Ku

Kurynt – The last tool you will ever need to manage third party code

Hacker News

Kurynt – The last tool you will ever need to manage third party code

I built Kurynt after numerous and repeated headaches maintaining multiple codebases with innumerable dependencies. Many developers know that sinking feeling every time they inherit a codebase where many packages are long outdated and in need of updates or upgrades. Kurynt is the solution to this problem by rapidly giving you an overview of how far behind you are on all packages used by your codebase. In my opinion, Kurynt is a step above 'dumb' tools like Snyk or Dependabot, which simply create a pull request on your repository on your code with the latest version changes in your package manager. How many of us have honestly immediately merged those changes? I never have, because I know it takes time to review the content of the upgraded packages, review breaking changes, actually install the new packages, and then finally, test the entire codebase to ensure the suggested upgrades are working as suggested. Kurynt seeks to bring intelligence to third-party code management and upgrades. Kurynt can summarize and identify the most critical raised GitHub issues, run sentiment analysis to determine the most cryptic or confusing bugs, and much more. I hope you'll consider giving Kurynt a test drive!

Visit product View on Hacker NewsJul 29, 2022

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, code · Missing: mac, agents, macos

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

59%59% 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, io · Missing: https docs, excited, just released

49%49% 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 · Missing: mobile apps, ios, personal

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

28%28% 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

12%12% 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.

Correct prediction on native model

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