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#python Tinygrad is a simple deep learning framework that is easy to understand and use. It is designed to be lightweight and flexible, making it easy to add new hardware accelerators. Tinygrad supports various devices like GPUs and CPUs, and it can run models like LLaMA and Stable Diffusion. Its simplicity helps users learn how deep learning works by providing a clean and readable codebase. This makes it a great tool for learning and experimenting with deep learning concepts. https://github.com/tinygrad/tinygrad

#python This tool helps you easily download PDF textbooks from the National Primary and Secondary School Smart Education Platform by extracting the book URLs and saving the files automatically with correct names. Since February 2025, the platform requires login, so you must set an Access Token (login credential) in the tool to download books. It supports batch downloads, shows progress, works on Windows, Linux, and macOS, and saves your token securely on your device. This makes getting and managing digital textbooks much faster and more convenient for study or teaching. https://github.com/happycola233/tchMaterial-parser

#python fairchem is a centralized open-source library by FAIR Chemistry that provides advanced machine learning models, datasets, demos, and tools for materials science and quantum chemistry. You can install it via pip and use pretrained models through the FAIRChemCalculator with ASE, enabling tasks like catalysis, inorganic materials, molecules, MOFs, and molecular crystals. It supports simulations such as structure relaxation and molecular dynamics. Version 2 is a major update and not compatible with version 1 models. Using fairchem helps you quickly apply state-of-the-art AI models to accelerate research and discovery in chemistry and materials science[1][2][4][5]. https://github.com/facebookresearch/fairchem

#python This library helps you test and compare language models by running standard benchmarks like math, reading, coding, and general knowledge tasks. It uses simple, clear instructions to measure how well models perform without complicated prompts, reflecting real-world use better. You can evaluate many models, including OpenAI’s and others, to see their strengths and weaknesses on tasks like problem-solving and factual accuracy. This transparency helps you pick the best model for your needs and understand their capabilities. The library supports easy setup and running of tests via APIs, making it practical for developers and researchers to assess model quality quickly and reliably. https://github.com/openai/simple-evals

#python Torchtitan is a PyTorch-native platform designed for easy and large-scale training of generative AI models like Llama 3.1. It supports advanced distributed training techniques such as multi-dimensional parallelism, activation checkpointing, and Float8 precision, enabling efficient use of many GPUs. Torchtitan is modular and cleanly coded, making it easy to extend and customize for different AI research and development needs. It also integrates with PyTorch’s latest features like torch.compile for faster training. This platform helps you rapidly experiment and scale AI model training with minimal code changes, boosting productivity and innovation in generative AI development[1][3][4][5]. https://github.com/pytorch/torchtitan

#python FieldStation42 is a project that lets you experience old TV like it was in the past. It uses a Raspberry Pi to simulate multiple TV channels with shows and commercials. You can set up different channels, schedule shows, and even add seasonal content. The system supports multiple channels playing at the same time and can automatically insert commercials. This project is great for people who miss the old TV experience and want to relive it with a nostalgic feel. It requires some technical setup but offers a fun way to enjoy retro TV. https://github.com/shane-mason/FieldStation42

#python The Jelly Evolution Simulator is a program that lets you watch jelly-like creatures evolve over time. You can run it using a simple command in Python. The simulator allows you to control various features like closing the program, toggling markers, storing species, and changing colors. It also lets you scroll through different generations to see how the creatures change. This tool is useful for understanding how evolution works in a fun and interactive way. It helps users visualize how small changes can lead to different outcomes over time. https://github.com/carykh/jes

Version 3.10 of the legendary programming language is now here: https://www.python.org/downloads/release/python-3100 No rush to update, though. #Python

#Python is the main language of data science, per this analysis on 10M Jupyter Notebooks: https://blog.jetbrains.com/datalore/2020/12/17/we-downloaded-10-000-000-jupyter-notebooks-from-github-this-is-what-we-learned/

📰 Feature freeze for Python 3.15 as first beta released JIT compiler much improved, but no reinstatement for leaky incremental garbage collector. 🔗 Source: https://www.theregister.com/devops/2026/05/11/feature-freeze-for-python-315-as-first-beta-released/5237934 #python

📰 New Python Backdoor Uses Tunneling Service to Steal Browser and Cloud Credentials Cybersecurity researchers have disclosed details of a stealthy Python-based backdoor framework called DEEP#DOOR that comes with capabilities to establish persistent access and harvest a wide range of sensitive information from compromised hosts."The intrusion chain begins with execution of a batch script ('install_obf.bat') that disables Windows security controls, dynamically extracts an. 🔗 Source: https://thehackernews.com/2026/04/new-python-backdoor-uses-tunneling.html #python

📰 PyPI package with 1.1M monthly downloads hacked to push infostealer An attacker pushed a malicious version of the popular elementary-data package Python Package Index (PyPI) to steal sensitive developer data and cryptocurrency wallets. 🔗 Source: https://www.bleepingcomputer.com/news/security/pypi-package-with-11m-monthly-downloads-hacked-to-push-infostealer/ #python