Additionally, the original paper uses the term “cognitive debt“ not cognitive decline, which may have an important ramifications for interpretation and conclusions.

I wouldn’t be surprised to see similar results in other similar types of studies, but it does feel a bit premature to broadly conclude that all LLM/AI use is harmful to your brain. In a less alarmist take: this could also be read to show that AI use effectively simplifies the essay writing process by reducing cognitive load, therefore making essays easier and more accessible to a broader audience but that would require a different study to see how well the participants scored on their work.

> Am I right?

With cursor you get reasonably flexible control at many levels. You can have it only suggest changes that you have to apply manually or you can have it make automatic changes with various ways to review, change, reject or accept. I usually have the changes made automatically but don’t accept the changes automatically. Cursor has a UI that lets you review each edit individually, for the whole file or all files. Depending on the situation I will use whichever level is appropriate. The UI also allows you to revert changes or you can ask the AI to undo or rework a change that you just approved so there’s plenty of ways to do large changes without giving up control. There’s also a stop button you can use to interrupt mid-stream if the work it’s doing isn’t what you want. It isn’t flawless but I haven’t found myself in a corner where I couldn’t get back to a happy path.

I used to do aeropress or pour over coffee every morning but now I brew a large batch of hot coffee in a jar and then leave it on the counter overnight. The next day I remove the grinds and put the coffee in the fridge. Then on weekdays I just pour some over ice. It’s barely more work than making one pour over and I get 7 days of coffee with no prep work in the morning: delayed instant gratification all in one.

I would recommend to you, the author of the OP and others in this thread read “Amusing Ourselves to Death” by Neil Postman. It is old but it has had amazing sticking powe in my idea of what is entertainment and what is informational. He argues that many things are made to be entertainment (even some books that are thousands of pages) and challenges readers to consider this when deciding what to invest into. We can’t all be experts on everything so we make decisions, consciously and unconsciously about when and where to draw the line based on what information is available, how it’s presented and how much the value/work tradeoff is to study/consume it in that format.

I think the sting comes when we see people ignore low hanging fruit especially willfully but we would be less critical of someone saying that the task at hand is beyond their current capacity that someone else might be better suited to a particular task or topic.

> Using smallpond and 3FS depends largely on your data size and infrastructure:

> Under 10TB: smallpond is likely unnecessary unless you have very specific distributed computing needs. A single-node DuckDB instance or simpler storage solutions will be simpler and possibly more performant.

> 10TB to 1PB: smallpond begins to shine. You'd set up a cluster with several nodes, leveraging 3FS or another fast storage backend to achieve rapid parallel processing.

> Over 1PB (Petabyte-Scale): smallpond and 3FS were explicitly designed to handle massive datasets. At this scale, you'd need to deploy a larger cluster with substantial infrastructure investments.

Makes it very easy to determine if this would be useful for me and how much work I would expect to do to use it.

Do you often do that many independent $incs (or any query) in a single second? I have gotten much better performance by using `BulkWrite` to do a bunch of small updates in a batch.

To go to a specific example from the "Driver Benchmark" on the link from your first reply:

   client[:users].insert_one(name: Digest::MD5.hexdigest(index.to_s))

For me the limiting factor is mostly around the performance of BulkWrite. I haven't hit any performance bottlenecks there that would merit benchmarking different ways to use it, but I would mostly be trying to fine tune things like how to group the items in a BulkWrite for optimal performance if I did.

Even in the case of one-off queries it almost always feels faster on 7+ than earlier versions. As I mentioned the one bottleneck we hit with migration was that we had some queries where we were querying on fields that were not properly indexed and in those cases performance tanked horribly to the point where some queries actually stopped working. However, once we added an index the queries were always faster than on the old version. When we did hit problems, it took only a few minutes to figure out what to index then everything was fine. We didn't have to make changes to our application or the queries themselves to fix any issues we had.