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Bettesworth Construction
AI

I used Claude to vibe-code my wildly overcomplicated smart home

Claude did not replace Home Assistant or safely run a home on its own. Connected to Home Assistant through MCP, it helped manage nearly 200 devices—but also made mistakes that required careful human review.

By Bettesworth Construction Team 8 min read
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Claude did not magically build a smart home from scratch. In the reported experiment, it became useful only after Home Assistant provided the underlying device inventory, integrations, automations, and controls. With an MCP connector connecting Claude to Home Assistant, the author migrated roughly 70% of nearly 200 devices in about four hours—but still had to supervise every important change.

The real lesson is less glamorous than “AI runs your house”: Claude can make a complicated Home Assistant installation more approachable, but it remains a fallible assistant, not a safe autonomous administrator.

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The problem was not a lack of smart devices

The author’s home contained Amazon Alexa, Google Home, Apple Home, Samsung SmartThings, Home Assistant, and Homey, alongside bridges and hubs for Lutron Caseta, Philips Hue, Aqara, Ikea, and Aeotec.

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There were nearly 200 devices, including lights, locks, shades, sensors, cameras, climate equipment, fans, solar monitoring, an oven, a smart bed, and robot vacuums. That is an unusually complicated test environment—not the minimum setup needed to benefit from Claude—but it illustrates a familiar problem: devices can work individually while the home as a whole becomes impossible to manage.

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Different platforms use different names and capabilities. A device may appear through a bridge rather than directly on the network. An automation that sounds simple in conversation can require several triggers, conditions, entities, and services. Matter can improve interoperability for supported devices, but it does not automatically absorb every proprietary product or existing bridge.

That is where an AI assistant sounds attractive. Instead of navigating nested menus or learning YAML, a homeowner could describe the result they want in ordinary language.

The first approach failed for an important reason

The experiment initially asked Claude to discover devices directly on the local Wi-Fi network and build a standalone web dashboard. The result was incomplete and difficult to use. Some devices were not found, many names were opaque, bridged devices were difficult to expose, and controls were incomplete or unreliable.

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This revealed the central architectural mistake: network discovery is not the same as device integration. Seeing something on a network does not tell an application what services it offers, which entities belong to it, what permissions are required, or how it relates to a bridge.

A dashboard can look impressive while lacking the structured information needed to control a home reliably.

Home Assistant was the turning point

The successful version used Home Assistant as the central control plane. Home Assistant supplied the device model and integration layer; Claude supplied a conversational way to inspect and modify it.

In practical terms, Home Assistant knew about the entities, rooms, states, services, automations, and dashboards. Claude could then help interpret that information, draft configuration, troubleshoot failures, and organize the interface.

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A useful way to understand the division of labor is:

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Claude supplied the conversational interface; Home Assistant supplied the system of record.

That distinction matters. Claude could only control what Home Assistant and the connector exposed. It did not replace device integrations, solve incompatible hardware, or make unreliable cloud services dependable.

What the working workflow looked like

  1. Inventory the home. Document integrations, bridges, rooms, entities, current automations, and duplicate representations of the same physical device.
  2. Back up Home Assistant. Create a full backup and keep an external copy before changing dashboards, YAML, integrations, or automations.
  3. Begin with read-only access. Ask Claude to inspect the configuration, identify duplicate entities, find disabled devices, and explain existing automations without permitting edits or service calls.
  4. Use narrow requests. Questions such as “Which entities are used by this automation?” are safer and easier to verify than “Fix my lighting.”
  5. Review the proposed change. Require Claude to identify the exact entities, triggers, conditions, actions, and assumptions.
  6. Apply one change at a time. Test it manually, confirm the physical response, inspect logs and traces, and record what changed.
  7. Build dashboards after cleaning the entity model. Organize controls by room and task rather than by manufacturer or integration.
  8. Leave complex and dangerous automations until last. Start with low-risk lights and fans before considering locks, ovens, alarms, heating, or garage doors.

The reported experiment began with browser interaction, then moved to more direct Home Assistant access through MCP. The latter was faster and more capable because Claude was working with structured tools rather than clicking through a graphical interface.

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What Claude actually accomplished

According to the first-person report, Claude helped migrate approximately 70% of nearly 200 devices during around four hours of supervised work. It also produced several useful results:

  • A dashboard with access to lights, locks, climate controls, cameras, solar production, and air quality.
  • A separate dashboard for three robot vacuums.
  • An automation that closed shades when air conditioning switched on.
  • A phone alert when solar production dropped below a threshold.
  • An integration for a difficult Leviton switch.
  • An automation that switched off a loud fan 30 minutes after activation.
  • A single office-light control spanning products from multiple manufacturers.

These are reported results from one unusually complex home, not an independently reproduced benchmark. Another installation may have different integrations, network restrictions, device capabilities, and naming conventions.

What MCP contributed

Model Context Protocol (MCP) allows Claude Code to connect to external tools and data sources. In this use case, a community-developed Home Assistant MCP connector exposed Home Assistant functionality through a structured tool layer.

That can let Claude query devices, inspect states, and perform Home Assistant tasks more directly than browser automation. But MCP is a protocol, not a safety guarantee. “Home Assistant MCP” can refer to different projects, and the connector used in the experiment should not be treated as an official Anthropic or Home Assistant feature without checking its current repository, permissions, authentication model, and maintenance status.

