| # Agent Trace |
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| This project is being built with OpenAI Codex as the coding agent. |
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| ## 2026-06-05 |
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| - Read hackathon rules, kickoff notes, sponsor details, and peer review feedback. |
| - Selected `Jawbreaker` as the product name. |
| - Chose Backyard AI as the main track. |
| - Established model bakeoff plan before committing to a default model. |
| - Created initial project scaffold for a Gradio Space and public GitHub repo. |
| - Created private Hugging Face Space under the hackathon organization. |
| - Pushed the same Git commit history to GitHub and Hugging Face Spaces. |
| - Added explicit Codex build evidence after organizer/Discord clarification that Codex Track eligibility depends on commit metadata and GitHub repo evidence. |
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| Open questions: |
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| - Which real/sanitized user story should anchor the demo video? |
| - What final latency numbers should be reported after the public Space is stable? |
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| ## 2026-06-06 |
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| Codex helped: |
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| - build the 100-case scam eval dataset and backend-aware eval runner |
| - wire configurable analyzers for heuristic, saved prediction, llama-cpp, and Transformers paths |
| - add JSON extraction and schema validation for model responses |
| - add a heuristic safety guard for weak small-model outputs |
| - switch the deployed runtime to ZeroGPU + `Qwen/Qwen3-0.6B` |
| - keep the llama.cpp path for local/eval evidence while avoiding it as the live judge-facing path |
| - harden Qwen thinking-token handling with `enable_thinking=False` where supported and `<think>` stripping as a fallback |
| - remove duplicate model calls from session memory saving |
| - add hidden page-load model warmup for the deployed Space |
| - redesign the Gradio UI around a calm safety-card experience |
| - add copyable trusted-person handoff text |
| - patch Gradio dark-mode/loading-opacity leakage |
| - pivot the deployed model default to `openbmb/MiniCPM4.1-8B` for OpenBMB eligibility |
| - add `trust_remote_code` support for MiniCPM's Transformers loader path |
| - add a generated Jawbreaker train/dev/test corpus for SFT experiments |
| - add a PEFT/LoRA MiniCPM training script and training-only requirements |
| - add Transformers eval support for scoring OpenBMB models directly |
| - add runtime fallback when model output is malformed or inference fails |
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| Current decisions: |
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| - Deployed model: `openbmb/MiniCPM5-1B`. |
| - Deployed adapter: `build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8`. |
| - Deployed backend: Transformers on ZeroGPU. |
| - Local/eval model path: llama-cpp-python remains available for GGUF models. |
| - Fine-tuning: completed through a Modal-trained MiniCPM5-1B LoRA adapter. |
| - Claimed bonus badges: Off Brand, Off the Grid, Sharing is Caring, Field Notes, Tiny Titan, and Well-Tuned. |
| - Pending bonus badge: Best Demo, once the demo video and social post are linked. |
| - Sponsor target: OpenBMB, because MiniCPM is central to the app. |
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| ## 2026-06-07 |
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| Codex helped: |
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| - run and compare MiniCPM5-1B LoRA evals against earlier 8B adapter evidence |
| - promote `build-small-hackathon/jawbreaker-minicpm5-1b-lora-v4` as the first strong 1B deployed adapter candidate |
| - commit 320-case and 394-case hard guarded eval reports |
| - update README, setup, eval, training, and honest-submission evidence |
| - refine the custom Gradio Server UI for readability, elderly-friendly wording, copy-to-trusted-person behavior, and final model disclosure |
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| Final model evidence: |
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| - 394-case hard guarded eval: `379/394` risk accuracy (`96.19%`) |
| - `0` dangerous-as-safe |
| - `0` dangerous-as-needs-check |
| - `0` suspicious-as-safe |
| - `0` unsafe action violations |
| - `0` invalid predictions |
| - `0` model errors |
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| ## 2026-06-09 / 2026-06-10 |
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| Codex helped: |
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| - add fresh public-pattern calibration data for wrong-number crypto/trading, marketplace money movement, task/job scams, MFA-code theft, toll/tax/benefit notices, and safe family/logistics contrasts |
| - train and evaluate the MiniCPM5-1B LoRA v8 path on Modal |
| - diagnose preemption during a long Modal eval and preserve the final successful run as public evidence |
| - tighten the deterministic safety guard for wrong-number investment grooming without over-promoting ordinary family/school logistics |
| - add regression tests for guard behavior |
| - promote `build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8` as the final deployed adapter |
| - update the Space README, model card, dataset card, collection notes, and final submission evidence so v8 is consistently framed as final |
| - add `CODEX_JUDGE_EVIDENCE.md` to map Codex-attributed commits to files, final metrics, and public artifacts |
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| Final v8 model evidence: |
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| - 632-case hard guarded eval: `579/632` risk accuracy (`91.61%`) |
| - `0` dangerous-as-safe |
| - `0` dangerous-as-needs-check |
| - `0` safe-as-dangerous-or-suspicious |
| - `0` unsafe action violations |
| - `0` invalid predictions |
| - `0` model errors |
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| Public final artifacts: |
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| - Space: https://huggingface.co/spaces/build-small-hackathon/jawbreaker |
| - Model: https://huggingface.co/build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8 |
| - Dataset/eval bundle: https://huggingface.co/datasets/build-small-hackathon/jawbreaker-scam-defense-data |
| - Collection: https://huggingface.co/collections/build-small-hackathon/jawbreaker-6a263632dcd0b6d41ca914ff |
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