precision
Beyond Market Intelligence keeps precision in one place: 3 stories so far. The section currently leads with “Balancing precision and cost to feed AI agents the right data”, “Explore how margin of error shapes what polls actually tell us”, and “Explore how AI models can now run directly on your mobile device.”. Feeding enterprise data to token-hungry AI agents isn't just about scale; it's about precision. Polls dominate how we talk about elections, yet their numbers often feel deceptively solid. 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 precision story on Beyond Market Intelligence, newest first.
Balancing precision and cost to feed AI agents the right data
Feeding enterprise data to token-hungry AI agents isn't just about scale; it's about precision. At TOTVS, Fabiane Nardon tackles this head-on by balancing deterministic logic with the non-deterministic nature of LLMs. Her approach is refreshingly practical: using data mesh principles, low-latency databases, and semantic ontologies to keep context windows lean and costs controlled. Dynamic MCP tool selection becomes the key to trimming token overhead. It's a smart, human-centered strategy for making AI genuinely useful in transactional systems.
Explore how margin of error shapes what polls actually tell us
Polls dominate how we talk about elections, yet their numbers often feel deceptively solid. A margin of error isn't a flaw or a hedge; it's a measure of honesty. Pew's explainer cuts through the noise, showing why that 3-point swing is less about bad polling and more about the limits of any sample. It's a useful reality check for anyone reading a crosstab like a fortune teller. For more on how data tools shape our understanding, explore the Forrester Function piece.
Explore how AI models can now run directly on your mobile device.
Running a private Qwen 35B MoE model on a Galaxy S26 Ultra is no small feat, and the early numbers are worth noting. Roughly 90 input tokens per second with about 8 output tokens per second shows real progress, especially when precision stays intact. What stands out is the path here: self-taught expertise, no formal PhD, just curiosity and access to resources. That matters. Four arXiv papers still on hold because there is no institutional backing? That is a familiar wall.