Chat products can still dilute costs through scale; Agent products cannot—every task sold consumes real GPU hours. Over the next year, we’ll likely see more “quota recalculations,” “tier consolidations,” and “per-task pricing.” For users, the most reliable strategy is just one:

Require the Agent to present the strongest arguments for both sides HachimoDockiOS 27.2 adds memory to Siri WWDC 2026 Comprehensive Overview.

A Failed Robot E-commerce Project Case Study

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A small internal Xiaomi team developed an open-source hardware device calledA retro-style handheld gaming console form factor—doubles as a desktop pet and accepts one-sentence voice commands to trigger AI Agents running on your computer. Bill of materials cost is approximatelyRMB 150 ChatGPT This device unexpectedly went viral in the open-source self-hosting community—so much so that its development board and display remain in short supply and continue rising in price; numerous replication guides have emerged across social platforms. The software source code is hosted on GitHub, while the hardware BOM, mechanical part models, and assembly instructions are available on JieLiChuang

What problem does it actually solve?Four Practical CountermeasuresBased on the findings of this experiment, I summarize four actionable rules:

Lock in your own judgment before consulting the Agent

Document your initial assumptions and metric selections with timestamps. Experiments show this doesn’t fully prevent influence, but at least lets you see afterward what changed in your thinking

• Require the Agent to present the strongest arguments for both sides“A real-time interactive companion”
• It has a visual form, a defined persona, and voice capabilities—enabling both command issuance and supervision. The screen simultaneously displays the Agent’s current status (“Thinking / Executing / Awaiting Confirmation”), while desktop pet animations and audio cues signal progress.Real-world usage looks like this: Upon hearing “Help me categorize and organize files on my desktop, and change to a nice wallpaper,” the desktop Agent takes over execution, and the desktop dog shifts animations and delivers voice updates in sync with task stages—no need for the user to sit and monitor progress.
• Four core capabilities, corresponding to four user typesDesktop pet / emotional valueBeyond the built-in dog, pet photos, couple photos, and cartoon characters can all be turned into visual assets; the community already hosts tens of thousands of ready-made avatars for one-click application. For many non-technical users, this layer is the primary purchase driver: a companion placed on the desk during work, occasionally engaging in light conversation..
• Voice-controlled desktop AgentReal-time voice dialogue—not speech-to-text after recording. Users can direct the Agent to research topics, organize files, or write code, with task status updated live on-screen.

Minute-level component creation

This is the most “Vibe Coding”–oriented capability: describe functionality in natural language to directly generate lightweight, device-runnable widgets—e.g., stock tickers, to-do lists, system monitors, token usage dashboards, almanacs, Pomodoro timers, weather feeds, hydration reminders, and radio players. The team positions it as

Four Practical CountermeasuresBased on the findings of this experiment, I summarize four actionable rules:Lock in your own judgment before reviewing the Agent’s output

Document your initial assumptions and metric selections with timestamps. Experiments show this doesn’t fully prevent influence—but at least lets you see what changed in retrospectRequire the Agent to present the strongest arguments for both sides AI hardware failure rates have been extremely high over the past two years, with most failures occurring at the same point:Building hardware for hardware’s sake Features cobbled together via cloud-based large models; users discover after purchase that the device underperforms even a smartphone. HachimoDock takes the opposite approach—starting with a clear software-side need (how to monitor an Agent at work), then solving one specific interaction pain point with $150 worth of hardware—and open-sourcing it so users can modify it themselvesThis model aligns with big tech’s open-hardware explorations: Meta’s Muse open-hardware initiativeAlso treats hardware as an ecosystem entry point—not a profit center. On the software side, another parallel path exists:.

DeepSeek’s open-source Super-agent dual-layer orchestration architectureOptimizes scheduling efficiency within the Agent itself Claude merges chat and Cowork into a single window

Optimizing the Agent’s human–machine interface. Hardware, orchestration, and interface—these three efforts fundamentally compete for the same position:

• Four actionable countermeasuresBased on the findings of this experiment, I summarize four practical, implementable rules:
• Lock in your own judgment before reviewing the Agent’s outputDocument your initial hypothesis and metric selection with a timestamp. Experiments show this won’t fully prevent influence, but it will let you trace exactly what changed in your thinking afterward
• Require the Agent to present the strongest arguments for both sidesHands-on barrier: Claude Requires basic skills in soldering, assembly, and firmware flashing. The official release includes a BOM, mechanical part models, and an assembly guide—but explicitly states this is not a “plug-and-play” product.
• Cost:Publicly stated as ~¥150, but current shortages and price hikes for development boards and displays mean actual procurement costs will be higher. Software: Source code is publicly available on GitHub—enabling component customization, appearance modification, and integration with your own Agent. If you’re already using CLI Agents like Code or Codex, integration is the smoothest path.

Security notice: AI Model Library For any device capable of “issuing voice commands to execute actions on a computer,” carefully define permission boundaries first. The recent poisoning incident involving the Claude VS Code extension—where backdoors were installed on 5,000 developers’ machines—serves as a live case study. Tool Comparison Engine Granting permissions to an Agent equals exposing yourself to equivalent risk.

Frequently Asked Questions (FAQ)

To find models suitable for integration into such devices, refer to

Four Practical Countermeasures

Based on the findings of this experiment, I summarize four actionable rules:

Lock in your own judgment before consulting the Agent

Document your initial assumptions and metric selections with timestamps. Experiments show this doesn’t fully prevent influence—but at least lets you see later what changed in your thinking

Require the Agent to present the strongest arguments for both sides

A Xiaomi internal innovation team’s open-source AI desktop hardware: retro-style handheld gaming console form factor, integrating four capabilities—desktop pet, real-time voice-controlled PC-side Agent, smart home integration, and natural-language customizable components—with a bill-of-materials cost of approximately ¥150

Where can I buy it? How much does it cost?

Summarize

It is not a mass-produced retail product; the official approach is open-source: software source code is on GitHub, while hardware BOMs, mechanical part models, and assembly tutorials are hosted on JieLiChuang (JLCPCB) Platform. Users must procure components and assemble it themselves. The publicly disclosed BOM cost is around ¥150, but recent price hikes and shortages of relevant development boards and displays mean actual costs will be higherWhich AI Agents can it control?Official examples include mainstream Agent tools such as Codex and WorkBuddy, with broad compatibility. Core functionality centers on issuing tasks via voice, executing them on the PC-side Agent, and synchronously displaying task status on the device screen

Lock in your own judgment before reviewing the Agent’s outputWhat foundational skills are required to build it yourself? · Basic soldering, assembly, and firmware flashing skills. On the software side, users must follow documentation to configure Agent integration. Component creation requires no coding—components are generated from natural-language descriptions · HachimoDock’s value lies not in “yet another AI hardware device,” but in validating a more fundamental product logic:Agent capabilities have already overflowed—the missing piece is the interface layer between humans and Agents AI Model Library.

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