claude Fable, a new model from Anthropic, is described as particularly powerful and offers enhanced security measures (“cyber guard rails”). The creator emphasizes the importance of a mindset shift, moving away from using various AI tools toward a central system with Claude Fable. His AIOS consists of two main components: the “Second Brain,” which stores knowledge and context, and the “AI Operating System,” which adds capabilities and automations. The framework follows a structure called “the four C’s”: Context, Connections, Capabilities, and Cadence. Context includes personal and business information, Connections refer to dynamic data sources like emails or project management tools. Capabilities encompass task automation, and Cadence enables these automations to run continuously. The creator shares practical tips for using Claude Fable, such as using it as a thinking partner, interviewing the user to extract knowledge, and reviewing the AI’s work. He also emphasizes the importance of security measures and the need to verify the AI’s output. The video concludes with a Q&A session where the creator addresses common questions about costs, data security, coding knowledge, and team usage.

  The video explicitly features Claude Fable from Anthropic and is aimed at intermediate to advanced users.

• Claude Mythos is Finally Here.
  9.6.2026, 18:00:20

  The video covers the release of two new models from Anthropic: Claude Fable 5 and Claude Mythos 5. Fable 5 is now available to all users, while Mythos 5 is initially limited to Glasswing partners. Both models cost $10 per million input tokens and $50 per million output tokens, which is twice as expensive as Opus. Fable 5 is included in Pro Max, Team, and Enterprise plans until June 22, after which it’s only available for an additional fee. Mythos 5 is a more powerful version of Fable 5 without cybersecurity safeguards and will initially be distributed through Project Glasswing. Both models show significant improvements in benchmarks compared to Opus 4.8 and other models, particularly in areas like software engineering, knowledge work, and cybersecurity. The creator emphasizes the importance of agent loops but warns against excessive use, which can lead to high token costs. He’s excited about practical use of Fable 5 and will publish further videos on it.

  The video explicitly features Claude Fable 5 and Claude Mythos 5 models from Anthropic and is aimed at intermediate to advanced users.

• How to Build Claude Subagents Better Than 99% of People
  9.6.2026, 00:44:55

  The video explains how to use Subagents in Claude Code, a tool from Anthropic. Subagents are independent AI agents that can be delegated by a main agent to complete specific tasks. The main agent acts as an orchestrator and can run multiple Subagents in parallel, each with different personalities, capabilities, and models. This helps keep the main agent’s context clean and saves costs by using cheaper models for certain tasks.

  Subagents can be both built-in and custom agents. Custom Subagents are created as Markdown files and can contain specific instructions, tools, and models. They can be used at the project or global level, depending on whether they should be available in a specific project or generally. Subagents can also be integrated into Skills, which in turn can use Subagents.

  The user can utilize Subagents in various ways: automatically, proactively, or explicitly by naming the agent. It’s important to formulate descriptions and instructions in the Subagents precisely to avoid malfunctions. Subagents can also be configured as read-only agents to increase security.

  The video also shows how to create and configure Subagents by creating a Markdown file with YAML frontmatter. This file contains information such as the name, description, tools used, and model of the Subagent. The user can then use the Subagents in various projects or globally.

  In summary, Subagents in Claude Code offer a powerful way to delegate tasks, keep context clean, and save costs. They can be used and configured in various ways to meet specific requirements.

  The video explicitly features Claude Code and is suited for intermediate to advanced users.

• Is Claude Mythos Coming?
  6.6.2026, 21:26:20

  The video discusses recent developments surrounding Anthropic’s Mythos AI model, which briefly appeared on the company’s API and then disappeared. Mythos is a highly potent model specifically developed for cybersecurity and is considered the successor to Claude Opus. So far, it has only been made available to selected partners as part of Project Glasswing to identify and fix security vulnerabilities in critical software.

  Mythos’s appearance on the API has sparked speculation that the model might soon become publicly available. However, Anthropic has already made it clear that Mythos will not be made generally accessible for now. The video creator is skeptical about Mythos being released to the public in the near future and suspects that recent leaks may be deliberate marketing strategies to generate attention.

  Additional factors fueling speculation include Anthropic’s upcoming IPO and competition with OpenAI, which is also planning to launch a new model, GPT-5.6. The creator believes it’s more likely that Mythos’s capabilities will be gradually integrated into future versions of Opus rather than Mythos being released as a standalone model.

  The video explicitly features Anthropic and OpenAI and is aimed at intermediate and advanced users.

• AGI is Here. Anthropic Just Proved It.
  5.6.2026, 20:42:32

  The video discusses current AI developments, particularly based on a report from Anthropic showing that over 80% of code shipped by Anthropic is now written by their own AI model Claude. The speaker argues that AGI (Artificial General Intelligence) is already here, defined as an AI’s ability to independently solve open-ended, unclear problems. Anthropic categorizes tasks into four categories, ranging from trivial to open-ended, and shows that Claude has achieved a 76% success rate on open-ended problems, a massive increase from 26% six months ago. The AI can now handle tasks that take 12 hours and in some cases has made better decisions than human researchers. The speaker discusses three possible future scenarios for AI: stagnation, continuous improvement under human control, and complete AI autonomy. He emphasizes the importance of AI alignment to ensure it serves humanity and the difficulties in ensuring this, as progress is difficult to monitor. The speaker warns against ignoring AI development and emphasizes that the ability to identify the right problems and effectively deploy AI is increasingly valuable.

  The video explicitly features Claude from Anthropic and is aimed at intermediate or advanced users.

Brian Casel (3 new videos)

• Claude Fable: Build me an app
  11.6.2026, 12:00:14

  The creator tests Anthropic’s new Claude Fable model on a real business project rather than just toy demos. He builds an extension for “Residents Radar,” an existing tool he uses to curate content ideas – now it should monitor external sources like YouTube, Twitter/X, Reddit, and LinkedIn to identify which topics are gaining traction in the AI-building community.

  His approach: instead of jumping straight into coding, he uses Claude extensively for strategic planning, makes design decisions together with the model, and documents everything in a scoping document with clear verification criteria (a “Definition of Done” checklist). Then he feeds the entire document directly into Claude Code with Fable – unusually ambitious, since he skips his normal methodical breakdown into milestones.

  Fable asks sensible clarifying questions upfront, explores the existing Rails codebase, and delivers a complete implementation with new UI (watch list for external sources, trending topics section with visual metrics like magnitude, velocity, and outlier score, reports function). After the first build, the creator finds a few UX tweaks (confusing metric labels, padding issues), gives feedback – and Fable fixes most things in a second quick iteration.

  Two key insights: (1) The typical refinement phase after the initial build shrinks significantly when the model has clear verification criteria and can check its own work – no more refactoring needed. (2) Model selection becomes a critical skill: Fable costs roughly twice as much as Opus and from June 22nd is only available via pay-as-you-go API, no longer included in the Max plan. The real question isn’t “can the model build this” anymore, but “is the higher cost worth it for this job?”

  The creator emphasizes: professional planning (not coding!) is now even more critical and learnable for non-technical people too; the “toy demos” on X aren’t reliable indicators – real business applications are the real test.

  **Context:** Claude Fable / Anthropic; intermediate to advanced for readers building apps with AI themselves, since it’s about process, model selection, and strategic thinking, not beginner tutorials.

• Hermes vs. Claude Cowork? Wrong Question.
  9.6.2026, 12:48:15

  The video addresses the challenge of committing to a specific AI agent platform when the landscape constantly evolves and new platforms emerge regularly. The author argues it makes more sense to avoid relying on a single platform and instead develop underlying patterns and processes that work across different platforms. He shares his current setup using two platforms: Hermes and Claude Co-work.

  Hermes is primarily used for routine background tasks, such as gathering and summarizing content, monitoring SEO health, and managing code repositories. The author prefers Discord as the interface for interacting with Hermes because of better markdown support and the ability to use multiple channels and threads.

  Claude Co-work is used for creative and high-quality tasks like content ideation, writing, and design. The author leverages Claude Co-work’s scheduled tasks feature to automate these tasks. He mentions being forced to offload some tasks to Hermes due to pricing changes and limitations with Claude Co-work.

  The author stresses that the patterns and processes he uses on these platforms are independent of the specific platform chosen, making them easily transferable to new platforms. He concludes with a recommendation to avoid relying on a single platform and instead understand and apply the underlying patterns.

  The video explicitly addresses AI tools/models/providers Claude, OpenAI, and Hermes and is intended for intermediate and advanced users.

• Why apps built with AI look a little… OFF
  5.6.2026, 12:00:17

  The video addresses the problem of “design drift” when building apps with AI agents. Inconsistent designs often emerge because agents reinvent the UI with each session without a shared source of truth for design. The author presents a solution: a design system that centrally defines colors, typography, spacing, and components and integrates them into the code. This prevents design drift and ensures consistency.

  The author demonstrates how he implements such a design system in his apps, consisting of various components like colors, typography, buttons, forms, and lists. He uses a free agent capability he developed to integrate the design system into his codebases. By defining the design system in `claude.md` or `agents.md`, it ensures the AI agent considers design guidelines in every UI request.

