For each problem it detects, LangSmith Engine offers three resolution actions. Opens a PR
Writes a targeted code or prompt change + opens against the repository. You can review and merge. Creates a custom inline evaluator
Proposes an evaluator
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LangSmith Engine offers three resolution actions
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Coding agent retry loop: 10,000 calls huge bill
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A coding agent gets stuck in a retry loop during the night. By morning, it has made 10,000 LLM calls. You now have a four-digit bill. Observability tells you what happened, but stopping these problems before they
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Anthropic reports 8x code, 52x optimization, and 64% better decisions
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Recursive self-improvement may no longer be just a theory.
Anthropic reports: 8x more code per engineer 76% success on open-ended coding tasks 52x training optimization Better research decisions than humans 64% of the time
The feedback loop is getting tighter.
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68% of AI code contains errors, No-mistakes stops them
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EL MEME DE “CLAUDE, MAKE NO MISTAKES” SE HIZO REALIDAD.
— Nico (@nicos_ai) 8 juin 2026
El 68% del código que genera la IA tiene errores, según las stats reales del creador de esta Skill.
No-mistakes los para antes de que lleguen a producción.
Cómo funciona:
→ git push no-mistakes en vez de git push origin… pic.twitter.com/LzT5XYPFftThe meme of 'Claude, don't make mistakes' has become reality.
68% of AI-generated code contains errors, according to the real stats from the creator of this Skill.
No-mistakes stops them before they reach production.
How it works:
→ git push no-mistakes au -
Nemotron 3 Ultra unable to recover HLE and code performance via OPD
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Nemotron 3 Ultra can't recover perf on HLE, code, etc. via OPD The teacher was trained on DeepSeek-V4-Pro traces (DSV4 Max achieves 37.7% on HLE!). Looks like the MOPD warmup failed to properly init the student? No good trajectory → No improvement via OPD
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AI shifts from reactive programming to proactive orchestration
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The deeper shift is from reactive programming to proactive orchestration, where AI anticipates needs rather than just responds. It's about designing systems that learn intent, not just behavior.
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Unifying Codex, agents, and chat boosts value for users and valuation
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Folding Codex, agents, and chat into one app makes sense for users and even more sense for the valuation.
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From C to Theano: The Evolution of Neural Network Frameworks
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I wrote my first neural networks in pure C, then in Matlab, then in NumPy, before eventually moving to Theano. Since then, I have seen and tried practically every neural network framework ever developed. Some are bad, some are good. The good
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Codex evolves from assistant to AI teammate across domains
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Codex use-cases: “From software engineering and design to data analysis and operations, Codex is becoming an AI teammate instead of just an AI assistant.”
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Design loops that prompt agents instead of direct prompting
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Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.