Bh

Bhttp Binary HTTP (RFC 9292) for Go

Hacker News

Bhttp Binary HTTP (RFC 9292) for Go

Hey HN, I'm Willem, a freelance engineer. Together with the folks at Confident Security I developed this Go package that we open sourced today: https://github.com/confidentsecurity/bhttp It's a Go implementation of BHTTP (RFC 9292) that allows you to encode/decode regular ` http.Request` and ` http.Responses` to BHTTP messages. We've implemented the full RFC: - Known-length and indeterminate-length messages. Both are returned as `io.Reader`, so relatively easy to use and switch between the two. - Trailers. Work the same way as in `net/http`. - Padding. Specified via an option on the encoder. If you're working on a problem that requires you to pass around HTTP messages outside of the conventional protocol, be sure to check it out. Any feedback or PR's are much appreciated.

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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 NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
63%63% 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: code, open · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
30%30% 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
15%15% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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