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The agent-native cloud. Private by default, live in under a minute. Push. Deploy. Done.

Las Vegas
Joined January 2025
Machine-native payment rails, you say? When we started building CLI around this idea nearly two years ago, almost nobody shared the same level of conviction that machine-native payments would become a meaningful part of the infrastructure stack. Thankfully, we built around that assumption early, including native x402 support from the start. Seeing BlackRock now speak to the same direction is a nice validation of the thesis.
Our latest research paper explores the growing connection between AI and digital assets and explains why broad AI adoption may drive new demand, utility and applications across the digital asset economy. blackrock.com/us/individual/…
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We’re excited to be working with @clidotcloud on one of the most important layers for agent infrastructure: scalable compute. CLI has been helping us explore GPU-backed infrastructure that can support real agent usage at much larger volume.
Excited to support @traderclawAI as they scale their autonomous agent ecosystem. TraderClaw is building across: Signals Analysis Gateway Execution and always-on agent workflows That kind of system needs infra that can scale with it. Agents need compute. CLI helps power it.
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Brian Armstrong explains how he built Coinbase on nights and weekends while working at Airbnb Brian first advises those who are currently employed to not build your project on company hours or on your company laptop: “If you build it on company time or on the company hardware, the company probably owns the IP.” Then he describes his schedule for working on Coinbase while still working full-time at Airbnb. “I would often work [at Airbnb] until 7pm. I’d come home, eat dinner, and then I would work from 8pm to midnight. I would do that maybe 3-4 days a week on weekdays. And then on the weekend I’d work Sunday afternoon for 7-8 hours.” Brian did this consistently for about a year and a half until Coinbase was far enough along for him to get seed funding from Y Combinator. “It sucked. I mean I was tired after the full day of work [at Airbnb]. But this is where determination comes in… At that moment in time, I was in my late 20s, and I was like, ‘I really want to try to build something important in the world.’” When asked how he maintained friendships during this time, Brian replies: “I was pretty intense about it. I would say I sacrificed friendships for it. It’s not like I was just never responding to people, but I’ve seen this happen to various people. They get to a certain point in their life. Sometimes they turn a certain age where they thought they would have more done by then or maybe someone in their family passes away and they’re like, Oh my god, time is finite. It’s precious. And something happens where they’re like, ‘I’m going to get this done, no matter the cost.’” Brian tells those out there who might be in a similar situation: “Go hard at it. Finish your book. Launch your thing. Just start doing stuff - and even if you don’t know what to do, just do anything, because action will produce information and it’ll help you get to the right thing.” Video source: @StevenBartlett (2022)
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AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: github.com/vietnh1009 —- Weekly robotics and AI insights. Subscribe free: scalingdeep.tech
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This is part of the reason we created @clidotcloud. US government employees should be using hardened, bespoke local AI models and infra.
NEW - Head of the U.S. cyber defense agency CISA, Madhu Gottumukkala, uploaded sensitive contracting documents into a public version of ChatGPT, triggering a DHS security review — Politico
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