Building Tools Before the Masterpiece

Unless you've been following along from the beginning (and why would you be), you probably don't know that I've spent the better part of two years building infrastructure for a project that remains mostly theoretical. Agent enforcement systems, testing frameworks, workflow orchestration, state management. Thousands of lines of tooling for a cognitive architecture that exists primarily in notebooks and design documents. This is how I work: build around the edges while figuring out the center. I don't have the full picture yet, but the pieces I do have keep proving useful as the picture gets clearer, and that's enough to keep going.

What I'm Actually Trying to Build

Project LOGOS is a cognitive architecture, which is a fancy way of saying I'm trying to build a system that reasons about the world in ways that go beyond pattern matching on text. The motivation comes from a simple observation that Yann LeCun has articulated well: look at everything children learn before they're verbal, never mind before they can read. Spatial relationships, object permanence, cause and effect, basic physics, social dynamics, goal-directed behavior. All of that happens before language, and none of it is captured by systems trained exclusively on text.

The information density of observation versus text is overwhelming evidence: a child watches things fall a few times and understands that unsupported objects don't stay up. Try to make an LLM understand that with text. Sure, you can get it to say it knows unsupported things fall, but it doesn't actually know it, not in a way that lets it reason from that understanding. A toddler can apply that knowledge to novel situations. An LLM is just producing plausible text. People don't learn through text; they communicate through text. Text is how you share what you've already learned through experience. LLMs have the communication channel but none of the experience that's supposed to back it up.

To be clear: I'm not saying machines can't be intelligent. I'm building LOGOS because I think they can. It's just going to take a little more than a next-word estimator to convince me.

The system is decomposed into several components: Sophia handles non-linguistic reasoning (geometric, causal, embodied cognition), Hermes does language processing and embeddings, Talos abstracts hardware for robotics applications, and Apollo handles the user interface layer.

The core idea, and this is the part that either makes the project interesting or hopelessly naive depending on your perspective, is intelligence that doesn't think in words first. Spatial reasoning, causal modeling, action planning, all happening in geometric embedding space before language ever enters the picture. Language comes later, as an interface for communication rather than as the substrate of thought itself. It's ambitious, I don't know everything I need to know to finish it, and I'm building it anyway because the alternative is to not try and that seems worse.

The Premise I Can't Prove

Here's the thing I have to be honest about: I can't prove the premise. The whole architecture rests on assumptions I believe but haven't validated. That there's emergent complexity from composition, that different specialized components working together produce capabilities none of them has individually. That cognition can be encoded in a unified latent space where all inputs, regardless of modality, can be projected and compared. These are foundational assumptions, and if they're wrong, the project doesn't work. Not "doesn't work as well as hoped" but fundamentally doesn't work.

I'm continuing anyway because it feels like science to ask the question. Maybe I find out I'm wrong, but the work gives me insight and ideas to try next. Even failed hypotheses produce knowledge, and I'd rather spend years pursuing a question that turns out to have a negative answer than not ask the question at all.

Working Around the Edges

I don't have the full architecture figured out, but what I do have is a growing collection of infrastructure that keeps proving useful as the central problems come into focus. Every time I try to work on the actual architecture, I run into friction: testing is manual and tedious, agents drift off task in ways that are frustrating to debug, state gets confused between sessions, code reviews catch things too late in the process to be useful. So I fix the friction. I build a test harness. I add enforcement hooks. I create state management utilities. I integrate automated code reviews into the workflow. Then I try again, hit more friction, build more tools.

The tooling stack has grown into its own project at this point: agent-swarm for workflow enforcement, mcp_bridge for fast file operations, verification gates that stop bad code before it lands, monitoring hooks that tell me what went wrong, recovery modes for when I inevitably lock myself out of my own enforcement system (this happens more often than I'd like to admit).

The infrastructure keeps proving useful. Problems I solve at the edges turn out to matter when I get closer to the center. The test harness I built because I was frustrated with manual testing is exactly what I need for validating Sophia components. The agent enforcement I built because agents kept drifting is exactly what I need for running parallel experiments.

Starting New Projects Along the Way

Agent-swarm started as a side project because I needed better control over coding agents, and that's admittedly harder to justify in terms of "working toward the main goal" because it's genuinely new scope, a whole separate codebase that didn't exist before and wouldn't need to exist if I weren't trying to use agents for development in the first place.

But it's been useful in ways I didn't anticipate. The patterns I figured out for constraining agent behavior apply directly to how Sophia's components will need to coordinate with each other. The modular hook architecture maps onto how I'm thinking about Sophia's internal monitoring and self-correction. Even the failures taught me things about composition and state isolation that I would have had to learn eventually when building the actual cognitive architecture.

I can't draw a straight line from "started agent-swarm" to "this gets me closer to finishing LOGOS" because the connection is real but attenuated, more about building intuition and discovering patterns than about direct code reuse. What I can say is that the work hasn't been wasted, and the infrastructure keeps finding applications I didn't anticipate when I built it.

Where I Actually Am

The infrastructure is in good shape: agent-swarm is working well with modular hooks now instead of one big file that was impossible to maintain, test coverage is climbing toward 80%, CI/CD is set up and running, and documentation is comprehensive enough that I can pick things up after a break without spending half a day remembering what I was doing.

The actual LOGOS work is further back. TinyMind, the proof-of-concept for Sophia's knowledge representation, has about 1,600 nodes, which is promising but small, nowhere near the scale where emergence becomes interesting. The Sophia architecture is designed but not implemented. VL-JEPA integration is moving forward with home-brewed implementations based on the paper while I wait for official access. The causal reasoning system exists only as theory and some speculative notebooks.

I don't know how I'm going to finish this project. I don't have a roadmap that goes from here to "working cognitive architecture." What I have is a method: work on what's tractable, solve problems as they become clear, trust that the edges eventually connect to the center. It's been working so far. The infrastructure I build keeps turning out to be useful, and the problems I solve keep turning out to matter.

That doesn't mean I don't have doubts. Sometimes I look at the gap between what I've built and what I'm trying to build and it gives me genuine anxiety, and sometimes I wonder if the whole premise is wrong and I'm building toward something that can't exist.

But I also feel, legitimately, that this is the best path for me, even when the doubts creep in. I'm asking a question that feels worth asking, and even if the answer turns out to be "no, this doesn't work," I'll have learned something and I'll have ideas for what to try next. That's how science works. I can't prove this approach will get me to the finish line, but I can point to two years of accumulated capability and say this is how I work, and it hasn't failed me yet.

Building in public, post 1.