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I wrote a new BitTorrent tracker in Elixir

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I wrote a new BitTorrent tracker in Elixir

Hello everyone! I'm currently in a journey to learn and improve my Elixir and Go skills (my daily job uses C++) and looking through my backlog for projects to take on I decided Elixir is the perfect language to write a highly-parallel BitTorrent tracker. So I have spent my free time these last 3 months writing one! Now I think it has enough features to present it to the world (and a docker image to give it a quick try). I know some people see trackers as relics of the past now that DHT and PEX are common but I think they still serve a purpose in today's Internet (purely talking about public trackers). That said there is not a lot going on in terms of new developments since everyone just throws opentracker in a vps a calls it a day (honorable exceptions: aquatic and torrust). I plan to continue development for the foreseeable future and add some (optional) esoteric features along the way so if anyone currently operates a tracker please give a try and enjoy the lack of crashes. note: only swarm_printout.ex has been vibe coded, the rest has all been written by hand.

Visit productView on Hacker NewsJun 19, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
87%87% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock, new, code · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
68%68% 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: month, way, para · 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: calls · Missing: plus, platform, intuitive
36%36% 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
18%18% 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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