How I used Cursor AI to migrate a Bash test suite to Python

The migration of a large Bash container test suite to Python using the Cursor AI code editor saved about 1.5 months of development time, with Cursor handling script conversion, function replacement, and automated PyTest suite generation. Although the migration was not entirely smooth and required some manual fixes, the resulting Python test suite passed tests successfully, demonstrating ...

https://developers.redhat.com/articles/2025/09/23/how-i-used-cursor-ai-migrate-bash-test-suite-python#our_real_world_results

VectorLiteDB

The SQLite for vector embeddings — A simple, embedded vector database that stores everything in a single file.

https://github.com/vectorlitedb/vectorlitedb

fastapi-radar

A powerful debugging dashboard for FastAPI applications. Monitor HTTP requests, SQL queries, and exceptions in real-time with a beautiful React UI. One-line integration, zero configuration needed.

https://github.com/doganarif/fastapi-radar

Compiling Python to Run Anywhere

The article discusses an innovative approach to compiling Python code into cross-platform, ahead-of-time optimized machine code executables without modifying the original Python source. It details building a custom symbolic tracer, propagating types for lowering to C++, leveraging AI to generate C++ operators, and empirically optimizing performance across multiple hardware targets to ena...

https://blog.codingconfessions.com/p/compiling-python-to-run-anywhere

MapAnything

Universal Feed-Forward Metric 3D Reconstruction

https://github.com/facebookresearch/map-anything

enso

enso is a functional programming framework for Python.

https://gitlab.com/evansemenoff/enso

OM1

Modular AI runtime for robots.

https://github.com/OpenMind/OM1

How Well Do New Python Type Checkers Conform? A Deep Dive into Ty, Pyrefly, and Zuban

The Python type checking landscape in 2025 includes three new Rust-based tools: Astral's ty, Meta's pyrefly, and Zuban. Ty emphasizes gradual adoption with fewer false positives, pyrefly focuses on aggressive inference to catch more issues early, and Zuban aims for seamless mypy compatibility; while conformance tests reveal differences, all show promise for real-world Python development.

https://sinon.github.io/future-python-type-checkers/

Cloud-Native Pipelines for Scientific Data Processing with Prefect and Dask

This article explains how to build scalable, cloud-native scientific data processing pipelines using Prefect for workflow orchestration and Dask for parallel computation. It covers cloud-optimized formats (like Zarr), integration with tools like xarray and echopype, and demonstrates end-to-end ETL pipelines that load, process, and store multidimensional data directly in the cloud.

https://oceanstream.io/cloud-native-data-processing-pipelines-with-prefect-and-dask/

LLM-Deflate: Extracting LLMs Into Datasets

LLM-Deflate is a technique for systematically extracting structured datasets from trained large language models by probing their internal knowledge with hierarchical topic exploration and prompt engineering. This reverse-compression process enables model analysis, knowledge transfer, training data augmentation, and debugging, potentially making knowledge extraction a standard tool as inf...

https://www.scalarlm.com/blog/llm-deflate-extracting-llms-into-datasets

The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data

The Kaggle Grandmasters Playbook presents seven proven techniques for tabular data modeling, emphasizing fast experimentation and careful validation powered by GPU acceleration to handle large-scale data effectively. Key strategies include advanced exploratory data analysis, building diverse baselines, extensive feature engineering, ensembling with hill climbing and stacking, pseudo-labe...

https://developer.nvidia.com/blog/the-kaggle-grandmasters-playbook-7-battle-tested-modeling-techniques-for-tabular-data/

How to Build Advanced AI Agents – Course for Beginners (LiveKit, Exa, LangChain)

The video teaches beginners how to build advanced AI agents, such as voice sales agents, research assistants, and multi-agent workflows, using LiveKit, Exa, LangChain, and Cerebras. It provides step-by-step guidance, hands-on code, and free API credits to help developers quickly create real-world AI applications.

https://www.youtube.com/watch?v=B0TJC4lmzEM

Python Singleton Pattern: Smarter Than You Think?

This video analyzes the strengths and weaknesses of the singleton pattern in Python, explaining why global state is risky but controlled instantiation can be valuable in certain cases. It recommends module-level singletons and thread safety measures, while cautioning against tight coupling and testing pitfalls with traditional singleton implementations.

https://www.youtube.com/watch?v=p_UQ7tzUFLo

LLMs from Scratch – Practical Engineering from Base Model to PPO RLHF

This video provides a hands-on guide to building a large language model entirely from scratch in PyTorch, covering every step from core transformer design to advanced alignment with RLHF. By the end, viewers gain practical experience in implementing, training, scaling, and aligning their own custom LLMs.

https://www.youtube.com/watch?v=p3sij8QzONQ