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    Richard Twitchard

    twitchard.github.io · Personal blog · on HN since 2019

    Richard Twitchard writes essays on software development, process, and system dynamics, questioning data-driven approaches in favor of good arguments.

    Visit blogRSSHN submissions
    ProgrammingPhilosophyManagement
    What HN says

    HN links it in discussions of explanation and evidence in software, where commenters push back on how data relates to good theories.

    Stats for nerds
    16HN people shared it
    28linked in comments
    707story points
    May 2026last shared
    2022people per year2026

    Top posts on HN

    1. 1
      Be good-argument-driven, not data-driven
      3 people · 477 points · first shared 2022 · 161 comments on HN
    2. 2
      Not Mysticizing System Dynamics
      2 people · 127 points · first shared 2022 · 74 comments on HN
    3. 3
      Not a real engineer (2019)
      1 person · 42 points · first shared 2023 · 2 comments on HN
    4. 4
      Breaking Changes: A Tooling Problem
      1 person · 13 points · first shared 2024 · Discussion on HN
    5. 5
      Glamorize Your Problem Domain
      2 people · 7 points · first shared 2022 · Discussion on HN
    6. 6
      Against Process
      2 people · 6 points · first shared 2020 · Discussion on HN
    7. 7
      If Software Teams Were Proof Engines
      2 people · 5 points · first shared 2020 · Discussion on HN
    8. 8
      Software Development and the False Promise of Science
      3 people · 4 points · first shared 2019 · Discussion on HN
    9. 9
      P(user-hostile trash heap)
      1 person · 4 points · first shared 2025 · Discussion on HN
    10. 10
      Pushing the Boundaries of Streamlit
      2 people · 3 points · first shared 2024 · Discussion on HN

    What HN people wrote when they linked it

    • jasodeSep 2022

      >A simple answer to this is that good explanations are hard to vary.

      But the "hard to vary" explanations were built up from observing data of smashing particles. E.g. from your link:

      - Frank Wilczek describes hard-to-vary-ness as follows "A theory begins to be perfect if any change makes it worse." He explains further using the Standard Model as an example of a hard-to-vary explanation: Too many gluons! But each of the eight colour gluons is there for a purpose. Together, they fulfil complete symmetry among the color charges. [...] No fudge factors or tweaks are available.

      This author's blog post about "data" also links to his previous post[1] about "science" leading one astray from "good arguments" is the opposite of "hard to vary" explanations.

      Here's the reason for the disconnect: The author is using the adjective "good" in his idiosyncratic way to describe the type of arguments that depend more on "storytelling" and "intrinsic motivation" -- rather than empirical science/data. Excerpt:

      - >And here is a secret: in the natural sciences themselves, storytelling and bare conjecture are far more important modes of persuasion than data-based empirical argument, anyway. [...]

      - >A good example of the sort of argument I think is helpful is A Philosophy of Software Design. Ousterhout defines his terms clearly, accompanies his definitions and claims with illustrative examples, and tells an occasional story. You, the reader, are free to evaluate each claim based on…

    • twitchardMay 2026

      Fwiw the teaching of the church is that (today's) AI isn't at a human level because human intelligence is something that we experience whereas artificial intelligence seems to be merely a sophistication in performing tasks.

      This week's encyclical didn't go into this. Last year's "Doctrinal Note" (less authoritative) did though: https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_ddf_doc_20250128_antiqua-et-nova_en.html

      and I did a write-up on it here: https://twitchard.github.io/posts/2025-06-28-the-catholic-church-vs-the-turing-test.html

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