Shows

In 2024, researchers from the University of Illinois found that GPT-4, when provided with a common vulnerabilities and exposures (CVE) description, could autonomously exploit 87% of a curated 15-vulnerability one-day dataset. Without the description, it could only exploit 7%. This provided a “margin of safety” for the industry because while AI could exploit known vulnerabilities,…

Enterprise AI agents are stalling — not because of model performance, but because of permissioning. Every agentic workflow eventually hits the same wall: what is this agent allowed to touch, on whose behalf, and how does the system know? Workday’s answer is to make its existing system of record the governance layer for agents. Gerrit…

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a framework from researchers at multiple universities, encodes new knowledge into a dedicated smaller memory model that operates separately from the main LLM. The…

At 620 million monthly users, calling a frontier model for every image recommendation isn’t a strategy — it’s a bill. Pinterest CTO Matt Madrigal solved it by gutting Qwen3-VL’s vision layer and rebuilding it with proprietary embeddings, cutting costs 90% and boosting accuracy 30%. Madrigal’s team has been heavily investing in customizing open-source models “foundationally…

As enterprise AI agents move into production, organizations are confronting a growing reliability problem. Many teams are discovering that LLM performance alone does not determine whether agents succeed in production. Long-running AI workflows must survive crashes, preserve state, recover from failures, manage inference costs, and coordinate across APIs, tools, and enterprise systems. After a first…