A correct answer can still hide stale or unauthorized memory. Our latest article shares 12 practical measures to observe agent memory after launch: write quality, retrieval, corrections, deletion, and task outcomes. Read more to know more 👇🏻

September 23 at S-Tron Shanghai 2026 Innovation Event, our founder & CEO, Feiyu Xiong @superbearmaster, joined Angus Dunn, Patricia Bubner, PhD, and Bing Li, to discuss a question many founders are facing now: If AI lowers the barriers to building a startup, do founders still need to move to Silicon Valley? For our team, the most relevant part of the conversation was the distinction between making execution easier and making decisions better. AI can help founders code, automate operations, and iterate with smaller teams. But the harder questions remain human: Which direction is worth pursuing? How do we evaluate what we’ve built? What expertise is needed to know whether an answer is actually useful? Feiyu shared that AI-era startups will need 2 kinds of talent: practitioners who can turn AI into deliverables, and technical specialists with deep architectural and domain expertise. Tool literacy will become widespread; industry knowledge will become scarcer and more valuable. The discussion also explored why Silicon Valley’s role is changing rather than disappearing. Teams can build globally, open source can create a path to early users, and a well-positioned lightweight model can gain traction without chasing the state of the art. But biotech still needs physical labs, and strong industry networks still benefit from being in the room. Our takeaway: AI is a powerful enabler, not a substitute for judgment. Sustainable advantage still comes from strategic clarity, domain expertise, and the ability to turn insight into something people can use. Thanks to Bing, Angus, Patricia, and the S-Tron team for a thoughtful conversation. We're bringing these questions back as we continue building for an AI-native world 🪐

MemOS can help agents carry memory across sessions, so customers don't repeat their story when switching. The example follows a headphone exchange, showing reusable workflows, policy retrieval, and isolated customer memories. Check the full walkthrough👇🏻

Retrieval can’t repair a memory that was wrong when written. One lucky query becomes a reusable rule, and the error spreads. Agents need an admission layer that separates user statements, facts, inferences, and procedures first. Read more 👇🏻

Anthropic's Claude made memory visible and editable, which is real progress. But production agents need a governed memory layer: what’s stored, who can retrieve it, how it’s updated or deleted, independent of any model or chat surface. Read more 👇🏻

Agents need memory that stays useful as users, facts, and tasks change. Our latest guide explains how agent memory works with RAG and compares memory solutions on lifecycle, access control, deployment, and fit. Read the full guide 👇🏻

MemOS is heading to SF @Techweek_ 🚀 On October 5, we're co-hosting the Builders Beyond Borders: Vibe Coding Showcase with EchoHer, @CalHacks, and @pebble_bed in San Francisco. Building with AI? We'd love to see what you're working on. → Submit your project: forms.gle/Mv1GYHJWru19y8UJA → Join us: partiful.com/e/mGyFYFRLUKRuP…