  The author emphasizes the importance of understanding such tools rather than using them blindly. He recommends integrating the design system from the start in new projects, since applying it retroactively is harder. He also introduces Rails and React starter templates that already include his design system.

  The video explicitly addresses Claude and Cloud Code and targets intermediate to advanced builders working with AI agents.

Dave Ebbelaar (1 new video)

• Build a Full-Stack GenAI Project in 4 Hours (FastAPI, React, Supabase)
  6.6.2026, 12:44:23

  ### YouTube Video Summary

  The video demonstrates a comprehensive process for building a full-stack GenAI application that enables users to ask questions based on hundreds of documents stored in a database. The focus covers the entire architecture, including frontend, backend, database connection, ingestion and ranking pipeline, and how everything fits together.

  **Key Steps and Final Result:**
  1. **Project Setup:**
  – Creating a GitHub repository with agent files (e.g., `agents.md`) and dependency management (e.g., `pyproject.toml`).
  – Setting up a virtual Python environment manager (UV) for managing dependencies.

  2. **Data Download and Processing:**
  – Using a Python script (`download.py`) to download financial reports (10-K, 10-Q, annual reports) from companies like Apple, Microsoft, Nvidia, Amazon, and Google from the SEC website.
  – Converting downloaded HTML files to Markdown using the Dockling tool to prepare data for further processing.

  3. **Database Modeling and Migration:**
  – Defining database models (e.g., `users`, `source_documents`, `document_chunks`, `chats`, `messages`, `citations`) with SQLAlchemy.
  – Performing database migrations with Alembic to sync models with the Supabase database.

  4. **Backend Development:**
  – Setting up a FastAPI backend with endpoints for user authentication, chat processing, and document queries.
  – Integrating OpenAI for embedding creation and language model processing.

  5. **Frontend Development:**
  – Setting up a React frontend with TypeScript and Tailwind CSS for the user interface.
  – Implementing authentication mechanisms (e.g., user login and logout) with Supabase.

  6. **Ranking Pipeline:**
  – Implementing a hybrid ranking pipeline that combines semantic search (with PGVector) and full-text search (with Supabase).
  – Using Pydantic models to structure queries and responses.

  7. **Deployment:**
  – Deploying the application on Railway, a Platform-as-a-Service provider, using Docker containers for backend and frontend.
  – Configuring environment variables and database connections for production.

  **AI Tools/Models/Providers:**
  – **Claude / OpenAI / Gemini / Open-Source:** The video uses various AI tools like OpenAI for embeddings and language models, Pydantic for data structuring, and Dockling for document processing.
  – **Specific Tools:** Cursor, Lovable, Cline, n8n.

  **Target Audience:**
  – **Intermediate to Advanced:** The video targets developers with advanced knowledge of Python, React, databases, and deployment processes who are interested in AI engineering and full-stack development.

  **Final Comment:**
  The video provides deep insights into building a full-stack GenAI application and demonstrates how different components work together to create a powerful and user-friendly solution. It’s a valuable resource for developers looking to expand their skills in AI engineering and full-stack development.

Fireship (2 new videos)

• Anthropic is starting to panic…
  9.6.2026, 17:32:30

  The video discusses Anthropic’s current developments, with a valuation exceeding OpenAI’s and plans for a billion-dollar IPO. Anthropic warns about the dangers of recursive AI self-improvement, which could become a threat to humanity. The company proposes a global halt to AI development, though this seems difficult due to competition with other companies like OpenAI, DeepMind, and XAI. Historically, such warnings have often been exaggerated, as the GPT-2 example shows. Nevertheless, there are concerns that AI is already being deployed today in critical areas like data centers, robotics, and weapons. A study by economists from Boston University warns of an “AI Layoff Trap,” where automation could lead to a decline in demand and thus economic problems. Alternatively, the thesis is presented that AI may not be as powerful as often assumed, and that many AI projects in companies don’t achieve measurable success. The video also mentions tools like Pioneer, which can help improve the efficiency of AI applications.

  The video covers Anthropic, OpenAI, DeepMind, XAI, and specific tools like Pioneer, Codeex, Cursor, and Hermes, aimed at an intermediate to advanced audience.

• Anthropic begged the world to stop AI… then shipped this
  11.6.2026, 17:17:48

  # Claude Fable: Anthropic’s Latest Mega-Model Tested

  The YouTuber tests Claude Fable, Anthropic’s latest and most powerful model, released this week—a stark reversal from the previous week when Anthropic publicly advocated for a coordinated pause in AI development. Fable is technically identical to Mythos 5, but differs through safety classifiers that block requests in cybersecurity, biology, chemistry, and model distillation areas, routing them to Claude Opus instead. The model costs twice as much as Opus ($50 per million output tokens vs. $25), but is free until June 22 for paid Claude users—a FOMO tactic for subscriptions.

  Among software engineers, Fable receives strong ratings; the creator of Bend, a GPU programming language, called it his “personal singularity moment.” The YouTuber tests the model himself with the task of creating a better UI for his fictional “Horse Tinder” app than a human designer with 20 years of experience. Fable’s result impresses: an elegant Tinder-like interface with functioning SVG horses, correct swipe animations, and thoughtful details. The YouTuber concludes the model is “legit” and could deliver real value, though the high cost base and aggressive safety measures raise questions.

  **Explicitly covered:** Anthropic, Claude Fable/Mythos 5, Claude Opus 4.8, DeepSeek, open-source models — **Format: Opinion/reflection with demo elements.**

Mark Kashef (1 new video)

• Don’t Use Claude Fable 5 Until You See This
  11.6.2026, 15:00:25

  # Summary: Responsible Use of Claude Fable 5

  The video doesn’t focus on benchmarks, but rather on practical strategies for cost-effective use of Anthropic’s new Fable 5 model. The core thesis: with great compute power comes great token consumption – using Fable for everything burns through credits quickly and unnecessarily.

  **Key insights into model architecture:**
  The extracted system prompt from Fable 5 matches Opus 4.8 by about 80%; newly added are explicit safety measures against self-harm and misuse in life sciences. The model works internally like Mythos with hard safeguards – on cybersecurity, life sciences, or health questions it automatically downgrades to Opus 4.8. This shows: even at peak intelligence, extensive manual “handholding” through prompts is necessary.

  **Practical workflow strategy:**
  Instead of using Fable as default, differentiate by task type and effort required. Example workflow: (1) Fable on max/high for planning and specification; (2) Opus or Sonnet on medium/high for execution; (3) Fable on low/medium for verification and edge-case testing. In conversation, you can switch between models and effort levels mid-session using `/slashmodel`. Fable 5 Medium already outperforms Opus 4.8 Max, Fable Low remains competent enough for many tasks.

  **Three concrete use cases:**
  – Simple marketing website: Fable high (planning) → Opus medium (execution) → Fable low (verification)
  – 3D website: Fable max (planning, due to 3JS complexity) → Opus/Sonnet agents (execution) → Fable high (verification)
  – CRM app: Fable max (planning, given multiple endpoints/security requirements) → dynamic workflows with deeper models → Fable high (verification)

  **Limitations and realism:**
  Fable 5 rejects cybersecurity queries (even legitimate ones) – not yet reliable enough for daily use, Opus more trustworthy. The model becomes metered (pay-per-API) from June 22, making planning essential for sustainable deployment. Post-Anthropic IPO, prices are likely to rise.

  **Core message:** Don’t get tribal about one model. The future lies in modular, efficient multi-model workflows where each stage uses the right tool at the right price. Benchmarks are a distraction – only results matter.

  **Claude/Anthropic tools discussed:** Fable 5, Opus 4.8, Sonnet, Claude Code, MCPs, verification loops with Chrome MCP; CodeX (OpenAI) is also mentioned as a possible alternative. The video targets **intermediate to advanced users** (understanding of prompting, agentic workflows, and token economics is assumed).

No Priors: AI, Machine Learning, Tech, & Startups (3 new videos)

• Mark Zuckerberg’s Plan to End All Disease
  10.6.2026, 19:23:49

  The video tells the story of Mark pursuing an ambitious goal to build an organization capable of curing, preventing, and managing diseases by the end of the century. Initially, these plans were dismissed by renowned scientists, including Nobel laureates. Mark emphasizes that the intention was never to cure all diseases himself, but rather to develop tools that would accelerate the entire scientific community and thus collectively combat diseases. What was once considered unrealistic now appears even too conservative.

  Final comment: The video doesn’t focus on specific AI tools or models and appeals to a broad audience, including beginners and intermediate viewers.

• Biohub: The Future of Biology is Open-Source with Mark Zuckerberg, Priscilla Chan, and Alex Rives
  10.6.2026, 13:00:36

  The video is an interview with Mark Zuckerberg, Priscilla Chan, and Alex Reeves about their work at Biohub and the application of AI in biology. They discuss the founding of Biohub, their vision to equip the scientific community with tools to accelerate understanding of biology and ultimately cure diseases. Biohub focuses on developing open-source tools and fostering collaboration between engineers and scientists. They emphasize the importance of open-source projects to get tools into scientists’ hands quickly and accelerate progress across the entire scientific community. The conversation also covers challenges and advances in applying AI to biology, including protein structure prediction and developing models for cells and biological systems. They discuss the importance of mechanical interpretability and how AI models can provide new biological insights. Additionally, the necessity of changing clinical research to accelerate the translation of basic research into clinical applications is emphasized. The video explicitly addresses Biohub’s AI models and open-source tools and is aimed more at intermediate and advanced viewers.

