SurfSense
Personal AI Assistant for World Wide Web Surfers. Research & Never forget anything you see on the Internet.
https://github.com/MODSetter/SurfSense
Personal AI Assistant for World Wide Web Surfers. Research & Never forget anything you see on the Internet.
https://github.com/MODSetter/SurfSense
GitHub
GitHub - MODSetter/SurfSense: Open Source Alternative to NotebookLM / Perplexity / Glean, connected to external sources such as…
Open Source Alternative to NotebookLM / Perplexity / Glean, connected to external sources such as search engines (Tavily, Linkup), Slack, Linear, Notion, YouTube, GitHub, Discord and more. - MODSet...
Building RAG with Postgres
A step by step guide to building a RAG system with Postgres.
https://anyblockers.com/posts/building-rag-with-postgres
A step by step guide to building a RAG system with Postgres.
https://anyblockers.com/posts/building-rag-with-postgres
Anyblockers
Building RAG with Postgres — any blockers?
A step by step guide to building a RAG system with Postgres
What are some well-known, universally understood things that a self learner might miss?
https://www.reddit.com/r/learnpython/comments/1fsol3z/what_are_some_wellknown_universally_understood/
https://www.reddit.com/r/learnpython/comments/1fsol3z/what_are_some_wellknown_universally_understood/
Reddit
From the learnpython community on Reddit
Explore this post and more from the learnpython community
Refactoring Python with Tree-sitter and Jedi
https://jackevans.bearblog.dev/refactoring-python-with-tree-sitter-jedi/
https://jackevans.bearblog.dev/refactoring-python-with-tree-sitter-jedi/
Jack's blog
Refactoring Python with 🌳 Tree-sitter & Jedi
I was toying around with a refactor the other day that would have taken me ages by hand as it involved 100s of files.
I wanted to rename every instance of...
I wanted to rename every instance of...
Inside Bento: Jupyter Notebooks at Meta
Meta has developed Bento, an internal platform that enhances Jupyter notebooks with features like version control, collaborative editing, and automated dependency management. This system, which handles over 150,000 notebooks and supports 18,000 monthly active users, aims to improve productivity and collaboration for data scientists and engineers across Meta's various teams and projects.
https://engineering.fb.com/2024/09/17/data-infrastructure/inside-bento-jupyter-notebooks-at-meta/
Meta has developed Bento, an internal platform that enhances Jupyter notebooks with features like version control, collaborative editing, and automated dependency management. This system, which handles over 150,000 notebooks and supports 18,000 monthly active users, aims to improve productivity and collaboration for data scientists and engineers across Meta's various teams and projects.
https://engineering.fb.com/2024/09/17/data-infrastructure/inside-bento-jupyter-notebooks-at-meta/
Engineering at Meta
Inside Bento: Jupyter Notebooks at Meta
This episode of the Meta Tech Podcast is all about Bento, Meta’s internal distribution of Jupyter Notebooks, an open-source web-based computing platform. Bento allows our engineers to mix code, tex…
RoboTwin
Dual-Arm Robot Benchmark with Generative Digital Twins.
https://github.com/TianxingChen/RoboTwin
Dual-Arm Robot Benchmark with Generative Digital Twins.
https://github.com/TianxingChen/RoboTwin
GitHub
GitHub - TianxingChen/RoboTwin
Contribute to TianxingChen/RoboTwin development by creating an account on GitHub.
Everything you need to know about Python 3.13 – JIT and GIL went up the hill
https://drew.silcock.dev/blog/everything-you-need-to-know-about-python-3-13/
https://drew.silcock.dev/blog/everything-you-need-to-know-about-python-3-13/
drew.silcock.dev
Everything you need to know about Python 3.13 – JIT and GIL went up the hill | drew's dev blog
All you need to know about the latest Python release including Global Interpreter Lock and Just-in-Time compilation.
MemoRAG
Empowering RAG with a memory-based data interface for all-purpose applications!
https://github.com/qhjqhj00/MemoRAG
Empowering RAG with a memory-based data interface for all-purpose applications!
https://github.com/qhjqhj00/MemoRAG
GitHub
GitHub - qhjqhj00/MemoRAG: Empowering RAG with a memory-based data interface for all-purpose applications!
