Ten Years of Tabs…

For more than ten years, my mornings started the same way. A charting tab. A fundamentals tab. A screener I paid for and used twice a week. A funding-rate dashboard that had been running all night, because crypto does not wait for a bell. Somewhere in the stack, a news feed I never entirely trusted.

Every one of those tabs was good at its job. Not one of them knew the others existed.

The reading was never the slow part. The slow part came after: holding a technical read next to a fundamental one, next to what the derivatives market was quietly pricing, and working out what their disagreement actually meant. Hours on a normal day to arrive at one honest sentence about one asset. On a day when something moved, considerably more.

I did that for a decade. I got faster at it. I never got to like it.

It took me an embarrassingly long time to name the problem properly. For years I assumed I needed better inputs, and I kept buying them.

I was wrong, and I can now show how wrong. I mapped all 121 analyses AstraEye runs today against wherever else a person could get the same thing. Eighty-one of them are available free, or inside an ordinary retail subscription. Relative strength, funding rates, token unlocks, insider filings, liquidation maps, protocol revenue. None of it was behind a wall.

It was behind twelve logins, in twelve formats, on twelve refresh schedules, with nothing in the world willing to put any two of them in the same sentence.

The information was never scarce. The reconciliation was.

Priced honestly, the lean version of that stack comes to roughly $429 a month across twelve products. The version that actually matches the depth runs closer to $1,561 across fifteen. And neither of them reconciles anything. You still do that part yourself. Every morning. For years.

What changed was not that AI developed opinions about markets. I would not have wanted that, and I would not have trusted it.

What changed is that models became genuinely competent at the unglamorous work: deriving a figure from a pile of numbers, compacting a hundred pages into a paragraph that survives checking, and saying plainly where the numbers disagree with each other. In seconds.

That is exactly the work that had been eating my mornings, and it is the only work I ever wanted to hand over. The judgement stayed mine. It still does, and it stays yours. AstraEye describes what it finds and shows how it got there. It does not tell you what to do about it, and it never did, because that was never the part that was missing.

I did a lot of this thinking at night, which may explain the shape it took. But the shape turned out to be structural rather than decorative, and it is now how the product is actually built.

A sky needs someone to read it, and no single reader is good at all of it. So I gave each field its own scientist, with its own expertise and its own room, permanently on call and waiting for research orders.

The workrooms are not a metaphor. Each scientist is a separate service with its own database and its own deployment, so one of them having a bad day cannot take the others down with it.

That part was not romantic to build. It is queues and retries and circuit breakers and refunds that fire correctly when a step is skipped. It is most of the work, and it is the difference between a demo that runs one analysis and a lab that runs them all day without losing your credits or your results.

The thing that made me most uncomfortable about every tool I had paid for was how rarely any of them would tell me how a number was reached. A score appears. It is between 0 and 100. You are invited to believe it.

So every AstraEye analysis carries a methodology section that names the public theory it rests on: Wilder’s RSI, an event study, a Gini coefficient, a Nakamoto coefficient. Things a student can carry into any textbook and check. And where our implementation is a scoring choice rather than a derivation, it says so outright, instead of inventing a formula to sound more rigorous than it is.

Then I added the part I assumed nobody would care about. At the end of a research run, SAGE writes you a test. Multiple choice, drawn from what that run actually produced, about the assets it covered.

I built it because I wanted to know whether I had genuinely understood a report or had simply agreed with it, which are not the same thing and do not feel different from the inside. When I later went looking for a competitor, I could not find one. Plenty of platforms teach investing in the abstract. Nothing I could find generates a graded test from the research it just produced for you.

Of everything in the catalog, the quiz is the piece I could not find an alternative to.

The last thing I wanted was to replace twelve platforms with one nicer one. So the research does not have to stay here. The AstraEye Research API hands you your own projects and report summaries, so you can pull them into whatever you already use: your spreadsheet, your notebook, your own tooling, your own models.

It is your research. It should meet you where you work.