Add durable memory to your AI agent in a few commands. The Atomic Memory Cloud OS QuickStart takes you from local setup to your first stored and retrieved preference with Docker, an OpenAI API key, and three CLI steps. Start building with a configurable AI memory: docs.atomicstrata.ai/open-so…

Imagine a @DBSbank AI assistant that goes beyond generic banking answers. Instead, it uses your approved financial context to give more personalized guidance on savings, cards, and travel benefits without making you explain your goals every time. Atomic Memory Cloud can help you achieve that. memory.atomicstrata.ai/?utm_…

Are you ready to reduce repeated context and lower token costs? Check out Atomic Memory Cloud: memory.atomicstrata.ai/?utm_…

So how does agent memory reduce that cost? Having an external memory system lets an agent extract durable information from each conversation and store it separately, rather than keeping everything in its active context. That information is indexed by meaning and task relevance. So the agent can then retrieve the specific facts it needs when a later task requires them. This gives the model a smaller, task-specific context to process, and doesn't require the agent to read the entire transcript again.

Cut your token costs by up to 90% with agent memory. Ever notice your agents reload the same files and repeat decisions? That's because context stays trapped in the transcript of every previous task. Each repeated file and decision adds more tokens for the model to process, which increases the cost of completing the task.

Fluency makes the question emotionally compelling, but useful behavior and subjective experience still need to be evaluated separately.

More agents only help when the work can be divided cleanly, otherwise coordination costs quietly become the main workload.

Autonomous systems need clear boundaries because a mistake involving external systems quickly becomes an organizational liability.

Having a configurable AI Memory is the key to this. 🔑

An AI safety framework needs auditable memory.

Congrats on the achievement! 👏

Agent safety has to be part of the runtime, since sandboxing determines which mistakes stay local and which ones become incidents.

Here’s what @OpenAI announced at DevDay: 1. ChatGPT is becoming a workspace through ChatGPT Space, which brings projects, goals, context, and previous work together. 2. GPT-6.1 Sol improves coding, computer use, and agentic tasks while costing less than Astra. 3. Astra Ultrafast brings faster performance to ChatGPT Work, Codex, and the API. 4. Dots are persistent Astra-powered agents with cloud computers, connected apps, configurable permissions, and ongoing task execution. 5. Codex is gaining parallel agents, cloud environments, mobile access, voice, worktrees, and automatic code reviews. 6. The Agents API is adding deeper computer use, multi-agent orchestration, and stronger tool handling. 7. Pages and Slides now support live dashboards, charts, connected data, and collaborative presentations inside ChatGPT. 8. Plugins are becoming interactive apps with sidebars, custom viewers, and event triggers. 9. ChatGPT is expanding into Slack and Teams, where it can investigate questions, prepare updates, and use connected tools. 10. Meeting agents can record discussions, save notes, extract decisions, and turn outcomes into follow-up work. 11. OpenAI introduced Sign in with ChatGPT, giving users a new way to access connected applications. 12. A new $500 monthly Pro tier offers higher limits and exclusive Astra Ultrafast access in Work and Codex. AI agents are moving closer to completing meaningful work across tools, environments, and teams. Reliable, portable context will become essential as these systems become more persistent.

My skills work across coding agents. I wanted my preferences to travel with me too. With @AtomicStrata Memory, @cursor_ai can pick up the stack preferences @opencode learned about me. Here’s what that looks like 👇

The most valuable part of your AI is its memory. In this clip, Atomic Strata co-founder @Aileentech explains why owning your AI data matters for keeping that context portable and useful over time. Is your AI context portable, or is it tied to a single platform?

Curious to know what people think about this as well 👀

This feels like a big step toward keeping teams and AI working from the same shared context 👀

Parallel work in the terminal is a great upgrade, especially when each task can keep the right context.

Dot is our new friend 🤝 if its running 24/7, definitely best paired with Memory

Well that was quick! GPT-6 Sol didn't even last long.