Launch Intel
AI

AI tool to that reaches top in machine-learning competition

Hacker News

AI tool to that reaches top in machine-learning competition

I've been experimenting with autonomous coding agents for ML workflows. ML-Ralph uses claude-code to run a continuous experiment loop - it forms hypotheses, writes training code, evaluates results, and iterates on what it learns. Added Weights & Biases integration for observability on long runs. As a test, I pointed it at Kaggle Higgs Boson and let it run for a few hours unsupervised. It placed top 30. I humanly participated on that competition too, and barely reached top 150. Still rough around the edges. Would appreciate feedback, especially on failure modes you'd want to see handled better.

Visit productView on Hacker NewsJan 29, 2026
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1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: lua, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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