Spaced repetition systems have gotten way better
domenic.me
How the Sun Enterprise 10000 was born (2007)
filibeto.org
Coding without a laptop: Two weeks with AR glasses and Linux on Android
holdtherobot.com
AniSora: Open-source anime video generation model
komiko.app
Show HN: A web browser agent in your Chrome side panel
github.com
The Conquest of Hell Gate [pdf]
nan.usace.army.mil
Project Verona: Fearless Concurrency for Python
microsoft.github.io
Lessons from Mixing Rust and Java: Fast, Safe, and Practical
medium.com
High Available Mosquitto MQTT on Kubernetes
raymii.org
What Every Programmer Should Know About Enumerative Combinatorics
leetarxiv.substack.com
Experts have it easy (2024)
boydkane.com
Show HN: Turn any workflow diagram into compilable, running and stateful code
workflows.diagrid.io
Craft Basic (Windows 95 and up)
lucidapogee.com
Why some friendships end after kids come into the picture
text.npr.org
How to have the browser pick a contrasting color in CSS
webkit.org
Dead Stars Don’t Radiate
johncarlosbaez.wordpress.com
If nothing is curated, how do we find things
tadaima.bearblog.dev
Palette lighting tricks on the Nintendo 64
30fps.net
Mice grow bigger brains when given this stretch of human DNA
nature.com
ARMv9 Architecture Helps Lift Arm to New Financial Heights
nextplatform.com
Understanding Transformers via N-gram Statistics
arxiv.org
I love decision trees. Conceptually simple, computational efficient and giving very good results for a lot of tasks. I especially use them on microcontroller grade system, via emlearn - which converts scikit-learn models to embedded friendly C code.
These articles are a good and pretty comprehensive introduction. I would have loved to have even more examples around the bias/variance trade off for forests, it is a key concept that not all practitioners have integrated.