Practical guidance

Practical AI guidance

Techniques published by their authors, each with what it is for, what it needs, the product version it applies to and where it stops working — in the source's own words, which are quoted beneath each point. None of it was written here, and an item is only listed when its source states all of these.

Agents

  1. The Agent Said It Was Done. The Database Disagreed.

    Hugging Face blog · · Reported by the source, not tested by us

    What it is for
    The article provides instructions for running the ThinkingBox-Bench agent benchmark through OpenEnv.
    This post covers what we found, what consistency costs, and how to run the benchmark yourself through OpenEnv
    What you need
    • Use Linux or WSL with Python 3.11 or newer, uv, and Docker.
      Tested on Linux and WSL, with Python 3.11+, uv and Docker.
    • Check out the thinkingbox-data repository at the pinned release and provide model endpoints for the agent, simulated user, and judge.
      You also need a thinkingbox-data checkout at the pinned release and model endpoints for the agent, simulated user and judge.
    • Start Typesense 30.1 and wait for its health check.
      start Typesense 30.1 and wait for its health check
    • Start the Session Proxy and MCP servers.
      start the Session Proxy and MCP servers
    • Start the OpenEnv server using a ThinkingBox YAML config that names the three models.
      start the OpenEnv server against a ThinkingBox YAML config naming your three models
    Applies to
    ThinkingBox-Bench v1.0 (we do not track this product's releases; check your version)
    git -C thinkingbox-data checkout thinkingbox-bench-v1.0
    Where it stops working
    • The cost metric is a comparative efficiency index, not an invoice or production serving price, and it prices single successes rather than consistency.
      This is a comparative efficiency index, not an invoice, and not the price of serving one production request. It also prices single successes, not consistency.
    • The readiness check cannot observe Typesense or live-probe every model endpoint, so those must be confirmed separately.
      It cannot observe Typesense or live-probe every model endpoint, so confirm those separately
    • The public benchmark tasks are synthetic reconstructions, and the customers are not real.
      every task in the public benchmark is a synthetic reconstruction. The workflows and policies are modeled on real AI agentic enterprise patterns; the customers are not real.
    • The article has not measured the lift from the recommended production practices on this benchmark.
      We have not measured the lift from any of these on this benchmark, which is exactly the kind of thing the environment now makes testable.

    Read it at Hugging Face blog

Evaluation

  1. ReviewBench: An open benchmark for AI code review

    GitHub blog: AI & ML · · Reported by the source, not tested by us

    What it is for
    The article explains how to use ReviewBench to evaluate an AI code review agent and submit results to its leaderboard.
    In this post, we'll walk through how ReviewBench is constructed, how it establishes reliable ground truth and scoring, and how to onboard your own code review system and submit results.
    What you need
    • Sign in with GitHub on the ReviewBench website.
      Sign in with GitHub on the ReviewBench website.
    • Provide a container image, your configuration, and your own model key when registering your agent.
      Provide a container image, your configuration, and your own model key.
    Applies to
    ReviewBench research preview (we do not track this product's releases; check your version)
    ReviewBench's research preview version is now available through the ReviewBench website
    Where it stops working
    • ReviewBench is an offline signal, while online experiments remain the ultimate measure of user impact.
      Online experiments remain the ultimate measure of user impact, but ReviewBench gives us greater confidence in which changes are worth taking there.
    • Comment volume alone does not capture comment quality.
      Comment volume alone, however, does not capture comment quality.
    • Leaderboard publication is limited to first entries or scores that outperform the agent’s current leaderboard score.
      Scores are published to the leaderboard only if they outperform the agent's current leaderboard score, or if this is the agent's first leaderboard entry.

    Read it at GitHub blog: AI & ML