All the cars are gone from Tesla Century City! All they have is Model Y L, a self-driving video playing on the background. I guess the best product is no product. Either that or they just sold the showroom cars.

All systems are looking good, and weather is favorable for liftoff. Propellant load is about to begin for Falcon 9’s launch of the USSF-385 mission from California →

All that agent trace data sitting in your production logs — what if it could train its own specialized model? LangSmith just made that a built-in workflow. Title: Introducing LangSmith Fine-Tuning URL: ❓ What is LangSmith Fine-Tuning? It's the `smithtune` CLI, which turns production agent traces into a fine-tuned model without building custom infrastructure. It covers dataset creation, training, evaluation, and deployment end to end. ❓ How does it build the training data? It pulls trajectories — ordered sequences of messages and tool calls — from LangSmith projects, then uses a multi-agent review process with custom rubrics to keep only high-quality examples. Crucially, it preserves the exact context at each turn, including which tools were available. ❓ How do training and deployment work? Supervised fine-tuning via LoRA runs through managed platforms like Fireworks and Baseten, so there's no GPU provisioning to manage. After a replay evaluation against the base model, `smithtune deploy` ships the fine-tuned model straight to production. ❓ What results did they see? On an issue-detection task, fine-tuning lifted Kimi K3's score from 90.0 to 96.0. On code review, it matched or beat base-model quality while cutting model calls by 29.8% and tool requests by 29.4%. #LangSmith# #FineTuning#

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All of this effort from the AI labs pouring into proofs, but there are so many other interesting problems in other fields For example, this historian used AI to make progress on the cyphers of John Dee & the intellectual antecedents that Darwin drew from.