BaseThesis x smallest.ai Teardown: Building Conversational Voice Agents That Remember YOU

The Physics of Real-Time Conversation: Building Conversational Agents That Remember YOU A hands on investigation into multi-session memory, persistent context, state management, and how production voice agents maintain understanding across days and weeks Current conversational memory systems fail because they record comprehensively, not intelligently. When someone says "I'm from Bangalore," most systems store: {"location": "Bangalore"}. BUT a great salesman stores: startup ecosystem context, infrastructure challenges, tech talent density AND then surfaces it naturally three conversations later.The difference is comprehension vs recording. Recording is cheap, BUT Comprehension requires deciding what information means for future interactions. Saturday, we're investigating how to build memory that demonstrates understanding, not just recall. The core questions are when a user returns for their 3rd conversation with your agent, what should it remember from Sessions 1 and 2? And how do you architect memory to make that possible without drowning in context? How do you architect memory that demonstrates understanding, not just recall? We're bringing Akshat Mandloi, the Co-Founder and CTO at smallest.ai, to tear down and ship these constraints with you on "what should an agent remember about a human?" The constraint is