How fast can we process a CSV file

The article explores the speed of processing CSV files, highlighting the use of PyArrow to enhance CSV reading speed significantly. It compares different methods like pandas with C engine, pure Python looping, and pandas with PyArrow engine, showcasing the efficiency of PyArrow in processing CSV files faster and more effectively

https://datapythonista.me/blog/how-fast-can-we-process-a-csv-file

6 ways to improve the architecture of your Python project (using import-linter)

The article discusses six ways to enhance the architecture of Python projects, focusing on maintaining clear dependency relationships between packages and modules to avoid tangled inter-module dependencies. It addresses challenges like high architectural understanding costs for newcomers and reduced development efficiency due to difficulties in locating code within large projects.

https://www.piglei.com/articles/en-6-ways-to-improve-the-arch-of-you-py-project/

FujiwaraChoki / MoneyPrinter

Automate Creation of YouTube Shorts using MoviePy.

https://github.com/FujiwaraChoki/MoneyPrinter

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/

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

Doubiiu / DynamiCrafter

DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

https://github.com/Doubiiu/DynamiCrafter

EvalPlus

EvalPlus for rigourous evaluation of LLM-synthesized code.

https://github.com/evalplus/evalplus

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

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/

openllmetry

Open-source observability for your LLM application.

https://github.com/traceloop/openllmetry

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

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

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

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/

ibis-project / ibis

the portable Python dataframe library

https://github.com/ibis-project/ibis