thorax

RE: syntax For casual use, I kinda always liked the whole MATCH/AGAINST syntax for old school Innodb, though obviously things have changed a lot since those days. But it felt less like calling embedded functions and more like extending SQL’s grammar.

Regarding the rest, it seems like a reasonable approach at first tinker.

In AI Studio, it seemed to let me upload pretty much any file and tokenize it without renaming, FWIW

Replaced my openers in the spring, and 100% wouldn't have chosen them if there wasn't HA MyQ integration. Such a silly move.

I used a local Meross install on my old garage doors, time to break them out, but ugh...

Don't think so, but there were some guesses on 3.5-turbo-- i.e. training a much smaller model on quality questions/answers from GPT-4. Same tactic worked again and again for other LLMs.

I'm definitely curious on the context window increase-- I'm having a hard time telling if it's 'real' vs a fast specially trained summarization prework step. That being said, it's been doing a rather solid job not losing info in that context window in my minor anecdotal use cases.

Do you have a link or gist of an example run you tried? I'd be curious to try something similar.

I've had to move to using Azure OpenAI service during business hours for the API-- much more stable unless the prompts stray into something a little odd and their API censorship blocks the calls.

Ah, really curious if it'll balk at the age old problem or just answer based on the code provided since it does inspect the nearby code to understand context for the generated code.

I'll have to try that when I get back to my desk!

> It returns factually incorrect data, and it returns code with subtle but important errors if you ask it anything that's not regurgitated a thousand times in the training dataset.

To be fair, that's what pretty much every person does. The bar does seem pretty high if we need more than that (especially if not specifically trained on a topic). It's not a universally perfect expert servant, but I've been exploring the code generation of GPT4 in detail (i.e. via the 'cataclysm' module I just posted about). In 1 minute it can write functions as good as the average developer intern most of the time.

We're keeping score in a weird way if we're responding quickly with it needing to "code without subtle but important errors". Because that's the majority of human developers, too. I've been writing code for 30 years, and if you put a gun to my head, I would still have subtle but important flaws in every first typing of any complex generated code.

I'm not saying you're bashing it, by the way, I get your point, but I do worry a bit when the first response is citing that the SOTA models get things wrong in 0-shot situations without full context. That's describing all of us.

Now that it's released, it's time to experiment with what it can do.

   from cataclysm import doom

   # App gets the img file from the command line and saves it as a new file at half size with _half appended to the name
   doom.resize_app()

Turned out to be all that's needed for a command-line file resize app (with PIL installed).

> I don't think GPT-4 will be a big deal in a month

Why do you think that? Competition? Can you elaborate?

Yeah I wrote my own plunkylib (which I don't have great docs for yet) which is more about having the LLM and prompts in (nestable) yaml/txt rather than how so many people hard code those in their source. I do like some of the features in langchain, but it doesn't really fit my coding style.

Pretty sure there will be a thousand great libraries for this soon.