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🚨 Anthropic Mythos System Prompt Leaked It reveals something many have suspected: it is not a raw model but a full agentic system with access to local files and tools like search, skills, context management and more. https://t.co/4WC9ZkKQIi

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In this clip from his PyTorch Conference Europe 2026 keynote, Patrick von Platen (@MistralAI) discusses why real-world machine interaction requires models that can take continuous input and produce continuous output. Using live transcription as an example, he explains how streaming architectures differ from traditional speech recognition approaches that process larger chunks of audio at once. 🔗 Watch the full keynote: https://t.co/PmM1ak29ay #PyTorchCon

Introducing Rork Games × Claude Fable 5 Rork can now one-shot 3D & 2D games you can play with friends online. Even Fortnite with voice chat. Play on Web and  iPhone. Generate 3D models from text and photos, 2D Assets, sound effects and music. https://t.co/3UXuzqDoK3

recovering from my operation going to be a fun afternoon https://t.co/4KoNr4AocO

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How to effectively run autonomous long-running coding agents? This is one of the most exciting discussions on agents I've ever had. I recorded it and am making it freely available. (bookmark it) The idea of autonomous long-running agents is a real thing. We talk about lots of things like /goal, /loop, and dynamic workflows, and what comes next. One interesting discussion was around how to make the agent run for longer while ensuring it stays on track. Most models today will struggle to coordinate work effectively. They sometimes pause the work early. Lots of mistakes happen, and lots of weird shortcuts (reward hacking). What helps is to be extremely clear about the goals it needs to achieve. To clarify the dos and don'ts clearly. Eliminate any assumptions you think the model would make. Deep expertise matters so much in this. But you can get far through careful planning. My formula currently is to use Opus 4.8 for planning carefully and GPT-5.5 for all executions. For the evaluator (via /goal), I am often using something like Deepseek or the latest models from Qwen, Kimi, and MiniMax, etc. Another insight we discussed to enforce goals is to provide strong visual cues for the agent to compare with. I found that a multimodal goal is a much stronger goal than a plain text one. And use agents to help you set clear goals. Watch here: https://t.co/ML3bSwGjUG

Your standards. Your tools. Every review. Copilot code review supports custom agent skills and MCP server connections. Now in public preview for existing Copilot Pro, Pro+, Business, and Enterprise users. Bring your org's context and standards right into the diff. 💡 https://t.co/cAuIkVClEm

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