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HyperWorker
Project management harness for AI agents
Your AI agent starts strong...then drifts, loses context, and skips steps. HyperWorker stops that. Six mechanisms in markdown and YAML keep long-running projects on track: Lock (no drift), Atomicity (no "mostly finished"), Dependency (no skipped steps), Memory (session survival), Precedence (rule conflicts), Verification (evidence-based completion). Point your agent at HARNESS.md and it scaffolds everything. Built in Cowork. Works with any LLM that reads files. Open source, MIT licensed.
Hey Product Hunt! I'm Spencer, and I built HyperWorker because I kept hitting the same wall running projects through AI agents.
The agent would be great for the first few tasks. Then it would forget what happened last session, edit files it was told not to touch, or mark something "done" based on nothing but its own confidence. The problem wasn't the model...it was the environment. There was nothing structurally preventing drift.
HyperWorker started as rules in a system prompt. When that stopped scaling (prompts compress, rules conflict, knowledge gets lost), I externalized everything to markdown and YAML files that the agent re-reads every session.
v4 adds Verification as the sixth mechanism...every task now produces an evidence trail proving what was checked and what happened. The ratchet principle means the project can only move forward.
The launch campaign for HyperWorker was managed by HyperWorker itself. That felt like the right test.
I'd love feedback from anyone running real multi-session projects through AI agents. What breaks for you? What patterns have you found?
About HyperWorker on Product Hunt
“Project management harness for AI agents”
HyperWorker was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #73 on the daily leaderboard. Your AI agent starts strong...then drifts, loses context, and skips steps. HyperWorker stops that. Six mechanisms in markdown and YAML keep long-running projects on track: Lock (no drift), Atomicity (no "mostly finished"), Dependency (no skipped steps), Memory (session survival), Precedence (rule conflicts), Verification (evidence-based completion). Point your agent at HARNESS.md and it scaffolds everything. Built in Cowork. Works with any LLM that reads files. Open source, MIT licensed.
On the analytics side, HyperWorker competes within Productivity, Open Source, Developer Tools and GitHub — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how HyperWorker performed against the three products that launched closest to it on the same day.
Who hunted HyperWorker?
HyperWorker was hunted by Spencer Heckathorn. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of HyperWorker including community comment highlights and product details, visit the product overview.
Spencer Heckathorn