Increasingly, I believe companies may need to be rebuilt from the ground up, where you have a single timeline of all observability product metrics file changes laid out in a retrievable system, like Datadog Posthog Google Drive Slack (really unified filesystem of Claude Code chats Codex chats). This might be the new data foundation for any and all companies to maximize AI. Needs to be rebuilt because keeping track of diffs on existing system basically impossible to produce longitudinal information on decisions and rollbacks, something coding agent storage companies are actively trying to figure out, but this should extend to businesses as a whole. Highly skeptical existing businesses will adopt this though because it means overhauling everything about their instrumentation and business data, but I think businesses built on this foundation probably can execute 100x better and faster

This memory engine is built to be swapped. Not lock you in. Memory providers will have you rewriting parts of your code tied to them to switch them out. This is what we built to avoid with Atomic Memory. The SDK is designed so that if you ever want to swap out the memory backend, you only touch the configuration. Your application code stays exactly as you wrote it. Start building without the lock-in. ⬇️ github.com/atomicstrata/atom…

AI agents forget everything between sessions. @AtomicStrata built the open-source memory layer that fixes it. 1,300 GitHub stars since launch. @jbruce got inbound from JPMorgan's head of agentic within days. Watch their pitch: youtu.be/nVOKw_Z0-Yo?t=3193

Run a Smarter Memory Layer at a Lower Cost for your AI Agents Your agents inject their entire memory file into every prompt, whether it is relevant or not. Hermes native memory includes the full MEMORY.md every turn. OpenClaw carries full cross-channel context on every query. That is a fixed token cost you pay regardless of whether any of what it retrieves is useful. Atomic Memory sits underneath both Hermes and OpenClaw and changes how memory gets injected. Retrieving only the facts the current query actually needs. Benchmarks show it does this at a lower cost per query than tools with comparable retrieval accuracy, which enables precise context injection without the token overhead.

We at Atomic Strata are excited to be pitching at the Stanford Blockchain Acceleartor Demo Day today 9-10:30a pt. Join here luma.com/x3dqhaw0

Our benchmark is out for v66! Atomic Memory delivers top-tier memory performance in each reported category while costing significantly less to run in real applications. The case for switching makes itself 😉 github.com/atomicstrata/atom…

This is @_HermesAgent backed by Atomic Memory in a real work setup. Every decision, update, and correction your team makes gets organized and stays inspectable across sessions. Atomic Memory improves your Hermes agent by replacing the 2.2KB native memory cap with unbounded, per-turn memory that resolves contradictions before anything hits storage. The memory layer your team actually needs. github.com/atomicstrata/atom…

We are excited to be partnering with Filecoin on a decentralized AI context solution for agents and develpers.

SuperNet is now Atomic Strata, and we just open-sourced Atomic Memory, our core AI context memory infrastructure. Our thesis is that AI memory is becoming a foundational layer of the AI stack. It will determine what agents and AI apps know about users, teams, projects, workflows, and organizations. That layer cannot remain a hosted black box. Most memory products today bundle storage, extraction, embeddings, retrieval, ranking, packaging, scope, and observability into one opinionated backend. That creates lock-in at exactly the layer where developers need flexibility. Atomic Memory is built around a more modular approach: a configurable SDK and self-hosted Core engine that developers can inspect, customize, swap, and run on their own infrastructure. The key idea is simple: applications should not be permanently wired to one memory vendor, one model stack, or one theory of context. This is the first step in the broader Atomic Strata rollout: open-source memory infrastructure first and then more exciting things to launching in the coming months.

It was a pleasure to attend my 8th Medici during the Milken conference. The team consistently delivers the highest quality crowd in crypto.

This is a great summary of the AI problems we are hearing from enterprise--significant fragmentation of AI tools, employees choosing their own (often non-compliant) tools and lack of cross platform context and governance. The problem is only getting bigger. venturebeat.com/orchestratio…

I had the pleasure of hanging with OpenClaw founder Peter Steinberger and talking AI agents at a TED conference dinner. Meta and TED assembled an amazing group of AI minds. Good times.

Watch LLM Wiki Compiler in action 👁️ An open-source tool inspired by @karpathy's pattern. At SuperNet, we are constantly experimenting with the state of the art in AI context, including alternatives to conventional RAG memory. Test it out and let us know your feedback! github.com/atomicmemory/llm-…

The TED conference is always a glimpse into the future. Im excited to be back for the final year in Vancouver, Chris Anderson’s final year and engaging conversations on the future of AI.

At MIT’s Imagination In Action AI summit talking about AI context. John Werner organizes this amazing conference that has TED vibes and is quickly becoming one of the most serious thought leadership conferences in AI in the world. Highly recommended.

The AI industry has a context memory benchmarking problem. High scores in current benchmarks are not necessarily correlating to good real world results. We need to fix this.

Internet without websites and apps is coming. Watched my former roommate Brett Taylor, CEO of Sierra and Chairman Of OpenAI, talk about the agentic future of the internet at HumanX. GUI interfaces with be replaced by conversations with agents.

At HumanX Ray Kurzweil just predicted AI will be implanted in our brains by the mid 2030s and we will not be able to differentiate between our own thoughts and those motivated by AI. What do you think of that?

At the HumanX conference in SF connecting with old friends and talking about enterprise AI. Context is on everyone’s mind.