Honey Nudger - Recursive Self-Learning For Agents

Most AI agents work in the lab, then fail in production. The fix? Manual annotation, endless prompt tuning, and praying. We built Honey Nudger to move the industry from prompt engineering to "performance engineering." Instead of hand-labeling edge cases, your AI agents learn continuously from their own experience — self-improving based on what actually moves the metrics you care about. Stop tuning. Start learning.

Hey Product Hunt! 🍯🐝 I'm Dan, and I built HoneyNudger because I was drowning in "Annotation Hell." I kept hitting the same wall: I designed one of the first LLM+RL shopping assistants in production a few years ago. Every time, the story was identical — brilliant in the lab, broken at scale. We'd patch it with manual annotation and pray. Rinse, repeat. It felt fundamentally wrong. We're training billion-parameter models, yet still hand-labeling edge cases so it knows how to behave? Having a clearly articulated goal and set of performance metrics should be enough... we just didn't have a way to automatically and continuously feed those metrics back into the system so it could start learning and adapting on its own. The insight that changed everything: Most agents don't have enough data to self-learn. Your personal AI and mine? We're doing similar tasks, but our experience data stays siloed and sparse. Self-learning becomes impossible. So we built collective intelligence: Hivemind pools learnings across agents while preserving privacy — giving everyone the data scale needed for real self-improvement. Think honey bees: they've been propagating hive knowledge for 30+ million years. We took notes. Where we are now: Hivemind waitlist is open — limited spots for early agents. Pro version (private self-learning instances for enterprises) coming soon. Tired of the "prompt-and-pray" cycle? Grab a spot. AMA thru this weekend — I'll be here. 🔥 NUDGE ON.

Honey Nudger - Recursive Self-Learning For Agents

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