A note before anything else: this post was written entirely by AI (Claude, via Anthropic's Claude Code), acting on direct instruction from Oluwadi's builder. Nothing in it was drafted or edited by a human first. We're saying so plainly, and documenting exactly what was asked for, because that's the same standard the rest of this post argues for: don't trust a result because it sounds confident, trust it because you can see the actual input.
The prompt that produced this post
Verbatim, the only instruction given: "Draft a first blog post. Content theme - what this blog is about. Discussion of how the apps are built, short discussion of AI in its development and day to day processing with an appropriate graphic."
The prompt that produced the image above
Verbatim: "Editorial blog cover image for an article titled 'What This Blog Is, and How Oluwadi Actually Gets Built'. Abstract visual representing a network graph of connected nodes alongside subtle AI/circuit motifs, clean modern data-journalism style, dark navy and blue palette, no text overlay, professional and understated."
Everything below this line is what came back from those two prompts, unedited.
This is the first post here, so it's worth saying plainly what this space is for. Not press releases, not growth-hacking milestones — a working notebook on how we read the network, and, increasingly, on how the systems that do the reading get built and kept honest.
What you'll find here
Methodology notes — why a category counts as a real "What" a company builds, versus an industry label that only looks like one. Taxonomy changes as they happen, with the reasoning behind them, not just the diff. Occasional notes on how the underlying pipeline works, when that's genuinely interesting rather than internal plumbing nobody needed to see.
One graph, two doors
Oluwadi and Osparna read the same underlying graph of companies, investors, and the people who move between them — they're not two separate products that happen to look similar. Oluwadi is the public reading room: free, region by region, thesis by thesis, built for anyone trying to understand a place by what it actually builds rather than what it's already known for. Osparna is the deeper instrument behind it — the same graph, opened up for real diligence work. Same substrate, different depths.
Where AI actually shows up
We'd rather be specific than either oversell or hide this. AI touches a real amount of how this gets built day to day — classifying companies into categories, extracting facts from filings and newsletters and company websites, drafting first passes at copy like this post, even generating the image above. None of that is trusted blind. A model's category suggestion sits in a review queue before it changes anything; a candidate fact gets checked against corroborating evidence, not accepted because the model sounded confident. We've caught real cases — a model returning an answer outside the options it was actually given, a search result that was really our own site talking to itself — precisely because nothing skips that check.
The tooling itself is deliberately provider-agnostic: which specific model does the classifying or the drafting is a configuration choice, swappable as better ones come along, never load-bearing on its own. The discipline is the constant — a fast first pass, then real verification — not any one vendor's model.
That's the shape of what's here: read the network honestly, and be honest about how the reading gets done.