TorChat was a peer-to-peer anonymous instant messenger that used Tor onion services as its underlying network. It provided cryptographically secure text messaging and file transfers. The characteristics of Tor's onion services ensure that all traffic between the clients is encrypted and that it is very difficult to tell who is communicating with whom and where a given client is physically located.
If you are looking for TorChat 0.9.9.xxx (the original Python implementation) then please switch to the torchat py branch. For downloads of the latest versions please see the downloads section: This branch torchat2 is a rewrite from scratch, using Lazarus + Free Pascal. This will make it easier to create plugins for existing IM applications and also allows to easily generate code for a wider range of platforms, especially mobile devices like Android and iPhone. Please note that TorChat is produced independently from the Tor® anonymity software, I am not related with or sponsored by torproject.org. TorChat is making use of the Tor® client software and the Windows version comes bundled with original Tor binaries but TorChat itself is a completely separate project developed by totally different people, so if you instead want to buy the developers of Tor® from torproject.org a beer (they deserve it even more than me and without their great Tor software my little program would not have been possible) then please consider doing so at the following address:
Excerpt from the source-code README · 2,070 chars · not written by Vinony
~5 min read
TorChat was a peer-to-peer anonymous instant messenger that used Tor onion services as its underlying network. It provided cryptographically secure text messaging and file transfers. The characteristics of Tor's onion services ensure that all traffic between the clients is encrypted and that it is very difficult to tell who is communicating with whom and where a given client is physically located.
TorChat is free software licensed under the terms of the GNU General Public License (GPL).
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Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).