Langchain is a publication focusing on programming languages, language design, and compiler development. Readers can explore articles covering topics such as language features, syntax design, and compiler optimization techniques. Additionally, they can learn about programming language theory, language implementation challenges, and practical applications of language design principles.

LangChain

Better-Harness is a system for iteratively improving AI agent harnesses using evaluations as a learning signal. The approach treats evals like training data in classical ML, using them to guide autonomous harness updates through a loop of sourcing evals, splitting into optimization/holdout sets, running baselines, optimizing, validating, and human review. Key design decisions include tagging evals by behavioral category, using holdout sets to prevent overfitting, and mining production traces for new eval cases. Results on Claude Sonnet 4.6 and GLM-5 showed strong generalization to holdout sets after hill-climbing. The system is open-sourced and integrates with LangSmith for trace logging, production monitoring, and eval generation.

Better Harness: A Recipe for Harness Hill-Climbing with Evals

Better-Harness: a recipe for hill climbing your harness

The Future: automated error detection & fixes