Empowering RAG with a memory-based data interface for all-purpose applications! - qhjqhj00/MemoRAG
Multi-dimensional Sparse Arrays in SciPy
The article explores multi-dimensional sparse arrays and their implementation in scientific computing, focusing on their efficiency in handling large datasets with mostly empty or zero-valued elements. It emphasizes the benefits of sparse arrays for memory optimization and computational performance in various data-heavy applications.
https://labs.quansight.org/blog/multi-dimensional-sparse-arrays
The article explores multi-dimensional sparse arrays and their implementation in scientific computing, focusing on their efficiency in handling large datasets with mostly empty or zero-valued elements. It emphasizes the benefits of sparse arrays for memory optimization and computational performance in various data-heavy applications.
https://labs.quansight.org/blog/multi-dimensional-sparse-arrays
labs.quansight.org
Multi-dimensional Sparse Arrays in SciPy
My work focused on extending support for COOrdinate sparse arrays in SciPy to n-dimensions.
How to Design an AWESOME Function Signature in Python
This video demonstrates how to design a great function header in Python, highlighting aspects that are often overlooked but can significantly impact the quality of the code. Important considerations will be discussed to enhance coding practices and improve overall functionality.
https://www.youtube.com/watch?v=WI6CqykFbDc
This video demonstrates how to design a great function header in Python, highlighting aspects that are often overlooked but can significantly impact the quality of the code. Important considerations will be discussed to enhance coding practices and improve overall functionality.
https://www.youtube.com/watch?v=WI6CqykFbDc
YouTube
Things (Almost) No One Thinks About When Designing Functions in Python
💡 Learn how to design great software in 7 steps: https://arjan.codes/designguide.
In this video, I'll show you how to design a great function header in Python. I’ll mention things you probably haven’t considered but that can make or break your code.
🔥 GitHub…
In this video, I'll show you how to design a great function header in Python. I’ll mention things you probably haven’t considered but that can make or break your code.
🔥 GitHub…
weather_landscape
Visualizing Weather Forecasts Through Landscape Imagery.
https://github.com/lds133/weather_landscape
Visualizing Weather Forecasts Through Landscape Imagery.
https://github.com/lds133/weather_landscape
GitHub
GitHub - lds133/weather_landscape: Visualizing Weather Forecasts Through Landscape Imagery
Visualizing Weather Forecasts Through Landscape Imagery - lds133/weather_landscape
unclecode / crawl4ai
🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper
https://github.com/unclecode/crawl4ai
🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper
https://github.com/unclecode/crawl4ai
GitHub
GitHub - unclecode/crawl4ai: 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://dis…
🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://discord.gg/jP8KfhDhyN - unclecode/crawl4ai
How to Use FastAPI for Machine Learning
Learn how to use FastAPI for machine learning. Explore the framework's benefits and why it's popular, and understand how to integrate it into your ML workflows.
https://blog.jetbrains.com/pycharm/2024/09/how-to-use-fastapi-for-machine-learning/
Learn how to use FastAPI for machine learning. Explore the framework's benefits and why it's popular, and understand how to integrate it into your ML workflows.
https://blog.jetbrains.com/pycharm/2024/09/how-to-use-fastapi-for-machine-learning/
The JetBrains Blog
How to Use FastAPI for Machine Learning | The PyCharm Blog
Learn how to use FastAPI for machine learning. Explore the framework's benefits and why it's popular, and understand how to integrate it into your ML workflows.
streamable
Stream-like manipulation of Python iterables: fluent, typed, lazy, concurrent.
https://github.com/ebonnal/streamable
Stream-like manipulation of Python iterables: fluent, typed, lazy, concurrent.
https://github.com/ebonnal/streamable
GitHub
GitHub - ebonnal/streamable: Pythonic Stream-like manipulation of iterables
Pythonic Stream-like manipulation of iterables. Contribute to ebonnal/streamable development by creating an account on GitHub.
Httpdbg – A tool to trace the HTTP requests sent by your Python code
https://github.com/cle-b/httpdbg
https://github.com/cle-b/httpdbg
GitHub
GitHub - cle-b/httpdbg: A tool for Python developers to easily debug the HTTP(S) client and server requests in a Python program.
A tool for Python developers to easily debug the HTTP(S) client and server requests in a Python program. - cle-b/httpdbg