Edit: And thats not a R vs D thing. The requirements have been this way under multiple administrations, and newspapers have not been a primary way of sourcing candidates for two decades.

I really wish this was quantifiable in some way -- I have no way to tell if this statement is accurate.

I've worked with some very skilled H1B that definitely deserved to be here. If companies like Infosys or Cognizant are taking "slots" that could be filled by people like those H1Bs, then I'd consider that a big problem. Companies like these are not doing "highly skilled" work.

Edit: I kinda wonder if the "highly skilled" value judgement is a holdover from the era where people thought you were a genius if you touched a computer.

They are wildly out of date compared to how jobs are actually filled. Regardless of any other enforcement actions, this needs to be fixed.

I don't think it even needs congressional approval to change these. The DoL could do it.

In my mind the issue isn't that it was crafted by AI, but that it wasn't crafted with the intention of getting the point across in a way that respects the reader's time or energy. The result is often needlessly wordy with information buried in a sea of flourish. This might be forgivable if the author had a certain entertaining style, but LLMs are aggressively mediocre by their nature, making wading through it painful.

Its certainly possible to get an LLM to write concise prose but thats not how most people are using them. We can talk all day on HN about the ideal LLM user that is conscientious, reviews the output, puts time and effort into stylistic concerns, etc. But none of that actually matters because thats not what people are doing; the entire reason they're using an LLM in the first place is so that they don't have to do that.

This is an unsubstantiated assertion. I'd want to see evidence that things like this actually move the needle on rent vs things like available housing stock, zoning, etc which seem much more likely to actually impact affordability.

In my professional experience, management tends to treat this as a dichotomy rather than a range. And its lead to situations where we're choosing to route every decision through a small number of 'experts' and expecting that to scale, or have people make very bad decisions about things like indices, data layout, etc.

I align with you on this. If you are using some sort of system, there is a sort-of minimum bar of expertise one should acquire. When using analytical platforms, having a basic understanding of how data is stored and the levers it gives you to optimize data processing should be standard.

Some examples:

MySQL - I would expect developers to be able to come up with a list of columns to index and why, understand why UUIDv4 is a bad choice for primary keys, know how to write performant SQL by hand and understand when their ORM is going to do the wrong thing, etc.

Redshift - Understand dist and sort keys and have some mental models on how data shuffles and is pruned based on each choice.

Iceberg + Spark/Trino - Understand that data tends to be stored/loaded as batch files. Understand how deletes work. Understand partitions and sort as options. Understand generally how bloom filters work and when you should decide to place a bloom filter. Understand why UUIDv7 is better than UUIDv4 when its part of a partition key.

I don't think thats a common position on this site. It'd be more accurate to say that people want balanced content.

I use AI quite a lot in my work, but a ton of AI content is just thinly-veiled marketing and not particularly interesting, IMO.

Edit: I'll also note that while its nice to see real novel AI news I find a lot of it is extremely marginal value. I mentally associate most discussion with debating what color of marker to use when marking the edge of your CDs to improve the sound.

Sorry, that doesn't really come across as a ringing endorsement. React apps are typically not doing anything complex so rendering at less than 60fps should only happen if you're doing very computationally intensive things or writing bottom-quartile quality code.

Sure, but thats not an interesting observation unless there is something about react-native that makes those apps consistently higher quality than native apps.

On the other hand, like-for-like C/C++/Rust performs better than javascript in terms of CPU and memory use, so the null hypothesis is that re-implementing would , in fact, improve things. There's little reason to think a priori that the end result would somehow be worse.

Scale actually matters. Things that are generally OK at a small scale become problematic at larger scales. A single police cruiser writing down license plates isn't able to track you in the same way a huge surveillance network is, and the opportunities for abuse are much lower.

In the current AI mania, there's a lot of due diligence simply being ignored. Plenty of "Well humans make mistakes too!" going on here on HN too.