Oblivious HTTP for Go

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I couldn't find a suitable go implementation for Oblivious HTTP Client & Gateway (RFC 9458). So, I'm open sourcing ours. Some background: OHTTP is a protocol that hides who you are from the data you send - if you've ever used products of Apple, Mozilla, Fastly, or Cloudflare (to name a few) you probably used OHTTP. Key Features: - implemented as http.RoundTripper - supports chunked transfer encoding - customizable HPKE (e.g., for custom hardware-based encryption) - built on top of twoway and bhttp libraries Repo: https://github.com/confidentsecurity/ohttp Detail: https://blog.confident.security/ohttp/ Explainer: https://support.mozilla.org/en-US/kb/ohttp-explained Specs: https://datatracker.ietf.org/doc/rfc9458/ , https://datatracker.ietf.org/doc/draft-ietf-ohai-chunked-oht... Feedback welcome!

Visit product View on Hacker NewsOct 29, 2025

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

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% 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: apple, coding, open · Missing: mac, agents, macos

45%45% 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

39%39% 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

29%29% 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

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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.

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