Alternatives
Products that do what 6DoF Object detection and tracking in web browser – WebAR.rocks.train does
6DoF Object Detection and Tracking in JavaScript/WebGL. A neural network is trained in the web browser using a 3D model rendered with ThreeJS. Then, the trained model is used for augmented reality with React Three Fiber. WebAR.rocks.train is released under the MIT License on GitHub.
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In browser PPO training demo, made possible by tinygrad: TinyJit -> WebGPU kernels. Requires WebGPU.
May 2026 · ppo.gradexp.xyz
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2023 · antimatter15.com
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This is a GPU "software" raytracer (i.e. using manual ray-scene intersections and not RTX) written using the WebGPU API that renders glTF scenes. It supports many materials, textures, material & normal mapping, and heavily relies on multiple importance sampling to speed up convergence.
2024 · github.com
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2022 · usdz.app
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Hi HN, author here. SHARP is Apple's recent single-image 3D Gaussian splatting model (https://arxiv.org/abs/2512.10685). Their reference code is PyTorch + a pretty heavy pipeline; I wanted to see if it could run in a browser with no server hop, so I exported the predictor to ONNX and ran it via onnxruntime-web with the WebGPU EP. What works: drop in an image, get a .ply you can download or preview live, all on your machine — your image never leaves the tab. The model is large (~2.4 GB sidecar) so first load is slow on a cold cache, but inference itself is a few seconds on…
May 2026 · github.com
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2018 · github.com
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Hey HN! We're the creators of Sieve API (https://www.sievedata.com) and we recently built this fun site that folks can play around with. Frontend is built with NextJS + Tailwind UI and backend is our API. It uses ffmpeg in the browser to transcode an uploaded video, chop it to 5 seconds, and then send it to our API endpoint which detects and tracks objects in the video. You can then "select" any object and track it over the video. We thought it'd be a fun thing for the community to play around with while also showing the power of video object tracking!
2022 · trackobject.xyz
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I’ve been experimenting with Rust lately and wanted a project that would help me explore some of its lower-level and performance-oriented features. Inspired by Sebastian Lague’s videos, I decided to implement my own ray tracer from scratch. The initial goal was just to render a simple 3D scene in the browser at a reasonable frame rate. It evolved into a small renderer that can: • Run locally or on the web using wgpu and WebAssembly • Perform mesh rendering with a Bounding Volume Hierarchy (BVH) for acceleration • Simulate both direct and indirect illumination for photorealistic results • Be…
Nov 2025 · github.com
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2021 · filter.mot.omg.lol
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I was fascinated by this [0] and this video [1]. After many struggles, I finally built this app that traces images using circles similar to what these videos had shown. The most challenging part (to me) is to find a way to convert images to vector lines. I had tried Potrace, but its output is not suitable for my use case: too many small elements share the same border. Potrace's goal is to represent the original image faithfully using vector lines. But I want to trace the image edges. After searching and trying some Potrace alternatives in vain, I finally found my keyword. Surprisingly (to…
2022 · phqb.github.io
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Hello HN! I want to share something me and a few friends have been working on for a while now — Zeroshot, a web tool that builds image classifiers using text-image models and autolabeling. What does this mean in practice? You can put together an image classifier in about 30 seconds that’s faster and more accurate than CLIP, but that you can deploy yourself however you’d like. It’s open source, commercially licensed, and doesn’t require you to pay anyone per API call. Here's a 2 minute video that shows it off: https://www.youtube.com/watch?v=S4R1gtmM-Lo How/why does it…
2023 · usezeroshot.com
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2015 · mattdesl.github.io
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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
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Check out this impressive project that enables running LLMs entirely in the browser using WebGPU. Key features: - Zero token costs, no cloud infrastructure required - Complete data privacy through local processing - Simple 3-line code integration - Built on MLC and Transformer.js The benchmarks show smaller models can effectively handle many common tasks. Currently the project roadmap includes: - No-code AI pipeline builder - Browser-based RAG for document chat - Analytics/logging - Model fine-tuning interface
2025 · github.com
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Hello! I've created a small library for tracking, along with a tutorial. I plan to continue developing it. Tracking is an important topic, closely related to object detection. However, I've noticed that it doesn't receive as much attention compared to machine learning approaches. Or, the focus is on filters like the Kalman filter. This tutorial begins with single object tracking and progressively complicates the tasks, introducing various models and a hypothesis tree to solve them.
2023 · github.com
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I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com
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It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)
Jun 2026 · github.com
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