💻 git-truck: 674⭐ 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.

💻 git-truck: 674⭐

Bus factor meets data visualization.

Regional ML weather forecasting got real. Data-driven models can go local without losing the global picture.

Regional ML weather forecasting got real.

Met Norway extended AIFS with a stretched grid that concentrates 2.5 km resolution over the Nordics while keeping a global context. Trained on ERA5 plus just 3.3 years of regional data, it outperforms MEPS for 2m temperature.

💻 fastapi: 96.9 k ⭐ 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.

💻 fastapi: 96.9 k ⭐

I built REST APIs in Flask for years — route decorators, manual request parsing, Swagger docs as an afterthought. FastAPI made all of that automatic.

I write about ML, Python, and occasionally zombies. It's a range. For when you want data science content that doesn't take itself too seriously.

I write about ML, Python, and occasionally zombies. It's a range.

The blog covers practical stuff like pytest patterns and clustering in Python alongside weirder pieces on the uncanny valley and AI vacation planning. No posting schedule, just whenever something bugs me enough to write it down.

That's very fair.

this is a good article. we are changing what parts of coding are hard. but make no mistake, there are still hard parts. And we need to not gloss over those.

💻 infinite-fractal-stream: 30⭐ 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.

💻 infinite-fractal-stream: 30⭐

What if your training dataset was literally infinite?

💻 picklescan: 397⭐ 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.

💻 picklescan: 397⭐

Every ML model you download as a pickle can run arbitrary code. That should concern you.

I made a course about AI art because I wanted to demystify the part most tutorials skip. For when you want to make AI art with intention, not just luck.

I made a course about AI art because I wanted to demystify the part most tutorials skip.

It covers Stable Diffusion and prompt engineering on Skillshare -- not just "type this and get that" but understanding why certain prompts work and how the model actually interprets your words.

💻 hypothesis: 8.5 k ⭐ 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.

💻 hypothesis: 8.5 k ⭐

I was writing unit tests with hand-picked inputs for years. Hypothesis found a bug in the first function I pointed it at.

💻 weatherbenchX: 196⭐ 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.

💻 weatherbenchX: 196⭐

This is literally what I work on, so I have opinions.

💻 The-Little-Book-of-ML-Metrics: 995⭐ 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.

💻 The-Little-Book-of-ML-Metrics: 995⭐

I still look up the difference between macro and weighted F1. No shame.

This is literally my day job and it still blows my mind sometimes. You can compare physics-based and ML forecasts yourself, right now.

This is literally my day job and it still blows my mind sometimes.

ECMWF now shows ML weather forecasts from FourCastNet, Pangu-Weather, and GraphCast alongside the operational IFS on our public charts. Same verification, same standards. Plus we open-sourced ai-models so anyone can run them.

💻 textual: 34.9 k ⭐ 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.

💻 textual: 34.9 k ⭐

I wanted a quick UI for a Python tool but didn't want to learn Qt or ship an Electron app. Textual let me build it in the terminal.

💻 pydantic: 27.2 k ⭐ 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.

💻 pydantic: 27.2 k ⭐

I used to write manual validation for every dict that came from an API or config file. Pydantic made me wonder why I ever did that.

💻 timbertrek: 169⭐ 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.

💻 timbertrek: 169⭐

Interpretable ML meets interactive visualization, and it works in a browser.

I counted and apparently I wrote nine books. That surprised me too. If any of those topics intersect with your life, there's probably a book in here for you.

I counted and apparently I wrote nine books. That surprised me too.

They span ML Recipes, a Stable Diffusion Lookbook, 70 years of ML in geoscience, a data science guide, ChatGPT for creators, and a resume guide for data jobs. Some are free, some are on Skillshare.

I needed a space for ML conversations that isn't a shouting match. If you want ML community without the LinkedIn energy, come hang out.

I needed a space for ML conversations that isn't a shouting match.

The Latent Space is a small, inclusive Discord for ML practitioners, makers, and creators. We talk about the normal stuff below the hype -- tooling, papers, career questions, creative projects.

💻 magic-wormhole: 22.3 k ⭐ 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.

💻 magic-wormhole: 22.3 k ⭐

I needed to send a file to another machine without thinking about it.

💻 pre-commit: 15 k ⭐ 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.

💻 pre-commit: 15 k ⭐

Every codebase I've inherited had broken or missing git hooks. pre-commit fixed that across all of them with one config file.

Two years of weekly ML newsletters and still zero sponsorships. That's a feature, not a bug. If you want a newsletter where every recommendation is genuine, this is it.

Two years of weekly ML newsletters and still zero sponsorships. That's a feature, not a bug.

Late to the Party covers real-world ML, data science, and Python -- tools, papers, and repos I actually use. No affiliate links, no paid placements, just curation.

I’m often concerned about how generative AI impacts gender equality ⚖️👩‍💼 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. 🌐📖

I’m often concerned about how generative AI impacts gender equality ⚖️👩‍💼

Especially so after this insightful read: "Patriarchal AI: How ChatGPT can harm a woman’s career" by Ruhi Khan.

I always strive to improve my public speaking skills! 🎤🌟 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! 🗣️✨

I always strive to improve my public speaking skills! 🎤🌟

Check out "How to Make a Great Conference Talk" by Sebastian Witowski.

I love being a largely self-taught programmer! 🛤️💻 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! 🚀📚

I love being a largely self-taught programmer! 🛤️💻

But it can be a bit lonely and uncertain on what to learn next.

I’m amazed by how tech tailors forecasts to where we live 🌍📡 This system zooms in on local regions while keeping a global perspective, offering hyper-accurate forecasts where it matters most.

I’m amazed by how tech tailors forecasts to where we live 🌍📡

Check out Met Norway’s Regional Data-Driven Weather Modeling with a Global Stretched-Grid!

I look around on LinkedIn and people want AI to do all the hard work! 😅🤖 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.

I look around on LinkedIn and people want AI to do all the hard work! 😅🤖

Here’s a great read on Stack Overflow on why Generative AI can't replace human engineering teams by Charity Majors.

💻 grafog: 132⭐ 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.

💻 grafog: 132⭐

Supercharge your graph data 📊🔍

Stay updated on the latest machine learning, data science, and Python trends in geophysics? 🌍🧑‍🔬 Don't miss out on the insights and sign up now. 🎉✨

Stay updated on the latest machine learning, data science, and Python trends in geophysics? 🌍🧑‍🔬

Join my newsletter and receive curated links to projects, data stories, job interview tips, personal updates, and more every Friday!

Coding with AI agents hast shown me that we're fine in the AI Apocalypse.

"I destroyed all of humanity (in these three examples and will pretend it's 100% and gaslight you that you misunderstood me)"

I love a good brain teaser, especially when it sharpens my coding skills! 🧩🤓 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!

I love a good brain teaser, especially when it sharpens my coding skills! 🧩🤓

Deep Learning Puzzles, crafted by the great minds at deep-ml offers a unique way to learn ML fundamentals.