Jesper Dr.amsch

Can we really trust AI predictions?! 🔮🧪 Our new paper in Nature Geoscience tackles this crucial question, when lives are at stake.

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.

💻 memray: 15.3 k ⭐ I had a Python process eating 8GB of RAM and no idea which line was responsible. Memray showed me in a flame graph.

💻 git-truck: 780⭐ Bus factor meets data visualization.

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.

💻 pynimate: 359⭐ I needed animated bar chart races and did not want to leave Python for it.

💻 prompttools: 3 k ⭐ I have been testing prompts by vibes. That is not engineering.

💻 the-incredible-pytorch: 12.5 k ⭐ I needed a single bookmark for the PyTorch ecosystem.

I went on The Data Scientist Show with Daliana Liu and we talked way longer than planned. We covered the Kaggle-to-career pipeline, why I switched from Keras to PyTorch, dealing with missing data in geoscience, and what self-taught data science actually looks like when you don't have a CS degree.

💻 tqdm: 31.1 k ⭐ I used to stare at a silent terminal wondering if my script had hung or was still processing. One import fixed that permanently.

Meta keeps open-sourcing what others charge for. Meta is reportedly working on free code-generating AI software, positioning it directly against OpenAI's commercial offerings. The open-source-as-strategy playbook continues.

I built this because I kept missing CFP deadlines by two days. PythonDeadlin.es tracks submission deadlines for Python conferences worldwide -- PyCon, PyData, EuroSciPy, and hundreds more. Filterable by category, exportable to your calendar, shown in your local timezone.

💻 machine-learning-notes: 839⭐ Sebastian Raschka's personal ML notes became a public resource.

💻 bat: 58.7 k ⭐ I've been reading source files with cat for 15 years. Plain white text, no line numbers, no context. bat showed me what I was missing.

I left Twitter when it stopped being a place I wanted to share work. Bluesky is where I landed. The ML, Python, and open source conversations actually moved there. Custom feeds mean I see what matters instead of what the algorithm wants me to rage-click.

💻 nicegui: 15.7 k ⭐ I keep wanting Streamlit to be simpler about state, and NiceGUI just is.

Defending a PhD in public is one of the stranger academic rituals. Mine's on YouTube now. The defence covers ML in geoscience: how neural networks can be applied to seismic data, what happens when you build physics into your architecture, and what I learned from four years of research.

💻 aquarel: 860⭐ I spend too much time tweaking matplotlib rcparams. This fixes that.

💻 uv: 83.8 k ⭐ I managed Python environments with pip, virtualenv, and pyenv for over a decade. Then I tried uv and genuinely couldn't go back.

Sixty seconds is apparently enough to explain most ML concepts. I've been testing that theory. Over on @jesperdramsch I break down machine learning ideas into short videos: one concept, no filler. It's a fun constraint that forces clarity.

💻 Unlearn-Saliency: 145⭐ AI ethics and model weights -- two things that connect more than people realise.

💻 git-truck: 674⭐ Bus factor meets data visualization.

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 ⭐ 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. 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.

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⭐ What if your training dataset was literally infinite?

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

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

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

💻 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. 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 ⚖️👩‍💼 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! 🎤🌟 Check out "How to Make a Great Conference Talk" by Sebastian Witowski.

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

💻 grafog: 132⭐ Supercharge your graph data 📊🔍

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

💻 the-incredible-pytorch: 11.9 k ⭐ I love finding curated resources for my Pytorch projects! 📚✨