Memory is what makes us human. It's also what makes AI truly intelligent. @mem0ai has raised $24M to build the universal memory layer for AI. Thousands of teams in production. 14M downloads. 41K GitHub stars. Intelligence needs memory & we're building it for everyone. More👇

We’re #hiring a Data Analyst at Mem0 You’ll build our analytics foundation from 0→1 - defining metrics that matter, setting up pipelines (Postgres, PostHog, LLM outputs), and turning data into insights that guide every decision. - Bangalore, India - Full ownership - High

Applied to YC 7 times before finally getting in. Here’s what I learned the hard way👇 When I first started applying to @ycombinator, I treated the application like a pitch deck, trying to convince YC to invest, instead of explaining why the problem mattered. Over time (and 7

100 billion tokens processed through OpenAI. Not a vanity metric but a signal of how fast developers are adopting the universal memory layer for AI. What matters more is what it represents, thousands of systems now learning, adapting, and remembering through @mem0ai. Onward🚀

Yesterday, I joined TBPN to talk about what it really means to give AI a memory. And, why that’s becoming the next big moat. We discussed: - Why today’s AI apps are stateless - How memory is quickly becoming the real moat for AI systems - How Mem0 is decoupling memory from

https://t.co/GE7Y0SPkNP

We’re hiring🚀 The past few days have been incredible, we’ve heard from so many founders, engineers, and researchers who believe in @mem0ai. It’s energizing to see how much excitement around where AI memory is headed. As we scale, we’re doubling down on building reliable

Blog: https://t.co/zigJRlBX1F TechCrunch:

Discover mem0 with Taranjeet Singh as he talks about building a memory layer for AI agents and the future of personalized AI experiences. Watch here: https://t.co/b4XqMFdVdE #AI #LLM #Mem0 #SiliconMindsHumanHearts

The future AI stack won’t be one giant platform... It’ll be a network of specialized layers and memory is the first one breaking free. 15 years ago, enterprise software looked the same. Everything was bundled into monoliths. Then the unbundling began - @stripe owned payments.

Bigger context windows can't eliminate the need for memory... A context window defines how much information a model can “see” at once. Expanding it to 200k or even a million tokens helps with short-term recall but it doesn’t help the model remember. Once the window slides, the

I reverse-engineered @interaction's Poke and figured out why it feels so magical. Introducing OpenPoke —an open-source implementation you can run locally. 🧵 on the multi-agent architecture and memory:

https://t.co/9pwXWAPd3G

We hit 40,000 stars on GitHub. What started as a small project has now grown into one of the fastest-growing memory layers for AI agents. Couldn’t have done this without the amazing community building with us 🙌

https://t.co/zMzO0Kq1Rq

Two years ago, it was just my co-founder and me. I had just moved from India for the first time, and he had left his job at Tesla. Today, we’re building the future of AI memory with @mem0ai. What started with just the two of us is now growing fast, and we’re looking for

We believe your next favorite human...isn’t. Today we’re Born: announcing a $15M Series A to reimagine AI friends. Here's our story:

The memory layer will be bigger than the model layer. Models will commoditize. Memory won't. In five years, the difference between GPT, Claude, and Llama will be marginal. They'll all be fast, cheap, and capable. The differentiator won't be the model you use. It will be what

Hot take: Bigger context windows are not solving the memory problem. Memory isn’t just recall. It’s: • Remembering what matters • Forgetting noise • Updating as things change

On Evaluating Memory for AI Agents There’s been a real surge in “memory” for AI agents lately. Most frameworks now prioritize persistent memory as a core feature. But how we actually evaluate that memory is still an open question. Because memory isn’t just about storing and