About
Only what can be checked gets built.
Five convictions the work here runs on. They come out of numerical methods and simulation, where a result is worth little until someone can say when it stops holding.
Principles
A method is not finished when it produces one good result. It is finished when you know when it fails. And the interesting part is rarely the model itself, it is everything beside it: data quality, boundary conditions, interfaces, operations.
Both are missing from a lot of projects. A convincing demo gets built, it fails on edge cases in daily use, and afterwards nobody can explain why. The work here runs the other way round: a small start, real data, a measurable result and a clear statement of what the system cannot do.
Not everything is an AI problem. The foundation is ordinary, inspectable engineering. A model is added where it measurably contributes, and left out where a rule, a query or a well described process does the job better.
Working locally is a consequence of practice rather than a principle held in advance. Anyone handling production data or measurement data cannot simply send it to an outside interface. That very good open models now run on modest hardware you own turns that restriction into a solvable technical problem. Where the data of a given process allows it, the same system runs in your own cloud.
In the end the system should belong to you: documented code, decisions you can retrace, handover with training. Something only its builder can maintain is rented, not built.
On the name
Why Krylov?
Krylov subspace methods solve systems of equations too large to write down in full. They need to know only how the system acts on a single vector, and approach the answer step by step. That is how I work too: start small, check each step, stop once it is accurate enough.
Contact
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