Hyper-local, hyper-cloud - by David Hoang
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Hyper-local, hyper-cloud<br>Issue 306: Have your cake and eat it: why agents belong local and cloud
David Hoang<br>Jul 12, 2026
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I’m not a person to make a declarative statement or hot take on the spectrum of options. That’s why my life principle is, “Have your cake and eat it.” I do this with life, especially food: the Arnold Palmer, Cronut, and Ramen Burger are abundantly clear examples of this principle in action. This also applies to technology, software design, and company building.<br>Recently, Peter Yang posted online regarding an interesting tension about agents on the cloud or run locally. His instinct is correct; the concepts feel conflicting.
Peter’s post on X<br>I have strong conviction on this dichotomy not only when it comes to AI and agents, but the general category of software computing once everything is re-written to be agentic (and it will). Many people rightfully believed in the 2000s that Cloud Computing is the future; still will. One desktop workflow at a time moved to the cloud: word processing became collaborative with Google Docs, file storage moved to Dropbox, design to Figma, and app building to Replit.<br>If this is the future, then why are people in 2026 (myself included) running local agents and models on their Mac minis and local hardware? If you look deeper at what the fusion of hyper-local, hyper-cloud brings, you’ll see how the concept complements instead of conflicts. The result is the best of both worlds for collaborative and individual productivity.<br>Let’s look at the split between cloud and local: the problems it solves, the strengths of each, and a few workflows where I’m already working this way.<br>Cloud is contextual and collaborative
Cloud remains critical infrastructure for obvious reasons, and it’s not going away. I run cloud agents as much as I do local agents, and the workflow will keep improving. But beyond cloud sessions, the cloud’s most critical role is as a facilitator—passing context between teams and individuals.<br>In the GitHub model, developers are used to pulling from a cloud-based repository. This model will flip: the cloud will push the latest to developers, designers, and knowledge workers working locally. The shift sounds small, but it’s significant. As generative work accelerates and people iterate faster, ambient awareness of what your teammate is working on becomes far more critical.<br>I often wonder, “What does the merge conflict look like for knowledge workers?” The answer is shared understanding, context, and alignment.<br>The natural thought is to put everything on the cloud. I think that’s a mistake. The early problem of the cloud was how much information could be stored; now terabytes of storage are economically approachable. The new problem is there is too much information for human beings to parse. This is what I call artifact slop: too many half-baked documents in the knowledge base, diluting understanding—the new bottleneck.<br>Local workflows are personal
Despite all the tech innovation, there is something special about a person’s local, personal workflow. People put up stickers, build widgets, and customize their wallpapers to make the space feel like it’s theirs—the same way you set photos of family and pets on your desktop. I think the desktop metaphor is shifting to the coworking space, but people still want their personal notebooks.<br>Take OpenClaw. The frontier companies’ AI assistants used at work feel very professional; the agents people make for personal use feel almost like a Tamagotchi virtual pet.
Left: Tomagotchi virtual pet, Right: Norm, my AI Assistant from town.com<br>The hyper-local, hyper-cloud thesis
In January of 2026, I wrote about the AI focus areas I’m most interested in that focused on five areas:<br>The natural language of drawing: returning to sketching as pre-work before spending time in front of terminals to prompt agents
Dynamic interfaces: continued work on how AI experiences will create composable and malleable software/content
From desktop to command center: how agent orchestration will quite literally feel more like playing a Real-Time Strategy (RTS) game
Personal LLMs and agents: how people need personal and customized tools in addition to the frontier models provided at work
Memory interpreters and boundary agents: the holy grail of accessing personal and work context in a trustworthy way
Personal LLMs and agents has come to fruition—I called this before Clawbot → OpenClaw. Memory interpreters and boundary agents will keep taking shape. Jason Yuan’s new startup, Hivemind, is a social AI that has one-to-one conversations with many people at once. To me, that’s the boundary agent I described, and I believe it’s coming to the workplace.<br>The hurdles to navigate
I’ve been passionate about decentralized systems—particularly blockchain technologies—for more than 10 years, even before crypto. Distributed ledgers (blockchain is one type) are...