ONNX
ONNX on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on onnx in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around onnx, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Millwright — experimenting with an end-to-end machine learning framework in Rust [P]
Millwright is an open-source project exploring a complete machine learning workflow built in Rust, addressing gaps often found when integrating individual ML libraries. This framework streamlines the classical ML lifecycle—ingest, explore, preprocess, and beyond—by providing a common abstraction layer over existing Rust libraries and interoperating with the Python/ONNX ecosystem. Currently featuring capabilities like AutoML and drift monitoring, Millwright aims to provide a valuable execution layer across training, inference, and production.
![Hybrid collaborative filtering recommendation system for judging and suggesting books based on their covers [P]](https://preview.redd.it/zcz8hf1u6skh1.png?width=140&height=113&auto=webp&s=eb3136507a3bc923edeaf86880a1d987971f6ef7)
Hybrid collaborative filtering recommendation system for judging and suggesting books based on their covers [P]
By-Its-Cover presents an innovative approach to book discovery, leveraging AI to judge and suggest titles based solely on their covers. This project utilizes a hybrid collaborative filtering recommendation system, combining CLIP embeddings for semantic searches and a two-tower neural network for personalized recommendations. Currently hosting around 2,000 books, the system dynamically grows with user interaction. Explore the project on GitHub and test the site – feedback is welcome!
No cloud, no GPUs, no problem: Liquid AI's new model LFM2.5-2.6B brings powerful AI agents to devices as small as a Raspberry Pi
Liquid AI has unveiled LFM2.5-2.6B, a new open-weight language model designed to bring powerful AI agents to devices as small as a Raspberry Pi – a significant step toward accessible edge AI. This model, boasting 2.6 billion parameters and a 128,000-token context window, runs entirely on local hardware without cloud inference or GPUs, ideal for high-volume tasks like automation and connectivity-limited environments. Explore how this innovative solution transforms data management and expands possibilities for enterprises, as highlighted in our recent coverage of Qwen 3.8-Max.
One encoder, seven heads: what we learned training a unified security classifier with masked losses [P]
We've consolidated seven distinct sequence classifiers into a single, unified model—our apex security classifier—streamlining data processing and enhancing efficiency. This architecture utilizes a shared mmBERT-small encoder with seven task heads, achieving impressive results across diverse security functions, including injection detection and threat type identification. Notably, we implemented masked losses to handle training rows with incomplete labels, a technique validated by a rigorous gradient self-test. Explore the released weights and detailed per-head metrics on Hugging Face.