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Products that do what Hurricane Path Prediction Using LSTM does
College project I picked back up and improved. Implementation in PyTorch, better results than NHC models as reported at : https://www.nhc.noaa.gov/verification/verify8.shtml
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- 2MP
I have added OpenZones to Flowx, my Android weather app[0]. OpenZones are regions covering disaster zones where all the pro data in the app is freely available. I have created an 1200km diameter OpenZone over Florida with Hurricane Dorian approaching. So if you add Florida to the app, you can view radar, NOAA's GFS, NAM and HRRR models, CMC GDPS and RDPS models, and the DWD (Germany) ICON model. There are also predicted hurricane tracks from the NOAA and CMC ensemble models which I find extremely valuable to predict the possible paths of Dorian. This YouTube video[1] shows an example of the…
2019
- 3FW
2012 · callum.com
- 4OA
2018 · openavalancheproject.org
- 5AC
2018 · allclearweather.com
- 6FM
As the title and description of the GitHub repo suggest, I’m working on a small project for purely educational purposes, with the goal of implementing generative model inference (small models capable of modeling 2D distributions) based on the Flow Matching paradigm in C. I’ve worked on generative AI models based on Flow Matching from a more “abstract” perspective, using frameworks like PyTorch, and I wanted to understand what goes on behind the scenes. The repository is still a work in progress and is also one of my first "serious" projects in C.
Jul 2026 · github.com
- 7BH
Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…
2019
- 8RW
Weather apps all claim to be accurate but never show their work. So I built a scoreboard that checks: it takes the forecasts each model published (ECMWF, GFS, ICON, AIFS and others, plus Apple Weather, Foreca, OpenWeather and Visual Crossing), waits for the weather to happen, and scores temperature, wind and rain against observations. A few things that surprised me: - AIFS performs very well, yet almost no commercial apps give you access to it or uses it in their blend (I suspect some do without disclosing it tho) - Foreca scores surprisingly well compared to other apps and raw models - ICON…
Aug 2026 · nickleenders.github.io
- 9LR
I just noticed it takes literally ~5 minutes to train millions parameters on slow CPU...but before you call Yudkowsky that "it's over", an important note: the main bottleneck is the corpus size, params are just 'cleverness' but given limited info it's powerless. Anyway, here is the project: https://github.com/bggb7781-collab/lrnnsmdds/tree/main couple of notes: 1. single C file, no dependencies. Below are literally all the "dependencies", not even custom header (copy paste from the top of the single c file): #define _POSIX_C_SOURCE 200809L #include #include…
Apr 2026 · raw.githubusercontent.com
- 10EW
I had been wanting to try experimenting with nowcasting since the death of Dark Sky and finally got around to it. This application essentially became a playground for me to experiment with various weather datasets and geospatial visualizations. Some interesting concepts that came from it: The nowcast is using pySTEPS optical flow under the hood: https://pysteps.readthedocs.io/en/latest/auto_examples/plot_.... This library is super cool. It bugged me that every radar visualization just gave you choppy snapshots in time, so I spent some time working on building a…
Jul 2026 · strataweather.com
- 11IB
This dashboard is a personal project I've developed to visualize every wind farm's energy production across Ireland. I'm trying to break into software development/data analytics sector and I wanted to build a project to practice my coding. I wanted to build a novel project rather than following along to a basic YouTube video project and not really learn anything. I'm interested in the renewable energy sector and after browsing the web noticed there isn't any public data on real time wind energy production in Ireland. I took the task onto myself to attempt to build a dashboard that…
2024 · irelandwinddashboard.com
- 12GF
Hello HN, We are pleased to introduce you graphlearn-for-pytorch (https://github.com/alibaba/graphlearn-for-pytorch), an open-source distributed graph neural network library based on PyTorch and compatible with PyG. Our library is designed to make it easy for developers to build and train large-scale graph models in a distributed environment. With graphlearn-for-pytorch, you can leverage GPUs to accelerate graph sampling and utilize UVA to reduce the overheads of feature collection. Following a scalable design, graphlearn-for-pytorch supports training GNN models on…
