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Find reward hacks and grader flaws in Harbor RL environments.

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GITHUB · launched Nov 6, 2026 · 54 upvotes (top 59%) · 0 views

Niche: ai benchmarking platforms · 145 launches, 4 in the last 7 days ▼ -56% · Explore this niche →

Category and tags

  • Horizontal
  • Observability & eval
  • Developer
  • AI-native
  • Commercial product

Visit website ↗ Source listing ↗

Could you build this?

Partial A UI and harness for running agents against Harbor environments and reporting suspected grader flaws is vibe-codeable, but finding real reward hacks requires genuine RL-evaluation expertise to judge what counts as a flaw.

What it would actually take: Build a runner that executes Harbor RL tasks in sandboxes, then use LLM-based agents to attempt exploits and static analysis to flag weak graders, with a dashboard for triage. The hard part is designing adversarial probes and validating findings so they are real exploits, not false positives, which requires RL and benchmark-design expertise plus compute to run many trials.

Competitors

Other products that read as similar to this one : 3697 launches clear the similarity bar, closest 8 shown.

Attention rank: #2138 of 3698 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 373 days after the earliest competitor.

  • REx-skill · github · 2026-09-23 · 96 upvotes · similarity 0.59
  • PwnPad · hn · 2026-08-27 · 5 upvotes · similarity 0.55
  • Permanent Underclass · hn · 2026-02-24 · 6 upvotes · similarity 0.55
  • grand-tour-rpocket · github · 2026-11-05 · 60 upvotes · similarity 0.55
  • nukerat · github · 2026-11-06 · 79 upvotes · similarity 0.54
  • blazing-rust · github · 2026-11-07 · 12 upvotes · similarity 0.53
  • skills · github · 2026-10-14 · 14 upvotes · similarity 0.53
  • Skill-up · hn · 2026-07-30 · 5 upvotes · similarity 0.53

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