# SignalProof Lab

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Ardhya Novri](https://cerebralvalley.ai/u/vanovri)
- **GitHub:** https://github.com/vanovri/signalproof
- **Demo video:** https://www.youtube.com/watch?v=Zn9r72hdEFs
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/58

SignalProof helps electronics students inspect low-voltage CSV captures before trusting an AI explanation. It measures DC mean, AC RMS, near-rail share, 48-52 Hz band power, timing gaps/jitter, and duration in the browser and via cli.mjs (including side-by-side --compare and --compare-demo differential E1-E6 tables). Every finding links to its measurement and calculation.

Try the workbench at https://vanovri.github.io/signalproof/: pin the synthetic 50 Hz example, load the reference, and compare a 73.545-percentage-point band-share change. Then load Missing samples: the tool withholds its spectrum. The FFT exposes its sample coverage and requires its retained block to have bin spacing at most 1 Hz. Comparison and AI output can be exported together.

Meta Muse Spark Open explains the derived evidence in English or Indonesian. Static Pages explicitly labels recorded responses. Live custom-CSV inference requires the Node server. The model receives derived measurements and fixed findings, never raw samples or filenames.

Verification: 54/54 code tests (unit, DSP, archive-integrity, HTTP, comparison, and CLI tests) and 15/15 deterministic synthetic scenarios. A reproducible archive audit covers 520 successful synthetic inference records, 410 Baseten and 110 Fireworks, with 1,313,133 recorded tokens. Full response text exists for 200 benchmark rows; 320 retain telemetry only. These are integrity checks, not a 100% semantic-accuracy score. All captures are synthetic. Hardware performance and user time savings remain unmeasured. Judge steps, walkthrough page, and corrected evidence: docs/JUDGE_GUIDE.md, https://vanovri.github.io/signalproof/docs/demo.html, and docs/benchmark_audit.json.

## More from Meta Global AI Developer Hackathon

- [ZTL Engineer](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/55)
- [SWORD GANG](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/56)
- [Muhammad Septian](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/57)
- [Daniel Quek](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/59)
- [VerifEye](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/60)
- [gradepack](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/61)

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