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Heyneo.so – We built the first autonomous Machine Learning Engineer
Hacker NewsHeyneo.so – We built the first autonomous Machine Learning Engineer
NEO is an autonomous machine learning engineering AI agent capable of implementing complex ML tasks - From data cleaning, preprocessing, handling structural issues to model exploration, training, optimization with its own reasoning and code execution capabilities. We are soon releasing our early beta access. Join our waitlist. Would highly appreciate your feedback and use-cases you could potentially use NEO for.
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Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, model · Missing: agents, macos, cursor84%84% 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.
AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released42%42% 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, podcasting39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal32%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, revenue16%16% 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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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