Demand for Intelligence is exploding, and we are facing a major supply crisis. https://t.co/EmAKgJ3nEk
— Nina Schick (@NinaDSchick) 3 avril 2026
Demand for Intelligence is exploding, and we are facing a major supply crisis.
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Demand for Intelligence is exploding, and we are facing a major supply crisis. https://t.co/EmAKgJ3nEk
— Nina Schick (@NinaDSchick) 3 avril 2026
Demand for Intelligence is exploding, and we are facing a major supply crisis.
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I probably should have, but I like Alex and he was just on my screen with a popular post about them. @BrianRoemmele has been yelling about local models for years. So has @3duaun on the thousand+ spaces we've done together. Amongst many others. And NVIDIA has the religion,
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I'd go with @NousResearch
's Hermes. Everyone is saying it's better than the Claw, including a guy working for me building automation systems for me. He switched last weekend and hasn't looked back.

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Multi-Agent Reinforcement Learning! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode
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AI Agents is a waste of time. AI Agents = LLM + Data + Tools + Web Codex / Claude Code = the same thing (CLI Agents = good at reading files and coding). Just use that. Best agent = markdown file and scripts running through the file structure. That’s it.

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Block just open-sourced mesh-llm, a peer-to-peer system that lets anyone pool spare GPU compute to run large open-source AI models without relying on any cloud provider. If a model fits on your machine, it runs locally at full speed. If it doesn't, the system automatically splits it across multiple machines on the network. Dense models get split by layers. Mixture-of-experts models like DeepSeek and Qwen3 get split by experts. Zero configuration required. Discovery happens over Nostr. Nodes find each other through relays, score by region and VRAM, and self-organize. No central server coordinates anything. Weights are read from local files, never sent over the network. Dead nodes get replaced in 60 seconds. It exposes a standard OpenAI-compatible API on localhost, meaning any existing AI tool can plug in without modification. Block is building infrastructure for AI that doesn't route through OpenAI, Google, or Anthropic. Frontier-class open models running across a mesh of commodity hardware, discovered via Nostr, with no cloud dependency. That's the direction AI needs to go. [Translated from EN to English]
→ View original post on X — @whiteafrican, 2026-04-02 23:14 UTC
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Same, I have a similar setup. A mix of Obsidian, Cursor (for md), and vibe-coded web terminals as front-end. Since I do a podcast, the number/diversity of research interests is very large. But the knowledge-base approach has been working great. For answers, I often have it generate dynamic html (with js) that allows me to sort/filter data and to tinker with visualizations interactively. Another useful thing is I have the system generate a temporary focused mini-knowledge-base for a particular topic that I then load into an LLM for voice-mode interaction on a long 7-10 mile run. So it becomes an interactive podcast while I run, where I ask it questions and listen to the answers to learn more. Anyway, heading out for a run now, thanks for the write-up 👊
→ View original post on X — @lexfridman, 2026-04-02 23:06 UTC

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Qwen3.6 Plus is now live on Poe. Delivers advanced vision‑language performance from Qwen, with clear gains in code‑heavy workflows like agentic and front‑end coding, plus stronger multimodal understanding including improved OCR and precise object localization. Designed for
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More evidence of move to local models.
— Robert Scoble (@Scobleizer) 2 avril 2026
This one running on the claw. On a laptop. https://t.co/oEGqhlQ9JQ
More evidence of move to local models. This one running on the claw. On a laptop.