121–130 of 615 posts
I'd love to read a semi-technical book on everything that we've learned about what works and what does not on LLMs.
It would be out of date in months. Things that didn’t work 6 months ago do now. Things that don’t work now, who knows…
Or do you actually mean that the same routines and data that didn't work before suddenly work?
Earlier quoted context omitted.
Been in the Mac ecosystem since 2008, love it, but there is, and always has been, a tendency to talk about inevitabilities from scaling bespoke, extremely expensive configurations, and with LLMs, there's heavy eliding of what the user experience is, beyond noting response generation speed in tokens/s. They run on a laptop, yes - you might squeeze up to 10 token/sec out of a kinda sorta GPT-4 if you paid $5K plus for…
I have a 2023 mbp, and I get about 100-150 tok/sec locally with lmstudio.
Spookily good at writing code? LLMs frequently hallucinate broken nonsense shit when I use them. Recognize what they do well (generate simple code in popular languages) while acknowledging where they are weak (non-trivial algorithms, any novel code situation the LLM hasn't seen before, less popular languages).
Did you try learning HOW to get good code out of them? As with all things LLM there's a whole lot of undocumented and under appreciated depth to getting decent results. Code hallucinations are also the least damaging type of hallucinations, because you get fact checking for free: if you run the code and get an error you know there's a problem. A lot of the time I find pasting that error message back into the LLM gets…
About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy. And I guess that in tech many folks try LLMs for the same use cases. Try Claude Sonnet 3.5 (not Haiku!) and tell me if, while still flawed, is not helpful. But there is more: a key thing with L…
I think John Gruber summed it up nicely: https://daringfireball.net/2024/12/openai_unimaginable OpenAI’s board now stating “We once again need to raise more capital than we’d imagined” less than three months after raising another $6.6 billion at a valuation of $157 billion sounds alarmingly like a Ponzi scheme — an argument akin to “Trust us, we can maintain our lead, and all it will take is a never-ending stream of…
Earlier quoted context omitted.
Hopefully things have narrowed but you can see from the trends data just how few people (API may be a different story) use claude relative to chatgpt.
Brand awareness is a hell of a drug.
Earlier quoted context omitted.
Did you try learning HOW to get good code out of them? As with all things LLM there's a whole lot of undocumented and under appreciated depth to getting decent results. Code hallucinations are also the least damaging type of hallucinations, because you get fact checking for free: if you run the code and get an error you know there's a problem. A lot of the time I find pasting that error message back into the LLM gets…
> Code hallucinations are also the least damaging type of hallucinations, because you get fact checking for free: if you run the code and get an error you know there's a problem. This is great when the error is a thrown exception, but less great when the error is a subtle logic bug that only strikes in some subset of cases. For trivial code that only you will ever run this is probably not a big deal—you'll just fix i…