DEDUPED Hot AI repos, turned into use cases Get the Monday 5

How to let your coding agent harness improve itself from requests with Reef

Run Reefine against a local model endpoint, then ask in plain language for harness changes.

Short answer: Install the package with uv pip install reef-infra inside an activated virtual environment; then start the harness recipe with reef serve --recipe reefine --inference.upstream-url http://127.0.0.1:11434 --inference.upstream-model gemma4:26b; create a scenario with curl -fsS -H "Content-Type: application/json" -d '{"name": "my-harness"}' http://127.0.0.1:8901/reef/scenarios. All steps.

Authorization notice. Reef is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Remote script. The documented install pipes a script from the local Reef server into bash, so read the script before running it and keep the install root outside your working project.

Who it is forA developer who runs a coding agent day to day and wants its prompts, rules, and skills to get better without training any model.

The problemAgent instructions and skills stay static, so the same corrections are repeated in every session.

The outcomeA versioned harness that reflects your requests, installed in a separate directory and ready to review or roll back.

What you need first

  • Python with uv installed
  • git-lfs system package for artifact and checkpoint features
  • A model endpoint, such as a local Ollama server, for harness work
  • A CUDA GPU stack that meets the Evolve your model requirements, for weight training

Steps

  1. Install the package with uv pip install reef-infra inside an activated virtual environment.
  2. Start the harness recipe with reef serve --recipe reefine --inference.upstream-url http://127.0.0.1:11434 --inference.upstream-model gemma4:26b. (inferred, check the README)
  3. Create a scenario with curl -fsS -H "Content-Type: application/json" -d '{"name": "my-harness"}' http://127.0.0.1:8901/reef/scenarios. (inferred, check the README)
  4. Install the harness with curl -fsS -H "x-reef-scenario: my-harness" 'http://127.0.0.1:8901/reef/harness/install?adapter=pi' | bash, after checking the script contents. (inferred, check the README)
  5. Ask for a change with reef-pi evolve "when I ask you to fix a bug, reproduce it with a failing test first".
  6. Review versions with /versions inside a reef-pi session and install one with /versions install.

Prompt for your agent

Using the Human-Agent-Society/reef repo, set up the Reefine harness recipe against the local model endpoint at <model endpoint URL> and install a scenario named <scenario name>. Report the output of reef-pi evolve and confirm the install root is outside the project directory.

Caveat. The README says the install root must stay outside the project the agent works in, because a session could otherwise change what the next session runs.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for Human-Agent-Society/reef and "Let your coding agent harness improve itself from requests"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

Cursor, ChatGPT, others

Share: X LinkedIn Hacker News Email Copy link

Third-party repos we don't own. AI-written use cases can be wrong. Disclaimer