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Articole: 14
  1. A Year of Linux, a Month of Omarchy Quattro—Part 3

    This is my Part 3 about Omarchy, after using it for more than a year full time, as my main daily driver first on Lenovo ThinkBook 14 AMD, and now TUXEDO InfinityBook Pro 14 Gen10, for my business and personal use, replacing my MacBook Pro M1 after using m…

  2. I’ve been using a personal second brain for many years.

  3. The Grammar of Data: From Definition to Execution

    In Part 1, we discovered the grammar for data: a way to define a data project with its complex requirements and how we define it declaratively as a grammar in one sentence with nouns (sources), transformations (verbs), templates, and modifiers, essentially

  4. There’s so much talk about new ways of working with agent engineering supported workflows.

  5. I had the pleasure to discuss and analyze the workflow of none other than Benn Stancil, legendary writer and storyteller in the data space and beyond, who started writing back in 2013 (with a seven-or-eight-year startup break in between).

  6. Writing really is an Emotional Rollercoaster

    [[Writing]] has become my passion over time; I have built a whole company based on technical writing services (even though most say that’s impossible).

  7. It’s hard to keep up with the AI evolution; new AI tools drop every week, but how are experienced practitioners actually using them?

  8. Book Recommendations and Notes

    These are my recently read books and some comments and notes from when I read them.

  9. The Act and the Outcome of Creation

    Creation is the ultimate form of pursuing ourselves, giving to the world when shared, and using the power of our subconscious.

  10. Grammars for languages or any other field are a beautiful thing.

  11. With ever-changing models, new and better ones coming out every few months, it’s great if we don’t have to rely on them too heavily.

  12. The Process of Smart Note-Taking

    I’ve taken notes over my whole life.

  13. Operating the data orchestration layer can be as critical as the source OLTP databases, when decisions are taken from the data assets produced by downstream data pipelines.

  14. This series interviews real practitioners to extract the patterns behind how they actually use AI in their data work today.