pynimate takes a pandas DataFrame with time-indexed data and turns it into animated visualisations -- bar chart races, line plot animations, and more. Pure Python, MIT licensed, pip installable. Nice tool for conference talks or social posts when a static chart does not tell the story.

prompttools lets you systematically compare prompts across 10+ LLM providers and 7+ vector databases, all running locally. Export results as CSV, JSON, or to MongoDB. There is even a Streamlit playground for non-coders. If you are building anything with LLMs, structured prompt evaluation saves you from yourself.

The Incredible PyTorch is a curated list covering everything from LLMs and object detection to reinforcement learning, quantization, and medical imaging -- all with PyTorch implementations. Papers, tutorials, libraries, and tools in one place. Solid starting point when you know what you want to build but not which library to use.

A good listen if you're figuring out your own path into ML.

Wrap any iterable with `tqdm()` and you get a live progress bar with percentage, speed, and ETA — 60 nanoseconds overhead per iteration. Works in terminals, Jupyter notebooks, pandas operations, and even as a CLI pipe. Zero configuration needed. If you've ever killed a long-running script because you couldn't tell if it was stuck, tqdm is the cheapest insurance you'll ever add.

The real winners of this AI arms race might be the developers who get free tools out of it.

If you've ever rage-googled a deadline you already missed, this exists for you.

This repo is a collection of Jupyter notebooks covering hyperparameter tuning, loss functions, learning rate scheduling, regression methods, model evaluation, and more. It started as personal reference material and grew into something genuinely useful for anyone who learns by reading worked examples. Sometimes the best learning resources are the ones someone made for themselves.

bat is cat with syntax highlighting, line numbers, git change markers in the margin, and automatic paging for long files. Detects language from the file extension or shebang. Drop-in replacement — aliasing cat to bat is all it takes. If you read code in the terminal and your output is monochrome, `alias cat=bat` is a one-line quality-of-life upgrade.

If you're still on the fence: jesper.drams.ch

NiceGUI builds web UIs in pure Python using FastAPI + Vue/Quasar under the hood. It gives you buttons, sliders, plots, 3D scenes, and real-time data binding without Streamlit's state magic or having to write JavaScript. Hot reload, Jupyter support, and a pytest-based testing framework included. Great fit for dashboards, internal tools, or any time you want a UI without leaving Python.

If you've ever wondered what a PhD defence actually looks like, here you go.

Aquarel is a lightweight theming engine for matplotlib with 13+ built-in themes (gruvbox, solarized, scientific, minimal, and more). Unlike global stylesheets, it uses context managers so you can style individual plots. Themes serialize to JSON for sharing, and transforms handle things like trimmed axes that rcparams cannot. Per-plot theming with a context manager is the detail that sold me.

uv replaces pip, pip-tools, virtualenv, pyenv, pipx, and poetry — one Rust-based tool, 10-100x faster than pip, with a universal lockfile. It installs Python versions, manages virtual environments, runs scripts with inline dependencies, and even publishes packages. No Rust or Python required to install. If you're still managing your Python environments with multiple tools, the switch is a single install and you'll feel it immediately.

ML in your scroll without the doom.

SalUn (ICLR 2024 Spotlight) makes models forget specific data by identifying and modifying only the most relevant weights. It achieves near-exact unlearning on CIFAR-10 with just 0.2% gap and works on diffusion models like Stable Diffusion too. As privacy regulations tighten, machine unlearning is becoming essential infrastructure.

Git Truck visualizes your git repository's file structure, contributor patterns, and activity hotspots. It runs locally with npx, works offline, collects no data, and is git-provider agnostic. One command and you immediately see who touched what and where the knowledge silos are. If you care about software sustainability (and you should), this makes the invisible risks visible.

Data-driven models can go local without losing the global picture.

FastAPI builds APIs from Python type hints — request validation, response serialization, and interactive docs (Swagger + ReDoc) all generated from your function signatures. Async-native, performance comparable to Go and Node.js, powered by Starlette and Pydantic under the hood. If you're starting a new API and reaching for Flask out of habit, try FastAPI for one endpoint. You'll notice the difference immediately.

For when you want data science content that doesn't take itself too seriously.

Infinite-fractal-stream generates procedural fractal images on-the-fly using parameterized Mandelbrot variants, implemented in Triton for GPU speed. No fixed dataset size -- just infinite unique samples for training classifiers, diffusion models, or VAEs. Configurable resolution, depth, and class counts. As ML shifts toward scaling on more data, benchmarking against finite datasets starts to feel like the wrong test.

PickleScan detects malicious globals in Python pickle files -- the kind that execute code during deserialization. It scans local files, URLs, zip archives, PyTorch models, numpy .npy files, and Hugging Face repos. ClamAV-style exit codes make it easy to integrate into CI pipelines. If you load untrusted model weights, this belongs in your workflow. Hugging Face already uses it.

For when you want to make AI art with intention, not just luck.

