Go, Python, Rust, and production AI applications
https://ajmani.net/2024/03/11/go-python-rust-and-production-ai-applications/
https://ajmani.net/2024/03/11/go-python-rust-and-production-ai-applications/
Sameer Ajmani
Go, Python, Rust, and production AI applications
In this article, I’ll talk about Go, Python, and Rust, and each language’s role in building AI-powered applications. Python was the first programming language I ever loved, and Go was the sec…
Guide to Time-Series Analysis in Python
A look at why Python is a great language for time-series analysis. Plus, tips for getting started today.
https://www.timescale.com/blog/how-to-work-with-time-series-in-python/
A look at why Python is a great language for time-series analysis. Plus, tips for getting started today.
https://www.timescale.com/blog/how-to-work-with-time-series-in-python/
Timescale Blog
Guide to Time-Series Analysis in Python
A look at why Python is a great language for time-series analysis. Plus, tips for getting started today.
python-docstring-highlighter
Syntax highlighting for Python Docstring in VSCode.
https://github.com/rodolphebarbanneau/python-docstring-highlighter
Syntax highlighting for Python Docstring in VSCode.
https://github.com/rodolphebarbanneau/python-docstring-highlighter
GitHub
GitHub - rodolphebarbanneau/python-docstring-highlighter: Syntax highlighting for Python Docstring in VSCode.
Syntax highlighting for Python Docstring in VSCode. - rodolphebarbanneau/python-docstring-highlighter
3DTopia / LGM
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation.
https://github.com/3DTopia/LGM
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation.
https://github.com/3DTopia/LGM
GitHub
GitHub - 3DTopia/LGM: [ECCV 2024 Oral] LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation.
[ECCV 2024 Oral] LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation. - 3DTopia/LGM
facebookresearch / jepa
PyTorch code and models for V-JEPA self-supervised learning from video.
https://github.com/facebookresearch/jepa
PyTorch code and models for V-JEPA self-supervised learning from video.
https://github.com/facebookresearch/jepa
GitHub
GitHub - facebookresearch/jepa: PyTorch code and models for V-JEPA self-supervised learning from video.
PyTorch code and models for V-JEPA self-supervised learning from video. - facebookresearch/jepa
Relieving your Python packaging pain
An opinionated take for bootstrapping Python
https://www.bitecode.dev/p/relieving-your-python-packaging-pain
An opinionated take for bootstrapping Python
https://www.bitecode.dev/p/relieving-your-python-packaging-pain
www.bitecode.dev
Relieving your Python packaging pain
60% of the time, it works every time
👍1
LLM4Decompile
Reverse Engineering: Decompiling Binary Code with Large Language Models.
https://github.com/albertan017/LLM4Decompile
Reverse Engineering: Decompiling Binary Code with Large Language Models.
https://github.com/albertan017/LLM4Decompile
GitHub
GitHub - albertan017/LLM4Decompile: Reverse Engineering: Decompiling Binary Code with Large Language Models
Reverse Engineering: Decompiling Binary Code with Large Language Models - albertan017/LLM4Decompile
Diffusion models from scratch, from a new theoretical perspective
The article provides insights into interpreting and enhancing diffusion models using the Euclidean distance function, offering a detailed exploration of diffusion models and their applications. It focuses on improving diffusion models through gradient estimation, efficient sampling techniques, and visualizing the impact of momentum terms on text-to-image generation.
https://www.chenyang.co/diffusion.html
The article provides insights into interpreting and enhancing diffusion models using the Euclidean distance function, offering a detailed exploration of diffusion models and their applications. It focuses on improving diffusion models through gradient estimation, efficient sampling techniques, and visualizing the impact of momentum terms on text-to-image generation.
https://www.chenyang.co/diffusion.html
www.chenyang.co
Diffusion models from scratch
This tutorial aims to give a gentle introduction to diffusion models, with a running example to illustrate how to build, train and sample from a simple diffusion model from scratch.
Every dunder method in Python
An explanation of all of Pytho's 100+ dunder methods and 50+ dunder attributes, including a summary of each one.
https://www.pythonmorsels.com/every-dunder-method/
An explanation of all of Pytho's 100+ dunder methods and 50+ dunder attributes, including a summary of each one.
https://www.pythonmorsels.com/every-dunder-method/
Pythonmorsels
Every dunder method in Python
An explanation of all of Python's 100+ dunder methods and 50+ dunder attributes, including a summary of each one.
Vchitect / Latte
Latte: Latent Diffusion Transformer for Video Generation.
https://github.com/Vchitect/Latte
Latte: Latent Diffusion Transformer for Video Generation.
https://github.com/Vchitect/Latte
GitHub
GitHub - Vchitect/Latte: [TMLR 2025] Latte: Latent Diffusion Transformer for Video Generation.
[TMLR 2025] Latte: Latent Diffusion Transformer for Video Generation. - GitHub - Vchitect/Latte: [TMLR 2025] Latte: Latent Diffusion Transformer for Video Generation.
Python 3.10.14, 3.9.19, and 3.8.19 is now available
https://pythoninsider.blogspot.com/2024/03/python-31014-3919-and-3819-is-now.html
https://pythoninsider.blogspot.com/2024/03/python-31014-3919-and-3819-is-now.html
Blogspot
Python Insider: Python 3.10.14, 3.9.19, and 3.8.19 is now available