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Functional data types for TypeScript development

Hacker News

Functional data types for TypeScript development

I would love some feedback on this library I'm making. I've dabbled in FP for years and my journey has led me to create this library. As a front end dev, I see a lot of complexity in code bases as data is wrangled into various shapes before finally getting turned into ui elements. An API request gives you back a dumb object, and if you don't turn that object into something "smart", inevitably it becomes complicated, hence the ubiquity of state managers like Redux and it's ilk. I've had great success in functionally modeling my data using the TS/JS class keyword, and I'm trying to make some of that power accessible to anyone who wants to use it. I believe that if you model your state properly, you can avoid the need for external state management completely in most cases. I started with a Bool monad because it was simple, and a Maybe monad because most of the `if` statements I see in code are for checking whether data is present or not. I think the next item will be a queue or deque. Thanks for checking it out.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
87%87% 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, using, code · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
64%64% 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
46%46% 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
41%41% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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