11–20 of 41 posts
> Videos, Podcasts, etc.: If I am rating 100+ articles a day I just can't spend the time it takes to look at time-based media. It makes me sad when I see an interesting looking post, and it turns out that it is only a video or podcast, with no proper writeup. I just don't have time to watch an hour-long video, for content that I could read at my own pace in ten/fifteen minutes.
> Off-Topic: ... If they'd cover it on TV news, it's probably off-topic. That would be nice but unfortunately in practice all the big news make their way to the top of HN, despite the fact that we've already read and heard about them from countless other sources. For example some of the top posts at the moment are: - Catalan parliament declares independence from Spain - New Zealand to ban foreigners from buying exist…
I'd like to be able to credit comments that got me to re-think or even change my position on a topic. Upvotes basically say, 'that's a good point', or 'I agree with you'. But I'd like to see a way to gauge 'influence', in terms of affecting what people think in a positive way.
Can that be worked in?
I don't have time to read the whole thing, so maybe it's already there, but, .... I'd like to be able to credit comments that got me to re-think or even change my position on a topic. Upvotes basically say, 'that's a good point', or 'I agree with you'. But I'd like to see a way to gauge 'influence', in terms of affecting what people think in a positive way. Can that be worked in?
That's an interesting project. I've been exploring this from a different angle, in the form of generated email newsletters ( https://www.findlectures.com/form?type=alert ). This uses the contents of the articles as well - I'm crawling links on programming subreddits. If you start from keywords you can use NLP (Word2vec) to measure how close articles are to your interests, so for instance "python, machine learning" gi…
The ultimate goal was to save me time and effort in manually classifying them, as I and everyone else do when we scan through what we come across on a daily basis. Instead of manually doing that, the hope was the program could do it for me, and I could just focus on reading the interesting articles and not even have to deal with the uninteresting ones.
From that experiment I learned a few things:
- First, that I'd have to manually scan through all the articles anyway, just in case the classifier made a mistake and maybe dumped an article I found intensely interesting in the uninteresting pile.
- Second, that having to consciously think about which article was interesting or uninteresting in order to do the training, about whether the classifier was working or not and which articles needed to be reclassified, about having to re-train it when it messed up, and so on was a hell of a lot more work than just scanning through my RSS feed manually and deciding on which articles I found interesting or not myself.
- Third, my interests were not static things that the algorithm could learn and classify on correctly from then on out. My interests were constantly changing. Sure, maybe there were a handful of things I always found interesting or uninteresting -- but overall what I found interesting or not changed from day to day. It was also kind of unpredictable, even to myself.
The third point kind of argues towards the approach of the HN front page, which is un-classified and un-tagged. I've read all sorts of great articles on HN that if I'd been going by some pre-written list of interests that I had, I would have never have read. I do still often wish for tagging on HN anyway, just because there are certain types of articles that I really never ever want to read, and I'd love to be able to exclude them. But the vast majority of HN articles aren't of that kind (or I wouldn't be here).
That experiment with bayesian classification turned out to be rather short-lived, as I found the whole thing way too much of a bother to maintain and to retrain when it misclassified articles. I'm still reading RSS feeds the old fasioned way today, and am a little suspicious about any AI/machine-learning-like approaches to article classification.
One aspect the author touched on is "I'd like to be suprised by relevant things that I don't know about." It's not immediately clear how to discover serendipitously items that are outside of the things you're already interested in, and therefore would already be in your feed. This is an area I'd like to know more about.
I'm also really keen on someone taking the time to automate curation of their own feed. One can still fall prey to the more negative aspects of Daily Me[1], but at least you're better aware of what's going into producing the feed you're reading and have the tools to update it if you find it's not serving your best interests.
> Off-Topic: ... If they'd cover it on TV news, it's probably off-topic. That would be nice but unfortunately in practice all the big news make their way to the top of HN, despite the fact that we've already read and heard about them from countless other sources. For example some of the top posts at the moment are: - Catalan parliament declares independence from Spain - New Zealand to ban foreigners from buying exist…
I don't have time to read the whole thing, so maybe it's already there, but, .... I'd like to be able to credit comments that got me to re-think or even change my position on a topic. Upvotes basically say, 'that's a good point', or 'I agree with you'. But I'd like to see a way to gauge 'influence', in terms of affecting what people think in a positive way. Can that be worked in?
What about stating this in a reply?
I would also argue that an upvote is not "I agree with you", but "this contributes to the conversation in a positive manner". I frequently upvote posts with which I disagree. Conversely, a downvote in my book says, "hey, you're kind of being a dick and dragging the conversation down" rather than "I disagree". I mean, I'll downvote something that is just demonstrably wrong, but more often than not it's "quit being a dick".
That's an interesting project. I've been exploring this from a different angle, in the form of generated email newsletters ( https://www.findlectures.com/form?type=alert ). This uses the contents of the articles as well - I'm crawling links on programming subreddits. If you start from keywords you can use NLP (Word2vec) to measure how close articles are to your interests, so for instance "python, machine learning" gi…
Many years ago, back when Bayesian spam filtering was the hot new thing, I wrote up a little program that would classify articles in RSS feeds based on whether I found similar articles to be interesting or not interesting in the past. The ultimate goal was to save me time and effort in manually classifying them, as I and everyone else do when we scan through what we come across on a daily basis. Instead of manually d…
There are a bunch of great curated email newsletters around specific interests (Javascript Weekly, etc) so I'm aiming to do something similar, but more granular. So far the ML thing has been promising, but it helps to start from a pre-vetted dataset.