Show HN Submissions:
Show HN Makers:
I manually checked the status of the top 100 Show HN submissions. Eleven of them happened to be dead. The others still deliver what they promised in the original submission:
The connection between age and death is not evident here, but it reveals itself in a larger sample, as we'll see later.
The top 10 users ranked by total scores in Show HN submissions:
Author N sum(score) ----------------------------------- olalonde 7 3589 negrit 9 1472 vicapow 9 1464 geoffschmidt 1 1386 timmorgan 4 1318 gkoberger 5 1306 dang 1 1302 omegaworks 1 1215 hannahmitt 1 1172 afshinmeh 25 1080
Top makers have few projects. In Top 100, the mode is one project. Only few authors, like afshinmeh, have more than a dozen. Even in these cases, most scores come from a single submission.
The best examples of systematic success are ianstormtaylor and erohead: they have a high median score in six and three projects, respectively.
To explore commercial success of Show HN projects, I combined submissions with the CrunchBase data. I joined both datasets by network location. Despite obvious problems with overlapping second-level domains (github.com hosts many Show HN projects, but belongs to a different entity in CrunchBase), most HN projects have individual domains and, therefore, match uniquely.
These stats are impressive, but they describe the best projects. What about the rest?
The front page appearances make a small fraction of the submissions. The median Show HN project looks modest:
Projects earned these scores over one-two days after submission. How are they doing how?
I took a random sample of 10,000 Show HN submissions and sent a request to each associated URL. The servers returned status codes, which I used for dividing Show HN projects into living and dead. I interpreted code 200 as saying that the project still exists and all other codes (301, 404, timeout, and others) indicating a defunct project.
You can think of this status as indicating relevance. Relevant projects persist and fads disappear.
In this random sample, servers returned these codes:
Description for each code is available here
Users judge these projects early, and success on Hacker News may help developers decide what to do next with their project. Sometimes they succeed. For example, Stripe and Dropbox started with Show HN. So how well does the Hacker News community detect future hits?
I regressed the current status (alive or dead) on relevant variables:
Translating into English:
These results hold across different specifications and estimation procedures (of which I included only the basic model). So a Hacker News reception has good predictive power, estimated around the OLS's R² = 14%.