Binary Serialization with TypeScript

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Based on the Borsh serialization format I have created an easy to use TypeScript library that lets you conveniently serialize your classes into a compact binary representation. Later, you can deserialize this directly back directly into classes or using other languages, like with the Rust implementation borsh-rs. One of many reasons for using this library compared to using JSON is that you can have messages behave like Rust enums, rather than plain JS object that you have to do type checking with in order to determine what kind of object you are dealing with. TypeScript: https://github.com/dao-xyz/borsh-ts Rust: https://github.com/near/borsh-rs

Visit product View on Hacker NewsAug 26, 2022

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

68%68% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

46%46% 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: io · Missing: https docs, excited, just released

40%40% 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

33%33% 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

10%10% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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