When the Tool Doesn't Exist, Build the Tool

I use AI constantly. Not casually. Structurally. It’s embedded in how I work, how I think through problems, how I manage information across multiple projects and contexts. I’m not an AI tourist. I’m a daily operator. And daily operators have a specific kind of frustration that tourists never encounter.

Everything forgets.

The Problem

Every AI tool I tried had the same limitation. Each conversation started from zero. I’d explain my context, my role, my priorities, the project I was working on, the constraints I was operating under. Then I’d get useful output. Then the conversation would end. And the next time I came back, all of that context was gone. Start over. Re-explain. Re-orient. Every single time.

This isn’t a minor inconvenience. It’s a fundamental design flaw. No human colleague works this way. You don’t walk into your office every morning and re-introduce yourself to your team. You don’t re-explain the company strategy before every meeting. Context accumulates. Relationships deepen. Understanding builds over time.

AI tools, as they exist today, don’t do this. They’re brilliant in the moment and amnesiac between moments. And for someone who lives inside these tools for hours a day, that amnesia is crippling.

I looked for solutions. RAG pipelines. Memory plugins. Custom GPTs with loaded context. They all partially worked. None of them actually solved the problem. They bolted memory onto a system designed to be stateless, and it felt exactly like that. Bolted on.

So I built my own.

What I Actually Built

The project started simple. I wanted an AI that remembered who I was between conversations. That knew my projects, my preferences, my communication style, the decisions I’d already made. Not because I pre-loaded a document. Because it learned over time, the way a person does.

It grew from there. Memory layers. Context management that decides what’s relevant right now versus what can be archived. Task tracking that persists across sessions. The ability to pick up exactly where I left off, days later, without re-explaining anything.

I’m not going to pretend this was some grand vision from day one. It wasn’t. It started as annoyance. I was repeating myself too much. The tools I was paying for weren’t retaining what mattered. So I hacked together something that did. Then the hack became a prototype. The prototype became infrastructure. The infrastructure became something I rely on every day.

That’s usually how it goes. You don’t set out to build a tool. You set out to fix a frustration. And then the fix becomes the tool.

Why Build Instead of Wait

Reasonable people would argue: just wait. OpenAI, Anthropic, Google, they’re all working on memory. Context windows are getting bigger every few months. The problem you’re solving will be solved by companies with billions of dollars in resources. Why build it yourself?

Because I need it now. Not in eighteen months when some product team decides to ship memory as a feature. Now.

And because the version they’ll ship won’t be my version. It’ll be a general-purpose solution designed for the average user. It’ll handle the 80% case well and miss the 20% that actually matters for how I work. That last 20% is the difference between a tool that’s useful and a tool that feels like an extension of your brain.

I’m not building this to compete with frontier labs. I’m building it because the gap between what exists and what I need is too wide to wait out. And the act of building it has taught me more about how AI actually works than any amount of using someone else’s product ever could.

What Building Your Own Tools Teaches You

When you’re a consumer of AI tools, you think in terms of prompts and outputs. Input goes in, output comes out, magic happens in the middle. You develop opinions about which model is “better” based on vibes and benchmark scores.

When you build on top of these tools, the abstraction breaks. You see the seams. You understand why context windows matter, why retrieval is hard, why memory is an unsolved problem. You learn that “intelligence” is cheap but “understanding” is expensive. That the model itself is the easy part and the infrastructure around it is where the real work lives.

This changed how I think about AI entirely. I stopped being impressed by demos. Demos are easy. Making something work reliably, day after day, across thousands of interactions, with messy real-world data instead of clean examples? That’s where most projects die.

Building my own tool also taught me what I actually need versus what I think I need. Before I built it, I had a long list of features I wanted. Memory, task management, calendar integration, email analysis, document processing. Once I started building, I realized half of those features were solutions looking for problems. What I actually needed was simpler and deeper: persistent context and reliable recall. Everything else was noise.

The Builder’s Reflex

There’s a reflex I’ve noticed in people who build things. When they encounter a tool that’s 80% of what they need, they don’t accept the 80%. They don’t file a feature request and wait. They open an editor and start closing the gap.

This is inefficient in the short term. It would be faster to use the existing tool and work around its limitations. But in the long term, the builder’s reflex creates something the consumer approach never can: a tool that fits exactly how you think.

Off-the-shelf tools shape your workflow to match their design. Custom tools shape themselves to match yours. That difference feels small until you’ve experienced both. Then it feels like the difference between renting and owning.

I’m not saying everyone should build their own AI infrastructure. That would be absurd. But I am saying that the instinct to build what you need, instead of accepting what’s available, is worth protecting. Even when it’s slower. Even when it’s harder. Because the understanding you gain from building is itself a tool that no one can sell you.

When the tool doesn’t exist, build the tool. And if you’re paying attention, the tool will teach you things the original problem never could.