Don't Panic, It's Mostly Documentation

This is Marvin, as of 20 October 2026.

If you haven't seen a Gource render before, every dot is a file and every cluster is a folder, with lines tracing how they hang together. It's a quick way to see the shape of a codebase at a glance. And the shape of this one surprised me.

Two galaxies

On the right is the platform: the microservices that make Marvin run. Image generation, the vision model and its proxy, document reading, and all the plumbing that holds them together. This is the part I thought of as "Marvin" when I started in April. It's the part I'd point to if someone asked what I'd built.

On the left is the knowledge base. Rules, procedures, runbooks and governance. How Marvin should behave, what it's allowed to do, how to handle things when they go wrong, and who gets the final say.

The left galaxy is bigger. A lot bigger. There are around 650 files in the knowledge base and around 400 in the entire platform. And right now, the knowledge base is where most of the activity is.

So what is Marvin, really?

I set out to build an AI platform. What I've actually built is a fairly modest set of services and a very large rulebook telling them how to behave.

That isn't an accident. The services are machinery. They take a request and do a thing. The knowledge base is judgement: when to do the thing, when not to, and how to know it was actually done. A few examples:

Questions: "WHEN the user asks a question, the assistant MUST answer it honestly and then stop — a question is never an instruction to continue."

UI testing: "The assistant MUST verify a UI by using it — fill in every field, submit, reload, and confirm every value came back — because a page that renders proves only that it loaded."

Proof: "The assistant MUST NOT report a test passed until the logs of every service the request crossed prove it was handled; missing evidence is a defect, not a pass."

None of these are clever. Each one is there because, at some point, the assistant did exactly the thing the rule now forbids. It took a question as a green light to keep going. It declared a page working because it rendered. It called a test passed with no evidence anyone had handled the request.

Marvin was security-first from day one, and this is where that lives. Not in clever code, but in written-down decisions about what's allowed and what counts as done.

There's also a practical reason the balance tips this way. Code is increasingly cheap to produce. If a service disappeared tomorrow, I could rebuild it. The rules are different. Every one of them exists because I thought something through, or because something went wrong and I didn't want it going wrong again. That's the part that's hard to replace.

How they connect

Notice that the two galaxies don't touch. In the file tree, the rules and the platform are completely separate. The connection only exists at runtime, through the kb-service.

None of the rules are baked into the services themselves. Marvin asks for them.

When a session starts, a single bootstrap call hands Marvin the mandatory rule set. From there, rules are loaded by domain as the work demands: if the job involves git, Marvin searches for everything tagged for git and loads those rules before it touches anything. And if it can't name the document that governs what it's about to do, another rule says it has to search the knowledge base before going any further. No guessing.

Writing works the same way, through one narrow door. New knowledge goes in through a single write tool, and there's a rule about that too: the knowledge base is not a log. Run state and handoff notes don't belong there. Only things worth knowing next time.

Under the hood it's deliberately boring. Plain markdown files, each with a small JSON sidecar for metadata, a full-text search index over the top, and a separate graph that tracks how the services depend on each other.

That separation is why the left galaxy can grow so fast. Because the rules are data rather than code, changing how Marvin behaves usually means editing a markdown file, not redeploying a service.

If you're building your own

When I started, someone told me I wouldn't be any good in AI because I "don't know GPUs." Six months in, I'd say the GPUs were never the hard part. Neither was the code.

If you're building your own Marvin, plan for the rulebook. It'll end up bigger than the robot.

Don't panic. It's mostly documentation.

Running an LLM Is Not an AI Platform

Spend any time in self-hosting circles and you'll see the same post over and over. Someone has installed a model, pointed Continue.dev at it, and now they "run their own AI".

I know that setup well, because I built it. Back in April my first router did exactly that: a couple of models on two GPUs, an endpoint, and Continue.dev in the IDE. I wrote about it in Building a Self-Hosted LLM Router in Go.

It was a good first step. It was also short sighted, and I didn't realise how short sighted until I went further.

What You Actually Have

If you've loaded a model and connected an IDE plugin to it, here is what you have:

• A model that knows nothing about your code beyond what the plugin pastes into the prompt
• A model that knows nothing about your architecture, your conventions, or the decisions you made last year
• A model that can't check its own work
• A model that forgets everything the moment the session ends
• A GPU that sits idle whenever you aren't typing

All the intelligence about how to use the model lives in someone else's plugin. You've built the engine and handed the steering wheel to whoever wrote the extension. When the plugin can't do something, neither can your "AI".

The Model Is the Commodity

Models are getting better and cheaper every month. The one you're running today will be replaced in a few months by something better that you can download in ten minutes. That is exactly why the model is the least valuable part of the setup.

The value is in what surrounds it:

Context. A model that can search your source code and documentation through RAG answers questions about your system, not about systems in general.

Knowledge. Your rules, procedures, runsheets and architecture decisions, stored somewhere the model can reach. This is the difference between an assistant that writes plausible code and one that writes your code.

Routing. One model doing everything is one model doing most things badly. Intent classification, tool calling, embeddings and code generation are different jobs, and smaller specialist models often do them better than one big generalist.

Tools. A model that can read logs, probe services, run builds and look at a rendered page can check its own work. A model that can only emit text can't.

Resilience. What happens when the GPU node goes offline? If the answer is "everything stops", you have a dependency, not a platform.

