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The ML API for engineers who don't have time for ML

Hacker News

The ML API for engineers who don't have time for ML

Hey HN - spending time adding machine learning to your product is almost never worth it for startups. Formulating the problem, managing training data, training models, serving them, etc... for a 10% increase in some metric just isn't worth the time. Using rules + heuristics, although not optimal, does a pretty good job for a long time. There should be a better solution between rules & hardcore ML -- an 80/20 way to add ML to any product. Just slap it on top of the rules you already have, get that 10% gain, and never think about it again. I built an API that does just that. It's used by a few companies to do everything from shopping cart recommendations to personalized landing page hero text. It takes 10-20 lines of code to implement. I'd love for anyone in the HN community to kick the tires on this and let me know what you think. Docs are at https://www.banditml.com/. You can even use this api key immediately without an account to test it out: 1e9d8ef7948e545d25f189addb17dae1.

Visit productView on Hacker NewsAug 5, 2021
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
77%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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
17%17% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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