uv-lock-report
A GitHub Action to report changes to uv.lock.
https://github.com/mw-root/uv-lock-report
A GitHub Action to report changes to uv.lock.
https://github.com/mw-root/uv-lock-report
GitHub
GitHub - mw-root/uv-lock-report: A GitHub Action to report changes to uv.lock.
A GitHub Action to report changes to uv.lock. Contribute to mw-root/uv-lock-report development by creating an account on GitHub.
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
DeepAnalyze is the first agentic LLM for autonomous data science, supporting:
🛠 Data preparation, analysis, modeling, visualization, and insight.
🔍 Data research and produce research report.
https://ruc-deepanalyze.github.io/
DeepAnalyze is the first agentic LLM for autonomous data science, supporting:
🛠 Data preparation, analysis, modeling, visualization, and insight.
🔍 Data research and produce research report.
https://ruc-deepanalyze.github.io/
The future of Python web services looks GIL-free
The free-threaded Python variant in 3.14 removes the Global Interpreter Lock (GIL), enabling true parallel multithreading for CPU-bound tasks. While it may have a modest performance cost on single-threaded code, it significantly improves memory efficiency and concurrency in web applications, simplifying deployment and boosting throughput, especially for ASGI and WSGI based services.
https://blog.baro.dev/p/the-future-of-python-web-services-looks-gil-free
The free-threaded Python variant in 3.14 removes the Global Interpreter Lock (GIL), enabling true parallel multithreading for CPU-bound tasks. While it may have a modest performance cost on single-threaded code, it significantly improves memory efficiency and concurrency in web applications, simplifying deployment and boosting throughput, especially for ASGI and WSGI based services.
https://blog.baro.dev/p/the-future-of-python-web-services-looks-gil-free
Fluxus by gi0baro
The future of Python web services looks GIL-free | Fluxus by gi0baro
Web frameworks benchmarks on CPython 3.14t looks promising
Three times faster with lazy imports
This post tests Python 3.15’s proposed PEP 810 explicit lazy imports, which delay loading modules until first use to cut startup time.? Using the feature on author's CLI tool pypistats, he found it ran 2.92× faster (reducing startup from 104 ms to 36 ms), demonstrating how lazy imports can significantly speed up Python applications with large dependency graphs.
https://hugovk.dev/blog/2025/lazy-imports/
This post tests Python 3.15’s proposed PEP 810 explicit lazy imports, which delay loading modules until first use to cut startup time.? Using the feature on author's CLI tool pypistats, he found it ran 2.92× faster (reducing startup from 104 ms to 36 ms), demonstrating how lazy imports can significantly speed up Python applications with large dependency graphs.
https://hugovk.dev/blog/2025/lazy-imports/
Hugo van Kemenade
Three times faster with lazy imports
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
DeepAnalyze is the first agentic LLM for autonomous data science, supporting:
🛠 Data preparation, analysis, modeling, visualization, and insight.
🔍 Data research and produce research reports.
https://github.com/ruc-datalab/DeepAnalyze
DeepAnalyze is the first agentic LLM for autonomous data science, supporting:
🛠 Data preparation, analysis, modeling, visualization, and insight.
🔍 Data research and produce research reports.
https://github.com/ruc-datalab/DeepAnalyze
GitHub
GitHub - ruc-datalab/DeepAnalyze: DeepAnalyze is the first agentic LLM for autonomous data science.
DeepAnalyze is the first agentic LLM for autonomous data science. - ruc-datalab/DeepAnalyze
Recursive Language Models
Recursive Language Models (RLMs) let language models recursively call themselves within an environment, like a Python REPL, to handle extremely long contexts without performance drop (context rot). They dynamically break down queries into smaller parts, delivering strong, cost-efficient results on big benchmarks and enabling scalable, interpretable reasoning beyond fixed context limits.
https://alexzhang13.github.io/blog/2025/rlm/
Recursive Language Models (RLMs) let language models recursively call themselves within an environment, like a Python REPL, to handle extremely long contexts without performance drop (context rot). They dynamically break down queries into smaller parts, delivering strong, cost-efficient results on big benchmarks and enabling scalable, interpretable reasoning beyond fixed context limits.
https://alexzhang13.github.io/blog/2025/rlm/
Alex L. Zhang
Recursive Language Models
We propose Recursive Language Models (RLMs), an inference strategy where language models can decompose and recursively interact with input context of unbounded length through REPL environments.
Introducing PyTorch Monarch
PyTorch Monarch is a distributed programming framework designed to simplify scaling AI workflows by enabling a single-controller model that orchestrates distributed resources like a single machine. It provides actor-based programming with scalable messaging, fault tolerance, and distributed tensor support, allowing seamless development, debugging, and efficient handling of large-scale tr...
https://pytorch.org/blog/introducing-pytorch-monarch/
PyTorch Monarch is a distributed programming framework designed to simplify scaling AI workflows by enabling a single-controller model that orchestrates distributed resources like a single machine. It provides actor-based programming with scalable messaging, fault tolerance, and distributed tensor support, allowing seamless development, debugging, and efficient handling of large-scale tr...
https://pytorch.org/blog/introducing-pytorch-monarch/
caniscrape
Know before you scrape. Analyze any website's anti-bot protections in seconds.
https://github.com/ZA1815/caniscrape
Know before you scrape. Analyze any website's anti-bot protections in seconds.
https://github.com/ZA1815/caniscrape
GitHub
GitHub - ZA1815/caniscrape
Contribute to ZA1815/caniscrape development by creating an account on GitHub.
Create Your Own Bash Computer Use Agent with NVIDIA Nemotron in One Hour
A tutorial on building a computer use AI agent capable of executing multi-step tasks in a Bash shell, powered by the NVIDIA Nemotron Large Language Model. It covers creating the agent's brain, the Bash interface for safe command execution, and the agent loop, demonstrating how to build and deploy an autonomous assistant within an hour.
https://www.youtube.com/watch?v=F7f-eFou2-o
A tutorial on building a computer use AI agent capable of executing multi-step tasks in a Bash shell, powered by the NVIDIA Nemotron Large Language Model. It covers creating the agent's brain, the Bash interface for safe command execution, and the agent loop, demonstrating how to build and deploy an autonomous assistant within an hour.
https://www.youtube.com/watch?v=F7f-eFou2-o
YouTube
Create Your Own Bash Computer Use Agent with NVIDIA Nemotron in One Hour
In this tutorial, you'll learn how to build a computer use AI agent from scratch. Powered by the NVIDIA Nemotron Large Language Model (LLM), this agent can execute multi-step tasks in the Bash shell, such as navigating files and summarizing documents and…