Alternatives
Products that do what 4d-Modeller – R library to make Bayesian spatiotemporal modeling easy does
Hi, I've been working with other colleagues at University of Bristol for the last year to build an R-package that makes bayesian spatio-temporal modeling easy. That is, if you have some problem like predicting sea level rise over time, disease transmissions, etc., this package may be useful for you. It centers on a collection of Shiny apps that guide you through using the various pieces of spatio-temporal modeling packages like R-INLA or inlabru. Hopefully people find this useful. I think the nice part is that we really tried to design the package so you can add it to your modeling tool kit…
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Simulate anything on a map from a text prompt -- and conduct risk analysis against LiveUA map's global realtime data points from social media and news sources. I trained a GPT-2-size model on historical incident data used to predict things that will go wrong. As historian Benjamin Breen mentions, the leading language models are good historians, so the application will simulate historical events pretty well also. I include a Multi-Agent RL Urban Mobility model in progress displayed on the map as small white cubes representing traffic and pedestrians. Around SF, it uses real census data and…
2025 · mused.com
- 2AS
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
2024 · github.com
- 3DA
Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020
- 4PS
We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is: https://github.com/S-FM/faim-python-client
Dec 2025 · github.com
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Interactive, animated architecture maps for any Hugging Face model — and whether it fits on your GPU.
18d ago · modelmap.cc
- 6MMapipedia▲61
A web platform I've written to make it easy for people to share goespatial time series data and display it on animated heatmaps. There's also a social media component that allows you to write comments, follow, share and like data sets that you want to engage with. You can also download the CSV data and embed animations into your own websites. People uploading datasets can also choose to make money by accepting donations. Please check out the home page for more details: https://mapipedia.com Here are some samples of things created with the platform (press the play button to start…
2019
- 7RP
2017 · github.com
- 8AM
This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location /…
2025 · github.com
- 9MT
2019 · github.com
- 10MR
Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance:…
2025 · github.com
- 11R
At RedisLabs, we've just released the first release candidate of RedisTimeSeries - a time series data structure for Redis. We would love to get your feedback! https://github.com/RedisTimeSeries/RedisTimeSeries/releases/...
2019
- 12RH
Hi Hacker News! I’m a Bayesian statistician that has been working on applying hierarchical mixture models (originally developed for genomics) to structure text data, and in the process, used these models to build (what started as a personal) tool for conducting literature reviews and deep research. My literature review process starts with a broad search to find a few key papers/groups, and from there expands along their citation networks. I needed to conduct a few rounds of literature reviews during the course of my research and decided to build a tool to facilitate this process. The…
Oct 2025 · sturdystatistics.com
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- 15GR
I built this as part of my quest to properly learn data visualization. The code is the easy part! Some lessons learned: - personal verification of the the general truth that pie charts are tough! and the returns are not great for the effort due to people's difficulties perceiving angles - may not use "vanilla" d3 with no React. was difficult to adapt for mobile - the GitHub API provides fairly standardized responses so building dynamic charts wasn't too bad. But when working with streaming data (say Kafka) I can see this getting interesting... schema registry should help but creating a view…
2024 · see-my-repo.netlify.app
- 16GA
All commands have the format `output = \func inputs` or just `\function inputs`. Points and scalars are built on the fly. Eg `\line a b` to an empty canvas creates points `a` and `b`, and joins them with a line. One can use broadcasting semantics similar to NumPy and PyTorch in a visual setting (imagine creating a list of circles where one dim corresponds to radius and another to the center). One can also use backpropagation, run gradient descent or visualize vector fields. Almost everything is reactive so changing a variable updates all of the downstream geometry. It also allows anyone to…
May 2026 · tinyvolt.com
- 17SI
I built a browser-based tool for detecting objects in satellite imagery using vision-language models (VLMs). You draw a polygon on the map and enter a text prompt such as "swimming pools", "oil tanks", or "buses". The system scans the selected area tile-by-tile and returns detections projected back onto the map as GeoJSON. Pipeline: select area and zoom level, split the region into mercantile tiles, run each tile with the prompt through a VLM, convert predicted bounding boxes to geographic coordinates (WGS84), and render the results back on the map. It works reasonably well for distinct…
Mar 2026 · useful-ai-tools.com
- 18PP
Predictobot lets you build a predictive model without any programming. You upload a spreadsheet of data, specify the column you want to predict, and it automatically builds a predictive model for a regression or classification problem based on the other columns. You download a new spreadsheet, with the model right in the Excel formulas. That lets you make predictions going forward. Other services like this are really aiming at helping programmers to build models. I wanted to make something that a regular non-programmer could use, to get insight into their data. It is open for registration…
2014
- 19RA
I’m a long-time time series nerd, I’ve worked with InfluxDB, TimescaleDB, ClickHouse, and more, across everything from monitoring fleets to tracking medical devices. But recently, I started exploring RedisTimeSeries again… and I was surprised by how much the Redis Stack has evolved. Between RedisTimeSeries, RedisJSON, RediSearch, and Streams, I realized: this could actually be the backbone for a full observability stack. So I built rtcollector, a modular, Redis-native observability agent. It’s written in Python, configured with YAML, and designed to push system, container, and database…
2025 · github.com
- 20BI
Hello HN, Recently an amazingly beautiful explainer was shared on HN: https://explainers.blog/posts/why-is-the-sky-blue/ I loved it so much that I wished more topics were explained that way. So, I decided to stress-test today's frontier models (Opus 4.6 in Claude Code) to generate similar explainer on any given topic WITH (almost) one shot and minimal nudging. I'm launching with four topics: Fourier transformation, scaling laws in bio, cellular automata and LLMs. I would let you be the judge, but I'm quite liking them. Some things I learned: - Prompting CC to test…
Feb 2026 · paraschopra.github.io
- 21SF
I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https://github.com/madman-bob/Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.
2018
- 22SO
Hi HN - Marcello and Vaibhav here. We built smolmodels to experiment with using LLMs for ML development. It's a fully open-source library that generates complete model training and inference code from natural language descriptions. It combines graph search with LLM code generation to find a model that gives as good predictions as possible. The core idea is that LLMs are overkill for a lot of predictive tasks. Smolmodels automates the trial-and-error process of finding the right model architecture and training approach, letting you build small, specialised models. You can either provide your…
2025 · github.com
- 23DA
Lately a friend of mine has been working on a personal project and I wanted to share it. The goal of Diffulab is to provide a flexible and modular framework for training diffusion models from scratch. The project is still in its early stages, and he is actively working on adding new features and improvements.
2025 · github.com
- 24RA
2022 · github.com
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