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Learning Loop
Consented outcomes improve future work.
Overview
What Learning Loop is
Learning Loop captures what happened during real production work locally, and uses consented outcomes to improve future prompts, workflows and recommendations. Its measure of success is that repeated work costs less, not that more data is collected.
Data handling
Local first, consent gated.
- Local by default
- Work and traces stay on your machine unless you choose to send them.
- Consent per decision
- Sharing is opt-in at the point it would happen, not a setting buried at install.
- Secrets redacted
- Credentials are stripped before anything is eligible to leave the machine.
- Your provider keys
- Model access uses keys held in a local vault: the project uses your keys, not a shared account.
Inputs
- Local traces of work performed
- Outcomes: what succeeded, what was rolled back
- Reuse signals from similar past work
Processing
- Capture
- Traces are written locally as work happens.
- Redact
- Secrets are stripped before a trace is eligible to be shared at all.
- Insight
- Patterns across traces become reusable solutions rather than raw history.
Outputs
- Reusable solutions for recurring work
- Improved prompts and workflow recommendations
- Local insight reports
Consent and data handling
- Local first
- Capture happens on your machine. Nothing leaves it by default.
- Consent gated
- Sharing is opt-in per decision, not a single setting buried at install time.
- Secret-free by construction
- Redaction happens before sharing is possible, not as a filter at send time.
Development
Where the work stands.
Objectives and dependencies. Dates appear only where a specific verifiable event backs them.
Active(1)
Learning Loop
Developer PreviewImprove future work from consented local outcomes, with secrets redacted before anything is shared.
- Depends on
- Project-aware agents
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