I created a game engine for Django?
The author built a multiplayer Snake game in the browser using only Python and Django LiveView, with no custom JavaScript, by keeping game state on the server and broadcasting rendered HTML over WebSockets.
https://en.andros.dev/blog/6e9e4485/i-created-a-game-engine-for-django/
The author built a multiplayer Snake game in the browser using only Python and Django LiveView, with no custom JavaScript, by keeping game state on the server and broadcasting rendered HTML over WebSockets.
https://en.andros.dev/blog/6e9e4485/i-created-a-game-engine-for-django/
en.andros.dev
I created a game engine for Django? | Andros Fenollosa
TL;DR: Complete multiplayer game in the browser made of 270 lines of Python and 0 lines of JavaScript running on Django thanks to Django LiveView.
Aft
Aft
PicoFlow – a tiny DSL-style Python library for LLM agent workflows
https://news.ycombinator.com/item?id=46706535
https://news.ycombinator.com/item?id=46706535
Microcode
Microcode is an efficient terminal-based AI agent with an internal REPL environment for coding assistance. It leverages Reasoning Language Models (RLMs) to help developers with coding tasks directly from the command line.
https://github.com/modaic-ai/microcode
Microcode is an efficient terminal-based AI agent with an internal REPL environment for coding assistance. It leverages Reasoning Language Models (RLMs) to help developers with coding tasks directly from the command line.
https://github.com/modaic-ai/microcode
GitHub
GitHub - modaic-ai/microcode: context-efficient terminal agent
context-efficient terminal agent. Contribute to modaic-ai/microcode development by creating an account on GitHub.
Prototyping a Live Product Recommender With Python
The article shows how to build a real-time product recommender prototype in Python using Contextual Multi-Armed Bandits to simulate user behavior and validate online learning algorithms like LinUCB. It explains why bandits handle cold-start and context better than traditional models, walks through data generation, feature engineering, offline evaluation, and sets up a live simulation as ...
https://jaehyeon.me/blog/2026-01-29-prototype-recommender-with-python/
The article shows how to build a real-time product recommender prototype in Python using Contextual Multi-Armed Bandits to simulate user behavior and validate online learning algorithms like LinUCB. It explains why bandits handle cold-start and context better than traditional models, walks through data generation, feature engineering, offline evaluation, and sets up a live simulation as ...
https://jaehyeon.me/blog/2026-01-29-prototype-recommender-with-python/
jaehyeon.me
Prototyping a Live Product Recommender with Python
Traditional recommenders struggle with cold-start users and short-term context. Contextual Multi-Armed Bandits (CMAB) continuously learns online, balancing exploitation and exploration based on real-time context. In Part 1, we build a Python prototype to…
earth2studio
Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
https://github.com/NVIDIA/earth2studio
Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
https://github.com/NVIDIA/earth2studio
GitHub
GitHub - NVIDIA/earth2studio: Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows. - NVIDIA/earth2studio
4 Pyrefly Type Narrowing Patterns that make Type Checking more Intuitive
Learn about 4 ways Pyrefly narrows types, reducing the need to explicitly cast in your code.
https://pyrefly.org/blog/type-narrowing/
Learn about 4 ways Pyrefly narrows types, reducing the need to explicitly cast in your code.
https://pyrefly.org/blog/type-narrowing/
pyrefly.org
4 Pyrefly Type Narrowing Patterns that make Type Checking more Intuitive | Pyrefly
Learn about 4 ways Pyrefly narrows types, reducing the need to explicitly cast in your code.
redress
redress is a failure-policy library for Python services. It treats retries, circuit breakers, and stop conditions as coordinated responses to classified failure, rather than independent wrappers.
https://github.com/aponysus/redress
redress is a failure-policy library for Python services. It treats retries, circuit breakers, and stop conditions as coordinated responses to classified failure, rather than independent wrappers.
https://github.com/aponysus/redress
GitHub
GitHub - aponysus/redress: Composable, low-overhead retry policies with pluggable classification, per-class backoff strategies…
Composable, low-overhead retry policies with pluggable classification, per-class backoff strategies, and structured observability hooks. Designed for services that need predictable retry behavior a...