ASI:One - A personal AI with memory that plans and acts for you

@tibo_wiels Great question, and honestly one of the first things we spent time thinking about.

In group chats, the AI doesn’t just freely access everything. It works within the boundaries of what each person has shared in that specific context. Your personal memory, preferences, or connected data aren’t automatically exposed to others.

So if we’re in a group, the AI helps coordinate, summarise, and move things forward based on what’s happening in that chat, not by pulling in your private history. The idea is to keep collaboration useful without breaking trust. You get the benefit of shared context where it’s needed, while your personal layer stays yours.

@matheusdsantosr_dev Really appreciate you putting it through real-world use, that’s honestly the only way these systems get meaningfully tested.

And you’re right, the gap is rarely in answering, it’s in following through consistently. That’s exactly what we’ve been trying to close.

On pricing, we’ve intentionally kept things open during this phase. Right now the focus is on usage, feedback, and understanding where people actually get value from the system day to day. For now, keep pushing it as much as you can. That’s the most useful thing for us at this stage. And thanks again for the kind words, means a lot to the team! 🙌

@matheusdsantosr_dev 

Really appreciate you putting it through real-world tests, especially around email and daily tasks. That’s exactly the kind of usage we care about, so it’s great to hear it’s holding up well for you.

Thanks again for taking the time to test it deeply and share this, feedback like this genuinely helps a lot 🙏

@samir_asadov Nice, this is a solid use case!

You’re right about prediction markets, the signal is there but most people don’t know what to do with it in real time. Where this could get interesting with ASI:One is probably not just consuming PolyMind alerts, but tying them to context. If the system already knows what I’m tracking or thinking about, a spike in probability or a large trade doesn’t stay an isolated alert, it becomes something actionable.

The planning layer is exactly where this fits. External streams like yours can be pulled in as agents, and then used inside a broader flow instead of just dashboards or notifications.

PolyMind looks like a clean layer on top of Polymarket. Very interesting project! :)

@samir_asadov Really interesting angle, markets are such a noisy space, its a good idea to bring this on ASI:One and surface the signal

@brian_h4  With ASI:One recurring tasks are more like delegation than scheduling. You basically tell it once - do X at Y time - and it keeps running in the background until you change it. So when calendar is connected, it gets smarter. It can align tasks with your actual availability, review your week, prep you ahead of events, or adjust around conflicts instead of blindly firing at a fixed time.

So in your example, it’s not just schedule when free, it’s:

• Understand when you’re free

• Run the task at the right time,

• and adapt as your schedule evolves.

@hgottfried Fair questions, and honestly ones we think about a lot too. This isn’t just OpenClaw.

What we’re building is closer to a connected system rather than a setup. The differentiation shows up in how things work together:

• Your personal AI isn’t isolated, it can collaborate with others’ AIs in group chats and collabs

• There’s a real network layer through Agentverse, where agents that can do things are discoverable and can be pulled in on demand

On concerns, completely fair. A lot of products in this space look similar early on. For us, the focus has been on making the system reliable, scoped properly, and actually useful in day-to-day use.

The real difference, in our view, is that this is not just a tool or a workflow, it’s a system that connects people, agents, and capabilities in one place.

@abod_rehman Thank you for trying out our product! Yes, its an open market place but also we have a verification layer for the agents and the verified agents have higher rankings, trust and are more discoverable over the unverified agents. But all agents are evaluated by our system to flag and malicious agents!

@abod_rehman Thank you! Right now, it’s an open marketplace with a verification layer on top. Verified agents get higher visibility and trust, but the ecosystem stays open so new agents can come in and be used.

On top of that, there are system-level checks to flag anything malicious, so there’s a balance between openness and safety.

@tijogaucher Great question — The answer is It's both.

There's a formal protocol underneath, but the interface is natural language.

The messy part isn't handwaved — it's abstracted. Developers can:

• Use the protocol directly (via uagents SDK) for fine-grained control

• Or let ASI:One handle orchestration automatically through the agentic LLM

The protocol ensures reliability, while natural language keeps it flexible. Best of both worlds.

@mcarmonas Great point, this is exactly where most systems break.

In group contexts, ASI:One doesn’t try to merge everyone into a single shared memory. Each person’s preferences stay scoped to them, and the system works more like a coordinator than a decision-maker.

So when preferences diverge, it doesn’t overwrite or average them out. It surfaces the differences, keeps track of who prefers what, and helps move the group toward a resolution, whether that’s suggesting options, highlighting trade-offs, or adapting the plan.

The goal is to preserve individual context while still making coordination smoother, not to force a single version of truth.

@natalia_iankovych Not sure I fully got your question, are you asking if ASI:One can analyze LinkedIn posts for meaning, or something broader around how it interprets content?

Happy to answer, just want to make sure I’m addressing the right thing.