Compared to what exactly? Snowflake? Hiring an engineer to deploy DuckDB? A hobby project? FWIW I work at MotherDuck so obviously biased, but curious to hear what makes you say that.
I mean, it is one thing to build things for yourself or your company this way. It's a whole other thing to dump it on the rest of the world. I'm still flabbergasted by the fact that maintainers of projects I highly value have to shut down contributions due to the amount of AI slop contributions they receive, making it impossible to maintain the project.
100% agree with the curators part. I think this is often implicit, but we look for signals of quality, whether that's Github stars or a person we trust. I feel the sense of what is a good curator has shifted, or even the curators are overwhelmed. Similar to the enshittification paradigm: once you find a good source of curated content, let's say Substack, then it grows and needs its own curation.
I've been overwhelmed with the flood of interesting new things being released. I tried to put a number on it and across the board from Github repos to package registries to Show HN submissions there is just such an immense increase in output, especially since Claude Code and Codex. I think everyone has felt this, but I have not yet found a way to deal with filtering out the genuine quality from the AI slop.
It’s interesting you mention linguistics because I feel a lot of the discussions around AI come back to early 20th century linguistics debates between Russel, Wittgenstein and later Chomsky. I tend to side with (later) Wittgenstein’s perception that language is inherently a social construct. He gives the example of a “game” where there’s no meaningful overlap between e.g. Olympic Games and Monopoly, yet we understand very well what game we’re talking about because of our social constructs. I would argue that LLMs are highly effective at understanding (or at least emulating) social constructs because of their training data. That makes them excellent at language even without a full understanding of the world.
Most of our jobs consist of working with tools. Yet it’s very hard to get insights into which tools are required most, are growing in your area, etc. So I decided to keep track of tools and technologies mentioned in the data space by keeping track of job openings for the last two years. Now I’ve opened up that data set. Here’s an analysis for jobs per data warehouse: https://selectfrom.work/insights/data_warehouses
By far the stupidest version of this to me has been Snowflake’s implementation of previews. This is a database, where people preview the content of a table, not in an app, not on a phone, and someone thought it was a good idea to make that an image. I have no idea who ever thought this was a good idea, but here i am constantly tricked into thinking I can select some preview data, only to realise I have to go on a 10 clicks and a SQl query diversion to get it done.