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Common FP – A New JavaScript Utility Lib

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Common FP – A New JavaScript Utility Lib

Who is this for? - Devs looking for utilities that treat data types generically. For example, common-fp has a `mapValues` function that takes an array, object, Map, or Set and returns a new instance of the data type passed in. - Devs interested in functional utilities without the jargon. In my experience, some functional concepts are too foreign for the broader community and pose a barrier to entry. Common FP lowers that barrier by naming the utilities in plain English, not currying, keeping the source intended for the common devs' eyes, and more. Other features - An in-browser playground: https://common-fp.org/try-it - Supports TypeScript: install types via `npm install common-fp-types` - Supports cjs: i.e. you can `require('common-fp')` - 100% test coverage. Types are tested as well via tstyche. - For every utility, I try to explain why it's useful along with a code example. This is more difficult than it seems, and I'm open to feedback. A personal note - I built this as a personal endeavor. I wasn't trying to solve a problem the community had, so I understand if people find Common FP to be odd or unnecessary. Finishing a project is difficult, and I'm mostly happy just to have done that and present what I consider to be a polished product. Link to source - https://github.com/common-fp/common-fp/tree/dev/pkg/common-f... Thanks for taking a look, Phil

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos
79%79% 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: supports · Missing: reddit linkedin, podcasting, created
69%69% 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
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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