What if understanding and generation were the same process? SenseNova introduces SenseNova-U1 — a native unified model that treats seeing and creating as a single process. It matches top understanding-only VLMs in text, vision-language perception, reasoning, agents, and
LLMS
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Seven Decision-Making Prompts and Frameworks
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I built these 7 prompts from frameworks that have driven billions of dollars in decisions across companies like Amazon, Toyota, and Berkshire Hathaway. The prompts are free. The thinking systems behind them are what separate good decisions from expensive mistakes. Also checkout
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Did AI depend on training on huge human knowledge?
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or is the question: did AI depend on training on enormous amounts of human knowledge?
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Fixing NotebookLM infographics: ChatGPT to the rescue!
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NotebookLM makes jaw-dropping infographics—until you hit "detailed" mode and get jumbled, unreadable text. The ultimate fix? Just drop the image into ChatGPT and prompt it to "generate this exact infographic but with every error corrected." If it misses a spot, download the
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AI is transforming mathematics, says Nature article
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‘It is incredible’: How #AI is transforming mathematics
by @dcastelvecchi @Nature Learn more: https://
buff.ly/NDPsRy2 #LLM #ArtificialIntelligence #MachineLearning #DeepLearning -
ChatLLM Routes Tasks to Best AI Models
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ChatLLM Will Route To The Best Model Based On Your Task Coding -> Opus 4.7 and GPT 5.5 Writing -> Gemini 3.5 Real Time – Grok 4.3 -> SeeDance 2.0 Voice -> ElevenLabs Images -> GPT Image 2
Open Source -> DeepSeek, Kimi and GLM 100+ top AI models in one place -
When extra test-time compute helps model convergence
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The key distinction: More test-time compute is not automatically useful. It becomes useful when the model has learned an internal landscape where extra iterations move the latent state toward solution-aligned attractors rather than spurious ones. That is why the convergence
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New paper introduces Equilibrium Reasoners for latent AI reasoning
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The next clue in AI reasoning: answers may be attractors. A new paper from Benhao Huang, Zhengyang Geng, and Zico Kolter introduces Equilibrium Reasoners (EqR) — a sharp mechanistic view of test-time scaling in latent reasoning models. The core idea is simple, but deep:
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Test-Time Compute and Solution-Aligned Attractors
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The key distinction: More test-time compute is not automatically useful. It becomes useful when the model has learned an internal landscape where extra iterations move the latent state toward solution-aligned attractors rather than spurious ones. That is why the convergence
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OpenAI and Anthropic’s contrasting AI launches in 2026 cinema
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OpenAI: carefully rolls out GPT-5.5-Cyber through Trusted Access for verified defenders Anthropic: “Claude Mythos is too powerful for public release” Also Anthropic: accidentally shows Mythos in the UI and immediately runs out of capacity 2026 AI launches are absolut cinema.