this looks like a website but it’s an interactive generative video 🫨 https://t.co/BIAxz5ryJk
— Yohei (@yoheinakajima) 22 avril 2026
this looks like a website but it’s an interactive generative video

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this looks like a website but it’s an interactive generative video 🫨 https://t.co/BIAxz5ryJk
— Yohei (@yoheinakajima) 22 avril 2026
this looks like a website but it’s an interactive generative video
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I’ve sometimes heard this referred to as “parallel construction” in an intelligence or law enforcement context. LLMs are presently a parallel construction goldmine. Whether that is for good or for ill, well, one of many things we’ll have to adjust to quickly.

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Improve agentic performance with accurate RL post-training on low-precision FP8. NVIDIA NeMo RL, an open-source library within NVIDIA NeMo, supports FP8 to speed up RL workloads by 1.48x on Qwen3-8B-Base—enabling faster iterations for agentic tool use and multi-step
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1) tips, stories, and experiences on leveraging AI as an individual (fav tools, workflows, etc.)
2) anything agent related
3) rapid fire reacting to or creating a whole bunch of startup ideas across categories and coming up with a quick GTM experiment
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Journalists: if you ever have a thing you understand to be true, but cannot cite it or get it past editors due to commitments made to sources, describe your belief about the world to an LLM and ask the LLM if it can find public evidence which unambiguously confirms the belief.

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For the record, the greatest community on X right now is the e/acc community. Great watching you all accelerate.

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UK tribunal sends £2B claim accusing Microsoft of overcharging for licensing to trial https://
go.theregister.com/feed/www.there
gister.com/2026/04/22/microsoft_licensing_claim_cat/?utm_source=dlvr.it&utm_medium=twitter
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You can create plugins on ClawHub, so everyone has a choice what to adopt. Please do not open more PRs for this.

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persistence always pays off, even for ml interns (btw, I haven't done anything for the past 30 mins, just observing the intern making mistakes and fixing them haha)

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For real agentic workloads (North), short-context calibration wasn't enough. We calibrated AWQ on long internal agentic traces (up to 64k tokens) and added token masking in llm-compressor to exclude repetitive chat templates/tool descriptions from calibration stats. Plus QAD