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Amid Fish

Personal blog · amid.fishaiprogramming

Amid Fish is a personal blog at amid.fish about ai and programming. Since 2015, 6 different Hacker News readers have linked it 13 times, most often “Lessons Learned Reproducing a Deep Reinforcement Learning Paper”. The last post was in 2021, but the archive is still widely read.

Visit the blog
13mentions?
6HN people?
2015–2023first – last linked?
Links on Hacker News per year · peak 5 in 2018

Too few sampled comments (3) to say how often HN recommends it.?

Why HN links it

HN readers link amid.fish for a write-up of reproducing a deep reinforcement learning paper, a Karplus-Strong reference, and the Fretastic guitar fretboard visualizer. Comments are questions and suggestions, not judgements of the writing.

Summarised by Claude from a sample of 3 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.

Most-linked posts?

  1. Lessons Learned Reproducing a Deep Reinforcement Learning Paper
    3 people · 3 mentions · first linked 2018-04-09
  2. The Humble Gumbel Distribution
    2 people · 2 mentions · first linked 2018-02-18

What Hacker News says?

  1. What is the tool you are using to write your 'logs'? (pictured in this image http://amid.fish/images/rl_logs.jpg )

    Comment linking /images/rl_logs.jpg

    on “Lessons Learned Reproducing a Deep Reinforcement Learning Paper”
  2. Submitted Lessons Learned Reproducing a Deep Reinforcement Learning Paper as a story · 230 points

    HN discussion
  3. I noticed that the strings do not actually sound close to a natural guitar. For the next set of enhancements perhaps you could incorporate Karplus-Strong[1] in your audio generation routine. [1]: http://amid.fish/karplus-strong

    Comment linking /karplus-strong

    on “Fretastic: Guitar fretboard visualizer – scales, intervals, backing tracks”
  4. Submitted Show HN: Karplus-Strong Guitar Synthesizer in JavaScript as a story · 171 points

    HN discussion
  5. A guide for astronauts (now, people doing machine learning) about what to do when things go wrong. GitHub: https://github.com/bkkaggle/machine-learning-flight-rules Product Hunt: https://www.producthunt.com/posts/machine-learning-flight-ru... There's a lot of "hidden knowledge" online on places like Stackoverflow, Kaggle, and the Pytorch discussion forums that is really useful but not easily accessible to people who…

    Comment linking /reproducing-deep-rl

    on “Show HN: Machine learning flight rules; tips and tricks for ml”
  6. Submitted Lessons Learned Reproducing a Deep Reinforcement Learning Paper (2018) as a story · 4 points

    HN discussion
  7. Submitted Machine learning productivity hacks as a story · 4 points

    HN discussion
  8. Submitted Distributed TensorFlow: A Gentle Introduction as a story · 4 points

    HN discussion
  9. Submitted Getting Better at Doing Scary Things as a story · 3 points

    HN discussion
  10. Submitted CFEngine Policy Servers with Docker as a story · 3 points

    HN discussion
  11. Submitted The Humble Gumbel Distribution as a story · 1 points

    HN discussion
  12. Submitted The Humble Gumbel Distribution as a story · 1 points

    HN discussion
  13. Submitted What If Everyone Were Barack Obama? as a story · 1 points

    HN discussion

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