Flask Wiki

A community-driven wiki for learning Flask.

https://flaskwiki.wiki/

awslabs / agent-squad

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

https://github.com/awslabs/agent-squad

daytonaio / daytona

Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code

https://github.com/daytonaio/daytona

2025 Python Packaging Ecosystem Survey

2025 Python Packaging Ecosystem Survey
If you make use of Python packaging tools (pip, ...

https://simonwillison.net/2025/May/18/2025-python-packaging-ecosystem-survey/#atom-tag

Q-Insight

Understanding Image Quality via Visual Reinforcement Learning.

https://github.com/bytedance/Q-Insight

Python Tutorial: Type Hinting vs Type Checking vs Data Validation - What’s the Difference?

In this video, we'll be learning about the differences between type hinting, type checking, and data validation in Python. These are three concepts that many developers get confused about, so we'll cover what each one does, when to use them, and how they work together. We'll also look at practical examples using tools like mypy for type checking and Pydantic for data validation. By the e...

https://www.youtube.com/watch?v=fM4O9bModsE

BioReason

Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model.

https://github.com/bowang-lab/BioReason

Traffic meter per ASN without logs

The author introduces asncounter, a Python tool that analyzes logs or network traffic to count and group incoming IPs by their Autonomous System Number (ASN), helping identify which organizations are generating the most traffic. It’s designed for quick deployment and practical insight—especially when logs are anonymized or attackers use distributed IPs—making it easier to spot patterns, ...

https://anarc.at/blog/2025-05-30-asncounter/

No GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL

Hugging Face’s new co-location feature lets vLLM inference and model training share the same GPUs and process group, eliminating idle GPU time and costly hardware overhead that plagued the old server-based setup. This integrated approach delivers up to 1.73X faster throughput for large language models, maintains model quality, and simplifies scaling—though it requires careful GPU memory ...

https://huggingface.co/blog/vllm-colocate

20 Pandas One-Liners That Can Save You Hours of Work

A curated set of 20 concise Pandas one-liners that leverage advanced features—like Arrow-backed dtypes, vectorized eval, and efficient group-by transforms—to optimize common data preprocessing, filtering, and aggregation tasks. These snippets are designed to streamline data analysis workflows on large datasets by reducing memory usage, speeding up computations, and minimizing boilerplate...

https://www.nb-data.com/p/20-pandas-one-liners-that-can-save

How local variables work in Python bytecode

The post explains how local variables are managed in Python bytecode: they’re stored in reserved slots at the bottom of each function’s stack frame, with the stack holding references to objects on the heap. By walking through a custom Python interpreter in Rust, the author illustrates how compiled bytecode uses indices (not names) to access these slots, demystifying the stack-based stora...

https://fromscratchcode.com/blog/how-local-variables-work-in-python-bytecode/

Surprisingly Fast AI-Generated Kernels We Didn’t Mean to Publish (Yet)

Stanford researchers show that AI-generated CUDA kernels—created without relying on standard libraries—can now match or even outperform expert-optimized PyTorch kernels on specific tasks, thanks to parallel search and synthetic data generation. Their approach demonstrates that combining strong reasoning with broad exploratory search yields rapid performance gains, highlighting a promisin...

https://crfm.stanford.edu/2025/05/28/fast-kernels.html

pyleak

Detect leaked asyncio tasks, threads, and event loop blocking in Python. Inspired by goleak.

https://github.com/deepankarm/pyleak