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rrrrrredy/README.md

Hi, I'm Song Luo

I build evidence-driven infrastructure for AI agents: memory that survives lossy context, completion claims bound to current state, and reusable skills that can be tested and operated.

My work sits between agent research, infrastructure engineering, and hands-on product building. I prefer public artifacts, repeatable checks, bounded experiments, and honest negative results over convincing demos.

Technical Staff of Myself.

Email · X / Twitter · Hugging Face

Start here

More work

Papers and technical reports
Research and applied systems

Working approach: Bind important claims to observable evidence. Use deterministic checks for deterministic behavior and model evaluations where behavior is stochastic. Keep sensitive raw evidence local; publish reviewed, redacted, reproducible artifacts, including real failures and user corrections.

Stack: Go · Python · TypeScript · JavaScript · Shell · HTML/CSS

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  1. agent-memory-system agent-memory-system Public

    Local-first, evidence-backed continuous memory for coding agents

    Go 1

  2. research-toolkit research-toolkit Public

    Methods, workflows, and checks for AI-assisted research reports. 研究工具箱:研究方法、工作流程与检查工具。

    Python 5

  3. beforedone beforedone Public

    Require fresh, file-bound verification evidence before coding agents declare completion.

    Go 2

  4. skill-security-guard skill-security-guard Public

    Static security scanner for agent skills: A-F risk rating, safe zip scanning, CI-tested rules

    Python 2

  5. governed-skill-evolution governed-skill-evolution Public

    A prospective ablation and cross-model transfer study of governed persistent Agent experience.

    Python

  6. runtime-evolution-workbench runtime-evolution-workbench Public

    Local-first Codex Run retention, diagnosis, bounded comparison, and rollback-safe capability evolution technical preview.

    TypeScript