FLYWHEEL — Task-Level RL Brain
A tiny reinforcement-learning brain that learns which approach wins for each kind of task. FLYWHEEL blends win-rate and average reward with an exploration bonus and time decay, then recommends the policy most likely to pay off next — record, recommend, repeat. Point it at any task with an outcome and it compounds. Free, open-source, zero dependencies.
How it works
The exact pipeline, end to end.
Per-task policy learning: win-rate + avg-reward + explore + decay FLYWHEEL — Task-Level RL Brain turns that from a chore into a few minutes — and you keep it for every project after.
This vs the alternative
Why it's clearly worth it.
The shift it creates
- Piecing it together from scratch
- Conflicting advice online
- No proven playbook to follow
- Per-task policy learning: win-rate + avg-reward + explore + decay
- record → recommend → policyFor: a compounding loop
- Works on any task with a measurable outcome
Use it to…
Real ways people put FLYWHEEL — Task-Level RL Brain to work.
Per-task policy learning
Per-task policy learning: win-rate + avg-reward + explore + decay
record → recommend → policyFor
record → recommend → policyFor: a compounding loop
Works on any task with a
Works on any task with a measurable outcome
What's inside
Who it's for
Solo founders & creators
ship pro results without a team
Agencies & operators
repeatable for every client
AI builders
drop-in capability
Questions, answered
Ready to put FLYWHEEL — Task-Level RL Brain to work?
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