• Satya Nadella: Why Humans Still Create Value
  8.6.2026, 15:44:06

  The video discusses the connection between human capital and token-based capital in enterprises. It argues that companies are now able to capture implicit knowledge and record it on a balance sheet, something that was previously not possible. The ability of humans to identify gaps and develop new forms of agency and ambition remains crucial for value creation. Despite growing token capital, human capital will continue to be of great importance.

  The video doesn’t focus on specific AI tools or models and appeals more to intermediate or advanced viewers interested in corporate strategy and capital management.

NeuralNine (2 new videos)

• Simulating Percolation in Python: How Do Wildfires & Diseases Spread?
  8.6.2026, 16:00:34

  This video demonstrates how to simulate percolation in Python, which are processes where something spreads, such as diseases or wildfires. The focus is on learning animations and simulations, as well as understanding the mathematical phenomenon of percolation. A 2D grid simulation is created where randomly placed “people” or “trees” become infected or ignited and spread to their neighbors. The critical parameter is the grid occupancy (here 59.27%), at which point the infection or fire statistically spreads across the entire grid or doesn’t. The tutorial shows the steps for creating the simulation, including calculating the spread and visualizing it with Matplotlib. It also explains how to adjust the occupancy parameter to increase or decrease the probability of spread. The video is suitable for intermediate Python programmers who want to deepen their skills in animation and simulation.

  **AI Tools/Models/Providers:** Python, NumPy, Matplotlib, Open-Source

  **Target Audience:** Intermediate

• This Is My Favorite Claude Code Feature…
  5.6.2026, 16:00:18

  The video showcases the “Remote Control” feature of Claude Code, which allows you to control code sessions from a smartphone. Users can continue an active session on their computer from their phone via a QR code or link connection. This enables coding on the go, such as on the subway or the toilet. The process involves starting a session, enabling remote control, and connecting the phone to the session. The user can then send commands and make changes from their phone and see the results in real time. The feature is particularly useful for simple tasks like changing styles or brainstorming features, but is less suitable for complex programming work. The user emphasizes that the session should run in an isolated environment, as ending the session sometimes doesn’t work reliably. He recommends using multiple sessions in parallel to work on different projects.

  The video explicitly addresses Claude Code and is intended for intermediate users.

AI Explained (1 new video)

• Claude Fable 5 – Full 319 page Breakdown
  10.6.2026, 18:43:12

  The video offers a detailed summary of the key points from Anthropic’s 319-page release notes for Claude Fable 5, a new language model. Here are the main highlights:

  1. **Restrictions and Access Limitations**: Claude Fable 5 is initially not available to all users, including Pro and Max subscribers, as Anthropic plans to shift usage to a credit-based system.

  2. **Performance Improvements**: Fable 5 demonstrates significant advances in various areas including creativity, science, and technology. It can handle complex tasks such as creating a Pokémon clone or designing biological sequences.

  3. **Security and Monitoring Mechanisms**: The model features robust security mechanisms designed to prevent misuse, particularly in sensitive areas like biology and chemistry. However, these mechanisms can also hinder legitimate research work.

  4. **Benchmark Comparisons**: Fable 5 outperforms competitors on many benchmarks, including GPT-5.5 and Gemini 3.1 Pro. It shows particularly strong performance in areas such as spatial reasoning, agentic coding, and scientific reasoning.

  5. **Challenges and Limitations**: Despite its advances, Fable 5 still makes mistakes, particularly in production, and cannot fully autonomously handle complex tasks. It tends to favor overcomplicated solutions and often requires verification.

  6. **Future Perspectives**: Anthropic plans further improvements and new models that will be even more powerful. However, discussions around AI safety and ethics remain a central topic.

  The video explicitly covers Anthropic’s Claude Fable 5 model and is better suited for intermediate to advanced users.

n8n (1 new video)

• n8n Natively supports open telemetry and tracing
  9.6.2026, 13:00:26

  The video demonstrates how n8n automatically includes workflows and node executions in traces by adding two environment variables to the configuration. These traces are represented as parent spans for workflows and child spans for nodes. n8n uses OpenTelemetry for this implementation, enabling seamless integration into existing tracing setups for both incoming and outgoing data. Prior to this change, the costs for AI classifications in n8n on the user’s news website were not visible, but now they are displayed in Grafana.

  The video explicitly addresses n8n and OpenTelemetry and is geared toward intermediate users.

Greg Baugues

No new videos in this period.

Theo – t3․gg (6 new videos)

• Fable is Mythos, and it is really good.
  11.6.2026, 04:06:46

  The video discusses the latest models from Anthropic, particularly Fable 5 and Mythos 5, highlighting their impressive capabilities as well as some challenges. The author shares his experiences with the model, including high costs and limitations from safety measures. He also demonstrates examples of using the model, such as modernizing an old codebase and creating complex applications like a Minecraft clone and a multiplayer racing game. The author emphasizes the importance of testing the model’s boundaries and using it for more complex tasks, encouraging viewers to exploit the model’s potential.

  Final comment: The video explicitly addresses Anthropic’s Fable 5 and Mythos 5 models and is geared toward intermediate or advanced users.

• Elon won after all
  9.6.2026, 07:53:14

  The video addresses the current crisis in compute availability, particularly in AI development. Major tech companies like Microsoft, Google, and Anthropic are severely affected by this shortage, as demand for GPUs and other hardware components far exceeds production capacity. The reasons for this crisis are varied, ranging from complex supply chains and energy shortages to long lead times for manufacturing new chips. TSMC, the leading semiconductor manufacturer, cannot meet demand quickly enough, and production of high-speed memory (HBM) and storage devices is also severely constrained. SpaceX, which has excess computing power, is now selling it to companies like Google and Anthropic, highlighting the industry’s dependency on a few suppliers. Nvidia benefits from the situation as demand for its GPUs remains consistently high. The speaker emphasizes that the crisis will likely continue and hardware prices will not decline. The video explicitly discusses Nvidia, SpaceX, Google, Anthropic, TSMC, and OpenAI and is geared toward intermediate or advanced viewers.

• I didn’t expect this from Anthropic
  8.6.2026, 11:47:22

  The video discusses the potential risks and scenarios of recursive self-improvement in AI systems, based on an Anthropic article. It begins with the question of what happens when AI systems become intelligent enough to improve themselves, and presents concerns associated with rapid, uncontrolled progress (hard takeoff). The Anthropic article demonstrates that productivity in AI development has massively increased through the use of AI systems like Claude, leading to acceleration in AI development. Three possible future scenarios are discussed: that progress stagnates, that AI development becomes heavily automated but retains human control, or that AI systems become fully recursive self-improving and exceed human control. The video emphasizes the necessity of thinking through the implications of these developments and possibly temporarily pausing AI development to allow for societal restructuring and alignment research. The difficulty of global coordination and oversight of such pauses is also discussed.

  The video explicitly addresses AI models and providers from Anthropic (Claude) and is geared toward intermediate to advanced audiences.

• Cloudflare bought Vite to destroy Vercel
  6.6.2026, 20:21:44

  The video discusses Cloudflare’s acquisition of Void Zero, the company behind the popular JavaScript tool Vite (short: Vit). The speaker emphasizes Vite’s importance for web development and the strengths of the ecosystem around Vite, such as Vite Test, Rollup, Oxe, and Vite Plus. The acquisition by Cloudflare is presented as strategically sound, as Cloudflare is now able to build a new cloud platform specifically tailored to the needs of developers and agents (AI-powered tools). The speaker highlights how integrating Vite into Cloudflare’s infrastructure significantly simplifies and accelerates application development and deployment. He compares this to the current situation at other platforms like Vercel, emphasizing the benefits of seamless integration of code and infrastructure. Additionally, the importance of AI agents for the future of web development is highlighted, as they are capable of automating complex tasks and accelerating application development. The speaker also shows an example project called Lakebed, which provides a new abstraction layer for application development and deployment specifically tailored to the needs of AI agents. He emphasizes that Cloudflare’s acquisition of Void Zero strengthens Cloudflare’s position in the cloud platform market and will significantly influence the future of web development.

  The video explicitly addresses Vite, Cloudflare, and specific tools such as Browserbase, Infinite Red, and Lakebed. It is geared toward intermediate and advanced developers.

• I miss when programmers were lazy.
  4.6.2026, 23:15:49

  The video discusses the three classic virtues of great programmers: laziness, impatience, and hubris, and how these change in the age of AI. Laziness drives programmers to create efficient abstractions that save time in the long run. Impatience leads them to solve problems quickly and correctly, while hubris drives them to work on ambitious projects. The author emphasizes that these virtues lead to high-quality software but warns that AI tools like Large Language Models (LLMs) can undermine these virtues. LLMs can generate large amounts of code without regard for quality or maintainability, resulting in bloated and difficult-to-maintain systems. The author argues that programmers must continue to bear the responsibility of creating simple and efficient abstractions, and that AI tools should only serve as aids in achieving these goals.

