Recently, I made a dumb little app for my kids and decided to try marketing it on social media just to see what it is like. It is fascinating in a sense and disheartening as well. I have been very unsuccessful, but the most signal tends to come from the dumbest content I have tried.

In doing this, I have come into contact with the social media feeds I never felt the need to look at and man… they are like a drug. I find myself mesmerized by random IG reels. It is one thing to understand what they are on an intellectual level and a totally different to feel it first hand.

I miss MySpace.

Some of my use cases are very latency sensitive. What sort of overhead are you seeing?

So much content is just straight copy/pasted from the LLM now. Articles, blog posts, linked in posts, reddit comments, etc. Even just using the LLM for 'editing' tends to shift the voice to an obvious LLM voice when used naively. It is getting worse too. Last week a co-worker sent me a screenshot of Claude for me to review their "work", which was just whatever Claude made up.

Usually, if something is very obviously unfiltered LLM output, I just stop reading.

I do use LLMs for writing myself. They are useful, but are poor authors.

When I was in school, decades ago now, very few people went into CS compared to other majors. Everyone I knew going into it did it because they loved it. I would have done it regardless of the career opportunities because I want to build stuff.

Interviewing candidates over the years since then, my experience has been there are still very few of those passionate nerds and a lot of people who did it for other reasons, like the money or similar. There is nothing inherently wrong with this. I don’t fault people for it.

Maybe if we get very lucky, it will go back to a relatively few passionate people building stuff because it is cool?

Something like this was always inevitable. I just hope it doesn’t ruin a good thing.

My model for skills is similar to this, but I extended it to have explicit use when and don’t use when examples and counter examples. This helped the small model which tended to not get the nuances of a free form text description.

Not the only reason of course.