Ezy’s universal, extendable, typesafe gRPC/gRPC-Web client library

Hacker News

While working on ezy ( https://github.com/getezy/ezy ) - open-source alternative to Postman and Insomnia, I had requests to move the gRPC client into independent library which users can use in their projects and get the same behavior like in ezy. The idea was to create extendable client as much as possible. You just need to pass strategies of protobuf definition loader and gRPC protocol implementation to the client factory and then make your requests! Out of the box it supports gRPC and gRPC-Web protocols, loader by protobuf file. Also loader by server reflection is coming soon. Typesafe and well-tested - 99% coverage. Give it a try!

Visit product View on Hacker NewsFeb 7, 2023

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

83%83% 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.

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, open · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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 · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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