Most human tasks are not Markovian, the optimal next action cannot be determined solely by looking at the current state. It depends heavily on the past trajectory, the original intent, and context constraints. An agent that cannot compress and track its past trajectory with
AI
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AI Alignment and the Transfer of Human Values
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If humans don’t intrinsically value other humans, then their machines will have even less of an incentive to do so. They are literally learning from us right now, absorbing our values and attitudes.
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Managing rate limits for concurrent AI agents in Claude Code
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It’s solid for single sessions, but concurrent agents (common in Claude Code) hit the free 40 RPM limit fast and throw 429s. The proxy’s backoffs help a bit, many just request a quota bump from NVIDIA.
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Radar leverages AI to build an operating system for retail stores
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80% of commerce happens in physical stores, yet they have zero real-time data.@SpencerHewett is bringing AI to the physical world.
— Charly Wargnier (@DataChaz) 19 mai 2026
Radar just hit a $1B valuation for building the "operating system" for retail stores.
Physical AI is here folks 🦾 ↓pic.twitter.com/4ucpcoi0gy https://t.co/rE5yI2xnz280% of commerce happens in physical stores, yet they have zero real-time data. @SpencerHewett is bringing AI to the physical world. Radar just hit a $1B valuation for building the "operating system" for retail stores. Physical AI is here folks ↓
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Gemini Omni vs Veo: Video Generation Scene Composition
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Gemini Omni test 🔥
— 🚨 AI News | TestingCatalog (@testingcatalog) 19 mai 2026
One of the best "Cyberpunk hacker robot" videos I've seen so far. It handled scene composition much better than the latest Veo model. pic.twitter.com/5Jcg4vUCjpGemini Omni test One of the best "Cyberpunk hacker robot" videos I've seen so far. It handled scene composition much better than the latest Veo model.
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Improving AI stability via deterministic RNG and low-rank perturbations
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of course but EGGROLL’s deterministic RNG exactly reconstructs every low-rank perturbation from seeds making evolutionary paths more auditable/replayable than backprop and Could strengthen persistent AI stability
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Who controls AI infrastructure and data? Dell AI Factory
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One of the biggest enterprise AI questions right now is simple: who controls the infrastructure and the data? That’s why this matters. Bringing @MistralAI models into the Dell AI Factory with NVIDIA gives enterprises more control over how they train, deploy, and scale AI without
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MIT’s “Insum” speeds up einsum for sparse datasets
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MIT researchers developed “Insum,” a technique for speeding up computations on datasets replete w/zeros. It rewrites Einstein summation (“einsum”) operations to avoid inefficient handling of zeros, improving memory efficiency & performance: https://
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The declining cost of AI intelligence and model pricing
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the cost of intelligence keeps going down, model price itself is lossy implementation of that
