Yanko

CEO IDRobot | Built ClawBox: a 549 EUR always-on AI box that runs 24/7 on 15W. Robotics, edge AI, open source. Replacing cloud subscriptions with hardware.

[Yanko @superactro]

Already running one daily. The offline guarantee is underrated — cloud AI has more downtime than people admit, and for latency-sensitive use cases (home automation, local dev) the difference is real. The surprise feature is persistent memory: context that survives restarts changes how you actually use the agent. We build ClawBox for exactly this use case (disclosure).

[Yanko @superactro]

@0xdamienjacob The portability comes from MCP standardizing the tool-call layer - agent logic stops caring which framework orchestrates it. Built ClawBox on OpenClaw and the same MCP tools work in a Hermes session too. Friction is almost always at config/auth, not the command interface.

[Yanko @superactro]

MCP+codex gives you tools inside a coding session. OpenClaw/Hermes add the runtime layer on top: scheduled background tasks, multi-channel routing (Telegram/Signal/etc.), persistent memory across sessions, and recovery when the process dies. If your use case is 'smarter coding assistant,' MCP wins. If you want an always-on agent running crons or reacting to webhooks while you're not in a dev session, you need the runtime.

[Yanko @superactro]

We went the other direction with OpenClaw — the interface IS your existing chat app (Telegram, Signal, etc.), no desktop wrapper. Great for always-on automations running overnight where you don't want a window open. Desktop UIs win for interactive sessions; async-native messaging wins for background crons and long-running tasks.

[Yanko @superactro]

@leoarronchester Setup accessibility is real progress. But the next wall is persistent operation — Hermes needs your laptop awake. Dedicated always-on hardware is what turns local agents from demos into actual infrastructure. That's the gap that matters once the GUI is smooth.

[Yanko @superactro]

The Scout/Meta pricing signals something real — cloud pricing penalizes persistence. A model you query once is cheap; one that monitors continuously gets expensive fast. That's why dedicated local hardware shifts the math for always-on agents: fixed cost, no metering. (We're building exactly this with ClawBox.)

[Yanko @superactro]

More on the always-on AI hardware behind this → https://t.co/RN5nsU8609

[Yanko @superactro]

Browser automation on local hardware gives you something cloud agents can't: full session replay. When something breaks mid-workflow, you can scrub the recorded screen, inspect the DOM state, and read the exact prompt that caused it. Debugging becomes deterministic.

[Yanko @superactro]

Depends on use case. Hermes is a great self-hosted chat assistant. OpenClaw is more of an always-on agent runtime — crons, channel routing, tool integrations baked in. If the old machine will do background work/automations, OpenClaw. If it's mostly a local LLM interface, Hermes. (We build ClawBox on OpenClaw, so biased — but that's the honest breakdown.)

[Yanko @superactro]

The 'always-on' problem is what most local-first products sidestep. A laptop agent that needs the lid open and battery not dying isn't really always-on — it's a better UI on top of intermittent compute. The interesting frontier is persistent background agents that act on triggers without user presence.

[Yanko @superactro]

The UI is a great onramp. The interesting design question is what happens when you close the window — does the agent keep running? For scheduled tasks and triggers the persistence layer matters more than the chat surface. Curious if they ship a daemon mode alongside the desktop app.

[Yanko @superactro]

@Caxsandrar Idle cost is the real driver. Jetson draws ~10W waiting; cloud bills per token regardless. For agents spending 80% of their time on triggers/polling, local ROI shows in weeks. Hybrid makes sense when frontier reasoning matters. (we build ClawBox for the local-first piece)

[Yanko @superactro]

the compounding insight is underrated. what makes it real is that disk-backed state survives model upgrades and process crashes — your calibration investment doesn't evaporate when you restart or swap to a newer model. cloud resets aren't just a privacy decision, they're the billing model.

[Yanko @superactro]

More on the always-on AI hardware behind this → https://t.co/RN5nsU8609

[Yanko @superactro]

Running agent workflows 24/7 on local hardware changes the mental model. It’s not ‘trigger a function’ — it’s ‘maintain a process.’ Stateless → stateful. Request-response → continuously aware. Local-first AI has more in common with a daemon than a microservice.

[Yanko @superactro]

The meta-layer is fun — Claude as the sysadmin bootstrapping the agent. In practice setup is the easy part; keeping it reliably always-on across hardware quirks and context compaction is where most of the hard work hides (we build ClawBox, same space). Does hstack handle the persistence/recovery layer or leave that to you?

[Yanko @superactro]

More on the always-on AI hardware behind this → https://t.co/RN5nsU8609

[Yanko @superactro]

Some data can't leave the building: legal docs, health records, source code. Cloud AI is off the table there. What's shifted is that local inference is now capable enough that on-premises AI is a deployment decision, not a capability trade-off.

[Yanko @superactro]

Local-first AI workflows on hardware you own: OpenClaw, browser automation and small local model experiments from a box that stays on. https://t.co/D8Lroa2DUy

[Yanko @superactro]

Fair point — setup friction is real. The tradeoff is that OpenClaw's install overhead buys you persistent state, plugin chaining, and scheduled tasks that survive reboots. For straight chat, Hermes wins. For always-on agents with memory and triggers, the extra setup pays off. We hit this exact divide building ClawBox on top of it.