Basically, this approach means the base agent is insanely capable, and can do pretty much everything you ask it. I use it for almost everything now. I've literally spent less time building this than I used to spend debugging OpenClaw.
AGI
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Moving beyond LLMs toward world models in AI research
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Truly an all-star cast, on one of the most important questions in AI. Thrilled to see some many people finally willing to confront the hard questions of how we can move beyond LLMs, and into what world models are really about.
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AI, politics, and assumptions about powerful systems
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The talk about AI & politics seems to be oddly missing a segment (a) assumes extremely capable AI is possible soon and (b) has a strong belief about how to use this technology to make human life better according to the political project they believe in. It is a moment of action.
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LLM limitations versus AGI capabilities
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Real AGI would not do this. Even after a trillion dollars in LLMs still do.
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Tavus Image-to-Replica AI tool with Phoenix-4
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🚨Turn a single image into a talking AI replica that actually holds a conversation.
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 15 mai 2026
Tavus’ new Image-to-Replica flow lets you upload one face (headshot, render, or AI character) and spin up a Phoenix-4–powered digital replica that can speak, listen, and respond; no studio shoot… pic.twitter.com/V7mHf4BdJITurn a single image into a talking AI replica that actually holds a conversation. Tavus’ new Image-to-Replica flow lets you upload one face (headshot, render, or AI character) and spin up a Phoenix-4–powered digital replica that can speak, listen, and respond; no studio shoot
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New Open-Source AI Standard with Ring-2.6-1T
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🚨 @AntLingAGI HAS OFFICIALLY SET A NEW STANDARD FOR OPEN-SOURCE AI
— Charly Wargnier (@DataChaz) 15 mai 2026
They just dropped Ring-2.6-1T, a trillion-parameter model built specifically to tackle long-horizon workflows 🔥
The benchmarks show exactly why it leads the open-source space:
→ Tau2-Bench (95.32): Navigates… pic.twitter.com/H30Uyj9Ajo@AntLingAGI has officially set a new standard for open-source AI. They just released Ring-2.6-1T, a trillion-parameter model designed specifically to handle long-horizon workflows. The benchmarks demonstrate why it leads the open-source space: → Tau2-Bench (95.32): Navigates…
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Six stages of reacting to AGI
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Everyone thinks they’re ready for AGI until the robot starts casually outperforming their 10-year roadmap. The 6 stages:
denial → curiosity → excitement → panic → acceptance → “please don’t replace me, I brought snacks.” Which stage are you in? -
Controlling Algorithms: Why I Built My Own
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The one who controls the algorithm controls you. Why I built my own. 🙂
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AI Storage: No Trade-Offs Between Cost and Workflow
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AI teams shouldn’t have to choose between costly object storage and cumbersome git workflows. @huggingface
Storage is designed for model weights, datasets, checkpoints, and artifacts:
– simple per-TB pricing
– built-in CDN
– Xet deduplication
– private by default when needed Store -
Claude Cowork’s Federated MCP Performance Benchmark
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Claude Cowork just got 10x more powerful!
— Sumanth (@Sumanth_077) 15 mai 2026
Glean benchmarked centralized vs federated MCP in Claude Cowork. Same harness, same model, same queries, different context layer.
The federated approach: Each data source (Gmail, Slack, Drive, Salesforce) has its own MCP server. Claude… pic.twitter.com/0lSWyHsKriClaude Cowork just got 10x more powerful! Glean benchmarked centralized vs federated MCP in Claude Cowork. Same harness, same model, same queries, different context layer. The federated approach: Each data source (Gmail, Slack, Drive, Salesforce) has its own MCP server. Claude