Glasswing Ventures

A few years ago, I discussed Mamba, an AI architecture that was designed as an alternative to transformers. Mamba represented an evolution in state space models (SSM), which is a neural network architecture class where a select amount of information is st

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This recognition lands at a meaningful time for our firm, as we continue to back more than 90 visionary teams building category-defining AI-native and Frontier Tech companies across enterprise and cybersecurity, markets we've been conviction-led investors

At the beginning of last year, I discussed a method of making AI more truthful called Factuality-Aware Alignment for Large Language Models (FLAME). Since then, there has been a wave of new techniques and methods that improve upon that foundation of augmen

"Boston does not have to out-NYC New York." That's Rudina Seseri's perspective in Aaron Pressman's latest Boston Globe Media piece on the growing competition among AI hubs. Boston's greatest advantage has never been trying to replicate another ecosystem.

Our Founder and Managing Partner, Rudina Seseri, joined Bloomberg Businessweek to share how Glasswing Ventures is navigating the next wave of AI infrastructure and frontier tech. As enterprise adoption shifts from basic experimentation to core business st

Last year, I discussed a class of AI known as a "world model," an AI architecture that builds an internal representation of its surroundings to predict outcomes and guide decision-making, often bridging the gap between physical and digital worlds. Since t

Glasswing Ventures Leads $5M Seed Investment in AI-Native Risk Lifecycle Management Platform, Enlaye We are excited to announce our Seed investment in Enlaye, the AI-native Risk Lifecycle Management platform for the Built-World, helping contractors identi

The post Recursive emerges from stealth with a bold bet on self-improving AI appeared first on Glasswing Ventures.

"Memory" is among the most consistent reasons that enterprise AI agents fall apart. Even with millions of tokens now available to state-of-the-art models, multi-step agents will eventually lose track of earlier instructions and drop critical details, even