I feel that the trend towards training models to autonomously go off and try to do everything themselves is anti-human. We should, IMO, be training LLMs to support humans in their learning, creativity, and iterative experimentation.
AI
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Inquiry about safetensors version availability vs ggufs
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Do you have a safetensors version published or only ggufs?
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Anthropic engineers’ token-saving habits, no setting changes needed
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this guy literally breaks down the exact habits Anthropic engineers use to save millions of tokens without changing a single setting 🤯
— Charly Wargnier (@DataChaz) 22 mai 2026
Watch the video, then bookmark the written guide 👇 https://t.co/gAHjGmFxwy pic.twitter.com/Hlb96srixBthis guy literally breaks down the exact habits Anthropic engineers use to save millions of tokens without changing a single setting Watch the video, then bookmark the written guide
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GPT-5.5 Generates 30k Lines QML with Agentic Reasoning
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GPT-5.5 cranking out 30k lines of QML for the Omarchy 4 branch + nailing subtle agentic reasoning!!
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Claude MD File: Top GitHub Repo Uses Karpathy’s 4 LLM Principles for AI Control
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DID YOU KNOW THE #1 GITHUB TRENDING REPO (146K+ STARS) IS LITERALLY JUST A CLAUDE MD FILE? It uses @karpathy
's 4 LLM principles to keep AI in check: → Seek clarity: Always ask before making assumptions.
→ Stay minimal: Avoid bloat and keep things simple.
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Thousands of tokens per second across parallel requests
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1000s of toks/sec across a dozen parallel requests if not more
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Gemini 3.5 Flash: Eager Model Built for Real-World Usefulness
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Gemini 3.5 Flash is definitely an over eager model, a bit of over correction from the “Gemini laziness” feedback, but definitely built to be real world useful!
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LLM trained on whole Internet is inefficient for language understanding
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Learning an LLM from the whole Internet is a spectacularly inefficient way to understand language.