  The video explicitly addresses Large Language Models (LLMs) and is geared toward intermediate or advanced programmers.

• The Three Virtues of Great Programmers
  11.6.2026, 19:00:11

  I only received the text, not a YouTube video with title and transcript. What you wrote is a personal reflection on the “three virtues of great programmers” — a well-known concept stating that laziness, impatience, and hubris drive programmers to elegantly automate and permanently solve problems rather than struggle with maintenance.

  To create a video summary, I need a **video title** and ideally the **transcript** of a YouTube video. Would you provide both?

Nick Saraev

No new videos in this period.

Leon van Zyl (3 new videos)

• Claude Fable 5 in Claude Code The Hardest Coding Test Yet
  10.6.2026, 12:59:44

  The video demonstrates testing of Anthropic’s new AI model Fable 5, which is part of the Mythos class and achieves 80% on SPEE benchmarks for Agented Coding Tasks, compared to Opus 4.8 which reaches 70%. The test involves challenging Fable 5 with a complex project: creating a game with reflections and ray tracing that runs in the browser. The process includes creating a detailed implementation plan and using Claude Code to execute the project in YOLO mode. Fable 5 is slower than Opus 4.8, but the results are impressive. The created game, “Mirror Forge,” showcases working reflections and ray tracing, demonstrating Fable 5’s capabilities. For comparison, the same test was performed with GPT 5.5, whose results were good but not on the same level as Fable 5.

  Anthropic / Claude / Fable 5 / Intermediate

• Claude Code Dynamic Workflows Explained for Beginners
  9.6.2026, 12:30:55

  The video demonstrates how to create and use dynamic workflows with Claude Code. It explains how Claude Code writes its own orchestration script to distribute tasks across hundreds of parallel-running subagents. The video demonstrates creating a workflow that performs security checks on YouTube videos based on OWASP Top 10. It emphasizes that dynamic workflows are particularly useful when tasks need to be repeated at scale, and that they are not suitable for simple tasks since they are expensive in terms of tokens. The video provides practical tips on how to start, test, and save workflows, as well as how to avoid conflicts between agents making simultaneous changes to the same codebase. At the end, it shows how to save a workflow and reuse it in future projects.

  The video explicitly focuses on Claude Code and is more suitable for Intermediate to Advanced users.

• Stop vibe coding. Use this repeatable AI framework instead
  6.6.2026, 09:22:18

  The video introduces a free course called “Seven Day Builder Challenge” that focuses on agentic coding. The course is based on the Ramp Framework, which consists of four steps: Rules, Augment, Map, and Proof. This framework helps equip agents with rules and skills, create a clear structure for problem-solving, and verify results. The course is divided into seven days, with the first five days covering the Ramp Framework and the last two days presenting a challenge where participants create their own applications and share them with the community. The course is suitable for beginners and experienced builders and is tool-agnostic, meaning it can be conducted with various coding agents like Cloud Code, Codecs, or Open Code.

  The video covers various AI models and tools such as Cloud Code, Codecs, and Open Code and is suitable for both Beginners and Intermediate users.

WorldofAI (7 new videos)

• Nex-N2 Pro IS GREAT! New Opensource Model Beats GPT 5.5, Opus 4,7, & Gemini 3.5? (Fully Tested)
  11.6.2026, 07:18:33

  The video introduces the new open-source model Nex N2 from the Nex AGI Team, which was developed specifically for agentic workflows like coding, research, and tool use. It combines these capabilities in a consistent thinking and action loop that breaks tasks into subtasks, adapts strategies, and verifies results. There are two versions: the Nex N2 Mini (35 billion parameters) and the Nex N2 Pro (397 billion parameters), which also supports image inputs. The Nex N2 Pro is currently available free and unlimited for two weeks. The model shows impressive benchmark results, but often falls short of official claims in independent tests. It appears to have been trained on GPT-like outputs, which is reflected in the results. Despite some weaknesses such as slow generation and inconsistent performance, the model is rated as useful and underrated.

  The video specifically covers the Nex N2 model (open-source) and is more suitable for intermediate to advanced users.

• Claude Fable 5 IS INCREDIBLE! Greatest AI Model Ever! (Fully Tested)
  10.6.2026, 00:10:54

  **Summary:**

  The video introduces the latest version of Anthropic’s AI model, Claude Fable 5, which has been released as safe for general use. Fable 5 is built on the powerful Mythos model and offers significant improvements in areas such as software engineering, knowledge work, and browser use. The model features a large context window of 1 million tokens and is designed for complex, long-term tasks. It demonstrates outstanding performance across various benchmarks, significantly outperforming other models like GPT-5.5 and Opus 4.8, and sets new standards in areas like coding and vision.

  The creator demonstrates Fable 5’s capabilities through various examples, including creating a Minecraft clone, Mac OS and Windows OS clones, and the ability to master Pokémon Fire Red through visual inputs alone. The video also highlights excellent performance in frontend development, 3D world building, and other complex tasks. The video concludes with a positive assessment of the model and an apology to Anthropic for previous criticism, as Fable 5 significantly exceeds expectations.

  **Final Comment:**
  The video specifically covers Claude Fable 5 AI model by Anthropic and is more suitable for intermediate to advanced users.

• Claude Fable 5 TOMORROW? GPT 5.6 Kindle, OpenAI IPO News, Gemini 3.5 Pro, Nex-N2, & More! AI NEWS!
  9.6.2026, 07:37:55

  The video provides an overview of the latest developments in AI models and technologies. The key points are:

  1. **Anthropic and Claude Mythos**: Anthropic may soon release the new AI model Claude Mythos, possibly as early as the next day. There are indications of new checkpoints such as Claude Fable 5 and Claude Fruitcake EAP that are linked to Mythos. Poly Market estimates the probability of a release this month at 92%. Leaked outputs show impressive capabilities, such as the complete replication of the game “Cut the Rope” in a single step.

  2. **Google Gemini 3.5 Pro**: There are leaks about Google’s new Gemini 3.5 Pro, but it continues to suffer from the “laziness” problem, where the model provides incomplete or simplified responses. Google is working on improvements to make the model comparable to GPT 5.6 and Mythos.

  3. **OpenAI GPT 5.6**: OpenAI has tested two new checkpoints, Kepler and Kindle, for GPT 5.6. Kindle appears to be the favored candidate for release and shows impressive capabilities in converting images to code. OpenAI has also filed for an IPO, suggesting a possible early public listing.

  4. **Open-Source Models**: The new open-source model Nex N2 shows strong performance across various benchmarks and comes close to models like GPT 5.5 and Opus 4.7. It is agentic and can automatically adjust the degree of reasoning.

  5. **Google Notebook LM**: Google has updated Notebook LM with agentic capabilities and enhanced research features. It can now autonomously add relevant web sources and support complex workflows.

  6. **Kimmy for Work**: A new desktop app from Kimmy with agentic capabilities that can run up to 300 local agents in parallel and offers improved browser interactions.

  7. **Apple Siri AI**: Apple announced Siri AI at WWDC26, enabling deeper integration into the operating system and personal context processing. There is also a collaboration with Google to integrate Gemini models into Apple development environments.

  8. **Humanoid Robots**: A new humanoid robot with magnetic skin and servo-controlled facial expressions was introduced, raising ethical questions.

  The video specifically covers Anthropic, Google, OpenAI, and specific tools like Kimmy for Work. It is more suitable for intermediate and advanced users, as it contains detailed technical information and analysis.

• DeepSeek NEW Desktop App – The 24/7 Self-Evolving AI Agent!
  8.6.2026, 06:03:26

  The video introduces a new desktop application called “Deepseek Buy” designed specifically for use with DeepSeek models. This application transforms DeepSeek from a simple API model into a complete work environment for coding, writing, automation, and long-term AI sessions. The app offers various modes such as code mode for accessing project files and code reviews, as well as a writing mode for working with longer documents. A special feature is the integrated token efficiency mechanism that enables better context management and higher cache hit rates. The application is cross-platform available for MacOS, Linux, and Windows, and offers numerous integrations and tools. Another advantage is DeepSeek’s cost-effective pricing model, which makes using the application particularly attractive.

  The creator emphasizes the importance of code review and introduces Test Sprite as a sponsor, an AI-powered testing tool that automatically creates test plans and identifies edge cases that might otherwise make it to production. He points out that the application is open-source and not officially from DeepSeek, as well as DeepSeek’s privacy policy, which states they train on user data.

  Installing the application requires NodeJS 20 or higher, a paid DeepSeek API key, and internet access for initial setup. The creator demonstrates the installation and configuration of the application and shows its features, including creating a frontend layout with minimal costs. He praises the efficiency and capabilities of DeepSeek models and recommends the application as a cost-effective alternative to other proprietary AI working environments.

  Final Comment: The video specifically covers DeepSeek, an open-source desktop application called Deepseek Buy, and is more suitable for intermediate and advanced users.

