#python tips for folks getting started.
Don't use the system-level python as that can update and break dependencies. E.g. Arch uses 3.13 if you update and Tensorflow can't go above 3.12.
Use pyenv to do version management per project. https://github.com/pyenv/pyenv
Don't install pip dependencies globally. Use virtualenv to set up dependencies per project. https://virtualenv.pypa.io/
Python versioning is rough out of the box and these tips can save you some pain.
The appeal of machine learning to me is how clean the actual structure of models can be once you split it up in the right layers.
A lot of "application" code is a tangled web of dependencies and small independent or coupled bits of state that you need to coordinate in bizarre ways to account for the difficulty of state management in distributed systems.
ML on the other hand needs maths knowledge but the blocks fit together in a nice clean linear way of input to output.
Found this #techno banger via tiktok. Very cyber.
https://soundcloud.com/nikolachenmusic/nikola-chen-lippo-pippo
Updating my code to the latest version of #veilid
Hopefully the route instability stuff will be fixed now!
Fascinating practical networking research that exposes the gap between networking theory & real-world implementations. Valuable for security researchers, network engineers, and anyone building custom network protocols. https://github.com/Hawzen/hdp
Occult Enby that's making local-first software with peer to peer protocols, mesh networks, and the web.
Exploring what a local-first cyberspace might look like in my spare time.