Search engines have been dropping in quality significantly within the past decade, and especially within this past year. The noise to signal ratio has been frankly painful.
Can you please share some resources you use when trying to find answers to technical questions?
For example, STEM, academia, engineering, programming, etc.
Lots of quoting for specific phrases. Instead of searching like I used to using what I feel are relevant key words:
react table sortable
I now have to search for something like
“Best sortable table component for react”
This will lead me to some bullshit listicle that will then give me at least a few items to review, then I take the best one and start typing the components name vs and seeing what auto completes after vs
It’s all become a game and I hate it.
most University Libraries have “guides”. Simply google " guide". eg Stanford Library engineering guide. Result like: [(https://guides.library.stanford.edu/aa)] From there use any free local library access you might have to get the details.
That’s not how links work in Markdown; some clients will include the
)]at the end and break the link. Just use the plain URL https://guides.library.stanford.edu/aa or create a hyperlink using[this syntax](https://www.markdownguide.org/cheat-sheet/).Thank you so much!
Thank you all for your answers!
I wanted to add one resource I found that has helped me find even more relevant search results:
A Lemmy Search Engine https://www.search-lemmy.com/
Oh, awesome! Thanks
I used to start with searching Reddit, though that has been of less help lately. Wikipedia is helpful for getting a baseline if I have no clue about a subject. Lately ChatGPT has been helpful there as well.
And then of course, all search engines still accept boolean searches but you kinda need to 1) know the syntax the engine uses and 2) have a rough idea of what you are looking for.
https://help.duckduckgo.com/duckduckgo-help-pages/results/syntax/
Sorry, no Google documentation was relevant.
When doing technical things, I find the best source to always be the provided documentation. For example, when using an external crate in Rust, docs.rs or when coding a Django Webapp the official Django documentation.
When starting out, these often contain examples or guides/tutorials.
When that does not help, it goes back to putting relevant keywords into the search engine and hoping for the best.
Note that this does not apply to Microsoft. Like, at all. :)
Wikipedia is pretty good for computer sciency stuff. I’ll often use it as a reference for things like protocols or if I need a quick refresher for some algorithm.
For all of those topics, I use domain specific sites. So for research I’ll look at arxiv or one of the sites that make research freely available. For programming, I’ll search language mailing lists, documentation, and examples. Searching GitHub also isn’t a bad idea, but watch out for license issues.
Be wary of using tools like got to summarize articles or outright answer questions. There’s no guarantee it will be correct, and if you don’t know the answer you won’t know it’s wrong.
For code I use chagpt for first pass questions. Then I try compiling it and see if gpt is telling the truth
I feel like this is a risky approach. LLMs are designed to spit out text that sounds good, that’s all. If it hallucinates important info away, your compiler will not always tell you.
Yeah, I check stuff with stackoverflow and the documentation
I like to look for relevant books on archive.org, but they don’t always have stuff for more obscure topics.
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