In it they explicitly call it out as a ranking metric
> Many of GitHub's repository rankings depend on the number of stars a repository has. In addition, Explore GitHub shows popular repositories based on the number of stars they have.
Yet another case of metric -> target -> useless metric
I found it very unfun. You end up in dungeons with a subset of the abilities you're used to. It felt especially bad, when leveling, if I queued for a random dungeon and got into a lower level one shortly after acquiring a new ability.
I think they understand that the people running businesses are going to look at this vs a human who uses Figma and realize how much more cost and time efficient it is to pay for a machine than a human.
Obsidian shows a warning about this. But the only issue it's pointing out is that mixing Obsidian's built-in sync with something that syncs your files is likely to cause problems. Otherwise it's a perfectly safe and normal way to sync.
How can people ever discuss something in public fora if spoilers have to be eternally avoided? Keep in mind, people often disagree on what even crosses the line into being a spoiler.
I'm with other people here. Make this a one-time purchase. If a major macOS update requires significant changes to keep the program working, make that a new version that people need to buy. A pretty standard way to keep people from feeling screwed if the break happens right after they bought your software is to give them the next version of your software for free if you release it within 1 year of their purchase.
I think you're actually likely to make more money that way because people will pass on adding yet another subscription to the pile they have already.
I make sure to hit not interested the second I see anything I very much don't want pop up in me feed. I don't want mine to drift towards the average feed of the lowest effort, sensationalist garbage.
It seems to help. But it's just one factor. I also have a lot of subscriptions to help guide the algorithm. And it seems most heavily weighted on things you've recently watched, so if you ever leave youtube playing while you're not actually watching it, you might need to manually remove videos from your watch history that don't align with what you want to see suggested.
In case you start watching such a video (and maybe in general), it’s probably more effective to downvote it and remove it from your watch history. And when you use “not interested”, there are two “tell us why” follow-up options “already watched” and “don’t like”. Selecting the latter may be necessary for “not interested” to have a stronger effect.
I don’t know if YouTube Premium makes a difference, but I don’t see highly clickbaity thumbnails very often.
The AI for everything thinking is really easy to let infect you. I was trying to figure out how to make some SQL alerting easier to understand quickly. The first thing my brain went to was "oh just shove it into an LLM to pull out the info of what the query is doing". And it unfortunately wasn't until after I said that out loud that I realized that was a stupid idea when you could just run a SQL parser over the query and pull the table names out that way. Far faster, more cost effective, and reliable than asking an LLM to do it.
Snowflake is typically used for data analytics in my experience. It's going to have financial stuff very likely, but not like raw documents. Definitely not source code.
I mean technically you can stuff documents into a column with the BINARY datatype provided they are under 67 MB each, but it's not really meant to be used as a document store.
In it they explicitly call it out as a ranking metric
> Many of GitHub's repository rankings depend on the number of stars a repository has. In addition, Explore GitHub shows popular repositories based on the number of stars they have.
Yet another case of metric -> target -> useless metric
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