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

lmms-eval

Accelerating the development of large multimodal models (LMMs) with one-click evaluation module - lmms-eval.

https://github.com/EvolvingLMMs-Lab/lmms-eval

Should You Replace Every For Loop With Map and Filter?

Think map() and filter() are always better than for loops? Not so fast. This video walks you through four situations where functional code actually makes things worse—and explain why the classic for loop still deserves a place in your toolbox.

https://www.youtube.com/watch?v=ylzo04lU9Xs

Create a React + Flask Project in 2025

The tutorial provides an updated 2025 workflow for building a combined React and Flask application, detailing how to structure, run, and connect a modern React frontend with a Flask backend using current tools and best practices.

https://blog.miguelgrinberg.com/post/create-a-react-flask-project-in-2025

TurboDRF

The dead simple Django REST Framework API generator with role-based permissions.

https://github.com/alexandercollins/turbodrf

Create your customized running plan: A step-by-step guide using Python, Elasticsearch, and Agno

The article provides a step-by-step guide to building a personalized, AI-powered running plan using Python, Elasticsearch, and Agno, leveraging your workout history to generate a four-week training schedule. It walks through extracting fitness data, storing it in Elasticsearch, using agentic AI to create a tailored plan, and exporting the results to Notion for easy tracking and progress ...

https://allthingsopen.org/articles/step-by-step-guide-python-elasticsearch-agno-agentic-ai-create-running-plan

Build an AI Agent with Python, Django, LangGraph, and Permit

This video demonstrates how to build AI agents using Django for data management, LangGraph for agent coordination, andPermit.iofor robust access control, enabling safe, granular, and flexible interactions with user data and APIs. The course walks through integrating these tools step-by-step, empowering developers to create scalable, multi-agent systems with strong permissions and rapid p...

https://www.youtube.com/watch?v=rir9B0ZShug

Tokasaurus

Tokasaurus is an LLM inference engine designed for high-throughput workloads.

https://github.com/ScalingIntelligence/tokasaurus

TradingAgents

Multi-Agents LLM Financial Trading Framework.

https://github.com/TauricResearch/TradingAgents

Permit.io

Fixing FastAPI Throughput Without Going Fully Async

https://Permit.io

Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model

Stream-Omni is an end-to-end language-vision-speech chatbot that simultaneously supports interaction across any modality combinations.

https://github.com/ictnlp/Stream-Omni

Building a Modern Python API with Azure Cosmos DB: A 5-Part Video Series

This five-part video series demonstrates how to build a robust, modern Python API with Azure Cosmos DB, covering topics from data modeling with Pydantic and async operations in FastAPI to batch processing and centralized error handling. Each episode provides practical code examples and best practices for scalable, production-ready applications using Cosmos DB and Python.

https://devblogs.microsoft.com/cosmosdb/building-a-modern-python-api-with-azure-cosmos-db-a-5-part-video-series

Python is Removing GIL, Gradually

This article will take you on a quick tour of the past, current, and future of the infamous GIL. More importantly, there is a beginner-friendly guide about how to install and try no-GIL Python.

https://medium.com/techtofreedom/python-is-removing-gil-gradually-b41274fa62a4?sk=9fa946e23efca96e9c31ac2692ffa029

Field Notes From Shipping Real Code With Claude

The article shares practical strategies for using Claude as an AI coding assistant to ship real production code, emphasizing the importance of clear documentation, structured prompts, and keeping humans in the loop for critical decisions and tests. It demonstrates how combining rigorous development practices with AI can dramatically accelerate productivity, illustrated by a case study re...

https://diwank.space/field-notes-from-shipping-real-code-with-claude

Ragbits

Building blocks for rapid development of GenAI applications.

https://github.com/deepsense-ai/ragbits