Alternatives
Products that do what Magika: AI powered fast and efficient file type identification does
We are very excited to announce the release of Magika our AI powered fast and efficient file type identification lib and tool - https://opensource.googleblog.com/2024/02/magika-ai-powered-fast-and-efficient-file-type-identification.html Thanks to its optimized Keras model, large scale training dataset, and Onnx Magika massively outperform other file identification tools while be very fast even on CPU. Magika python code and model is open sourced on Github: https://github.com/google/magika and we also provide an experimental TFJS based npm package…
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Hi HN! I’m Alex from Parabola (https://parabola.io). Parabola is a visual programming tool for creating functional data flows that everyone can use. It’s entirely drag-and-drop, handles data sizes much larger than a traditional spreadsheet, calculates everything live, and can run your flows on a schedule of your choosing. I used to work in strategy consulting, doing data analytics for SMBs and Fortune 500 companies. The amount of time wasted on menial tasks was astounding. Things like cleaning data, generating custom reports, creating human workflows to solve shortcomings in third…
2018 · parabola.io
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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
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2020 · github.com
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2018 · github.com
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Hi HN! I just released a blog on the fastest, easiest, and most collaborative AI data version control tools out there. If you’ve been iterating on AI datasets lately, you know how annoying and difficult it is to version and share your data. If you want a detailed comparison on the best data exploration and versioning options from Oxen.AI (where I am currently building a solution), Hugging Face, DVC, etc. check out the blog and lmk what you think:) Here’s the link: https://www.oxen.ai/blog/the-best-ai-data-version-control-to...
2024 · ghost.oxen.ai
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Hey HN, We’re excited to announce Zant v0.1, an open-source TinyML SDK built in Zig, designed to optimize and deploy neural networks on resource-constrained devices. Unlike existing solutions, Zant focuses on performance, portability, and ease of integration, making it a strong alternative for anyone working on Edge AI and embedded ML. Why Zant? Most TinyML frameworks are either too high-level (requiring bloated runtimes) or too low-level (requiring extensive manual optimization). Zant bridges the gap by offering: - A lightweight but powerful code generation system to translate ML models…
2025 · github.com
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Hey everyone! I am excited to share updates on four of my & my teams' open-source projects that take large-scale search systems to the next level: USearch, UForm, UCall, and StringZilla. These projects are designed to work seamlessly together, end-to-end—covering everything from indexing and AI to storage and networking. And yeah, they're optimized for x86 AVX2/512 and Arm NEON/SVE hardware. USearch [1]: Think of it as Meta FAISS on steroids. It's now quicker, supports clustering of any granularity, and offers multi-index lookups. Plus, it's got more native bindings than probably…
2023 · usearch-images.com
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24 browser-based PDF, image & AI tools. No uploads.
Jul 2026 · aman2007-king.github.io
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Hi HN, I’m Linn, the creator of ToolKuai (https://toolkuai.com). Like many of you, I’ve always been wary of "free" online file converters. Most of them are black boxes: you upload your private documents or images to a remote server, and you have no idea where that data ends up or how it’s being used to train models. I wanted to build a suite of tools (Video/Image compressor, OCR, AI Background Remover) that runs entirely in the browser. No files ever leave your machine. The Tech Stack To make this performant enough to rival server-side processing, I leaned heavily into modern…
Jan 2026 · toolkuai.com
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Today you can easily adopt AI coding tools because you have git for branching and rolling back if AI writes bad code. We haven't seen this same capability for data and decided to build it ourselves. Nile is a new kind of data lake, purpose built for using with AI. It can act as your data engineer or data analyst creating new tables and rolling back bad changes in seconds. We support real versions for data, schema, and ETL. We'd love your feedback on any part of what we are building - https://getnile.ai/ What do you think?
Jan 2026
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I’ve been working on Code2Docs, an open-source CLI tool that helps developers automatically generate inline documentation (docstrings + comments) for Python code using AI. It’s built to solve a common problem I’ve faced (and seen often in teams): We code by "vibe" — fast iterations, minimal docs, and then forget what the logic was months later. Code2Docs helps bridge that gap by documenting as you go — without breaking your flow. Right now it supports function-level documentation. Planned features include: - README.md generation for projects - API endpoint docs - Database schema…
2025 · code2docs-open-source.netlify.app
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I was recently playing with Apple's CoreML and had several painful observations on tooling. It's not enough for a long read but should be for an HN post. In short, you can take a simple BERT-like encoder model in PyTorch, convert it into an f32 CoreML checkpoint, and run it on CPU or GPU, but not NPU. Let's unpack this. Having a simple and extensible format to exchange common ANN architectures is a big issue for anyone who uses more than one framework or programming language to run the same model. ONNX is the closest we have to that standard, but it's hard to call anything Protobuf-related…
2024 · github.com
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I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Jun 2026 · apeg.dev
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Hi guys, For a few months now I've been working on a web GUI to build, visualise, train and share deep neural models. It's currently reaching a state where opening it for Beta release make sense. Currently the tool support: - Fully connected and Convolutional architecture - Cloud and local, saving / loading of models - Edit / delete layers - Visualise Convolutional layers filters - Freeze / Unfreeze layers - More datasets: Fashion MNIST, QuickDraw(10 and 30) The editor can be found here: https://aifiddle.io. Your feedback, ideas, suggestions are greatly useful, so…
2019
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runNburn is an Apache-2.0 Rust inference engine for quantized GGUF models that are too big for your fast memory. The core idea: weights stay file-backed (mmap), host residency stays under an explicit byte budget (--ram-budget), and GPU caches are sized from detected free/total VRAM — never from device-name presets. There is no conversion step, no sidecar cache files, no silent requantization. The GGUF on disk is the single source of truth. The result that made me want to post this: Tencent's Hy3 (295B total / 21B active sparse MoE, a single 97.8 GiB Q2_K GGUF) runs on my desktop…
Jul 2026 · github.com
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