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Jeremy Jordan

Personal blog · jeremyjordan.meaiprogramming

Jeremy Jordan's personal blog at jeremyjordan.me covers ai and programming. Since 2019, 5 different Hacker News readers have linked it 5 times, most often “Effective testing for machine learning systems”. It is still writing: the last post was on 2026-06-17, and you can follow it by feed.

Visit the blog
5mentions?
5HN people?
2019–2024first – last linked?
Links on Hacker News per year · peak 2 in 2020

Too few sampled comments (2) to say how often HN recommends it.?

Why HN links it

HN readers link Jeremy Jordan's blog for posts on training neural networks and on why software engineering processes and tools fit machine learning poorly. The comments here are mostly a bare link and one reaction.

Summarised by Claude from a sample of 2 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.

Thoughts, ideas, and new things I've learned.

Most-linked posts?

  1. Effective testing for machine learning systems
    2 people · 2 mentions · first linked 2020-08-31

What Hacker News says?

  1. https://www.jeremyjordan.me/neural-networks-training/

    Comment linking /neural-networks-training/

    on “Fuzzy Metaballs: Approximate differentiable rendering with algebraic surfaces”
  2. Submitted Effective testing for machine learning systems as a story · 95 points

    HN discussion
  3. It's sad to see such "to hell with it" naivete (and the comments here with broad supporting evidence that data scientists are poor software developers. There are also a lot of DS people wrestling with the deeper challenges at the heart of MLOps and beyond just advocating sane team-based workflows, are looking at ways to harden and structure ML specific processes in the planning, design, build, and deploy phases. Sea…

    Comment linking /testing-ml/

    on “Why software engineering processes and tools don’t work for machine learning”
  4. Submitted An Introduction to Kubernetes as a story · 6 points

    HN discussion
  5. Submitted A simple solution for monitoring ML systems as a story · 1 points

    HN discussion

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