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Comment by ssivark | Hacker News Reader

(Apologies in advance if this sounds snarky, don't mean to be)

There are hundreds of posts on using Keras/TensorFlow/PyTorch/etc to do MNIST classification, and many examples on Github. All these resources are very easy to find by Googling. This post doesn't seem to have anything different to add to the conversation. So, why do such articles continue to be written, and why do they still get upvoted on HN? Is it that a lot of people want to learn these things, but haven't gotten a chance to, so they upvote with the hope of staying in touch with the topic? Is it FOMO? One might be forgiven for considering this spam.

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Yes, moderators routinely downweight tutorial articles for that reason. It took us years to realize it, but tutorials aren't a good fit for Hacker News.

HN is for articles that gratify intellectual curiosity. Most tutorials don't do that; it doesn't fit with being a recipe for a task. If a recipe were to dive deeply into how and why its steps work, how they got that way, and compared other recipes that have (or don't have) similar steps, it would gratify intellectual curiosity better. But then it would be a worse recipe. It would also be more work to write.

The worst kind of tutorial from an HN point of view is the kind filled with arbitrary details that apply only to a particular program or product, like "Now put a file named foo in a directory called baz". But that is almost all of them.

https://news.ycombinator.com/newsguidelines.html

I think this is just the nature of any open forum where everyone has an equal vote (democracy).

In any large-enough population, there will invariably be a significant non-cognoscenti segment who will upvote this type of content. And who knows, for this segment, this content may be new despite repeated iterations elsewhere.

It seems inefficient, but I'd rather have this type of system than systems that weight upvotes according to karma. This way, at least you avoid polarization by high karma users. The occasional low-quality content is not such a bad price to pay for a self-renewing system.

After the hype dies down, there will naturally be fewer MNIST articles. :)

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