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    Codemanship

    codemanship.wordpress.com · Personal blog · on HN since 2023

    Codemanship is Jason Gorman's blog on software development practice, with recent posts on AI coding tools and team performance.

    Visit blogRSSHN submissions
    AI & Machine LearningProgrammingManagement
    What HN says

    HN commenters call its AI-ready developer index series gold, noting that good software with AI follows the same principles as without it.

    Stats for nerds
    20HN people shared it
    30linked in comments
    951story points
    Mar 2026last shared

    Top posts on HN

    1. 1
      Comprehension debt: A ticking time bomb of LLM-generated code
      1 person · 532 points · first shared 2025 · 338 comments on HN
    2. 2
      The Gorman Paradox: Where Are All the AI-Generated Apps?
      1 person · 160 points · first shared 2025 · 221 comments on HN
    3. 3
      Things that have a bigger impact than coding assistants
      1 person · 110 points · first shared 2025 · 59 comments on HN
    4. 4
      Is Software the UFOlogy of Engineering Disciplines?
      1 person · 99 points · first shared 2025 · 177 comments on HN
    5. 5
      AI Software Development – What Does the Data Say?
      3 people · 7 points · first shared 2026 · Discussion on HN
    6. 6
      The Bluffers Guide to the Mythical Man Month
      1 person · 7 points · first shared 2023 · Discussion on HN
    7. 7
      The Great Filter, Why High Performance Still Eludes Most Dev Teams, Even with AI
      2 people · 4 points · first shared 2026 · Discussion on HN
    8. 8
      Coding Is When We're Least Productive
      2 people · 4 points · first shared 2026 · Discussion on HN
    9. 9
      The Gorman Paradox: An Explanation? – Codemanship's Blog
      2 people · 3 points · first shared 2025 · Discussion on HN
    10. 10
      Do You Know Where Your Load-Bearing Code Is?
      1 person · 3 points · first shared 2025 · Discussion on HN

    What HN people wrote when they linked it

    • nyrikkiMar 2026

      I think there is a bit of the map territory relation here.

      > First, it's not a question of decidability but of tractability

      The question of decidability is a form of many-to-one, reduction. In fact RE-complete is defined by many-to-one reductions.

      In a computational complexity sense, tractability is a far stronger notion. Basically an algorithm efficient if its time complexity at most PTIME for any size n input. A problem is "tractable" if there is an efficient algorithm that solves it.

      You are correct, if you limit your expressiveness to PTIME, where because P == co-P, PEM/tight apartness/Omniscience principles hold.

      But the problem is that Church–Rosser Property[0] (proofs ~= programs) and Brouwer–Heyting–Kolmogorov Interpretation[1] (Propositions at types) are NOT binary SAT, and you have concepts like mere propositions[3] that are very different than just BSAT.

      But CodeSpeak doesn't have formal specifications, so this is irrelevant. Their example code output produced code with path traversal/resource exhaustion risks and correctness issues and is an example.

      My personal opinion is that we will need to work within the limitations of the systems, and while it is trivial to come up with your own canary, I would recommend playing with [3] before the models directly target it.

      Generating new code from a changed spec will be less difficult, specifically when the mess of real world specs comes into play. You can play with the example on CodeSpeak's front page, trying…

    • wmeredithMar 2026

      Right here: https://codemanship.wordpress.com/2025/10/30/the-ai-ready-software-developer-index/

      This series of articles is gold.

      Unsurprisingly, writing good software with AI follows the same principles as writing it without AI. Keep scopes small. Ship, refactor, optimize, and write tests as you go.

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