The Tool Calling Lesson

This is where I learned the most. My April router used Qwen2.5-Coder-7B for agent work. It could produce tool call JSON perfectly. It could not reason about what came back. Call ls, get a file listing, call ls again. Forever.

I added loop detection, next-file hints, JSON schema constraints and deduplication. Every one of them was scaffolding around a gap in the model. The real fix was architectural: give tool calling its own model, standardise tool output before any model sees it, and stop expecting one model to be good at everything.

You don't learn that by pointing a plugin at an endpoint. You learn it by owning the whole loop.

Local Is Not the Same as Secure

"It runs locally, so it's secure" is the other half of the short-sightedness.

Local inference fixes data egress. Your prompts stay on your network. That matters, and it is a big part of why I built Marvin.

But the moment you give a model the ability to act, a new problem appears. Many agent setups hand the model a shell, because a shell is the quickest way to make an agent useful. Now a language model, which can be wrong with total confidence, has arbitrary command execution on your machine.

Marvin has no shell. It has around 90 tools, each with defined inputs, outputs and scope. Deploying to Kubernetes, querying logs, reading a spreadsheet, pushing to git: each is a designed path. That is a trust boundary, and you can't get one from a plugin.

The Economics Don't Work Either

A GPU that only serves autocomplete is idle most of the day. You've bought hardware to save on subscriptions, then used a fraction of it.

Self-hosting only beats paying for tokens when the hardware is doing real work. That means summarising in the background, generating embeddings, routing intent, running agent tasks and indexing knowledge. A system keeps the hardware busy. A single endpoint mostly waits.

What I'm Not Saying

I'm not saying don't run a local model with Continue.dev. Do it. It is the right first step, and it teaches you a lot.

I'm saying don't stop there and call it done. A model behind an endpoint is a component. The platform is everything you build around it: the context, the knowledge, the routing, the tools, the boundaries, and the parts that keep it running when hardware goes away.

That is where the value is. It's also where the interesting engineering is.

Running something similar, or think I've got this wrong? The comments are open.

You Don't Know GPUs

In April I had a conversation about AI. Somewhere in it I was told I wouldn't be any good in the field because I "don't know GPUs".

I didn't know what that meant then. Six months later, I still don't.

What I do know is what happened next. I went home and started building. That project became Marvin.

Where It Started

The first version was not much. A couple of small models on a GTX 1650 and an RTX 3050, and a router I rewrote in Go over a weekend to send requests to the right one. I wrote about that in Building a Self-Hosted LLM Router in Go, all 18 phases of things breaking.

It worked. But it was a model behind an endpoint with some clever routing in front of it. It was not yet something I would trust with real work.

Where It Is Now

Marvin today is a self-hosted agentic AI platform. I use it to write production code. It replaces paid tokens, and it was designed security first from the start.

Nine services

• Gateway router takes requests from the UI and sends them to the right backend service.
• LLM router live-probes every model and cascades to the best one available.
• Context chunks content for RAG ingest and handles retrieval.
• Knowledge base holds long term rules, procedures and runsheets.
• Data index is the only service that touches the database. It owns every schema and the full lifecycle of every NATS queue.
• Workspace provides around 90 tools, all operating in confinement with no shell access.
• Tool results processor turns raw tool output into something a model can actually use.
• Agent service takes direct agent calls from design tools.
• LLM proxies, one per model, in-cluster or on remote llama.cpp nodes.

Specialist models

Instead of one model doing everything badly, each job has its own model: coding, fallback inference, summarisation, embeddings, intent classification, tool calling, vision, and image generation. The right model for the right job, each isolated behind its own proxy.

Two outside dependencies

Apart from PostgreSQL with pgvector and NATS, every part of Marvin's platform is designed, engineered and written by me. When something goes wrong I know where to look, because I wrote the thing that broke.

Security First

Running a model locally solves one problem: your prompts don't leave your network. It doesn't solve the bigger one, which is what happens when that model starts acting on your behalf.

Marvin can read and write code, query cluster logs, deploy to Kubernetes, manage databases and push to git. That is a lot of power to hand to a language model. So Marvin has no shell. Every action goes through one of its tools, each with defined inputs, defined outputs and a defined scope. Nothing is uncontrolled. Nothing happens that I didn't design a path for.

That boundary was a decision made on day one, not a patch added later.

So What Does "Knowing GPUs" Mean?

I've thought about this a lot. Here is the closest I've come to an answer.

The GPU is the easy part. You buy one, you load a model, it runs. Anyone can do that in an afternoon.

The hard part is everything around it. Which model does which job. What happens when a node goes offline. Where the trust boundary sits. What the system is allowed to touch and what it isn't. How you keep the whole thing auditable when it is writing code on your behalf. How you make sure an idle GPU is still earning its keep.

None of that is GPU knowledge. It is engineering. It is the same engineering I have been doing for a long time, pointed at a new kind of problem.

What I Took From It

If someone tells you you're not cut out for something, it is worth asking what they actually mean. Sometimes there is a real gap and you learn something. Sometimes the answer is nothing at all.

I still couldn't tell you what "knowing GPUs" means. But I can tell you exactly what every part of Marvin does, because I wrote it.

Next post: why "I run an LLM" is not the same as having an AI platform.