Hypothesis generates hundreds of random inputs within ranges you define, checks that your assertions hold for all of them, then shrinks any failure to the minimal reproducing case. You write one test instead of twenty, and it finds edge cases you'd never think to check — empty strings, boundary values, Unicode weirdness. If your tests only cover the cases you thought of, that's exactly the problem hypothesis solves.

WeatherBenchX is Google's next-gen framework for evaluating weather forecasts, succeeding WeatherBench 2. Built on xarray, modular by design, and scalable via Apache Beam. It handles sparse station data and satellite observations, not just gridded fields -- which is where real-world evaluation gets hard. If you evaluate ML weather models, this is the benchmarking infrastructure to watch.

NannyML's Little Book of ML Metrics covers evaluation across regression, classification, clustering, ranking, CV, NLP, GenAI, probabilistic models, bias/fairness, and data observability. Free digital version, open source. Goes from accuracy to the obscure ones you forget exist until you need them. A reference that covers metrics you use daily and the ones you will eventually need.

You can compare physics-based and ML forecasts yourself, right now.

Textual is a TUI framework with CSS-like styling, a widget library including data tables, tree views, and input forms, plus a command palette out of the box. Apps run in the terminal or can be served in a browser with no code changes. From the team behind Rich. If you've ever wanted to build an interactive Python tool with more than print statements but less than a full GUI, this is the sweet spot.

Define a Python class with type hints, pass in data, and pydantic validates, coerces, and structures it automatically. Strings become datetimes, nested JSON becomes typed objects. V2 is a ground-up Rust-backed rewrite — faster, stricter, and still backward-compatible with V1. If you're still validating data with if-statements and try/except blocks, pydantic will save you thousands of lines.

TimberTrek lets you explore Rashomon sets -- collections of equally accurate decision trees -- and pick the one that aligns with your domain knowledge. Runs as a web app, in Jupyter, or in Colab. Published at IEEE VIS 2022 by researchers from Georgia Tech and Duke. When multiple models perform the same, choosing the one that makes sense to humans is an underrated step.

If any of those topics intersect with your life, there's probably a book in here for you.

If you want ML community without the LinkedIn energy, come hang out.

Magic Wormhole transfers files between computers using short, human-pronounceable codes. Sender generates a code, receiver types it in (with tab-completion), done. Encrypted, single-use codes, supports files, directories, and text snippets. Written in Python, packaged in most operating systems. The best file transfer tool is the one where you do not have to configure anything.

pre-commit manages git hooks as a YAML config — linting, formatting, type checking, secret detection, whatever you need. Hooks run in isolated environments, update automatically, and work across any language. Every contributor gets the same checks, no manual setup required. If your team still relies on "remember to run the linter before you push," add a .pre-commit-config.yaml and stop relying on memory.

If you want a newsletter where every recommendation is genuine, this is it.

This article delves into the ways AI models like ChatGPT can reinforce gender biases and affect women's professional lives. A must-read for anyone interested in ethical AI and gender issues… or involved in hiring honestly. 🌐📖

This guide offers practical tips and strategies to captivate your audience and deliver memorable presentations. Perfect for anyone looking to ace their next conference talk! 🗣️✨

Mapping out my coding journey has never been easier than these "Developer Roadmaps". This resource offers detailed roadmaps for becoming a proficient developer, covering various programming paths. Ideal for anyone looking to level up their coding skills! 🚀📚

This system zooms in on local regions while keeping a global perspective, offering hyper-accurate forecasts where it matters most.

AI can assist with coding and automation, but the creativity, problem-solving, and collaboration of a skilled engineering team remain irreplaceable. But most importantly, software engineering is an apprenticeship, and we’re cannibalizing our future.

Try Grafog, the Graph Data Augmentation Library for PyTorch Geometric by rish-16. This library provides a simple and efficient way to apply data augmentation techniques to graph data, allowing you to improve the performance and generalization of your machine learning models.

Don't miss out on the insights and sign up now. 🎉✨

These puzzles blend fun challenges with practical deep learning concepts, making it easier to grasp complex topics while keeping you engaged. Perfect for both beginners and seasoned experts!

Check out "The Incredible Pytorch" by Ritchie Ng. This repository is a treasure trove of PyTorch tutorials and projects, perfect for deepening your knowledge and skills in PyTorch. 🚀🔧

Check out "caption-upsampling" by @sayakpaul. This repository showcases the concept of "caption upsampling" from DALL-E 3 using Zephyr-7B, along with results gathered using SDXL. It's a fascinating project for anyone interested in advanced AI and image generation! 📈🎨

This article explores the limitations and repetitive nature of generative AI, offering a fresh perspective on its creative potential. A must-read for anyone curious about the future of AI! 🔍📖

Enroll now and discover the future of art creation!

This site tracks the openness, transparency, and accountability of large language models and reinforcement learning from human feedback (RLHF). It's essential for anyone interested in ethical AI development and usage! 📚✨