Because tokens are still pretty cheap, and reviewing what someone else's LLM did is a lot more work than reviewing what yours did where you remained in complete control of how you prompted it
LLMS
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How to Write Skills That Never Fail for AI Agents
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How to write Skills that never fail.
— Akshay 🚀 (@akshay_pachaar) 1 mai 2026
Writing good skills for agents is one of the highest-leverage skills you can develop today.
But many Claude skills fail silently. They never trigger, they trigger on the wrong prompts, or they skip the steps they were written to enforce.… pic.twitter.com/IeEgQCPuWWHow to write Skills that never fail. Writing good skills for agents is one of the highest-leverage skills you can develop today. But many Claude skills fail silently. They never trigger, they trigger on the wrong prompts, or they skip the steps they were written to enforce.
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o1 Outperforms Doctors on Medical Benchmarks and ER Cases
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New paper (on an old AI) tests o1 against doctors on medical benchmarks & real ER cases: “across a variety of scenarios and applications, the large language model outperformed both human physicians and older models” The potential suggests an “urgent need for prospective trials.”
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Is a Model Trained on Claude Outputs Without Permission Open Source?
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Would you still call a model open source if it was trained on Claude's outputs without permission?
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Why Looped Transformers Excel at Multi-Step Reasoning
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Researchers just figured out why looped transformers are so powerful. Most language models store huge amounts of knowledge but fail to combine facts in a single pass. Ask one a 10-step reasoning question and it breaks. A new paper explores looped transformers, an architecture
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Open Source AI Models and the Secret of Knowledge Distillation
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Some "open source" AI models have a secret. They were trained using outputs from closed models like ChatGPT and Claude. The weights are free. The code is public. Anyone can run them.
But the intelligence inside came from somewhere else. This technique is called distillation.
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YC Bench: AI Agent CEO Simulation Benchmark for Startups
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YC Bench by @CollinearAI
: Benchmark for Agents who play CEO of an AI startup for 1 simulated year via CLI tool use against a deterministic discrete-event simulation. Score = final $$ amount achieved by @nazneenrajani and team Also a good opportunity to showcase this recent hf -
Embedding Hybrid Deterministic AI With Transformers for Neurosymbolic AI
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All the more a reason to embed hybrid AI with deterministic traits alongside ANNs in particular Transformers with Self-Attention mechanism into Neurosymbolic AI
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Agentic Harness Engineering for Automatic Coding-Agent Evolution
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Paper: Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses https://
arxiv.org/abs/2604.25850 https://
github.com/china-qijizhif
eng/agentic-harness-engineering
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Practical Path to RSI: Improving the Model Harness and Tools
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The near-term path for RSI (recursive self-improvement) is not necessarily about the model directly improving its own weights. A more practical path is improving the harness around the model: tools, middleware, long-term memory, skills, evals, feedback loops. I saw a recent