Prior to this I’ve mostly only seen it on dribbble.
I actually like this style a lot, and I wish more apps would use it. But at this point I thought that this style was one that “came and went” before it saw any significant actual use in any apps or OSes. Maybe there is still hope after all :)
Edit: oh and I had to try asking your tool for sentiment about neumorphic design after this of course. It returned my own comment lol :p and it called it “neutral”. Is it only evaluating the first paragraph that the word appears in in the comment? (Also I guess other people more commonly refer to it as “neumorphism” than as “neumorphic design” and maybe that’s why when I asked it for neumorphic design it returned my own comment.)
I tried "remote work" like the initial instructions recommended as an example. The graph it gave me showed large spikes of "neutral" sentiment with a few negligible bouts of negative sentiment and even smaller bouts of positive sentiment. The sample comment it gave was from a "Who Wants to be Hired" post where the poster demanded exclusively remote offers, which the tool classified as "neutral" (with 98.7% confidence!)
Very slick tool, but if the sentiment analysis itself doesn't really work well then I don't see what value this could have.
Is it actually doing anything?
Also, you could drastically improve styling on mobile. Lot of wasted space.
> you can track trends with it
No, no, you can't.
So that we could compare terms based on this result metric: google vs microsoft, rust vs go, rust vs microsoft, etc
(Will not work for Go as it's a common word in addition to the programming language, but anyways)
I searched "apache" and the "randomly sampled comment" was a mystery:
> I think the author has a point with one-way doors slowing down the adoption of distributed systems. ...
I had to search google with that phrase to get the actual context. ( https://news.ycombinator.com/item?id=41363836 )
Which turned out to be about "Apache Beam" not the Http server.
Note: I would suggest just removing dark mode for now. Works WAY better in light mode. I almost missed the light mode, and that would have been too bad.
Here's my user test: https://news.pub/?try=https://www.youtube.com/embed/2eac5XZe...
Still, great idea and execution
Stockholm Syndrome?
Took me a minute to figure out what had happened, but I was able to submit a phrase. The response was
> Randomly sampled comment from the pulled data: Sorry, there was an error processing your message.
Also, it seems like putting in the same phrase twice generates different graphs and results at least sometimes. So it’s difficult to use comparatively.
> Ah yes, because blockchain is the 100% true source of ultimate truth.
The model can't detect sarcasm.
useless junk.
nothing
>>hobos in san francisco
nothing
>>accommodating my neurodivergence
error
>>is everyone I don't like hitler or just some people
nothing
Bug report, I saw inaccurate results, I asked about “native apps” and I got negative sentiment. This is contrary to my experience, afaik HN loves them.
The example comment[1] quoted “non-native apps” and is part of the discussion where people say they don’t like non-native apps.
Edit: Then I asked about non-native apps, got sentiment “neutral” and this comment (the one I’m editing now) as the example. Very unexpected!
Thanks for sharing :) TIL sentiment for “communism” is slightly less negative than for “capitalism” on here! Tho both are, surprisingly, mostly neutral.
For example if you search for bitwarden it ranks three comments as negative, all others as neutral. If I as a human look at actual comments about bitwarden [1] there are lots of comments about people using it and recommending it. As a human I would rate the sentiment as very positive, with some "negative" comments in between (that are really about specific situations where it's the wrong tool).
I've had some success using LLMs for sentiment analysis. An LLM can understand context and determine that in the given context "Bitwarden is the answer" is a glowing recommendation, not a neutral statement. But doing sentiment analysis that way eats a lot of resources, so I can't fault this tool for going with the more established approach that is incapable of making that leap.
1: https://hn.algolia.com/?dateRange=pastMonth&page=0&prefix=tr...