Hello World: Why Sushi & Syntax?
What this blog is about, and why raw fish and clean code belong together.
// notes from the counter
AI and data science,
raw and well prepared.
I'm Daniel. By day I work in AI and automation. Here I write about the models, pipelines and papers I'm chewing on, and turn the ones that stick into short, hands-on lessons.
Where I would start with AI today
Uni colleagues keep asking me where to begin with AI. Here is the honest answer, in five steps, none of which is a course.
ai-diaryFrontier models got cheap, open weights got good
Three AI labs shipped new models within a day of each other, prices dropped, and an idea I had shelved as too expensive suddenly cost less than lunch.
ai-diaryHow I know my AI thing actually works
Forty real questions in a plain text file taught me more than every benchmark I read this year.
ai-diaryRAG is not dead, it grew a brain
Agentic retrieval in practice: I let the model decide when and where to search, deleted half of my old pipeline, and finally understood which half mattered.
ai-diaryThe month everything became an agent
April 2026: every major lab shipped agent products in the same four weeks, an API I depended on got deprecated, and MCP quietly won.
ai-diaryMy RAG pipeline lied to me (three times)
I rebuilt my document assistant properly this time. It still gave me confident wrong answers, and all three lies had the same cause.
ai-diaryTwelve models in one month, one rule for staying sane
February 2026 shipped a dozen serious models. I tried to evaluate all of them, failed, and found a better rule.
ai-diaryThe papers that actually helped (and the ones I bounced off)
A reading path into modern AI for people who did not do a PhD: what to read, in which order, and what to skip on the first pass.
ai-diaryStarting over in the loudest month ever
I have been learning AI since 2019, but I got serious about the modern stack in the exact 25 days when four frontier models dropped. Bad timing, good decision.
ai-diaryscroll sideways · plates keep coming