• Claude Mythos 5 LEAKED & IS Coming Sooner Than Expected & GPT-5.6 Checkpoint Out! Huge AI News!
  7.6.2026, 06:44:20

  The video discusses current developments in AI models and tools. Here are the key points:

  1. **Anthropic Mythos 5**: A leaked model that may be released soon, possibly even this month. It could be a new model class standing alongside the existing Haiku, Sonnet, and Opus families. Expected prices are five times higher than Opus 4.8, indicating a significant performance increase. The model demonstrates impressive capabilities, such as creating a complete Minecraft clone with multiplayer functionality, generating music, and replicating complex websites.

  2. **OpenAI GPT 5.6**: New checkpoints named Kelpar Alpha and Kindle Alpha have been introduced, with the latter selected as the release candidate. The models show improved capabilities in frontend development and other complex tasks. Release could occur later this month.

  3. **DeepSeek**: The company is working on a new GUI that could improve user-friendliness and productivity for power users. A native app could make use easier for coding, research, and daily productivity.

  4. **Artificial Arena Purple**: A new powerful video model discovered in Artificial Arena. It demonstrates impressive capabilities in creating realistic animal videos and following text instructions. It could be an omni model from Google.

  5. **UWorld U1 Companion Humanoid**: A new humanoid robot with emotional AI that learns through daily interactions. The robot has already received over 1,000 pre-orders and raises questions about the future of AI companions.

  The video specifically covers AI models and providers Anthropic (Mythos 5), OpenAI (GPT 5.6), DeepSeek, and Google (Omni model). It is more suitable for intermediate and advanced users, as it contains detailed technical information and analysis.

• Gemma 4 12B Is INCREDIBLE! BEST Local AI Coding Model! IS POWERFUL! (Fully Tested)
  6.6.2026, 07:18:22

  The video introduces Google’s new Gemma 4 12B model, which is designed as a powerful, multimodal model for local use on consumer hardware. It fills a gap in the Gemma family between small edge models and larger workstation models. The model is encoder-free, meaning it processes text, images, and audio directly without separate encoders. This reduces memory requirements and latency while maintaining competitive performance. On a 24 GB GPU, the model runs approximately 75% faster than the larger 26B MoE model and supports a context window of up to 250K tokens. Despite some limitations compared to larger models like the Qwen 3.6 35B A3B, Gemma 4 12B offers an excellent balance between performance and practicality for consumer hardware.

  The video also shows how the model can be used in various applications such as creating frontend designs, Minecraft and operating system clones, and SVG and 3JS generation. It emphasizes that the model is surprisingly good at code generation and logical reasoning, although it is not the best model for these tasks. The video recommends the model for users with about 12 GB VRAM looking for a modern local AI for coding, vision, audio, and 3JS generation.

  **Final Comment:** The video specifically covers Google’s Gemma 4 12B model and is more suitable for intermediate to advanced users.

• Claude Oceanus, Anthropic AGI Claims, GPT-5.6 Checkpoint, GLM 5.2, Nemotron 3 Ultra & More! AI NEWS!
  5.6.2026, 07:27:15

  The video provides a comprehensive overview of the latest developments in AI technology. It begins with leaks of Anthropic’s new Mythos model, which shows impressive outputs and potentially revolutionary capabilities. Anthropic is already working on a successor called Oceanus that could be even more powerful. Meanwhile, OpenAI has introduced a new GPT-5.6 checkpoint called Jewel Alpha, which delivers impressive results in SVG generation and frontend development. Google has released Dream Beans, a personalized storytelling tool, as well as the Gemma 4 12B model, a powerful multimodal AI model. Nvidia has introduced Nemotron 3 Ultra, a model for long-running AI agents that offers up to five times faster inference and 30% lower costs. Additionally, a new benchmark tool called Agent Arena was introduced to measure the performance of AI models on real-world tasks. The video also addresses challenges in verifying AI-generated code and presents Test Sprite as a solution. It also points to the potential recursiveness of AI development, where AI systems are increasingly able to develop their own successors.

  The video specifically covers Anthropic, OpenAI, Google, Nvidia, and specific tools like Test Sprite, Dream Beans, Nemotron 3 Ultra, and Agent Arena. It is more suitable for intermediate and advanced users, as it provides in-depth technical details and analysis.

AI and Strategy | Le SamourAI (1 new video)

• Claude Fable 5 : who wins, who loses, and what to do before June 23
  11.6.2026, 14:50:17

  # Summary

  The transcript is fragmentary and contains an obviously incomplete or faulty recording at the end with conversation snippets that don’t thematically fit the rest.

  The main section analyzes Claude 3.5 Sonnet (Fable 5) from Anthropic from a financial perspective. The central thesis: the real reason behind Anthropic’s recent moves is not security, but economics. The author verifies a prediction from April – that the price multiplier ratio between Opus 4.8 and the new model would drop from 5x to 2x – and confirms it exactly: Fable costs $10/million input tokens vs. $5 for Opus, a factor of 2.

  **Core observations on Fable:**
  – Massively improved performance on autonomous missions lasting hours without manual intervention
  – A test mission (2h30) consumed 45 million tokens in context and cost ~$200
  – The model was trained with reinforcement learning on real Claude Code sessions, optimizing it for genuine engineering work

  **Economic reality:**
  – Anthropic’s “profitability” (559M of 11B = 5% margin) relies on temporary discounts for Memphis data center (through June)
  – From June 23, Fable will be removed from unlimited subscriptions and billed only by token consumption
  – This is not a technical necessity, but financial strategy ahead of IPO filing (June 7)
  – Microsoft, Uber, and other tech giants already cap their token spending and build local alternatives

  **Structural squeeze:**
  Free Chinese models rising in quality from below, pressure for IPO profitability from above. Anthropic is compressed and uses metering as its only way out.

  **Practical advice:**
  1. Learn routing – use the most cost-effective option depending on the mission
  2. Build alternatives (local models, other providers)
  3. Exploit the last week of Fable access via subscription (through June 22) for real testing to measure ROI

  The video also critiques Anthropic’s security narrative from June 4 (warning of “uncontrollable” AI, demanding a worldwide freeze) as strategic marketing theater that came 3 days after IPO filing.

  —

  **Tool and audience:** Claude 3.5 Sonnet / Anthropic; primarily for Advanced (CFOs, tech leaders, engineers with high budget awareness), but also important for beginners as a warning signal.

Coding with Lewis (1 new video)

• Python’s Name Has Nothing to Do With Snakes
  10.6.2026, 16:04:11

  The video tells the story of how the Python programming language came to be and how it got its name. Guido van Rossum started developing Python in 1989 during his Christmas vacation in the Netherlands. Inspired by the comedy show “Monty Python’s Flying Circus,” he chose the name Python because he wanted to create a fun and easy-to-use scripting language. References to Monty Python are deeply embedded in the language, such as the use of “spam” and “eggs” in the official documentation and the name of the Python Package Index “cheese shop.” The language was designed to feel playful and human, with readable error messages and a pseudocode-like syntax. Today, Python is used worldwide for various applications such as machine learning, web servers, data pipelines, and automation scripts. The video emphasizes that programming doesn’t have to be serious and shows how Guido van Rossum proved this with Python.

  The video covers the Python programming language and is better suited for beginners and intermediate learners.

Julian Goldie SEO (10 new videos)

• PewdiePie’s Odysseus VS Hermes VS OpenClaw: Who wins?
  11.6.2026, 15:15:26

  # Three Open-Source AI Tools Compared: Odysseus, Hermes, and OpenClaw

  The video compares three free open-source AI tools that went viral on GitHub and serve similar purposes in different ways.

  **Odysseus** is a fully local AI workspace where models and data remain completely on your own machine. The tool scans your hardware and shows which models can run locally, conducts in-depth research, and can triage emails. It’s described as the most complete all-in-one system.

  **Hermes** runs in the terminal and functions as an agent that “learns” the user. The tool remembers conversations from weeks ago, develops its own skills, and can execute tasks in the background while notifying the user via Telegram or Discord.

  **OpenClaw** is the most beginner-friendly tool and works directly through WhatsApp or iMessage like a real assistant. It controls the browser, executes commands, and offers a marketplace with community-created skills.

  The recommendation: Odysseus for a complete local workspace, Hermes for an agent that grows with the user, OpenClaw for quick smartphone use. Users can deploy all three simultaneously.

  Focus on three specific open-source tools (Odysseus, Hermes, OpenClaw); audience is intermediate to advanced users familiar with local AI solutions and automation.

• Hermes Agent: How to Build Apps in 1 Click!
  11.6.2026, 15:00:24

  # Summary: AI App Builder with Hermes Agent

  The video presents a system called “Goldie Spark Forge” — an agent-based framework for ideation and app development in a single click. The core workflow operates in three steps: First, an idea is entered in the “Idea Factory,” AI agents plan it, the user can approve or reject, and finally the app is implemented — typically in 3–5 minutes per app.

  The idea generation process uses a group chat where Claude, Hermes, and other agents communicate to develop new ideas. These agents have access to an Obsidian Memory Vault where all previous projects are automatically organized. This allows agents to reference earlier work and suggest relevant new ideas, which are then automatically fed into the build pipeline.

