Tornago – Cross-platform Tor wrapper in Go (client and server)

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tornago is a Tor wrapper library written in Go, created because some tools — especially around fraud prevention and privacy — need safe, anonymized network routes. It’s intended for production use, but it hasn’t seen enough real users yet, so there are bugs that will only surface through actual deployments. Highlights - Works on Windows, macOS, Linux, and BSDs (FreeBSD, OpenBSD, NetBSD, DragonFly) - Supports both client and server, including .onion Hidden Services - Manages the tor daemon lifecycle + SOCKS5 communication I’d like to see it tested in real projects — whether that’s fraud investigation, anonymous crawling, privacy-focused services, or simply protecting your own identity when accessing the network. If Tor reliability matters to your system, try it and let me know what breaks. Issues and PRs are welcome. GitHub: https://github.com/nao1215/tornago

Visit product View on Hacker NewsNov 25, 2025

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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 · Strong signals: users · Missing: mobile apps, ios, personal

34%34% 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

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.

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