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SuperUtilsPlus – A Modern Alternative to Lodash

Hacker News

SuperUtilsPlus – A Modern Alternative to Lodash

Hey HN! After years of wrestling with Lodash's quirks and bundle size issues, I decided to build something better. SuperUtilsPlus is my attempt at creating the utility library I wish existed. What makes it different? TypeScript-first approach: Unlike Lodash's retrofitted types, I built this from the ground up with TypeScript. The type inference actually works the way you'd expect it to. Sensible defaults: Some of Lodash's decisions always bugged me. Like isObject([]) returning true - arrays aren't objects in my mental model. Or isNumber(NaN) being true when NaN literally stands for "Not a Number". I fixed these footguns. Modern JavaScript: Built for ES2020+ with proper ESM support. No more weird CommonJS/ESM dance. Actually tree-shakable: You can import from specific modules (super-utils/array, super-utils/object) for optimal bundling. Your users will thank you. The best parts IMO: compactNil() - removes only null/undefined, leaves falsy values like 0 and false alone differenceDeep() - array difference with deep equality (surprisingly useful) Better random utilities with randomUUID() and randomString() debounce() that actually works how you expect with proper leading/trailing options Also genuinely curious - what are your biggest pain points with utility libraries? Did I miss any must-have functions?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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, user · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
52%52% 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, way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, users · Missing: platform, intuitive, reviews
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
19%19% 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.

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