At the recent ICML 2025 conference, Mark Khoury from Hudson River Trading (HRT) described the firm’s development of foundation-style transformer models trained on decades of market microstructure data. The result is a trillion-token-scale dataset spanning equities, futures, and crypto. Matt Robinson from AI Street, provided some great coverage of the presentation. It’s a huge step…
How Software Development Really Works I’ve been developing software for 40 years. I’m mostly self-taught and because of that (and my personality), I’ve spent a lot of time thinking about the process of developing software, not just the code. While most of my software development has been in small companies I founded or personal side projects, my…
Last April, Tina He asked a deceptively simple question that still echoes when we consider agent math: What happens when AI agents become your primary users? That question opened a clear line of inquiry. If AI agents are the ones clicking buttons, reading documents, and deciding what APIs to call, then a lot of software…
AI Math doesn’t have to guess. See how reliable, deterministic calculation unlocks real AI workflows like ROI analysis, what-if modeling, and scenario planning.
In July 2025, Anthropic announced a new enterprise offering: Claude for Financial Services. It’s a natural evolution of where generative AI is heading. Smarter interfaces for domain-specific reasoning, tuned for the complex, regulated world of financial institutions. Banks, asset managers, insurers, and fintechs all have similar goals: interpret a mountain of financial data, reduce manual…
LLMs Are Powerful — But Unreliable for Math Large Language Models (LLMs) have unlocked a new era of productivity: summarizing documents, generating code, writing policy memos, and answering complex questions with ease. But when it comes to mathematical reliability, their probabilistic nature becomes a liability. A new study by IBM Research — LLM Output Drift:…
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