# Raveenthecoder

- **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:** [Raveen Dissanayaka](https://cerebralvalley.ai/u/Raveenthecoder)
- **GitHub:** https://github.com/RaveenDi/NullLab
- **Demo video:** https://youtu.be/q4EyUdFeG1Q?si=C7xCu9xt_yoRZQ45
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2810

A model scores 97%, everyone trusts it, and nobody checks whether the test measured the right thing. NullLab is a research workdesk for ML researchers, engineers and the reviewers who rely on their results: it lets an author question their own evaluation before anyone else has to. Theme: Build for Your World (a problem every ML author faces: validation that quietly reuses the same machines, sessions or records in training and testing).

HOW IT WORKS
The author brings a question, a CSV, a compatible Python pipeline and notes. NullLab preserves the original files and inspects their structure without executing the upload. The author's answers become source-quoted draft suggestions; the author reviews and confirms the study brief (target, prediction time, population, input availability). Before anything runs, a concrete plan shows the metric, fixed seeds and proposed comparison, and known incompatibilities stop there, before training or model spend.

Meta models sit in the loop: Muse Wonder independently reviews the method, Muse Spark proposes a typed intervention, deterministic guards check applicability and preserve the original classifier, and local execution supplies the measurements. Wonder then challenges the interpretation. Everything is exportable: exact inputs, tested controls, predictions, environment and recorded objections. Reopening restores the investigation, and a changed input cannot inherit an earlier measurement.

RESULTS SHOWN IN THE VIDEO
1) A saved synthetic recovery case: the same Decision Tree goes from 0.974 to 0.290 accuracy under rig-disjoint validation. The outcome is labelled inconclusive because row intervals do not calibrate uncertainty across rigs, and the original unsupported attempt stays preserved.
2) A separate recorded example on real UCI SECOM semiconductor data (CC BY 4.0) with a resampling flaw we deliberately introduced: average precision goes from 0.983 to 0.139 on three fixed seeds. The evaluated population and prevalence also change, so we present a protocol comparison, not a pure causal amount of leakage, and Wonder disputes the interpretation.

HONEST LIMITS
NullLab supports binary tabular evaluation today. It has internal synthetic/API and browser acceptance checks only. We make no outside-user validation, adoption or time-saving claim. Hostile hosted Python and general paper evaluation are unsupported; audits run locally.

WHAT'S NEXT
Test with outside researchers, and extend uncertainty handling for grouped and time-ordered data.

BUILT DURING THE HACKATHON
The code, experiments, safeguards and workdesk were built during the hackathon (Oct 26 to Nov 4, 2026); the idea predates it. Third-party: UCI SECOM data, scikit-learn, pandas, NumPy, FastAPI, Meta inference.

## More from Meta Global AI Developer Hackathon

- [Muna Lens](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2807)
- [ClerkifyDx](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2808)
- [aSKii](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2809)
- [VRON](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2811)
- [Agent Rizz](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2812)
- [NeuralSyncLabs](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2813)

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