5 ms·

I don't quite understand these awesome lists. From what I've seen it usually ends up being a way for creators to promote their stuff and for the list creator to

by dvcrn 11y ago

I don't quite understand these awesome lists. From what I've seen it usually ends up being a way for creators to promote their stuff and for the list creator to have a big project with a few thousand stars in their profile. So when I did something in say, electron, I would go to the awesome-electron list and add it there for promotion sake. I couldn't find a usecase for these lists myself yet. There is no way to verify the quality of the product or the activity (stars for example? last commit date?). In one case I searched for aws adapters for a language, clicked on all links inside awesome-{{language}} just to find that all of them are inactive or a few days young. I ended up using something I found on google instead.

detaro 11y ago
It really depends on how well they are maintained and how selective they are. (E.g. a long list with a few links for many categories is often more useful than one that lists everything that possibly fits in one category). And some basic information for each entry is really needed. GitHub offers a reasonable way to manage contributions to them, compared to many other solutions. It is easy to suggest/fix something for external contributors, but the owner can work as a gatekeeper. This is something many link aggregators or bookmarking sites lack. One example that isn't perfect, but I found interesting was https://github.com/Kickball/awesome-selfhosted https://github.com/Kickball/awesome-selfhosted It has a sentence about each project, license information, and tries to purge unmaintained projects.
7952 11y ago
I think the benefit is as a source of inspiration. Finding data can require a lot of domain knowledge which students especially tend to lack. The problem is that the lists are always incomplete and other data may be more appropriate for a particular use case. Also, quality is more complex than just stars or activity. You need to know what the advantages or limitations are and how that applies to your project.
elcapitan 11y ago
When I started learning Golang last year I found the awesome-go list quite helpful. Not because I needed every single library mentioned, but because it quickly gave me an impression of how the ecosystem plays out and what typical ways to build stuff are. Because that's quite important in case you're not starting from an existing framework (.net or Rails etc).
Kluny 11y ago
I'm quite keen to start learning about data science. A bunch of big datasets like this is exactly what I need.

by dvcrn 11y ago

detaro 11y ago

It really depends on how well they are maintained and how selective they are. (E.g. a long list with a few links for many categories is often more useful than one that lists everything that possibly fits in one category). And some basic information for each entry is really needed. GitHub offers a reasonable way to manage contributions to them, compared to many other solutions. It is easy to suggest/fix something for external contributors, but the owner can work as a gatekeeper. This is something many link aggregators or bookmarking sites lack. One example that isn't perfect, but I found interesting was https://github.com/Kickball/awesome-selfhosted https://github.com/Kickball/awesome-selfhosted It has a sentence about each project, license information, and tries to purge unmaintained projects.

7952 11y ago

I think the benefit is as a source of inspiration. Finding data can require a lot of domain knowledge which students especially tend to lack. The problem is that the lists are always incomplete and other data may be more appropriate for a particular use case. Also, quality is more complex than just stars or activity. You need to know what the advantages or limitations are and how that applies to your project.

elcapitan 11y ago

When I started learning Golang last year I found the awesome-go list quite helpful. Not because I needed every single library mentioned, but because it quickly gave me an impression of how the ecosystem plays out and what typical ways to build stuff are. Because that's quite important in case you're not starting from an existing framework (.net or Rails etc).

Kluny 11y ago

I'm quite keen to start learning about data science. A bunch of big datasets like this is exactly what I need.