ONNX
Beyond Market Intelligence keeps ONNX in one place: 4 stories so far. The section currently leads with “Exploring what end-to-end machine learning looks like in Rust”, “Discover how AI recommends books by analyzing their covers”, and “Local AI agents arrive on devices as small as a Raspberry Pi”. Millwright is asking a question most ML tooling avoids: can Rust serve as a common execution layer across the entire classical lifecycle, from training to monitoring? By-Its-Cover isn't just another side project. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every ONNX story on Beyond Market Intelligence, newest first.
Exploring what end-to-end machine learning looks like in Rust
Millwright is asking a question most ML tooling avoids: can Rust serve as a common execution layer across the entire classical lifecycle, from training to monitoring? The project doesn't aim to replace Python's ecosystem. Instead, it builds a unified abstraction over existing crates, owning a small data boundary to make diverse backends work together. That's a pragmatic, honest approach. The focus on integration over reinvention is the right instinct.
Discover how AI recommends books by analyzing their covers
By-Its-Cover isn't just another side project. It's a focused experiment in whether cover art alone can power meaningful book discovery. The developer paired CLIP embeddings with a two-tower collaborative filtering model, then added a Determinantal Point Process to keep recommendations from feeling repetitive. That's thoughtful engineering. The honesty is refreshing: a few thousand books, a rate-limited API, and a frontend even the builder admits needs help. Yet the system grows smarter with every search. That's the kind of iterative, human-centered approach worth exploring.
Local AI agents arrive on devices as small as a Raspberry Pi
Liquid AI's new LFM2.5-2.6B is built for a simple premise: capable AI shouldn't require a data center. By running entirely on local hardware, down to a Raspberry Pi, this open-weight model targets high-volume agentic tasks like tool calling and workflow automation without cloud dependency. It's a practical step for enterprises prioritizing privacy and latency. Explore how this small-footprint approach could fit your operations.
One shared encoder, seven heads: training a unified security classifier
Masked losses are the quiet workhorse behind multi-task models, and this team's self-test for zero gradients on absent tasks is a genuinely smart safeguard. It caught two subtle bugs, which is exactly why you write those checks. The unified encoder's routing head lags at 0.916, and the semantic overlap there feels like a data problem, not a modeling one. We'd point curious readers to our related piece on deduplication stages, since both tackle messy real-world data pragmatically.