fak10
“Computer, do it.”
As a teenager I had a lot of time to think, and I liked using it. I could lose myself in a train of thought for hours. On top of that came a fascination with technology, with computers, and with anything where one action sets off the next. A marble run where one ball nudges something that triggers something else: I could watch that forever.
At my business-focused high school something new was added. I learned how rationally people act to meet their needs. And the longer I looked, the clearer it became: this can be modelled. If you know a person’s needs, a computer can work out what someone actually wants. And then you would no longer have to wrestle with the machine the way we all did back then.
I was a science-fiction fan. I wanted the computer from the films, the one you just say “Computer, …” to, and it takes care of it. Not only out of convenience: I consider laziness one of the fundamental principles of the universe. It makes sure the most efficient solution wins.
Excel, Java, a thesis
I couldn’t program yet. So I used what I had: Excel. I started at sixteen, in 1996. The oldest version I still have is from 1999, a file called An@lyse.xls. One sheet held my needs as a tree, each with a value (how well it is met right now) and a priority (how much it matters to me). A cell comment holds the sentence everything revolved around:
“Actions should be chosen so that the highest possible level of satisfaction can be expected in the short, medium and long term.”
And elsewhere, already back then, the limit of the whole idea:
“While Mozart was alive, a war broke out ⇒ two true statements that extensional logic cannot capture.”
A life doesn’t fit into a spreadsheet, because things are true at a time, under a condition, from a point of view.
In the summer of 2009 came the second attempt, now in Java. The project was called Annalyse, and for the first time it contained a class named Anna. She was meant to understand German (and Polish), with a grammar of case, gender, tense and mood, a word net, and a reasoner drawing conclusions from what she understood. Her goal, as the description still says: take a sentence apart and work out what job each word does in it. She had carried the name for a long time by then: she has been Anna since the spreadsheet, hidden in An@lyse. And since I knew a few Annas, the name came naturally. So Anna is thirty years old.
At the time I was studying business informatics in Siegen, and there I met what dominated artificial intelligence back then: ontologies and reasoning, knowledge as a web of statements from which a program draws conclusions by fixed rules. My diploma thesis (2010) designed a method to compare complex data for similarity, by content and by structure. I opened it with a quote by Fritz Mauthner:
“All classifying, all language, is built on similarity, not on sameness.”
fak10 still wrestles with exactly this: when are two entries the same person, and when are they merely alike?
In 2017 I brought Anna back: your-life, a web app with Anna as a module of her own. She got intents, statements and sentence analysis, hooks for Alexa, Siri and Messenger, and a collection of quotes from Marvin, the depressed robot. After two months, work needed me again.
The fourth attempt, and why it holds this time
In 2021 I tried again, and this time the project was called fak10. I had a few months, then work needed all of me again. Anyone with a passion project next to a day job knows the feeling.
What has changed since is, of all things, AI. With its help the work has become so much easier that I can finally finish the original approach. I’m using the big AI to build the small one I always wanted to build.
Why not just use the big AI?
Because I still see the advantage of the other path.
Large language models are impressive, but they guess. They sound certain even when they aren’t, and you can’t see where an answer comes from. They forget, or they remember everything on a server you don’t know. And every answer needs a data centre.
That’s why fak10 has a companion, Anna, who works differently: deterministically. She runs on the iPhone, offline. She understands language through a grammar, rule by rule: part of speech, case, tense, negation, in German and in English. From a sentence she makes a statement with a source and a date, and you can trace why she decided the way she did. She doesn’t guess. When she doesn’t understand something, she asks. And as long as such an approach does the job, it uses far less energy than a language model.
Anna is still learning. As of late September 2026, she understands “Jonas ist mit Miriam verheiratet.” (“Jonas is married to Miriam.”), even negated or in the past tense. She still fails at “Miriam gibt Clara ein Buch.” (“Miriam gives Clara a book.”): who gives what to whom, three parties in one sentence, is something she can’t put together yet. Instead of guessing, she asks.
What fak10 is meant to be
Above everything stands a line I once wrote down: experience over outcome. It’s not about optimising a life like a metric, but about choosing good experiences, weighing things wisely and being able to handle obstacles.
For that, fak10 is the diary of your time. One that thinks along:
- It keeps what makes up your life: people, contracts, documents, needs.
- It reminds you of what never made it into a calendar: the insurance policy that runs out in four weeks; the need you haven’t looked after in a long time.
- It helps you act, for example by filling in a form from what it already knows about you. “Computer, do it.”
And it belongs to you. No account, no cloud for your data. If a document does need the big AI, you call it in, and you see what leaves your phone.
Why I’m telling this in public
Because a project that started in Excel at sixteen deserves a few readers. From now on I’ll write here regularly: how Anna learns language, why word order in German lies, what separates a need from a goal, and what goes wrong.
If you’d like to be there when fak10 comes out: Join the waitlist
— Maciek