I do that a decent chunk of the time yeah especially for learning. I also have a bunch of marimo notebooks that double as clis and they're lovely.
But sometimes I want to do something too specific or high fidelity and it's just easier to get the clanker to write typescript and make a webpage/components.
hello, please fix needing to reauth every day (sometimes with email verification). This started happening this month. It's tedious and makes switching very tempting.
Deeply unserious company, flip flopping on policy every week with ludicrous, sometimes invisible, guard rails on their top model. Imagine trying to make business decisions about AI use with this.
I love Fable for many things including coding but I cannot justify basing any internal AI tools or LLM powered products on top of Anthropic's offerings.
Yep agreed completely. I couldn't imagine torturing myself with a small model for local coding. But Gemma 4 31B is so fucking good for a variety of language modelling tasks.
Google is singlehandedly carrying western open source models. Gemma 4 31B is fantastic.
However, it is a little painful to try to fit the best possible version into 24GB vram with vision + this drafter soon. My build doesn't support any more GPUs and I believe I would want another 4090 (overpriced) for best performance or otherwise just replace it altogether.
Not necessarily with speculative decoding. Whitespace would be trivial to predict and they would petty much keep using the same amount of compute as before.
I don't think that's their primary motive for doing this but it is a side effect.
> Data extraction tasks are amongst the easiest to evaluate because there’s a known “right” answer.
Wrong. There can be a lot of subjectivity and pretending that some golden answer exists does more harm and narrows down the scope of what you can build.
My other main problem with data extraction tasks and why I'm not satisfied with any of the existing eval tools is that the schemas I write change can drastically as my understanding of the problem increases. And nothing really seems to handle that well, I mostly just resort to reading diffs of what happens when I change something and reading the input/output data very closely. Marimo is fantastic for anything visual like this btw.
Also there is a difference between: the problem in reality → the business model → your db/application schema → the schema you send to the LLM. And to actually improve your schema/prompt you have to be mindful of the entire problem stack and how you might separate things that are handled through post processing rather than by the LLM directly.
> Abstract model calls. Make swapping GPT-4 for Claude a one-line change.
And in practice random limitations like structured output API schema limits between providers can make this non-trivial. God I hate the Gemini API.