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A boilerplate to finetune and deploy AI models

Hacker News

A boilerplate to finetune and deploy AI models

Hey HN, this is Patrick. I've been working as an software engineer for a few years, fine-tuning models and getting them production-ready. After a lot of frustration with the process, I decided to build FinetuneFast – a boilerplate that helps developers, indie makers, and businesses finetune and deploy AI models faster. FinetuneFast is designed for anyone who wants to ship and deploy customized AI models easier and faster. I’d love any feedback or thoughts on the project and hope this is helpful for you! Btw: if you're on Producthunt I'd highly appreciate your support there pls: https://www.producthunt.com/posts/finetunefast

Visit product View on Hacker NewsSep 27, 2024

Actual performance

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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.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% 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, intuitive

32%32% 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 · Missing: arr, mrr, revenue

18%18% 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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