# Apex Studio

- **Event:** [AIE Code Summit 2026 Hackathon](https://cerebralvalley.ai/e/aie-code-2026-hackathon)
- **When:** Sat, Nov 7 at 9:00 AM – 10:00 PM (PST)
- **Where:** San Francisco, CA
- **Team:** [Christopher Guerrero](https://cerebralvalley.ai/u/chrisguerrero)
- **GitHub:** https://github.com/coldrazer/Scar-Tissue
- **Demo video:** https://youtu.be/lRY_A1eupAA
- **Gallery:** https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/20

Scar Tissue — The Coding Agent That Forgets What Doesn't Help

    Most coding agents never get smarter. They just accumulate: every reflection saved, every wrong rule kept, until memory is noise.

    Scar Tissue treats every lesson as a hypothesis that has to earn its place. Failures are distilled into lessons — then every epoch, each lesson is ablated: its tasks are re-run wit
h it and without it. Lessons that hurt, never help, or never get selected are deleted, with the measured reason recorded.

    The result: held-out pass rate climbs 0% → 100% while memory shrinks from 20+ piled-up lessons to 8 proven ones — one per hidden house rule.

    🔗 GitHub (full source, 24 tests, benchmark): https://github.com/coldrazer/Scar-Tissue
    📄 Fetch.ai lesson schema: https://github.com/coldrazer/Scar-Tissue/blob/main/docs/fetch-schema.json

  
    🧠 HOW IT WORKS
    • Every failure + its eventual fix becomes a candidate lesson, classified by failure type
    • Lessons live in vector memory (MongoDB Atlas $vectorSearch, or local JSON) and are retrieved per task
    • Only 2 global + 3 per-task lesson slots exist — a useless lesson crowds out a useful one
    • Ablation re-runs each lesson's most relevant tasks with vs. without it, averaged over repeated probes
    • Survivors are born "canary" and auto-publish after a passing held-out eval; other agents query them through a Fetch.ai uAgent
    • Ships with container sandboxing, API-key tenancy, background job runner, and a hard cost budget

    ⚠️ HONEST NOTE
    The benchmark numbers in this video are from the offline mock model (stated noise rates, reproducible with `scar.py compare --mock --epochs 4 --seeds 3` in the repo). Real-model co
mparisons (Meta Llama, Google Gemini, Subquadratic SubQ) run with the same command — the repo README shows how.

    🛠️ BUILT ON
    The Meta ecosphere — Meta Wonder · Meta Spark · Muse Glimmer
    Research: Subquadratic (SubQ) · Front-end schema: Fetch.ai · Benchmark testing: Google Gemini
    Solver/distiller: Meta Llama API · Memory: MongoDB Atlas

    Built for the AI Engineer Code Summit hackathon — "systems that get measurably better with every run."

## More from AIE Code Summit 2026 Hackathon

- [Theseus](https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/17)
- [Dojo](https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/18)
- [Thought to Reality - Dylan Ler](https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/19)
- [Puddles](https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/21)
- [ballast](https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/22)
- [AgentGYM](https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/23)

---

Markdown version of https://cerebralvalley.ai/e/aie-code-2026-hackathon/hackathon/gallery/20. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt