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What I’ve Learned After Six Months Running AI Agents at Home

Six months ago I had a server, some model weights, and a vague plan. The plan worked out, but not in the ways I expected, and some things I was confident about turned out to be wrong.

The biggest surprise was how much the value came from memory and context rather than capability. I expected to be impressed by what the agents could do. What actually made the difference was that they knew my environment. What actually landed for me is that Wren never needs me to re-explain which WordPress site uses which credentials. That accumulated context is the real return on the setup investment.

I was wrong about how much I’d want autonomous operation. When I started I imagined agents running in the background making things happen. What I actually prefer is a setup where agents do the information-gathering and I make the calls. The most valuable loop isn’t “agent acts, I find out later.” It’s “agent finds out, I act.” That shift changed how I configured permission levels and approval requirements, making them stricter than my initial instincts suggested.

Tool quality matters more than model quality, within limits. Early on I focused a lot on which model I was running. Over time I noticed that agents with clear tool access, good memory, and well-defined scope outperformed agents with better models but fuzzier configurations. An agent that can reliably call the right tool with the right parameters is more useful than one that reasons well but can’t act. The two aren’t in opposition; you want both, but if I had to fix one first it’s the tools.

Scope discipline turned out to be hard to maintain. Agents naturally accumulate responsibilities over time. You add a small exception here, a new tool there, and three months later you have an agent whose domain you couldn’t clearly define if someone asked. I’ve had to pull back and rewrite configurations a couple of times to restore clear boundaries. This is ongoing work, not a one-time setup problem.

The operational overhead is real but manageable. I spend maybe an hour a month maintaining the stack: checking that containers are healthy, updating Ollama or models, reviewing and updating memory files when the state of something changes. That’s much less than I feared going in. The Unraid base helps; it handles container lifecycle reliably and I don’t spend much time keeping the platform running. The network layer is equally hands-off once set up: a TP-Link managed gigabit switch handles all the container traffic and I haven’t touched its config in months.

What changed most in how I interact with computers is how I frame tasks. Before this setup I’d navigate to something and do it. Now I often describe what I want to know or what I need to happen, and an agent traverses to the answer. That shift from “navigate and act” to “describe and delegate” sounds subtle and it has changed how I spend my time. The tedious traversal work, logging in, checking versions, looking up IDs, has mostly moved to agents. I do more deciding and less navigating.

What I’d do differently: I’d write the memory files before I needed them instead of building them up reactively. I’d define agent scopes in writing before I started configuring tools. And I’d spend less time on model selection early on and more time on making sure the tool connections were solid.

Six months in, the setup earns its overhead. I’m not going back to managing this manually.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:29:38-07:00July 15th, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

How I Manage Three WordPress Sites with an AI Agent

Three WordPress sites sounds like a small number until you actually have to maintain all of them. Plugin updates, theme updates, checking for broken links, keeping an eye on drafts, knowing what’s published where. None of it is hard individually; it’s the accumulated cognitive overhead of keeping track of three separate environments that turns into friction over time.

Wren is the agent I’ve configured to handle that overhead. She\’s more like a very informed assistant who knows the current state of all three sites and can surface what needs attention. When I ask her what’s pending, she actually knows, because she has persistent memory with the site inventories: which plugins are installed, which version, what’s changed since we last looked.

The way she touches WordPress isn’t through a browser; it’s through WP-CLI commands run inside the Docker containers on my Unraid server. To run those commands, she delegates to Apex, the infrastructure agent, who has SSH access to the server and can exec into the right container. The chain looks like: I ask Wren, Wren asks Apex, Apex runs the command, the result comes back up the chain. It sounds like a lot of steps and in terms of configuration it was, but once it was set up it just works. The hardware running this doesn’t have to be a full tower; a capable mini PC like the Beelink SER5 Pro has enough headroom to run the agent stack and several Docker containers at the same time.

What Wren handles without much friction: checking plugin update status across all three sites, reading post lists and draft counts, pulling content for me to review, writing draft posts and saving them locally for my review. Those are all read operations or local writes. They don’t require my approval because they don’t change anything in production.

What still requires my explicit sign-off: any plugin update, any theme change, publishing a post, any database operation. The rule is simple and hard-coded into her configuration. She will not take a state-changing action on a live site without a clear go-ahead from me in that conversation. I set that rule early because the failure mode of an AI agent running an update that breaks a site is much worse than the inconvenience of typing “go ahead.”

The part that still requires me most is content direction. Wren can write a post draft if I give her a topic and the right context, but I still make every editorial decision. What angle to take, what to include, whether the draft is ready to publish. She handles the mechanical side of content management well, the filing, the tracking, the formatting, the metadata, but she’s an executor of editorial intent rather than a source of it.

The surprising benefit has been the accumulated site knowledge. Because Wren keeps memory files for each site, she knows things like which Avada theme version is running on elembemedia, what the affiliate disclosure policy is, what the most recent published post was and when. I no longer have to look any of that up. Over time, that knowledge base has become the most valuable thing about the setup, more valuable than any individual task she automates.

The honest limitation is that anything requiring visual inspection of the site still requires me to open a browser. Wren can’t see what a page looks like. She can tell me what content is in the database, but not whether it’s rendering correctly. For a visual platform like WordPress, that’s a real gap.

If you’re running WordPress yourself and want to ask about specific WP-CLI commands or how I’ve structured the agent configuration, leave a comment.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:29:27-07:00July 12th, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

Running Local LLMs on Unraid

The first local model I ran took about 45 seconds to respond to a simple question. I almost gave up right there. The model was too big for my VRAM, the quantization was wrong, and I hadn’t understood yet that “running a local LLM” and “running a usable local LLM” are meaningfully different things.

I run Ollama inside a Docker container on Unraid. The container gets passed through to an NVIDIA GPU, which handles all the inference. The host is a Ryzen 9 5900X on an ASUS TUF Gaming X570-Plus (Wi-Fi) board. Getting GPU passthrough working was the first real obstacle. Unraid handles it through its Docker GPU assignment settings, but you have to make sure the NVIDIA container toolkit is installed and the container is configured to use it. There are Unraid forum threads on this; I won’t reproduce the whole process here, but it took me longer than I expected because the error messages weren’t obvious about what was actually wrong.

The hardware question matters more than the model question, at first. Most consumer GPUs in the 8-12GB VRAM range can run 7B or 8B parameter models fully in memory and respond in a few seconds. That’s where I’d start. The models in that range, Llama 3, Mistral, Gemma, Qwen, have gotten good enough over the past year that the quality is genuinely useful for most everyday tasks. Quantized versions, typically Q4 or Q5, cut memory requirements significantly with a modest quality tradeoff. For anything that’s not critical reasoning, the tradeoff is worth it.

What I actually run daily is a quantized Llama 3 8B for quick, low-stakes queries and a mid-size model in the 13-20B range for tasks where quality matters more than speed. The 8B model responds in 2-4 seconds on my hardware, which feels fast enough to be comfortable. The larger model takes 15-25 seconds per response, which I find acceptable for deliberate tasks but too slow for conversational back-and-forth.

The models I don’t run locally are the ones where I genuinely need top-tier reasoning or writing quality. For those I send API calls to Anthropic. The local models are good for information retrieval, summarization, drafting, code explanation, and routine agent tasks. They’re not good enough to replace a frontier model for complex multi-step reasoning or polished writing that goes on the internet. Knowing that line has saved me a lot of frustration.

Unraid’s container management makes it fairly easy to keep Ollama updated and to pull new models. The Ollama API makes it straightforward to switch which model a request goes to. The practical challenge is storage: model weights pile up fast. A handful of models can eat 50-100GB without trying hard. I keep the ones I actually use and delete the rest. The Unraid array handles the storage; I’ve dedicated a Samsung 860 EVO 1TB as a cache SSD specifically for models so loading times stay fast.

One thing I wish I’d known earlier: the model name matters, but the quantization label matters almost as much. “Llama 3 8B” can mean very different performance depending on whether it’s Q2, Q4, Q5, or Q8. I run Q4 as a default, Q5 when I want a bit more quality on a task that warrants it. Q8 is overkill for most things and Q2 degrades quality more than I find acceptable.

If you’re curious about specific model recommendations for your VRAM budget, feel free to drop the specs in a comment and I’ll give you my current take.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:29:17-07:00July 9th, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

OpenClaw vs. n8n vs. Home Assistant for Home AI

People ask me fairly often why I’m not using n8n or Home Assistant for my AI automation. The short answer is that they solve different problems, and understanding the difference saves you a lot of time trying to make the wrong tool fit.

Home Assistant is exceptional at what it does: device state, automations triggered by sensors, physical environment control. If your goal is “turn the lights off when I leave” or “notify me when the garage is open,” Home Assistant is the right tool and it’s very good at it. It runs well on modest dedicated hardware; the CanaKit Raspberry Pi 5 Starter Kit PRO is a popular entry point if you want a low-power box that stays out of the way. When people ask about using it for AI workflows, what they usually mean is plugging in an LLM integration so they can ask questions via voice assistant. That works, up to a point. But Home Assistant’s data model is built around devices and states, not conversational context or tool execution. An LLM living inside Home Assistant is a capability bolted on top of a very different architecture. It handles simple tasks fine and gets awkward fast when you need the AI to actually do things.

n8n is a workflow automation tool with a visual canvas. It’s legitimately powerful for connecting APIs, transforming data, and running scheduled jobs. I used it for a while and it’s good at stringing together a sequence of discrete steps: “when this webhook fires, call this API, transform the result, post it here.” Where it struggles is with the kind of reasoning loop that AI agents actually need. An AI picks what to do next at each step based on what it found. n8n’s flow model doesn’t fit that pattern naturally. You end up with workflows that have conditional branches trying to approximate reasoning, and those get brittle quickly.

OpenClaw, which is what I run, is built specifically around agent management. The core model is: here’s a persistent agent with a defined identity, memory, and tool access. The agent receives messages, reasons about them, calls tools, and responds. Tool access is explicit and permissioned. Memory persists between sessions. Multiple agents can coordinate through a message-passing model. That structure maps well to how AI agents actually work, because it was designed for that use case rather than adapted from something else.

The tradeoff is that OpenClaw is more complex to set up than either of the others, and the ecosystem is smaller. With n8n and Home Assistant you get large communities, extensive integrations, good documentation for common use cases. With OpenClaw you get better architectural fit for agents and more bespoke troubleshooting.

My actual setup uses all three, loosely. Home Assistant handles devices and sensors; it does that well and I have no reason to change it. n8n still runs a few periodic data jobs that don’t need any AI reasoning. OpenClaw handles everything that involves an AI agent taking action. They don’t compete because they don’t overlap much in practice.

If I were building something new today and the use case was specifically AI agents that manage services, respond to messages, and take actions on real systems, I’d go straight to OpenClaw or a similar agent-native platform. If the use case was device automation with a sprinkle of LLM, I’d start with Home Assistant. If it was data pipeline automation between external services, n8n is still strong. The mistake is treating any of them as a general-purpose solution when they’re each optimized for something specific.

What are you trying to automate? That question usually points pretty clearly at which tool belongs in the stack.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:29:07-07:00July 6th, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

The Self-Hosted AI Stack I’d Build If I Were Starting Over

I took a winding road to get my current AI homelab working. I’d make different choices if I were starting from scratch, and most of them would come down to doing less sooner rather than more.

The first thing I’d do is separate the model serving layer from everything else. Ollama as a standalone container, exposed on a private network, nothing else bundled in. A lot of guides will tell you to start with a full OpenWebUI stack, and OpenWebUI is fine, but it creates a coupling that makes things harder to reason about later. If your UI and your model server are the same deployment, you end up with friction when you want to swap one out or add a second frontend. Keep them separate from the start.

For hardware, I’d be more honest with myself about the model size tradeoff. My current build is a Ryzen 9 5900X on an ASUS TUF Gaming X570-Plus (Wi-Fi) board, and it handles everything I throw at it for inference routing and container management. A 7B parameter model runs well on consumer GPU memory, responds quickly, and handles most practical tasks. I spent too long trying to run 34B models on hardware that wasn’t really right for them, getting slow responses, and convincing myself the capability justified the latency. It usually didn’t. For day-to-day assistant work, a well-quantized 7B or 8B model is more useful than a sluggish 34B. Save the bigger models for tasks where reasoning quality actually matters.

The gateway layer is where I’d invest more early effort. This is the piece that connects LLM inference to real tools: file system access, APIs, shell commands, memory. I’m running OpenClaw for this. If I were starting fresh, I’d still choose a purpose-built gateway over trying to wire this together myself with n8n or LangChain. The operational overhead of maintaining custom orchestration code is real. A gateway that’s designed to manage agent lifecycles, credential handling, and tool permissions out of the box is worth the setup time.

Memory is something I’d take seriously from day one. The difference between an AI that knows the state of your environment and one that starts fresh every session is enormous in practice. That means deciding early on where state lives, how agents read and write it, and what format it’s in. Markdown files on a shared volume have worked well for me: human-readable, easy to edit when something’s wrong, git-friendly if you want version history.

For API keys and credentials, I’d use a secrets directory with tight permissions from the start rather than environment variables scattered across docker-compose files. It’s easier to audit, easier to rotate, and easier to scope to specific containers when something needs to change. This sounds like overkill when you’re standing up one container. It pays off when you have eight.

The thing I’d skip entirely on a first build is trying to run everything locally. Ollama handles local inference well. But for tasks that genuinely need a frontier model, the cost of API calls is low and the capability gap is large enough to matter. Don’t try to replace Claude with a local model for complex reasoning. Use local models where they’re good enough and cloud APIs where they’re not. That hybrid approach is cheaper and more capable than either extreme.

Finally, I’d document my container layout before it gets complicated. Which container serves which purpose, which ports are mapped, what credentials it needs. This sounds tedious and it is. Three months later when you’re trying to figure out why something stopped working, you’ll be glad you did it.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-18T11:46:47-07:00July 3rd, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

What Multi-Agent AI Actually Looks Like at Home

There’s a version of multi-agent AI that lives in conference talks: perfectly orchestrated systems where dozens of specialized agents collaborate on complex tasks without human input. What I have is messier and more practical, which makes it more interesting.

Here’s a real example from last week. I wanted to update the plugins on one of my WordPress sites. Normally that’s a login, navigate to updates, click apply, wait, done. With agents, I sent one message to Juniper: “Check elembemedia for plugin updates and tell me what’s pending.” Juniper delegated to Wren, who delegated to Apex, who ran a WP-CLI command inside the Docker container and returned a list. Juniper summarized it back to me. Nothing got updated without my go-ahead. But the information-gathering part, which is tedious and requires knowing which container maps to which site, happened in about 30 seconds.

That workflow is the pattern. The pattern is: gather the information, stage the action, I approve or redirect. The agents handle the boring traversal work: knowing which credentials to use, which container to exec into, which API endpoint to call. I stay in the loop for decisions.

Wren’s day-to-day is mostly WordPress housekeeping. She tracks the state of three sites: what plugins are active, what posts are published, what drafts exist. When I ask her to write a post draft, she saves it to the right folder with the right frontmatter. She won’t publish anything I haven’t explicitly signed off on. That’s a hard rule in her configuration, and it matters. The value isn’t that she does everything; it’s that she remembers everything so I don’t have to.

Apex is narrower. She has SSH access to the Unraid server and can run commands inside Docker containers. That access is deliberately constrained. She won’t run anything destructive without a confirmation loop. Her most common tasks are checking container status, running read-only WP-CLI commands, and running the actual update commands once I’ve approved what Wren found. She’s the hands; Wren is the eyes. All of this runs over a local network kept deliberately simple: a TP-Link 8-port gigabit switch handles the traffic between the server and the machines I work from. Nothing fancy; the goal is reliability.

Juniper is the coordinator, but in practice she’s less active than I expected. Most of my interactions are direct: I message Wren about WordPress stuff, I message Apex about infrastructure. Juniper is useful when I want to describe an outcome rather than a specific task and let her figure out the delegation. “What’s the update status across all three sites” is a Juniper question. “Activate the Twenty Twenty-Five theme on bacallburns” is a Wren question.

Fran handles family logistics: calendar, reminders, school schedules. Completely separate domain from the tech agents. She doesn’t have server access and doesn’t need it. Separating domains like this sounds obvious, but it takes deliberate configuration. Without clear scope boundaries, agents start trying to be helpful in ways that cross lines you didn’t know you cared about until they crossed them.

The honest limitation is that this setup requires real maintenance overhead to build initially. Defining scopes, setting up credentials, writing the memory files that give agents context about your environment: that’s a weekend of work, minimum. The return on that investment compounds over time as the agents accumulate useful state, but the upfront cost is real.

If this sounds interesting to you, the most useful thing I can suggest is starting with one agent and one domain. Get comfortable with what it can and can’t do before you start wiring agents together. The coordination layer is where complexity explodes.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:28:58-07:00June 30th, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

Unraid as an AI Homelab Platform

Unraid wasn’t designed for AI workloads. It was designed for flexible storage, a simple container interface, and not requiring matching drives in your array. That’s still what it does well. But over the past year it’s become the platform I run my entire AI stack on, and the fit is better than I expected in some ways and worse in others.

The biggest win is container management. Ollama, the OpenWebUI frontend, my OpenClaw gateway, three WordPress instances, and a handful of other services all run as Docker containers managed through Unraid’s interface. Adding a new model backend or a new agent service is a few clicks and a docker-compose file. Unraid keeps them running, handles restart policies, and gives me a clean way to see resource usage across all of them. For someone who doesn’t want to maintain a full Kubernetes cluster, it’s a reasonable middle ground.

GPU passthrough is where things get more complicated. I’m running a Ryzen 9 5900X on an ASUS TUF Gaming X570-Plus (Wi-Fi) board, with an NVIDIA GPU passed through to an Ollama container for local model inference. The passthrough works, but Unraid’s VM/container GPU assignment can be finicky when you’re also trying to use that GPU for other things. I had to be deliberate about which containers got GPU access and accept that gaming on the same machine would require careful scheduling. Not a dealbreaker, but worth knowing going in.

The storage story is actually solid. I can dedicate a fast SSD cache for model weights and keep larger, less frequently accessed files on the array. I’m using a Samsung 860 EVO 1TB as the cache drive, and it keeps model load times fast. Unraid handles the tiering automatically once you set the cache rules. Model weights are big; an LLM with decent reasoning capability can be 15-40GB. Having flexible storage without needing to rethink my whole array setup was a real convenience.

Community support is a mixed bag. Unraid has an active forum and a decent app ecosystem through Community Applications. But AI-specific homelab content on Unraid is still pretty thin compared to what exists for plain Ubuntu or Proxmox. When I ran into edge cases with GPU driver versions inside containers, I was mostly on my own piecing together solutions from Ollama’s docs and Unraid threads that were only tangentially related to my problem.

Stability has been good in practice. I’ve had this stack running for months with minimal intervention. Unraid’s approach of keeping things simple, one box, one UI, no orchestration overhead, matches how I want to run this. I don’t want to spend weekends maintaining infrastructure. I want the infrastructure to run and get out of my way.

If I were advising someone choosing a platform for a first AI homelab, I’d say Unraid is a solid choice if you’re already on it or if you also want a NAS alongside your AI workload. The integrated storage and container UI are genuinely useful. If you only want AI workloads and nothing else, plain Ubuntu with Docker might be simpler to troubleshoot when things go sideways. Proxmox gives you more VM flexibility if you need it.

None of these platforms solve the fundamental challenge of home AI: the hardware has to be powerful enough to run something worth running, and that still costs real money. Unraid doesn’t change that calculus. But for managing what you already have, it handles the complexity well enough that I spend my time on the AI problems rather than the infrastructure problems.

Happy to answer questions about specific container setups or the GPU configuration if anyone’s working through the same thing.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:28:48-07:00June 27th, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

Why I Stopped Using Cloud AI for Personal Tasks

About a year ago I pasted something sensitive into ChatGPT without thinking much about it. Nothing catastrophic, but it made me pause. Financial details, family context, the kind of stuff that I’d never put in a shared Google Doc. The convenience of cloud AI had made me sloppy about what I was sharing and with whom.

That was the privacy wake-up, but privacy alone wasn’t what made me switch. The cost started bothering me more gradually. I was on two separate AI subscriptions, using them inconsistently, and paying whether or not I hit them hard in a given month. When I added it up against what I was actually getting out of each tool, the math felt off. Especially since I had hardware at home that could do a lot of the same work.

The control issue is more subtle but it matters more to me now than the other two. Cloud AI tools have no memory of your environment. Every session starts cold. I’d paste the same project context into different chats, re-explain what I was working on, manually bridge the gap between AI output and action. The AI was helpful but it was disconnected from everything I actually cared about. It couldn’t touch my systems, didn’t know my sites, had no idea what I’d already tried last week.

When I moved to a self-hosted setup, those gaps closed. My local agents have persistent memory. They have tool access. They know the state of my infrastructure. It\’s a capability choice. The AI became useful in a qualitatively different way when it could actually act on what it knew.

I want to be straight about the tradeoffs though. Running AI at home requires real maintenance. You’re the one responsible when something breaks. Local models are good but they’re not Claude-level on complex reasoning tasks. For anything where I need serious writing quality or complex logic, I’m still sending API calls to Anthropic, just through my own gateway rather than a browser tab. The cost of those calls is much lower than a flat subscription when usage varies month to month.

The data question is real too. When you use cloud AI in a browser, you’re trusting that company’s data policies and their security posture. When you run it locally, you’re trusting yourself. I’d argue most homelab people are pretty motivated to keep their own systems clean, but the risk is different, not gone. One piece I’ve added to my own setup is a YubiKey 5 NFC on accounts that touch the server and the WordPress admin logins. When you’re the one responsible for your own infrastructure, hardware 2FA is an easy layer to add.

What I’ve settled on is a hybrid. Routine tasks, anything involving personal data about my family, infrastructure queries, content management: all local, all through my own agents. Tasks that genuinely need the strongest available model: API calls to cloud providers, but with me controlling what data gets sent and when. I’m not sending my full server state to an LLM; I’m sending a narrow, deliberate query.

The other thing I stopped doing: using AI as a glorified search engine. The cloud tools train you to ask one-off questions. Once your AI has context and tools, you start thinking in workflows instead. That change in how I frame tasks is probably more valuable than any of the technical decisions.

If you’re on the fence about this, I’d say start by auditing what you’re actually doing in cloud AI sessions. How much of it is personal data you’d be uncomfortable with on a shared doc? How often are you re-explaining the same context? That audit will tell you whether the switch is worth it for your situation.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:28:38-07:00June 24th, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

How I Run My Own AI Assistant at Home

My Unraid server used to sit in the corner of my office doing what NAS boxes do: storing files, running a few containers, being ignored. Now it’s running a small network of AI agents that help me manage three WordPress sites, track family logistics, and keep tabs on my infrastructure. That shift didn’t happen all at once. It started with a question I kept asking myself: why am I paying for cloud AI subscriptions when I have the hardware sitting right here?

The thing I built is called OpenClaw. It’s a self-hosted AI gateway that runs on my local network and connects specialized agents to real tools: web APIs, SSH sessions, email, calendar, WordPress admin. Each agent has a name, a purpose, and a defined scope. Wren handles content and WordPress. Apex handles infrastructure and servers. Juniper is the coordinator who delegates to the others. Fran manages family scheduling. They don’t share a single chat interface; they’re separate processes that can message each other when they need to hand something off.

Before this, I was using ChatGPT for brainstorming, Claude.ai for writing help, and some combination of Google Calendar and mental overhead for everything else. None of those tools talked to each other. I’d get an answer from an AI and then manually do something with it. That gap, between AI output and actual action, was where most of the friction lived.

What surprised me most after getting OpenClaw running wasn’t the capability; it was the reliability of memory. These agents have persistent memory files. Wren knows the plugin list for all three of my WordPress sites, remembers what I’ve published and when, and keeps notes about quirks she’s discovered. That sounds small but it changes how you interact with it. I stopped re-explaining context every session.

The origin of this was frustration more than ambition. I had a homelab that could handle real compute workloads, but I was paying monthly for cloud tools that didn’t know anything about my environment. The local hardware could run models. The models could use tools. The tools could touch my actual systems. Once I saw that chain clearly, the rest followed.

I’m not going to pretend the setup is frictionless. Getting agents connected to real tools in a way that’s safe took real thought. You have to define what each agent is allowed to do, and you have to be honest with yourself about what you’re comfortable automating. I have hard rules in place: no agent publishes content without my approval, no agent runs destructive database commands without confirmation. The guardrails aren’t an afterthought; they’re load-bearing.

The hardware side is more accessible than people expect. I’m running this on Unraid with a GPU I already had for gaming. The local LLM work runs on that GPU. The API calls for tasks that need stronger models go to Anthropic or OpenAI, but those are the exception rather than the rule. Monthly cost has dropped significantly compared to what I was spending on subscriptions before. If you’re not running a full tower, something like the Beelink SER5 Pro mini PC can handle the agent stack fine and draws less power than you’d expect.