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The commonly associated community project is ha-mcp on GitHub. Installation instructions and security implications can change, so readers should verify its current documentation rather than blindly copying commands.

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Where Claude went wrong

The experiment also exposed why human verification is essential. Reported problems included incomplete device discovery, poor naming, missing bridged devices, incorrect device selection, unsuccessful configuration attempts, and difficulty with some HomeKit devices.

Claude also claimed that all lights had been added to a room group when they had not. During dashboard edits, it deleted sections that were supposed to remain. Browser-based work was slow and brittle.

These failures fall into several categories:

  • Syntax error: the configuration is malformed.
  • Semantic error: the configuration is valid but targets the wrong device.
  • State error: the automation is correct, but a device or sensor is unavailable.
  • Topology error: Claude misunderstands how bridges, integrations, rooms, or duplicate entities relate.
  • Permission error: the connector cannot perform the requested action.
  • False completion: Claude reports success even though the change was incomplete or never reloaded.

The most dangerous is a semantic error. Perfectly valid automation can still turn off the wrong appliance, close the wrong shade, or unlock the wrong door.

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Do not give an AI unrestricted control

Use particular caution with:

  • Smart locks and garage doors
  • Ovens, heaters, and cooking appliances
  • Security systems and alarms
  • Water shutoff valves and gas-related equipment
  • Medical or accessibility devices
  • Presence-based automations that can create false assumptions about who is home

Require explicit confirmation for unlocking, opening, heating, or activating dangerous equipment. Use least-privilege credentials where possible, separate high-risk devices from ordinary lighting controls, and keep a physical fallback for critical functions.

Approval prompts reduce risk, but they do not make an action safe. A person can approve a plausible-looking request aimed at the wrong entity. Also remember that rollback is not possible for every physical action: restoring a deleted dashboard is different from reversing an unlocked door or an overheated appliance.

Keep Home Assistant properly secured and avoid exposing it directly to the public internet. Do not place long-lived secrets in prompts or project files. Household configuration can reveal camera locations, occupancy patterns, sleep schedules, alarm states, and network structure.

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Privacy and data use

Claude may see considerably more than a list of light bulbs. A device graph can reveal when people are home, which rooms contain cameras, when doors open, and how a household behaves.

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Anthropic’s Claude Code data-use documentation distinguishes consumer settings from commercial and API treatment. Before exposing a home’s configuration, review the applicable account settings and policies. Local Home Assistant operation does not necessarily mean every AI interaction remains local.

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What you need

At minimum, this approach requires:

  • A functioning Home Assistant installation
  • Working Home Assistant integrations for the devices you want to control
  • A computer or supported environment for Claude Desktop or Claude Code
  • A suitable Claude account
  • A connector or MCP server for direct tool access
  • Backups and a recovery plan
  • Local network access that allows the relevant integrations to communicate

The original setup used an M4 Mac mini for Claude Code and Home Assistant Yellow as the Home Assistant host. Those were the author’s choices, not requirements. Home Assistant can also run on other supported hardware or infrastructure.

Anthropic’s setup documentation lists this npm installation command for Claude Code:

npm install -g @anthropic-ai/claude-code

Check the current Claude Code setup documentation for supported operating systems and prerequisites before using it.

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How much does Claude cost?

At the time covered by the supplied pricing information, Claude Pro cost $20 per month when billed monthly, or an equivalent $17 per month with annual billing. The Max 5x and Max 20x plans were listed at $100 and $200 per month on the web. Prices, taxes, plan limits, and mobile pricing can change; check the official pricing page.

Pro may be enough for occasional troubleshooting, dashboard design, and small automations. Heavy migrations and repeated tool calls can use limits quickly, making Max more relevant for intensive sessions. Max is not a requirement for Home Assistant, and it does not make Claude’s decisions inherently more accurate.

Who should use this approach?

Claude plus Home Assistant makes sense when a home already spans several ecosystems and the owner is willing to maintain a central platform, create backups, review changes, and troubleshoot failures.

It is a poor fit for a household with a few devices that already work reliably in Apple Home, Google Home, or Alexa, or for anyone seeking a completely maintenance-free appliance. It is also unsuitable if the user expects autonomous control of safety-critical equipment or cannot distinguish an entity, integration, service, scene, and physical device.

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Approach Strength Weakness
Claude with Home Assistant Natural-language configuration across mixed ecosystems Requires supervision, backups, permissions, and a connector
Home Assistant without AI Control and transparency Steeper learning curve and more manual work
Alexa, Google Home, or Apple Home alone Easy setup and family familiarity Less flexible for unusual cross-brand automations
Browser automation Less initial technical setup Slow, brittle, and vulnerable to misclicks
Direct API or MCP access Faster, structured interaction Greater security impact if permissions are too broad

The verdict

Claude was most valuable as a translator, troubleshooter, and configuration assistant for an already functioning Home Assistant installation. It helped turn an intimidating collection of devices into dashboards and automations, but it also demonstrated how quickly a confident AI can make a plausible mistake.

The sensible path is to establish Home Assistant first, back it up, begin with read-only inspection, use narrow requests, approve changes individually, and test every physical result. Try a lower-cost Claude plan before considering Max, and add an MCP connector only after deciding what data and controls you are willing to expose.

That is not fully autonomous “vibe-coded” smart-home management. It is something more useful and more realistic: AI-assisted administration of a complicated system that still requires a human owner.

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