Voft – VAVR‑style objects for Typscript

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Hi HN, Some time ago I released voft (Various Objects For TypeScript), a tiny utility library that brings some of the ergonomics of the Java VAVR functional library to TypeScript/JavaScript. Why? I missed VAVR's Try monad when writing Node back‑ends and React front‑ends: it lets you chain calls without drowning in try/catch blocks. I wanted an async‑aware variant (AsyncTry) for promise chains. Existing FP libs (fp‑ts, purify, etc.) cover similar ground but felt heavier for small projects, and I also wanted to challenge myself. What's inside? - Try – wrap any sync code, then .map, .flatMap, .recover… - AsyncTry – same, but for Promise‑based flows; awaitable and chainable. - Optional – lightweight Maybe. - Either<L, R> – success/failure container with ergonomic helpers. Early days – the API is still settling and there are probably bugs. I'd love feedback, issues and PRs! Thanks for reading!

Visit product View on Hacker NewsJun 19, 2025

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

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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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.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

41%41% 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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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