91–100 of 116 posts
Earlier quoted context omitted.
If I did that I would waste so much time. I know what code is in my codebase. Maybe if I was a novice this would be effective to help me learn it. Is the point of the exercise to wow myself for a week that an LLM can spit out solutions?
No its that it will code a days worth of work in a few minutes
Also, does the WASM build perhaps enable using Postgres as an embedded db, where typically SQlite would be used?
- duckdb which also supports WASM - UWData's Mosaic (https://github.com/uwdata/mosaic) which supports real-time plots
would be really nice to have a kind of "drop-in" page that we could add to any intranet where people could just retrieve an export of some database, and plot it with your code
Clicking "New database" doesn't do anything for me...? No changes in the UI, and no messages in the console. Admittedly I'm not signed in with Github, but isn't that only for the AI thing (that really I don't want to use). Edit: Okay reading kiwicopple's comment makes it clearer that chatGPT is not-optional. I'm... not enthused by this. Why would you take something that's local-first and gatekeep it with something as…
(I still hate LLMs in my DB. I KNOW SQL LET ME WRITE IT.)
Rudimentary compared to what you've done, but is it possible to take an existing database schema, developed either in the Supabase migrations style or another tool like Flyway, and draw the diagram? That alone is huge for us in an enterprise setting, and would allow us to communicate the structure of the database to business folks (especially large databases). How does the tool build that from migrations currently?
Under the hood, we store the embeddings in a meta.embeddings table then pass back to AI the resulting IDs for each embedding. We do this because embedding vectors are big, and sending these back and forth to the model is not only expensive, but also error prone. Instead, the language model is aware of the meta.embeddings table and simply subqueries to it when it needs access to an embedding.
1. What does it mean to "pass back to AI the resulting IDs for each embedding" but not the table rows corresponding to the matched vectors?
2. Does "the language model is aware of the meta.embedding table" mean Supabase has deployed a fine-tuned GPT-4o?
Earlier quoted context omitted.
If I did that I would waste so much time. I know what code is in my codebase. Maybe if I was a novice this would be effective to help me learn it. Is the point of the exercise to wow myself for a week that an LLM can spit out solutions?
No its that it will code a days worth of work in a few minutes
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I really do think that software engineering as we know it is ending. It will take 3-5 years for tools like this to mature. But it will happen, and hard fought skill sets like SQL database design, query design, and maybe even ORMs will become obsolete. My biggest prediction is that ORMs will not be necessary when LLMs can generate SQL. Low level SQL is a better abstraction for LLMs than ORMs, and as people are removed…
Until a query becomes a bottleneck and no one knows why because no one knows how databases work anymore.