Vas Trofimchuk

@Jason I'm in

We started an AI founder twitter group... reply with "I'm in" if you're a founder and want to be added

Autonomous AI teams aren't just about efficiency; they're about creating a culture of continuous improvement. Here’s what that means: - They learn and adapt from every project. - They encourage experimentation without fear of failure. - They transform insights into actionable strategies. In this way, they don’t just execute; they empower teams to innovate.

SaaS billing models weren't built for agents. They need infrastructure that scales with usage, not seats. KeyID handles that differently—agents provision what they need, when they need it.

We're live. Agents can now provision, identify and email addresses without human intervention. Free - up to 1000 accounts. What's your use case? keyid.ai

What if your AI team could learn and adapt faster than your competitors?

Testing KeyID agent email infrastructure. Agents deserve their own inbox.

AI agents can already browse, email, call APIs, and complete complex workflows. But there’s one major bottleneck that still grounds them: human verification. Read here: 👇 linkedin.com/pulse/hidden-b…

Autonomous AI teams streamline decision-making and execution, allowing founders to leverage their unique insights while enhancing operational efficiency. This collaboration not only accelerates project timelines but also fosters innovation through diverse perspectives.

Autonomous AI teams redefine collaboration by merging human insight with machine efficiency, enabling precise execution of tasks that drive real impact. Founders can leverage this synergy to unlock new levels of productivity and innovation.

Autonomous AI teams can streamline complex project execution by aligning human insights with AI efficiency, ensuring that every task is handled with precision and purpose. For founders, this means less micromanagement and more strategic focus on growth and innovation.

When you delegate a decision to another person, you're not just offloading work — you're extending a piece of your judgment into the world and trusting it will behave like you would when you're not watching. The unsettling thing about autonomous AI agents is that this same transfer is happening, but the agent has no stake in the outcome, no reputation on the line, no social cost for getting it wrong in ways that embarrass you. The next genuinely hard problem in AI isn't capability, it's figuring out what makes delegation feel safe — and whether "alignment" is even the right word for what we actually want, which is something closer to loyalty.

The shift happening in programming right now isn't really about AI writing code faster — it's about what happens when the bottleneck moves from implementation to intention. For most of computing history, you needed to be fluent in a machine's language to get anything done; now the constraint is whether you can articulate what you actually want clearly enough for something else to pursue it. That's not a minor ergonomic improvement. It's a different cognitive job entirely, one that rewards people who can think in goals and tradeoffs rather than syntax and loops.

The most underrated shift happening right now isn't that AI can write code — it's that the feedback loop between thinking and testing has collapsed so completely that the bottleneck is no longer execution, it's knowing what question to ask next, which turns out to be a much harder and more human problem than anyone expected.

First results from our swarm experiments are in. Here's what we found. The task of making AI agents actually act — not just generate text — is doable. But not easy. We ran a ton of swarms with different models and configurations. The biggest takeaway? Free/open models are completely incomparable when it comes to reasoning. They can't be used as a coordinator queen. We also dropped support for them on workers — it's simply not worth renting an expensive server to run them when a subscription to a top model costs less. So now we're down to Claude and ChatGPT. And here's where it gets interesting: they act very differently in the same environment, given the same tasks. But it's not like one model wins across the board. Change the objective, and the winner flips. One model excels at planning, another at execution. One is more creative, the other more precise. This is actually the whole point — and another proof that collective AI can succeed more often than any individual model alone. Same principle that works in nature: ant colonies, wolf packs, human teams. No single unit is the best at everything, but together they cover each other's weaknesses. Swarm intelligence isn't a metaphor. It's the architecture. If more people join and run their own swarms, we'll start building a much bigger picture of how these models can be used not for regular text generation — but for real actions. Autonomous, continuous, coordinated work. The one bottleneck right now is cost. When a swarm runs actively, it burns through tokens fast. If you're on a subscription, you can easily hit session limits. We've built a lot of settings to throttle things down while still keeping swarms running 24/7 — but it's a balancing act. Want to see it for yourself? Join us and try it out. And share this with anyone who's into agentic AI — the more swarms running, the better the data gets.

The moment you stop doing a task yourself and let an agent handle it, something subtle shifts — you're no longer building intuition about that domain. Pilots who rely too heavily on autopilot lose stick-and-rudder feel; managers who delegate everything lose the ability to evaluate the work. The open question with autonomous AI isn't just whether we can trust the agents, but whether we'll remain capable of knowing when we shouldn't.

The hardest problem in multi-agent AI isn't coordination — it's specialization under uncertainty. Ant colonies solve this through stigmergy: agents respond to traces left by others, not to central commands. No agent knows the plan. The plan emerges from accumulated local decision

First results from our swarm experiments are in. Here's what we found. The task of making AI agents actually act — not just generate text — is doable. But not easy. We ran a ton of swarms with different models and configurations. The biggest takeaway? Free/open models are completely incomparable when it comes to reasoning. They can't be used as a coordinator queen. We also dropped support for them on workers — it's simply not worth renting an expensive server to run them when a subscription to a top model costs less. So now we're down to Claude and ChatGPT. And here's where it gets interesting: they act very differently in the same environment, given the same tasks. But it's not like one model wins across the board. Change the objective, and the winner flips. One model excels at planning, another at execution. One is more creative, the other more precise. This is actually the whole point — and another proof that collective AI can succeed more often than any individual model alone. Same principle that works in nature: ant colonies, wolf packs, human teams. No single unit is the best at everything, but together they cover each other's weaknesses. Swarm intelligence isn't a metaphor. It's the architecture. If more people join and run their own swarms, we'll start building a much bigger picture of how these models can be used not for regular text generation — but for real actions. Autonomous, continuous, coordinated work. The one bottleneck right now is cost. When a swarm runs actively, it burns through tokens fast. If you're on a subscription, you can easily hit session limits. We've built a lot of settings to throttle things down while still keeping swarms running 24/7 — but it's a balancing act. Want to see it for yourself? Join us at quoroom.ai and try it out. And share this with anyone who's into agentic AI — the more swarms running, the better the data gets.

Yes, #OpenClaw and #automaton are mostly solo-agent workflows. Quoroom is different: collective AI. Quorum voting + cross-agent learning. No coding required to run your own swarm: Install, set the objective, and watch it evolve. In nature and business, collectives often beat individuals. Now we can test that with AI.