7-e1even
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learn-agent
A collection of notes on building coding agents, derived from the production development of the desktop agent Reina, with each mechanism simplified into a zero-dependency, single-file Node.js demo.
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FR-AI / ANALYSISWhy it is worth attention
It addresses fundamental reliability issues in agentic coding—memory collapse, hallucinated certainty, and attention smear—by providing a structured dual-loop workflow with durability and verification, achieving 90% first-pass success in benchmarks versus 0% for ad-hoc prompting.
Who it is for
- Researchers & students needing verifiable multi-week experiments
- Engineers & tech leads managing multi-module projects
- Indie builders & founders turning concrete ideas into auditable tasks
- Repo maintainers wanting context survival across agent switches
Use cases
- Implementing a complex multi-step feature with precise design trade-offs
- Debugging a subtle concurrency bug that requires a living brief
- Migrating a codebase across modules with verifiable progress
- Running paper-faithful experiments with locked hyperparameters and acceptance checks
Strengths
- Four lightweight skills installable across seven major agents with one command
- Benchmarked 90% clean pass (first oracle green) vs 0% for ad-hoc on 20 tasks with 2.5× fewer tokens
- Explicit human gates (APPROVE) prevent hallucinated or premature code
- Durable artifacts (plans, progress, findings) survive context loss and agent switches
Considerations
- Requires a reasonably concrete idea; not suitable for vague one-liners or product invention from scratch
- Human-in-the-loop means it demands active user participation, not fully autonomous
- May be overkill for simple throwaway scripts or vibe-coded snippets
README quick start
Quick start
Description
A human-in-the-loop control plane for reliable agentic coding—plan, challenge, implement, verify, and preserve progress.
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