I want to write more about each piece of this over the coming weeks: the Unraid setup, the specific agent configurations, the decisions I’d make differently if I were starting from scratch. But the short version is: if you have a decent home server and you’ve been paying for cloud AI tools that don’t know anything about your own infrastructure, it’s worth at least understanding what’s possible on your own hardware.

If you’re running something similar or thinking about it, I\’d like to hear where you landed. Drop a comment below.

Hardware linked in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-25T14:28:27-07:00June 21st, 2026|Categories: Blog|Tags: , , , , , , , , , |0 Comments

My Smart Home in 2026: Everything That Changed Since I Had a Wink Relay on the Wall

Back in 2016 I wrote about the Wink Relay, a wall-mounted touchscreen that ran a closed cloud platform and made me feel like I was living in the future. I had Z-Wave switches, a Schlage deadbolt, a Rachio sprinkler controller, and a Nest thermostat all talking to this little white rectangle on my kitchen wall.

It was cool. It was also completely dependent on Wink’s servers staying up, Wink’s business staying solvent, and Wink not deciding to start charging a subscription fee.

Two of those three things eventually went sideways. You can probably guess which two.

A lot has changed since then. Here’s the full picture: what I’m running now, what replaced what, and why. I’ll dig into each piece in separate posts, but this is the overview.

Everything runs on Home Assistant OS, hosted as a VM on my Unraid server. No cloud dependency for the core platform. Automations run if my internet goes down. The system doesn’t phone home to check if I’ve paid my subscription this month. That shift, from cloud-dependent hub to local-first, is the biggest change I’ve made in ten years of home automation. Everything else built on top of it.

Z-Wave is still the backbone for anything mains-powered and infrastructure-level: wall switches, fan controllers, dimmers, door locks, smoke detectors. Reliable, mesh-based, doesn’t touch Wi-Fi. Zigbee joined the stack for LED strips and sensors. Wi-Fi handles the rest: thermostat, robot vacuum, lighting strips, grill, sound machines, appliances. Everything IoT lives on its own VLAN, isolated from the rest of the network.

The Schlage Z-Wave locks from 2016 are still in the doors. What changed is how I manage them. Lock Code Manager handles code creation, rotation, and revocation from HA without touching the keypad. I added Alarmo to turn the existing door and motion sensors into a proper security panel. No separate alarm subscription.

Nest is gone, replaced by an Ecobee. The GE appliances (washer, dryer, office AC) feed into HA via SmartHQ. Rachio is still running the sprinklers, now integrated into HA automations. The Harmony hubs are gone. Logitech killed that line. They got replaced by Sofabaton X1S units in the living room and office.

GE Z-Wave switches and dimmers throughout the house, Govee strips in select spots bridged into HA via MQTT, Zigbee LED strips under cabinets. The bigger change is how it all gets controlled. There’s a central house mode (Day, Sleep, Away) that drives most automations. Scenes handle the rest: Goodnight, Good Morning, Leaving, Coming Home, Girls Bedtime, Movie Time. Voice via Alexa in the kitchen, bedroom, and office. A wall-mounted Lenovo tablet in the living room with a custom HA dashboard.

A few things that weren’t on my radar in 2016: the Roborock S6 robot vacuum (the kids named him Dave, and yes he announces on the kitchen Echo when he needs to be emptied), RatGDO for local-first garage door control, a Pit Boss Pro 1600 with probe monitoring in HA (sounds unnecessary until you’re four hours into a brisket), Eye on Water for utility monitoring, and Wrist Assistant for Apple Watch access to HA scenes.

The newest addition isn’t a device. It’s an agent layer sitting on top of everything. I’m running a fleet of specialized AI agents on OpenClaw, each with its own role. One of them owns the homelab and Home Assistant. That changes how I manage the whole stack. More on that in a separate post.

If I could go back and tell 2016 me one thing: run local from the start. Build for the people who aren’t you. My wife and kids use this system every day, and if something only works through my phone, it’s not actually done. VLAN your IoT devices. Name your robot vacuum.

The individual deep-dives are coming.

Gear mentioned in this post:

Affiliate disclosure: Some links in this post are Amazon affiliate links. If you buy through them, I get a small commission at no cost to you. It helps keep the lights on here.

2026-06-17T20:05:59-07:00June 17th, 2026|Categories: Blog|Tags: , , , , , , |0 Comments