Yurl.ai – LLM based url shortener in F#

Hacker News

Built a URL shortener in F# to explore CQRS – feedback welcome Hey folks, I’ve been experimenting with F# and decided to build a small project to try out CQRS in practice. The result is a basic URL shortener I named YURL. The backend is all in F#, structured around command and query separation. I wanted something minimal yet cleanly architected—so no heavy dependencies or complicated setup. The project helped me better understand event flow and separation of concerns in functional style. If you’re curious, here’s the code: github.com/OnurGumus/YURL Would love thoughts from other F# folks or anyone doing CQRS in a minimalist way.

Visit product View on Hacker NewsMay 25, 2025

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

73%73% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, 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

37%37% 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: way, para · Missing: mobile apps, ios, personal

29%29% 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.

Correct prediction on native model

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