HN Attention Cliff

About HN Attention Cliff

# hn-attention-cliff

How fast does attention die after a Show HN launch? This repo measures it: every Show HN post from a full year, plus the complete comment tree of every launch that got at least 10 comments, scraped from the [Algolia HN API](https://hn.algolia.com/api) and reduced to a handful of uncomfortable numbers.

Written up at [jonno.nz](https://jonno.nz/posts/your-show-hn-dies-in-7-hours/).

## Reproduce every number

```sh make reproduce ```

That runs `analyze.py` (recomputes `results.json` from the CSVs in `data/`, which are included in the repo) and `charts.py` (regenerates the four SVGs in `charts/`). No dependencies beyond Python 3 stdlib. No network access.

To rebuild the dataset from scratch instead of using the included CSVs:

```sh make scrape # ~40 min cold, resumable, <3 req/s against the Algolia HN API ```

## Headline numbers

| Stat | Value | Function | |---|---|---| | Show HN posts, Jun 2025 - Jun 2026 | 41,301 | `scrape.fetch_stories` | | Median outcome | 2 points, 0 comments | `analyze.all_posts_outcomes` | | Launches with one comment or none | 78.9% | `analyze.all_posts_outcomes` | | Hours to 50% of lifetime comments (median launch) | 7.17 | `analyze.half_life` | | Hours to 90% | 26.17 | `analyze.half_life` | | Lifetime comments arriving after hour 48 (median) | 4.2% | `analyze.tail_share` | | Top-decile launches vs the rest | same cliff (7.6h vs 7.1h to 50%) | `analyze.cohort_split` |

Every stat quoted in the post maps to one function in `analyze.py`, and every headline number is recomputed a second, independent way (the `*_check` keys in `results.json`).

## Methodology

**Corpus.** Every story tagged `show_hn` on the Algolia HN API with a creation time between 2025-06-18 and 2026-06-18 (UTC). The window ends two weeks before the scrape date so every story has at least 14 days of comment history.

**Attention proxy.** HN does not publish vote timestamps, so comment timing is the proxy for attention. A story's "lifetime comments" are the live comments posted within 336 hours (14 days) of submission. Decay curves are cumulative shares of that lifetime total, so slow-burn stories are not penalised by the cap in shape terms; the cap itself is a stated limitation.

**Deep subset.** Comment trees are fetched for stories reporting >= 10 comments, and decay stats use stories with >= 10 live in-window comments. Everything about the *distribution* of outcomes uses the full corpus.

**Caveats, plainly:**

- Comments are not votes and neither is traffic. Public traffic reports suggest traffic decays even faster than comments. - The decay curves describe launches that got traction (>= 10 comments, about 5% of the corpus). The median launch never has enough comments to have a measurable decay curve at all. - The Algolia API excludes flagged/dead stories and deleted comments, so the corpus undercounts failures, which makes the medians *optimistic*. - HN's second-chance pool re-timestamps a small number of stories, which can make comments appear to predate their story; those comments are dropped and counted (`sanity.negative_hour_comments_dropped`). - Author replies in their own thread count as comments (19% of all comments); excluding them moves the half-life by four minutes (see `sanity.median_t50_excluding_author_comments_check`).

## Files

| File | Purpose | |---|---| | `scrape.py` | Resumable scraper (stories + comment trees, disk cache) | | `analyze.py` | Every stat in the post, one function per stat | | `charts.py` | The four SVG charts + OG image | | `data/stories.csv` | id, title, created_at, points, num_comments, url, author | | `data/comments.csv` | story_id, comment_id, hours_since_post, by_story_author | | `results.json` | Everything analyze.py computes, including the curves |

MIT licensed. Data is from the public Algolia HN API.