On GitHub, you volition discover a lot of repositories alongside the prefix 'awesome'. They normally merge curated cognition and resources connected to a certain domain, specified as awesome-python (see, e.g., this repository for a catalog of notable repositories). I used to see these notable repositories as possible goldmines of fine content. However, nowadays I see most of them as irrelevant and useless. There are two reasons for this, and they are the two connected to AI.
First, as many of these notable lists get older, they merge a lot of outdated matter if they are not actively maintained. You are in most cases improved off merely asking a decent AI example for inspiration or the matter you need. That is, I discover it difficult to accept that most of these notable lists are of involvement to group anymore. If they remain useful, it volition mostly be for agents looking to discover and inspect applicable resources.
I had a quick appearance at the awesome-R repository. The repository was final updated additional than a twelvemonth ago, and most of the resources included in the overview are either outdated or volition not be of broad involvement to group using R. One of the archetypal links in the repository is an overview of the top 50 downloaded packages - as of 2016! This is merely not a fine asset if you desire to examine R in any form or form.
Second, a lot of these notable repositories are being inundated alongside AI slop. Go to any random notable repository these days and you volition most apt discover a lot of drag requests suggesting the most generic tools and resources. For example, obtain a appearance at this awesome-free-online-tools repository. It is a small, unidentified repository alongside 13 stars but 91 open drag requests at the period of writing. I did not appearance at all of these drag requests, but the ones I looked at all suggested generic vibe-coded tools.
I established this asset since of a drag request made to one of my repositories, a uncomplicated project anywhere I shop matter I akin connected to statistics. The recommendation was to add StatOrigin ("a free, open repository of industry statistics"), and formerly I had a quick appearance at the asset and the another drag requests made by the user, I could effortlessly near the PR. This person solitary has created at smallest six vibe-coded websites (StatOrigin, ScanChess, Sticker Printer Hub, QuizQuip, LiquidityPools, DefiLPKit) and submitted additional than 100 drag requests to assorted notable lists to add these websites.
It did not obtain me lengthy to discover examples anywhere these tools were merged into the applicable repositories (see, e.g., here, here, and here). However, I additionally established examples anywhere group did the essential activity of checking out the material, specified as here, anywhere the PR was closed alongside the following explanation: "There is not yet adequate evidence of qualified instructor review, autonomous validation of the instructional content, or demonstrated learning outcomes." Good stuff!
My item is not that these notable repositories are now all bad. They can motionless be awesome. But as a broad method to finding fine resources, notable lists are small notable than they used to be. As AI gets improved at finding and creating resources (and creating drag requests for stated resources), these notable repositories volition rotate into small applicable and small useful.