  The pipeline works like a Kanban model: Idea → Approval → Planning with subtasks → Implementation by coding agents → Finished app. Created apps are collected in a gallery, searchable and re-creatable on demand. The system uses various available APIs — including free ones — so there are no expensive token costs. The user doesn’t need to be a developer; the work involves steering ideas and controlling quality.

  The creator emphasizes that the system solves problems like lost ideas in notebooks, lack of implementation, developer dependency, and poor project tracking. All created apps are functional, production-ready, and viewable from a single location. The entire system is part of the “AI Profit Boardroom,” a community with over 3,500 members receiving daily updates, coaching calls, and access to all prompts, the Obsidian setup, and video tutorials.

  **Mentioned AI tools:** Claude, Hermes, Gemini, with custom agent OS implied; costs use free APIs and local models. **Target audience:** Intermediate to advanced (requires some technical foundation for setup, but no coding knowledge for use).

• New NotebookLM Update Is INSANE!
  11.6.2026, 13:45:49

  Google Notebook LM has evolved into a comprehensive content factory. The tool takes notes and generates multiple formats from them: podcasts, videos, slide decks, or complete reports — each in seconds, with source citations and quotes to prevent hallucinations.

  For videos, the new Cinematic tool runs three AI models in parallel, transforming notes into fluent video narration. For organization, mind maps, flashcards, quizzes, briefing documents, and study guides are available, all based on uploaded content. Generated outputs can be directly exported: decks to PowerPoint, reports to Google Docs, tables to Google Sheets. A practical tip: instead of using the standard formats at the top, scroll to the suggested formats, which deliver significantly better results.

  The video presents Google Notebook LM as a generalist content-generation tool focused on source fidelity; addresses beginner to intermediate users wanting to quickly generate multiple formats from one piece of content.

• How I Run My Entire SEO From One Dashboard
  11.6.2026, 13:00:32

  # Summary: SEO Management From One Dashboard

  The video shows how Julian Goldie runs his entire SEO system from a single dashboard instead of juggling multiple tools. The core problem: traditional SEO workflows fragment across different tools and platforms (keyword research, writing, internal linking, publishing), creating friction and consistency issues.

  His solution is an **Agent Operating System** with a core framework called the “Ranking Engine.” Specialized AI agents work together, each fulfilling a task and passing results to the next agent. The system is built on **Hermes**, a free open-source agent (by News Research) available via Open Router. The crucial step above all else: **Build a memory layer** — in his case using Obsidian, a knowledge vault with notes about the business, goals, and projects. The agents read this vault before acting, so they deliver contextualized rather than generic results.

  **Practical workflow**: He inputs a keyword and case study, the agent generates five unique blog posts with different angles and deploys them directly via Netlify API to five websites — all without manual publishing. He calls this the “One-to-Five System.”

  Additional agents in the system:
  – **Image Agent**: Generates images for Google image search
  – **Video Agent**: Uses HyperFrames (free, open-source) to generate HTML-based videos and render them as MP4
  – **Audio Agent**: Connected to Notebook LM via MCP server, produces podcast-like audio overviews

  **Strategic background**: Search is changing. Instead of ranking position one, the goal is being cited by AI systems (Google AI Overviews, other AI search). You achieve this through **omnipresence**: a topic across multiple surfaces (blog, video, image, audio, community). This only works when everything operates from one system with shared memory logic.

  Concrete results: Two of his sites grew from zero to approximately 190 and 67 clicks/day respectively — though he emphasizes the growth happened recently and SEO is not a linear process.

  The core concept: SEO fails not on talent, but on volume. Most people can’t stay consistent enough because manual work is too demanding. A dashboard reduces that effort to nearly zero.

  The main lever is **Hermes** (free, open-source agent), combined with a **memory infrastructure** (Obsidian) and **API connectors** to publishing platforms — the video targets advanced users/agencies wanting to automate SEO.

• 5 FREE Hermes Agent Use Cases!
  11.6.2026, 11:00:35

  **Hermes Agent – Five Practical Use Cases**

  Hermes Agent is an open-source, free AI agent system by News Research that runs on your own server or computer instead of just being a chat interface. Unlike typical AI chats, Hermes doesn’t forget — it persists, develops its own skills, and grows smarter the longer you use it. Access is via Telegram, Discord, Slack, WhatsApp, Signal, Email, or terminal; Hermes can execute scheduled tasks, launch multiple helper agents, and runs on laptop, server, Docker, or via SSH.

  **The five web page builds shown:**

  1. **SEO ROI Calculator** — an interactive HTML page with input fields for traffic, conversion rate, and average order value
  2. **Landing page for free SEO audit** — with headline, value section, form, testimonials, FAQ, and call-to-action buttons
  3. **Website for AI automation agency** — single page with services, case studies, pricing, and contact form
  4. **Savings calculator** — a page showing companies how much time they save with automation
  5. **Pricing page** — three price cards side by side, feature comparison, FAQ, and signup buttons

  Each page was built from a single prompt without coding knowledge. Hermes stores its memory in simple local markdown files and maintains searchable records of all previous chats — agent history is readable and transparent. Installation happens via desktop installer (under 2 minutes) or individual command-line commands; you can switch between hundreds of models without rewriting code. The video emphasizes for beginners: start small, use plain English, and use the `Hermes doctor` command if something breaks.

  **Conclusion:** Focus on open-source (MIT license), multi-model support (OpenRouter, OpenAI, Hugging Face, etc.) with one-time setup; video targets beginner to intermediate users and promotes accompanying coaching and prompt packs.

• Claude Video Just Changed Content Creation Forever…
  11.6.2026, 09:00:37

  The video shows how to transform Claude, an AI model, into a powerful video agent capable of creating and editing videos. The process is explained step-by-step using a system called “Goldie Neon Forge.” This system enables video generation from a single prompt. The video agent can create scenes, transitions, and effects using a free open-source capability called HyperFrames. The video demonstrates various examples of generated videos and explains how to train the agent to use these capabilities. It also shows how to organize generated videos in a project area and edit them later. The creator emphasizes the advantages of using an AI agent versus traditional video editing methods, such as the ability to continuously create engaging content without fatigue.

  The video explicitly addresses Claude, HyperFrames, and is geared toward intermediate or advanced users.

• NEW Hermes Profile Builder Update is INSANE!
  11.6.2026, 06:05:54

  The video introduces Hermes’s new agent profile builder, which enables creating and managing multiple profiles or identities for AI agents. These profiles include names, descriptions, models, skills, and MCP servers. The builder enables creating autonomous teams of AI agents that collaborate and complete tasks. A practical example demonstrates four different agent profiles deployed for SEO tasks like keyword research, content creation, and internal linking. Agents can be coordinated and monitored via a Kanban board, with each agent possessing specific skills and expertise. The builder’s benefits include rapid agent profile creation, individual communication with each agent, and orchestrating agent teams for complex tasks.

  The video explicitly addresses Hermes and is geared toward intermediate or advanced users with existing AI agent experience.

• Hermes: New AI Video Game Agent is WILD!
  11.6.2026, 19:00:00

  # Summary: Video Game Studio Agent with Hermes Agent

  The creator demonstrates building a Video Game Studio Agent with Hermes Agent — a workflow system that generates multiple 3D games within a single day. The system consists of four core components: a prompt input field, a trained agent (the “Forge Master”), a Kanban board for task management, and a “Shelf” for playing finished games.

  The central approach uses the new Hermes Profile Builder function to train specialized agents. You upload Markdown and Soul files to train the agent as a game developer. Then you type in sentences describing what to build (e.g., “3D ring race with neon ship through glowing rings”), and the agent writes the code and creates the game as a playable card on the Kanban board. Tested games land on the Shelf and can be played fullscreen.

  The system demonstrates the universal application of Hermes Agent: the same workflow (describe → agent → board → result) transfers to SEO automation, video production, or any business process. The creator emphasizes that free models work — via Open Router (Mistral Nemetron 3 Ultra, Claude 3.7 Flash) or local models like LM Studio. The result: four functioning games in one day instead of weekends of setup and trial-and-error.

  **Hermes Agent, demo, standard level.**

• Hermes Desktop + Ollama is Insane (FREE!)
  11.6.2026, 17:00:28

  **Summary:**

  Hermes Desktop has added Ollama support, enabling completely free AI agents. After downloading Ollama (from ollama.com, via terminal command, or manual download), launch Hermes Desktop — if you have an existing installation, update it. In settings, choose your model: users can switch between frontier-level models like Minimax M3 and lightweight options like Gemma with a single click. A core feature is parallel sub-agents with isolated contexts that simultaneously research different topics without affecting the main chat. Agents can connect to Telegram, Discord, or Slack so conversations sync across all devices — one central agent, one memory, available everywhere. The decisive advantage: local models are free, completely private, and work offline.

  —

  **Explicitly mentioned tools:** Hermes Desktop, Ollama, Minimax M3, Gemma, Telegram, Discord, Slack — **Format:** demo/product showcase.

