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I found the development of my Triclock[1] interesting. Stayed in Show HN for 3 days, never reached the frontpage, 65 upvotes. So a popular 3 day evergreen. All other of my Show HN were Crash & Burn or Burn & Shine

[1] https://news.ycombinator.com/item?id=46975399

Yeah Show HN has a pretty interesting distribution compared to standard posts due to the long-term visibility on the Show page. The odds of a Show HN post breaking 10 points is significantly higher than an average post, but of the posts that clear 10 points, I recall the likelihood of breaking 100 points to be similar to a regular post.

As a sidenote: That clock is so cool: I was just mesmerized for multiple minutes!

Cool. I think it would be nice to normalize against users (or active users).

2016-era HN had its share of negativism, but it also had a lot less people - the light green from those charts is misleading.

Pre-2020 had far more informative posts and discussions. While we still have decent conversations post-covid, the quality has slid downhill somewhat.

I totally agree that the metric is imperfect for a long term analysis. I was initially leaning toward a quantile based approach to really focus in on topic trends over time, but when I was initially exploring the data, the relative challenge of having a Show HN become popular in 2025 compared to previous years caught my curiosity, and for this decade I felt a static cutoff provided a simple and easy to understand threshold.

I do think as a metric for total reach, a static cutoff actually works reasonably well. I think some form of square root normalization over total users is probably the best balance.

Great. Do you have any details on how you produced this? The "reproducible code" isn't really reproducible. The "hierarchical topic model" that you mentioned - which model was used?

The code provided is to reproduce the analytical results from the annotated data; my impression is that you're more interested in the details of the annotation process than running into an issue with that code?

My company's core technology extends topic models to enable arbitrary hierarchical graphs, with additional branches beyond the topic and word branch. We expose those annotations in a SQL interface. It's an alternative/complementary approach to embeddings/LLMs for working with text data. In this case, the hierarchy broke submissions down into paragraphs added a layer to pool them into submissions, and added one more layer to pool them by year (on the topic branch).

Our word branch is a bit more complicated, but we have some extended documentation on our website if you are interested in digging a bit deeper. Always happy to chat more about the technical details of our topic models if you have any questions!

Overview of Our Technology: https://blog.sturdystatistics.com/posts/technology/

Technical Docs: https://docs.sturdystatistics.com

So the analysis from the last image is not available - not even for money, right?

We are going to publish that publicly next time we have a free day, though its publication will likely render the analysis redundant :)

Very nice analysis

Do you have any insights into the Clawd spam ravaging /new and /show?

I'm in there, being part of a (down) "voting ring" (not coordinated)

Thank you! I currently don’t have much insight to this current trend. At the time of this analysis I hadn’t even heard of Clawd but that would definitely be worth my revisiting.

I was planning on doing this yearly but the Clawd excitement is definitely worth diving into.