Li

Lit-torrent – Basic BitTorrent client in your terminal

Hacker News

Lit-torrent – Basic BitTorrent client in your terminal

Hey everyone! Quick overview of the project. lit-torrent is a BitTorrent client that implements the original BitTorrent Protocol [1] (not fully as of now) built in Go that you can run in your terminal. I've been working on lit-torrent for the past couple of weeks. It's far from feature-complete but I wanted to get something out before it was fully "polished". A nice end goal for this project would be a fully featured BitTorrent Client TUI that runs completely in your terminal. My main reason for building this was to get a deeper understanding of a peer-to-peer protocol, take a deeper dive down the stack as I mainly work on the web/mobile applications layer, and pick up Go. Try it out, would love to hear any thoughts or feedback you may have! [1] https://www.bittorrent.org/beps/bep_0003.html

Visit product View on Hacker NewsJan 12, 2024

Actual performance

2points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, 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: 000, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

24%24% 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

19%19% 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.

Incorrect prediction on native model

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