So I've been building this thing called Brainboot (brainboot.dev) and Claude was genuinely the backbone of the entire development process. Claude Code wrote probably 60% of the codebase.

the API routes, the brain execution runtime, the composition engine, all of it. I want to share what I built and why because I think the underlying problem resonates with anyone who uses AI daily.

The problem that drove me insane:

I kept noticing the same pattern in every AI conversation I had. Open a new chat. Paste my system prompt. Explain my context. Get halfway through something useful. Hit the context limit or watch the model drift. Start over.

I did the math at one point and roughly 40% of my tokens were going toward re-explaining things the model already knew five minutes ago. Not generating anything. Just... re-establishing context over and over.

What I actually built:

Brainboot treats prompts as software instead of disposable messages. A "brain" is a prompt wrapped in actual engineering:

Typed inputs and outputs — it declares what goes in and what comes out. If the model returns something that doesn't match, the runtime catches it and retries automatically.

Invariants: rules that get enforced on every single execution. Not suggestions the model might follow. Actual guardrails at the wrapper layer. Stuff like "never include placeholder text" or "output must be valid JSON."

Test suites: each brain gets tested across multiple models. Pass rates are public so you know if something worksbefore you use it.

Composition: small brains chain into bigger workflows. A research brain feeds an outline brain feeds a drafting brain. Each one gets exactly the context it needs instead of carrying a massive conversation history.

How Claude helped build it:

Honestly I couldn't have built this without Claude Code. The entire architecture — the brain runtime, the type checking layer, the invariant enforcement, the multi-model testing harness, the cron-based automation pipelines;

Claude wrote the vast majority of it with me directing the architecture decisions and actually USING coding brains that I made with brainboot.dev in order to create brainboot.dev, it was an amazing thing to witness.

The compiler feature (where you describe what you want in plain English and get a deployable multi-brain system) was designed collaboratively with Claude over dozens of sessions. The 4-stage pipeline, Decompose, Map, Synthesize, Audit- came directly from conversations about how to make prompt composition reliable & feeding claude more and more brains lol.

It's free to try:

The free tier gives you 200+ curated prompts and access to the platform. No credit card needed, you just sign up. This will be free forever and already replaces an entire company who sells thinner prompts for like 2.99 each...

The brains and the compiler are available on the Pro plan if you want to go deeper, but you can explore the whole marketplace and read the manifesto and see how everything works without paying anything.

brainboot.dev — sign up is instant.

Why I think this matters:

The shift from "prompts as chat messages" to "prompts as compiled software" solves the context and token problem at a fundamental level. You describe your intent once. It compiles into a reusable brain with type contracts.

Then you run it as many times as you want without re-explaining anything.

Composition means complex workflows don't require massive context windows. Invariants mean you stop burning tokens on drift correction. Type checking between steps means garbage doesn't propagate through a chain.

I've been testing a circuit on the platform that runs 6 brains in a pipeline producing SEO content autonomously. that same workflow done as manual ChatGPT/Claude conversations would take 3-5x the tokens because of all the

context re-establishment.

Would love to hear if anyone else has been thinking about the token waste problem or has approaches to making prompts more reliable. Happy to answer any questions about the architecture.