A flavorings company filed under "AI Application" led to a full audit of Oluwadi's own 11,839-company database -- corporate research labs mistaken for government facilities, tribal governments read as advocacy nonprofits, entire universities crushed into one department, and an honest account of what still isn't fixed.

Contracts don't exist to describe the deal working. They exist to allocate what happens when it doesn't -- why our diligence reads termination, indemnification, and liability clauses first, not last.

The dominant narrative about AI and developers is replacement. That's not the only real experience out there. A 40-year developer's own account of shipping three real, working apps in three months -- and why the actual mechanism wasn't speed for its own sake, it was iteration.

BLM, EIA, and FRED are all real federal data sources, integrated the same afternoon -- a silently-null field, a row that splits five ways without warning, and one that just worked. None of it was documented anywhere. All of it required checking, not trusting.

Reports explain the now. Trends explain the path. Analysis explains why. Three sections, three different questions -- with a real example from each.

41 built Analysis pieces, 4 blog posts. A short tour of nine of them, grouped by what they actually argue -- place, history, and markets.

Four companies, one new industrial-policy tool -- and private capital did not move in a consistent order relative to the government stakes.

A Nobel laureate said it couldn't be done. Forty-four years later it's a real, traceable input into how fast the 2020 labor market recovered.

Four empires, one measurable cycle, three thousand years -- and every hinge was still one person's decision. Both halves matter for timing.

Geography, government, and culture compete to explain why an industry clusters in one place. Two real cases show the weighting is never the same twice.

Job scarcity, not opportunity, is now the majority reason Americans start a business -- and the share is still rising, not holding steady.

Two real datasets on founder age point in opposite directions -- and knowing which one you're looking at changes how you should read a pitch.

We found a single $65 billion round logged four times in our own data. Why that's not just a bug -- and what it says about signal vs. noise in venture capital.

Venture capital wasn't always an industry. Two 1978 policy decisions created it -- the boom-bust pattern from then still shapes where capital moves today.

Ayni's shape will keep changing as we collect more data and understand what we've already collected. That's not a bug to apologize away -- it's an invitation: what connections do you see that we don't, and what would you like to know?

Alaska and Hawaii both report into the San Francisco Fed district — the same district that already dominates every national reading. Their own sentiment is invisible by construction, and so is almost everything that makes either place what it is.

A Fed district's sentiment reading looks like a bare data point — expanding, mixed, contracting. Underneath the label is population, money, and formation history that the word alone never shows.

Osparna evaluates a deal. Oluwadi maps the network the deal sits inside. Ayni is the outer ring — what a place is, apart from whether it produces fundable startups at all. DoAyni seeks to put the U.S. in context. Starting with companies, expanding to government and civic society.

Why this exists, how Oluwadi and Osparna share one graph behind two different doors, and an honest account of where AI does and doesn't get trusted in building and running both.

Oluwadi no longer requires an account, and anyone can propose a company, investor, university, or organization for inclusion.

Oluwadi is live: a public, continuously updated way to read the U.S. venture network region by region and thesis by thesis. Why a region's single label hides more than it tells -- and what it means to read a place for what it actually builds.

Steve Yegge stopped fighting his coding agent's 'hallucinations' and started building the commands it kept guessing at instead. That's just Convention over Configuration for the LLM age -- and it changes what "being right" as a developer means.

How writing a genuinely detailed spec -- and following a Research/Plan/Implement discipline with Claude Code -- took a demo from a month down to five hours, and why "Vibe Coding" and "AI Slop" are really just names for skipping that step.

The battle-tested framework we use ourselves to pressure-test a deal across eight domains -- team, product, financials, business model, traction, competition, and legal -- before the check gets wired.

A field guide to technology due diligence, sharpened on the deals we've won and the ones we wisely walked away from -- architecture, security, infrastructure, IP, team, and resilience, down to the exact artifacts you should ask to see.

AI adoption is in its honeymoon phase -- and some users are already forming emotional attachments to their chatbots. What that says about the MIT study on failed AI pilots, and what it actually means for how investors should be using AI.

Fully remote jobs going back to the office doesn't correlate with productivity -- so why are large corporations doing it anyway? A look at what return-to-office is actually buying, and at what cost.

Osparna's team builds things outside of work too -- and that's not incidental. It's exactly what makes the diligence sharper. I work with people I like.

Coachability, character, and the ability to work well in a distributed environment now matter more than raw technical expertise -- what COVID-era diligence is teaching investors about what to actually look for in a team.

Why "Moneyball for Investing" became our catchphrase, why it's the wrong analogy, and what Osparna actually is: the diary you keep to track what you were thinking when you made the call.

Before Osparna focused on investors, the alpha version was general enough to score any decision -- including which house to buy. Here's how establish-weight-rate-review played out on a real move to Florida.

Biases underpin our intuition about investing, whether we admit it or not. Why declaring your criteria explicitly -- and being able to look back at what you decided and why -- is the real defense against them.

The first post on osparna.com: why we built a platform to bring decision science to investing, and what to expect from this space going forward.