2023 · github.com
- 13FS
Hey HN! I’ve been building Fatebook for the past couple of months. It’s a slack bot to help your team make and track predictions, right where you work. I see forecasting as anti-bullshit technology: - It gives you truthseeking incentives - You communicate your uncertainty as a probability, which is way clearer (70% is better than “probably”) - You can aggregate forecasts to get wisdom of the crowd effects - You can see everyone’s track record, and pay more attention to people who are consistently accurate I’m a fan of prediction markets [0] and forecasting platforms [1]. But predictions on…
2023 · fatebook.io
- 14IM
I’m 15 and self-taught. I'm learning ML from scratch because I want to really understand how things work. I’m not into frameworks. I prefer math, logic, and C++. I implemented a basic MLP that supports different activation and loss functions. It was trained via mini-batch gradient descent. I wrote it from scratch, using no external libraries except Eigen (for linear algebra). I learned how a Neural Network learns (all the math) -- how the forward pass works, and how learning via backpropagation works. How to convert all that math into code. I’ll write a blog soon explaining how MLPs work in…
2025 · github.com
- 15MM
Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This allows you to embed 50-100k documents per second on a cpu on a macbook. This reduction of course comes at a cost: distilled models are worse than their parent models. Even so, they are actually a lot better than large sets of conventional…
2024 · github.com
- 16NH
I'm looking for some help developing this front end: http://equationdesigner.appspot.com/ I'm pretty much a n00b when it comes to what web technologies to use, my focus has been the back end. This is also my first attempt seeking help on HN, lets hope this GOes well. I started this project for a group of lake ecologists (GLEON.org), but I think the computer science can be used more broadly.
2012
- 17IC
I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.
2024 · bell-boy.github.io
- 18TS
Hello Hacker News community! I'm currently working in financial risk management within the banking sector, and I began my career as a Data Science specialist. For quite some time, my friend and I have been developing a small pet project just for fun. This tool has repeatedly helped us save time when testing various hypotheses and machine learning models. The core idea is to combine different scripts—created in various programming languages and virtual environments—within a minimalist graphical interface. Whether you're building models, running a local neural network, or sending requests to…
2024
- 19PW
2014 · github.com
- 20UD
Hi HN! I was inspired by Andrej Karpathy's llm.c (https://github.com/karpathy/llm.c), and wrote a full diffusion model training loop in CUDA. I learnt a lot about CUDA from Simon Boehm's Matmul blog (https://siboehm.com/articles/22/CUDA-MMM). Currently there is still a lot of room for optimization: the model is running at 45% speed of PyTorch with torch.compile. I'm curious about any thoughts or CUDA tips for convolutions.
2024 · github.com
- 21SG
Hi HN, I built NOMA (Neural-Oriented Machine Architecture), a systems language where reverse-mode autodiff is a compiler pass (lowered to LLVM IR). My goal is to treat model parameters as explicit, growable memory buffers. Since NOMA compiles to standalone native binaries (no Python runtime), it allows using realloc on weights mid-training. This makes "self-growing" architectures a system primitive rather than a complex framework hack. I just pushed a reproducible benchmark (Self-Growing XOR) to validate the methodology: it compares NOMA against PyTorch and C++, specifically testing how…
Dec 2025 · github.com
- 22NT
I built a CLI tool that turns codebases and PRs into diagrams so you can quickly understand how things fit together. Originally made it because I couldn't follow my own AI-generated repos. Just shipped a big update: - Switched from D2 to Mermaid for rendering - Tree-sitter AST parsing + agentic flow instead of raw LLM calls. ~50x faster. - Works on any GitHub repo or PR, not just local - Dropped the web frontend, it's just a CLI now - Published as a pip package Still a ton to improve and I'm building fast. Feedback, issues, PRs all welcome.
Feb 2026 · github.com
- 23FW
Jul 2026 · wfy24.com
- 24GB
Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…
2024 · github.com
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