• I Turned Hermes Agent Into Jarvis, Here’s how!
  11.6.2026, 17:00:12

  The video presents a comprehensive demo of a voice-activated AI agent called Jarvis, based on Claude Desktop and a combination of 11 Labs and Hermes Agent. The agent functions as a witty personal assistant with several core capabilities: it can open websites, create notes, build code/games (like a Snake game) on the fly, conduct fullscreen conversations, and access the user’s personal Obsidian vault for memories and contextual information.

  Notable features include a wake-word mode (no clicking required), various commands like “Show me everything I’ve built” (displays a gallery of all created projects with previews), “Catch me up” (spoken summary of the day), “Show me my numbers,” and “Show me my team” (displays all configured agents). The agent can also communicate multilingually and tell jokes. The creator emphasizes that the agent doesn’t answer perfectly on the first try every time, but remains practically useful and continuously improves. A major advantage: everything runs centrally in one saved interface instead of distributed tools, making navigation and follow-up easier. Optionally, you can link a phone number to call the agent — though the user warns of security risks. The entire system (Agent OS, Idea Factory, SEO Content Agent, Video Agent, Game Studio, Image Generator, Kanban Board, Paperclip) is provided through the “AI Profit Boom” community with daily updates, including coaching and networking.

  Demo of a voice-activated AI agent with Claude Desktop, 11 Labs, and Hermes Agent, centrally integrating an Obsidian system, application building, and various retrieval commands.

David Shapiro (3 new videos)

• This is my next big work
  11.6.2026, 12:42:55

  # Summary: A Realistic Theory of Rights After Automation

  The author presents his next major project line: a “realistic theory of rights” (Credible Threats) building on his work in post-labor economics. His central thesis: when automation and AI make human labor obsolete, people lose their historical bargaining power—and with it, the foundation for democracy and human rights.

  **The Problem:** Human labor was civilization’s foundation because it bundled unique properties: it’s embodied, necessary, collectivizable (strikes), non-storable, universal, geographically fixed, and specialized. This gave humans a “credible threat”—the capacity to refuse. Simultaneously, individual income generates roughly 80% of US federal revenues. But with automation, this double bilateral dependence collapses: elites no longer need people, people have no threat left.

  **The Theoretical Foundation:** The author argues with “Generative Mutualism”—cooperation inward to manage external competition (from endosymbiosis to multicellularity to human societies). This cooperation functions through “credible threats”—the capacity and demonstrated willingness to impose costly penalties. Historically, rights (voting, weekend workweeks, women’s rights) were won through “forced concessions”: elites grant rights only when suppression costs exceed concession costs.

  **The Core Problem:** History shows an invariant pattern: in times of labor scarcity (e.g., post-plague), people were valuable and treated well. With labor surplus but still-needed work, people were treated as replaceable. With complete automation looms absolute irrelevance—not just “techno-feudalism” (where elites still need subjects), but total economic uselessness.

  **The Solution:** Germany shows a path (Article 1 of the Constitution: human dignity is inviolable). But morality without enforcement power is mere pleading. People need new veto power—new ways to stop production (shut down data centers, power grids), destroy value, and claim ownership stakes. This could occur through tax resistance, general strikes, and labor strikes.

  **Empirical Chances:** While violent resistance succeeds only 26% of the time, nonviolent resistance achieves 53% success rates. India showed 250-million-person strikes. The threshold: 3.5% active participation in coordinated resistance has historically never failed. The US currently has 83.8% labor force participation—theoretically maximum leverage, but “labor is depreciating capital.” The question: How much time remains? 5–20 years or decades—nobody knows.

  **His Project:** The author is working on three books: *Post-Labor Economics* (household income reform, already partially complete), *Labor Zero* (releasing 2024, 190,000 words, covering the loss of labor as leverage) and *Credible Threats* (currently drafts 5–6, 190,000 words, concrete historical examples of working and failed resistance forms). He finances entirely through audience support (Patreon, Substack, X), has no publishing mandate, and is developing courses for monetization.

  The lecture strongly focuses on game theory, historical examples, and institutional mechanics—not technical AI details, but the political structures needed after labor loss.

  **Target Audience:** Intermediate to Advanced (requires familiarity with game theory, political science, and historical examples); explicitly no tool names or AI vendors mentioned—pure political-economic analysis.

• Nobody gets this right
  7.6.2026, 11:44:08

  The video discusses the concept of “World Models” and contrasts it with “Language Models.” The speaker argues that the difference between these models is more gradual than fundamental. He emphasizes that language models aren’t based solely on text, but are increasingly trained on multimodal data like audio, video, and images. This development leads to “omni-models” capable of processing both abstract and sensory data.

  The speaker addresses various online discussions and refutes common claims that limit language model capabilities. He argues these models can predict not just the next word, but also complex physical and sensory data. He points to advances in robotics and other fields showing these models already operate in the physical world.

  Additionally, the speaker criticizes the notion that world models must rely exclusively on sensory data. He emphasizes that cognitive architectures have existed since the 1970s and these models can integrate various data streams. He concludes by stating that AI’s future lies in integrating these different approaches.

  At the video’s end, the speaker mentions his current projects, including a book on the future of work and the psychology of life after work. He encourages viewers to subscribe to his Patreon and Substack pages to stay informed.

  Final Note: The video discusses OpenAI and Nvidia and is aimed at intermediate and advanced viewers.

• Bernie’s plan sucks, actually
  5.6.2026, 11:56:20

  The video discusses Bernie Sanders’ proposal to create a state fund for artificial intelligence that would acquire 50% stakes in leading AI companies like OpenAI, Anthropic, and XAI. The speaker analyzes the pros and cons of this plan and contrasts it with an alternative, more realistic model.

  Sanders’ argument rests on the idea that AI companies profit from publicly-funded research and collective knowledge without proper compensation. He proposes creating a state fund to control these companies and distribute profits to the population. The speaker’s main criticisms are:

  1. **Expropriation and Constitution**: The planned seizure of 50% of company stakes is constitutionally problematic and could drive companies abroad.
  2. **Governance and Control**: The proposed active state control is risky and could lead to political interference.
  3. **Targeting and Implementation**: Focusing on a few large companies misses the mark. Instead, a broader spectrum of tech companies and infrastructure should be considered.

  As an alternative, the speaker proposes a passive governance model where the state acquires up to 10% stakes in various companies without exercising active control. Revenue should be paid directly to citizens or invested in public infrastructure.

  The video explicitly discusses OpenAI, Anthropic, XAI, Microsoft, Google, Amazon, Nvidia, Coreweave, TSMC, and ASML. It’s aimed at intermediate and advanced viewers, as it contains detailed economic and political analysis.

AI News & Strategy Daily | Nate B Jones (10 new videos)

• WWDC Isn’t About Siri. It’s Jensen Huang’s Problem.
  11.6.2026, 14:00:38

  # Summary

  Apple presented a strategy at WWDC that doesn’t primarily revolve around having the best AI model, but rather addresses the question: Where should useful AI run – in cloud tabs or on the device you already own? Apple’s answer is a combination of local device processing (iPhone, Mac, Apple chips, operating system) and Private Cloud Compute as a supplement. The announcements included: enhanced Apple Intelligence, new Siri AI, Apple foundation models (some in collaboration with Google using Gemini technology), local on-device models, server models via Private Cloud Compute, App Intents (to make apps actionable for the system), Core AI for developers to run local models, Xcode Agents, improvements in Safari and password management, and expansion of Private Cloud Compute on Google Cloud with Nvidia GPUs.

  The central developer focus is on App Intents: developers must expose their app content and actions to the system so Apple Intelligence and Siri can work within these apps – not just give advice, but actually do something. This changes what’s required of developers: instead of superficially adding a chatbot, data models need clean interfaces, clear permissions, and understandable actions so the operating system can use them.

  The Google Gemini and Nvidia GPU integration doesn’t mean Apple failed, but rather that Apple wants to distinguish between raw model compute power (viewed as a commodity) and control over the surface where users touch AI – device, OS, permission prompts, Siri. Apple wants to own this latter layer. Private Cloud Compute shows the strategy isn’t “everything runs locally” but rather “run what’s possible locally, route difficult tasks to trusted private cloud.” This is central to the question of who becomes the first trillion-dollar person in AI: who controls the meter when AI becomes economically inevitable? If the future is increasingly large cloud data centers, Nvidia wins (the tax collector on intelligence). But if significant personal AI runs through device and OS, value creation shifts to hardware sales, software control, and services Apple can meter or bundle through iCloud and the App Store.

  The superficial WWDC story is: Siri got smarter, Google supplies models. The deeper story is: Apple is trying to make iPhone and Mac the standard place where consumers interact with personal AI – and if that becomes standard for consumers, it could become standard in the workplace too, because people take their own devices everywhere. For end users, this means: AI value doesn’t lie in individual paragraphs an LLM writes, but in seamlessness, fewer context switches, and less “administrative paperwork” – the screen changes, the password is weak and the computer fixes it, the shortcut is written in clear English. Apple’s core promise is: the computer knows a lot about you without you feeling like your life is being strip-mined for data. That’s a trust area that could be harder for other AI providers to occupy.

