We've built a registry for open source KitOps–checkout the preview

Hacker News

Hey everyone, Jesse here. I'm one of the maintainers for open source KitOps, an MLOps tool that makes it easy to handoff AI projects across data science, app dev, and SRE/DevOps teams. We've seen a ton of adoption and excitement around KitOps, in fact last week we had our first 1k install week! One of the main things that KitOps users have been asking for is a purpose built hub to host their ModelKits. Today, we're excited to show off what we've been working on. You can check it out at jozu[dot]ml - If you like what you see you can sign up for early access on jozu[dot]com Any feedback is welcome.

Visit product View on Hacker NewsJul 24, 2024

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Actual performance

2points

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Launch Intel predictions

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

85%85% 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: excited, open source, io · Missing: https docs, just released, exist

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.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

46%46% 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, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · 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 · Missing: arr, mrr, revenue

15%15% 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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