PythonHub

Homebrew all the Python things https://blog.davep.org/2024/03/10/homebrew-all-the-python-things.html

fructose LLM calls as strongly-typed functions. https://github.com/bananaml/fructose

Understanding Context Manager and its Syntastic Sugar https://bjoernricks.github.io/posts/python/context-manager/

Speed up Django’s collectstatic command with Collectfasta The post introduces Collectfasta, an updated fork of Collectfast designed to enhance the performance of Django's collectstatic command. By optimizing the repository and improving performance, Collectfasta offers faster execution and efficiency compared to the standard Django command, providing a valuable tool for developers seeking enhanced performance in their Django projects. https://jasongi.com/2024/03/04/speed-up-djangos-collectstatic-command-with-collectfasta/

ibis-project / ibis the portable Python dataframe library https://github.com/ibis-project/ibis

Python Gevent in practice: common pitfalls to keep in mind Learn more about the common pitfalls of using the asynchronous Python library, Gevent, and how to resolve them in this article. https://upsun.com/blog/python-gevent-best-practices/

Large Language Models On-Device with MediaPipe and TensorFlow Lite The article discusses the release of the experimental MediaPipe LLM Inference API, enabling Large Language Models (LLMs) to run fully on-device across platforms. This transformative capability addresses the significant memory and compute demands of LLMs, which are over a hundred times larger than traditional on-device models, achieved through optimizations like new ops, quantization, cac... https://developers.googleblog.com/2024/03/running-large-language-models-on-device-with-mediapipe-andtensorflow-lite.html

We Hacked Google A.I. for $50,000 This article discusses the author's experience of participating in a hacking event in Las Vegas where vulnerabilities were discovered, leading to the successful hacking of Google. Despite the initial achievement, the Google VRP team extended the competition deadline to encourage more creative findings, highlighting the ongoing challenges and opportunities in the realm of cybersecurity https://www.landh.tech/blog/20240304-google-hack-50000

Insecurity and Python pickles https://lwn.net/SubscriberLink/964392/498a12fe44f51139/

Parsing URLs in Python https://tkte.ch/articles/2024/03/15/parsing-urls-in-python.html

Create A Machine Learning Powered NCAA Bracket Dive into the fascinating world of machine learning and AI as we guide you through developing a model designed to predict NCAA tournament outcomes. From initial setup to final predictions, we’ll cover everything you need to create your own powerhouse model. https://www.youtube.com/watch?v=cHtAEWkvSMU

openllmetry Open-source observability for your LLM application. https://github.com/traceloop/openllmetry

GGUF, the long way around This is an article about GGUF, a file format used for machine learning models. It discusses what machine learning models are and how they are produced. https://vickiboykis.com/2024/02/28/gguf-the-long-way-around/

Using LLMs to Generate Fuzz Generators The post explores the effectiveness of Large Language Models (LLMs) in generating fuzz drivers for library API fuzzing. It discusses the challenges and benefits of LLM-based fuzz driver generation, highlighting its practicality, strategies for complex API usage, and areas for improvement based on a comprehensive study and evaluation. https://verse.systems/blog/post/2024-03-09-using-llms-to-generate-fuzz-generators

JSON Serialization https://www.reddit.com/r/Python/comments/1bfs8cy/json_serialization/

Python Hub Weekly Digest for 2024-03-17 https://pythonhub.dev/digest/2024-03-17/

EvalPlus EvalPlus for rigourous evaluation of LLM-synthesized code. https://github.com/evalplus/evalplus

Doubiiu / DynamiCrafter DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors https://github.com/Doubiiu/DynamiCrafter

Analyzing "Sorting a million 32-bit integers in 2MB of RAM using Python" SummaryWe are going to revisit Guido's famous "Sorting a million 32-bit integers in 2MB of RAM ... https://www.bitecode.dev/p/analyzing-sorting-a-million-32-bit

Get started with conda environments This post explains the benefits of virtual environments and how to use virtual environments in conda. https://www.dataschool.io/intro-to-conda-environments/