

Christopher Sniffen recently sat down with Rezaur Rahman — CIO / CISO / CAIO at the Advisory Council on Historic Preservation — for a conversation on what it actually takes to build frontier AI for federal infrastructure. They get into the limits of frontier models on geospatial reasoning, mechanistic interpretability for applied AI, the trick that makes vision models useful…
At our latest Snorkel AI Reading Group, Carter Wendelken of Google DeepMind walked us through two related papers he presented at ICLR: Code World Models for General Game Playing and AutoHarness: Improving LLM Agents by Automatically Synthesizing a Code Harness. Both ask the same question from opposite ends: when you want an LLM to act reliably in a complex, possibly…
Coding agents have moved from tab-complete to teammate. They autonomously inspect repositories, edit files, run commands, diagnose failures, and work through multi-step engineering tasks. That creates a harder reliability problem. A model that only suggests code is easy for a human to evaluate. A coding agent refactoring your repository and testing its own changes is much harder to supervise –…