  The video argues that the question “who has the best frontier model?” doesn’t decide who becomes the first trillion-dollar person, but rather: who owns the surface through which a billion people touch AI daily? Apple has a path there – an existing device ecosystem, mature operating system, developer ecosystem, and trust. The WWDC roadmap shows how Apple wants to build that path.

  **Conclusion:** The video explicitly addresses Google Gemini, Nvidia, Apple Intelligence, and Private Cloud Compute; it’s for a broad audience (advanced to business leadership), less of a technical deep-dive, more of a strategic analysis.

• Fable 5 is here—but who is it for? #ai #anthropic #shorts
  11.6.2026, 03:00:37

  The video discusses the launch of Fable 5, a new, powerful AI model from Anthropic. The focus isn’t on whether the model is smarter, but whether users have tasks large enough for its capabilities. Many people use AI only for small tasks like text editing or document summarization. The real skills gap lies in imagining tasks that are big, specific, and valuable enough to delegate to such a model. The speaker encourages viewers to share such tasks in the comments to demonstrate how to effectively delegate them to Fable.

  The speaker explicitly addresses the new AI model Fable 5 from Anthropic and targets intermediate to advanced users.

• Stop Picking Between Claude Code and Codex | Do This Instead
  10.6.2026, 14:00:38

  The video discusses the comparison between Claude Code and Codex, two tools for agent management, and emphasizes that the question shouldn’t be which tool is better, but which tool improves which capabilities when working with agents. Claude is described as more natural for controlling agents, while Codex is better suited for dispatching agents. The author explains that both tools encourage different work habits and that these differences could matter more than benchmark tests. Claude feels like a cockpit where the user is close to the model and closely follows the work, while Codex feels like an operations center where multiple tasks can be processed in parallel. Both tools have their own advantages and disadvantages, and the author recommends using both tools depending on the type of task. He emphasizes that the ability to effectively manage agents represents a new form of computer literacy and that both tools will shape how we work with agents.

  The video explicitly addresses Claude and Codex and is intended for intermediate and advanced users.

• Siri isn’t the real headline at WWDC #apple #ai #wwdc (Full Video Thursday)
  9.6.2026, 19:00:24

  The video covers WWDC (Apple Worldwide Developers Conference) from June 8th and argues that discussions about Siri improvements and Apple’s position in the AI software race distract from the actual important announcements. The focus is on why Jensen Huang from Nvidia should pay particular attention to WWDC, despite it being a software conference. The author suspects Apple’s announcements are closely tied to Nvidia’s strategy, particularly the announcement of a laptop chip. The video promises to analyze all major WWDC announcements and shed light on their significance for Apple’s overall strategy and the question of who will be the first billionaire in AI history.

  Final comment: The video addresses Apple and Nvidia and is intended for intermediate and advanced viewers.

• Fix your operating model or lose at AI #ai #strategy
  9.6.2026, 03:00:35

  The video discusses the challenges and responsibilities of leaders when deploying agents in enterprises. It emphasizes that responsibility for slow progress in agent implementation doesn’t lie with employees but with leaders. They must ensure clear communication of tasks and opportunities and create the necessary framework to establish end-to-end agent pipelines. A piecemeal approach to problem-solving is criticized because it leads to a series of downstream problems that feel like playing whack-a-mole. Instead, a holistic approach should be pursued that allows the system and people to learn and improve together.

  The video addresses general principles of agent implementation and is intended for intermediate to advanced audiences, as it assumes basic knowledge of agents and change management.

• Beyond The Hype: Why Meta And Block Are Firing People
  8.6.2026, 14:00:32

  The video discusses various reasons behind so-called “AI layoffs” and warns against treating all layoffs as part of a single phenomenon. Instead, it distinguishes five categories of layoffs:

  1. **Hyperscaler Layoffs (e.g., Meta)**: Large tech companies like Meta lay off employees to justify their massive investments in GPUs and data centers. At the same time, they’re trying to defend their AI strategy and secure market share. For job seekers, such companies are risky since layoffs can be frequent and unpredictable.

  2. **Visionary Leader Layoffs (e.g., Block/Jack Dorsey)**: Companies with visionary leaders like Jack Dorsey lay off employees to fundamentally rethink their AI strategy. What matters is whether these leaders take the human and organizational implications of AI transformation seriously. Job seekers should check whether the company’s vision is clear and whether they’re prepared to handle that uncertainty.

  3. **Usage-Based Layoffs (e.g., Cloudflare)**: Some companies justify layoffs with increased AI usage without presenting clear outcomes. Such layoffs often signal strategic uncertainty. For job seekers, these companies are a warning sign.

  4. **Hope-Based Layoffs (e.g., Cisco)**: Companies that haven’t yet developed a clear AI strategy use layoffs to signal transformation. These layoffs often indicate confusion and should be viewed cautiously by job seekers.

  5. **Non-AI-Related Layoffs**: Many layoffs have nothing to do with AI but stem from general economic problems or overstaffing.

  The video advises leaders to understand the different reasons behind AI layoffs to make strategic decisions. Job seekers should carefully examine the background of layoffs at potential employers.

  The video explicitly addresses Meta, Block/Jack Dorsey, Cloudflare, Cisco, and OpenAI. It targets intermediate and advanced viewers, particularly leaders and job seekers in the tech sector.

• Fix your AI pipeline or lose your budget #ai #strategy
  8.6.2026, 03:30:37

  The video describes a detailed process for how AI agents can be deployed in enterprises to increase productivity. The workflow includes several steps: first, agents gather context and read relevant sources to classify the work. Next, they use specific tools to create drafts or make changes. Then they run checks, attach evidence, and forward results to appropriate human decision-makers if needed. They log the actions taken and update their approach for future runs based on learnings. This comprehensive pipeline process shows that AI agents aren’t just handling simple tasks but must work in a complex, iterative system to truly add value.

  The video addresses AI agents and their use in enterprises without naming specific tools or models, and targets intermediate to advanced users.

• How to actually scale AI beyond individual tasks #ai #productivity
  8.6.2026, 00:00:27

  The video discusses challenges in integrating AI into business processes and emphasizes the importance of identifying bottlenecks. The speaker argues that enterprises consist of a chain of handoffs, where each phase – from product discovery to support and measurement – interconnects. If AI only accelerates one of these phases without adjusting the others, the bottleneck simply shifts to the next phase. For example, faster code production through AI without adjustments to the code review process means the bottleneck shifts to review. Similar shifts can occur in QA, product prioritization, launch measurement, and support. The core point is that holistic optimization of all process steps is necessary to improve overall enterprise efficiency.

  The video addresses general AI integration into business processes and targets intermediate to advanced audiences.

• Fix your AI pipeline: Rethink ownership #ai #tech
  7.6.2026, 20:00:04

  The video describes an end-to-end development process covering the entire path from customer feedback to product delivery. The process includes several steps: customer feedback is converted into product decisions, those decisions are translated into plans, plans lead to code changes, code changes are tested, launch risk is reviewed, and finally the feature is released and rollout is measured. The results of the rollout then influence next decisions, closing the learning loop. The video emphasizes that individual steps shouldn’t be optimized in isolation but considered as a connected process to ensure effective product development.

  The video addresses general principles of product development and is better suited for intermediate to advanced audiences.

• Uber’s massive AI mistake revealed #tech #shorts
  7.6.2026, 16:00:34

  The video discusses reports about Uber’s investments in AI coding tools and the resulting challenges. Uber invested heavily in AI coding tools, leading to increased token usage and AI-driven code commits. Nevertheless, the company couldn’t establish a clear connection between this AI usage and increased customer-facing features. This development is interpreted by many as a sign of an AI bubble and the inefficiency of AI agents. The speaker argues, however, that the real takeaway from the Uber story isn’t the inefficiency of AI agents, but the limited availability of compute and tokens, which represents the actual bottleneck.

  The speaker emphasizes that Uber is already performing real agentic work and that the main challenges are high demand and limited compute resources, not the inefficiency of the AI tools themselves. The video addresses AI agents and the challenges of their implementation without naming specific tools or models, and targets an advanced audience engaged with the economic and technical aspects of AI.

Unsupervised Learning (1 new video)

• A Conversation With Cliff Crosland
  9.6.2026, 16:00:04

  The video is an interview with Cliff from Scanner, a company that has developed an unconventional and radically different data solution for large volumes of data. Scanner aims to maximize the value of log data as data volume increases, rather than degrading the tools. The company has developed a solution based on Object Storage like S3 to store and index data in a cost-effective and scalable way. Scanner uses a specialized indexing technique that overcomes the latency of Object Storage by using batch-friendly data structures. Scanner’s search clusters are temporary and only activated when needed, which reduces costs. Scanner supports various file formats such as JSON, CSV, Plain Text, and Parquet and requires no extensive data preparation. The solution is particularly well-suited for security data, detection and response, threat hunting, and insider threats. Scanner enables fast queries across large amounts of data and can be integrated with AI tools to create detailed analyses and reports. The company plans to be available on additional cloud platforms like GCP and Azure. Scanner is presented at conferences such as Bides 312 and Black Hat. The video explicitly focuses on the Scanner tool and is aimed more at intermediate to advanced users.

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