Anthropic released Fable 5, the public version of its Mythos model previously restricted under Project Glasswing. It is available on Pro, Max, and Team plans, free until June 22 after which usage credits apply. The model is designed for multi-hour agentic sessions, autonomously spinning up sub-models, gathering data, and writing and testing its own code. Hard safety blocks on cybersecurity, biology, and chemistry cause it to fall back to Opus 4.8 when triggered. A Reddit user is asking for community feedback on its real-world performance and weak points.
A Reddit user proposed a concept for AI-driven robot locomotion where a human operator provides high-level control via a joystick while the AI handles leg movement intuitively, akin to a spinal cord reflex. The user envisioned a library of objects that the robot should avoid stepping on, enabling smarter environmental interaction. The post is speculative and does not reference any existing system or research. This idea highlights ongoing interest in blending human direction with autonomous low-level control.
Reddit user KobyStam built the open-source tool 'The AI Counsel,' packaging Andrej Karpathy's LLM Council concept into a configurable Docker container. It offers two deliberation modes: a Council mode with individual replies, anonymous peer reviews, and a chairman synthesis for factual questions; and an Advisors mode where multiple personas debate a query across configurable rounds for decisions and tradeoffs. The tool includes a built-in MCP server for agent integration, supports local Ollama models and cloud providers like OpenAI, Anthropic, Mistral, and DeepSeek, and embeds web search via DuckDuckGo, Serper, Brave, and TinyFish with Jina AI for full article retrieval. Everything from system prompts to temperatures is configurable, and the project is entirely free and open-source on GitHub.
A team built an autonomous churn risk pipeline with six agents: ML scoring, product recommendation, availability check, pricing/promotion, transaction creation, and email drafting. MCP served as the tool layer, a pluggable server providing a uniform interface for tools, decoupled from any LLM or frontend. A2A acted as the smart routing middleware, leveraging an LLM to interpret intent, select tools, handle failures, and determine task completion. The jump from MCP to A2A shifts from static endpoint listings to dynamic orchestration. The main governance challenge was securing system-to-system communication; the team opted to pre-certify every backend connection rather than allowing open access, especially crucial for agents autonomously creating transactions.
GitLab has revealed that Git is being reengineered for 'machine scale,' targeting a future where AI agents plan, code, review, deploy, and repair software under human oversight. The company envisions agent-specific APIs, machine-scale Git infrastructure, and agents acting as first-class users of development platforms. Earlier projects like GitLawb proposed similar 'Git for agents' concepts, which are now being validated by this shift. The move suggests software development may transition from humans using AI tools to humans managing teams of AI developers, requiring version control systems to evolve accordingly.
A live, hands-on bootcamp on evaluating AI agents will be held on June 27, led by AI engineer Ammar Mohanna, PhD. The 5-hour session covers four evaluation layers: component, trajectory, outcome, and adversarial evaluation. Attendees receive a practical evaluation framework, 6 months’ access to an AI Evals assistant, implementation templates, a capstone project, and a Packt-endorsed certification. The event targets teams that struggle with agent failures in production due to poor evaluation practices.