ML
MLE-Agent – A lightweight AI agent to help you win Kaggle
Hacker NewsMLE-Agent – A lightweight AI agent to help you win Kaggle
We build a small tool to help you develop Machine Learning projects. You only need to provide your requirements (e.g., from Kaggle or your business). Then it will help you come up with a plan by referring arxiv and paper with code. Also, it will write and debug code for you. The code is very well structured. This is a tiny step and we are looking forward to your comments
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, tiny · Missing: agents, macos, cursor77%77% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue19%19% 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, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Bu
Built a stoic AI agent to help me be more centered20%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Built a stoic AI agent to help me be more centered
Hacker News3A
A lightweight compiler for untrusted AI Agent scripts29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
A lightweight compiler for untrusted AI Agent scripts
Hacker News2AI
AI Agent in Jupyter – Runcell41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
AI Agent in Jupyter – Runcell
Hacker News8MV
MVAR – Deterministic sink enforcement for AI agent20%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
MVAR – Deterministic sink enforcement for AI agent
Hacker News1Kollab14%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
All-in-one AI Agent
Indie HackersKollab37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
All-in-one AI Agent
Indie Hackerscommitment-full-timeLinkedNav14%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
AI Agent
Indie HackersLinkedNav14%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
AI Agent
Indie HackersXTEN-AV73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
First ever AI agent for AV
Indie Hackerscommitment-full-time
Bilbo84%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Metabase AI Agent
Product Hunt+171Chrome Extensions