Show HN: A practical AI Evaluation pattern

Category: ai-ml

Tags: ai-agents, benchmarking, evaluation, containers, reinforcement-learning

Score: 7.5/10 (Innovation: 7, Technical: 8, Documentation: 7, Utility: 8)

Harbor is a framework for evaluating and optimizing AI agents and language models in containerized environments, with support for running benchmarks like Terminal-Bench-2.0 across local Docker or cloud providers in parallel. It enables users to build and share custom benchmarks, run thousands of concurrent experiments, and generate rollouts for RL optimization. It stands out for its official harness role in Terminal-Bench-2.0 and its practical approach to production-oriented AI evaluation beyond public leaderboards.

Target audience: AI researchers, ML engineers, DevOps engineers, and teams deploying AI agents

Repository: https://deepsense.ai/blog/the-top-scoring-model-is-not-always-the-best-production-choice-how-to-evaluate-ai-systems-beyond-public-benchmarks/ · Python · Apache-2.0 · 6345 stars

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