GitHub repository
GitHub repository at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Trace AI agent decisions by tracking tool arguments before execution”, “Explore how your phone visualizes AI attention with each word it chooses”, and “Explore how hierarchical routing cuts memory while preserving long-context accuracy”. Tracking tool arguments before an AI agent executes them is a simple idea that could make a big difference in safety. Watching your phone visualize attention as it generates each word feels like peeking inside a working brain. 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 GitHub repository story on Beyond Market Intelligence, newest first.
Trace AI agent decisions by tracking tool arguments before execution
Tracking tool arguments before an AI agent executes them is a simple idea that could make a big difference in safety. Jay299792458 built a test harness that records where each argument came from, checks if the same input gives the same decision, and tests whether a model follows someone else's text instead of its user's. It's practical, transparent, and easy to extend. If you've been wondering how to verify agent behavior, this is worth exploring.
Explore how your phone visualizes AI attention with each word it chooses
Watching your phone visualize attention as it generates each word feels like peeking inside a working brain. This browser-based demo runs SmolLM2-135M entirely on-device, no server, just int8 precision and ONNX runtime. You see token probabilities in real time, plus attention links drawn back to earlier words, weighted and clickable. The developer initially thought his code was broken; everything kept attending to the first token. That turned out to be the attention sink phenomenon, a fascinating bug that became the lesson.
Explore how hierarchical routing cuts memory while preserving long-context accuracy
Hierarchical routing is often a trade-off between memory and accuracy. ALHR, Adaptive Learnable Hierarchical Routing, challenges that assumption. By using a static binary tree paired with learnable functions, it reads fewer keys while preserving long-context performance. Less memory used, better VRAM scaling with tokens. For those building efficient AI systems, this is a practical step forward. If you're exploring sparse attention models, our related piece on how one model reads 94% fewer keys while keeping accuracy offers deeper context.