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
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
Red Hat Developer
How I used Cursor AI to migrate a Bash test suite to Python | Red Hat Developer
Migrating a large codebase from one language to another can be time-consuming. What if an AI tool could do the heavy lifting for you? Our team recently needed to migrate our Bash container test suite
VectorLiteDB
The SQLite for vector embeddings — A simple, embedded vector database that stores everything in a single file.
https://github.com/vectorlitedb/vectorlitedb
The SQLite for vector embeddings — A simple, embedded vector database that stores everything in a single file.
https://github.com/vectorlitedb/vectorlitedb
GitHub
GitHub - vectorlitedb/vectorlitedb: The SQLite for vector embeddings — A simple, embedded vector database that stores everything…
The SQLite for vector embeddings — A simple, embedded vector database that stores everything in a single file. - 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
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
GitHub
GitHub - doganarif/fastapi-radar: A powerful debugging dashboard for FastAPI applications. Monitor HTTP requests, SQL queries,…
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. ...
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
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
Codingconfessions
Compiling Python to Run Anywhere
A guest post on building a Python compiler that generates optimized kernels while preserving the language’s simplicity.
MapAnything
Universal Feed-Forward Metric 3D Reconstruction
https://github.com/facebookresearch/map-anything
Universal Feed-Forward Metric 3D Reconstruction
https://github.com/facebookresearch/map-anything
GitHub
GitHub - facebookresearch/map-anything: MapAnything: Universal Feed-Forward Metric 3D Reconstruction
MapAnything: Universal Feed-Forward Metric 3D Reconstruction - facebookresearch/map-anything
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/
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/
Rob's Blog | Python • Rust • Ramblings?
How Well Do New Python Type Checkers Conform? A Deep Dive into Ty, Pyrefly, and Zuban — Rob's Blog | Python • Rust • Ramblings?
A comparison of three new Rust-based Python type checkers through the lens of typing spec conformance: Astral's ty, Meta's pyrefly, and David Halter's zuban
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/
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/
OceanStream
Cloud‑Native Pipelines for Scientific Data Processing with Prefect and Dask
An extended tutorial on the open-source libraries that we use to build the OceanStream cloud‑native data processing stack used to ingest data from sonar instruments and other marine sensors.
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
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
ScalarLM
LLM-Deflate: Extracting LLMs Into Datasets
Large Language Models compress massive amounts of training data into their parameters. This compression is lossy but highly effective—billions of parameters can encode the essential patterns from terabytes of text. However, what’s less obvious is that this…
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/
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/
NVIDIA Technical Blog
The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data
Over hundreds of Kaggle competitions, we’ve refined a playbook that consistently lands us near the top of the leaderboard—no matter if we’re working with millions of rows, missing values…
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
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
YouTube
How to Build Advanced AI Agents – Course for Beginners (LiveKit, Exa, LangChain)
Learn how to build real-world AI apps in this 3-part workshop series. You'll learn to build voice agents, deep research tools, multi-agent workflows, and more.…
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
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
YouTube
The Real Reason the Singleton Pattern Exists
💡 Learn how to design great software in 7 steps: https://arjan.codes/designguide.
Singletons are often criticized for introducing global state and making code harder to test—but there’s more to the story. In this video, we explore the real problems with…
Singletons are often criticized for introducing global state and making code harder to test—but there’s more to the story. In this video, we explore the real problems with…
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
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
YouTube
LLMs from Scratch – Practical Engineering from Base Model to PPO RLHF
Learn to build a complete large language model from scratch using only pure PyTorch. This course takes you through the entire lifecycle, from foundational concepts to advanced alignment techniques. By the end, you'll have the deep, hands-on experience needed…
Unlocking Performance in Python's Free-Threaded Future: GC Optimizations
A description of the performance optimizations made to the free-threaded garbage collector for Python 3.14.
https://labs.quansight.org/blog/free-threaded-gc-3-14
A description of the performance optimizations made to the free-threaded garbage collector for Python 3.14.
https://labs.quansight.org/blog/free-threaded-gc-3-14
labs.quansight.org
Unlocking Performance in Python's Free-Threaded Future: GC Optimizations
A description of the performance optimizations made to the free-threaded garbage collector for Python 3.14.
Air
The new web framework that breathes fresh air into Python web development. Built with FastAPI, Starlette, and Pydantic.
https://github.com/feldroy/air
The new web framework that breathes fresh air into Python web development. Built with FastAPI, Starlette, and Pydantic.
https://github.com/feldroy/air
GitHub
GitHub - feldroy/air: The new Python web framework by the authors of Two Scoops of Django
The new Python web framework by the authors of Two Scoops of Django - feldroy/air
onyx-dot-app / onyx
Open Source AI Platform - AI Chat with advanced features that works with every LLM
https://github.com/onyx-dot-app/onyx
Open Source AI Platform - AI Chat with advanced features that works with every LLM
https://github.com/onyx-dot-app/onyx
GitHub
GitHub - onyx-dot-app/onyx: Open Source AI Platform - AI Chat with advanced features that works with every LLM
Open Source AI Platform - AI Chat with advanced features that works with every LLM - onyx-dot-app/onyx
memvid
Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed.
https://github.com/Olow304/memvid
Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed.
https://github.com/Olow304/memvid
GitHub
GitHub - Olow304/memvid: Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic…
Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed. - Olow304/memvid