Earlier quoted context omitted.
Stenography with a known signature perhaps
Still easily defeated when the scheme is known.
My point is most won't bother
Things we learned about LLMs in 2024
361–370 of 615 posts
Re: Things we learned about LLMs in 2024
#362Earlier quoted context omitted.
What do you use so that you can throw in a set of documents and/or a nontrivial code base into an LLM workspace and ask questions about it etc.? What the cloud-based services provide goes way beyond a simple chat interface or mere code completion (as you know, of course).
I use my https://github.com/simonw/files-to-prompt tool like this: files-to-prompt . -e py -e md -c | pbcopy Now I have all the Python and Markdown files from the current project on my clipboard, in Claude's recommended XML-like format (which I find works well with other models too). Then I paste that into the Claude web interface or Google's AI Studio if it's too long for Claude and ask questions there. Sometimes I'…
Thanks. Google AI Studio isn’t local, I think, is it? I’ll have to test this, but our project sizes and specification documents are likely to run into size limitations for local models (or for the clipboard at the very least ;)). And what I’d be most interested in are big-picture questions and global analyses.
Re: Things we learned about LLMs in 2024
#363Earlier quoted context omitted.
I use my https://github.com/simonw/files-to-prompt tool like this: files-to-prompt . -e py -e md -c | pbcopy Now I have all the Python and Markdown files from the current project on my clipboard, in Claude's recommended XML-like format (which I find works well with other models too). Then I paste that into the Claude web interface or Google's AI Studio if it's too long for Claude and ask questions there. Sometimes I'…
Thanks. Google AI Studio isn’t local, I think, is it? I’ll have to test this, but our project sizes and specification documents are likely to run into size limitations for local models (or for the clipboard at the very least ;)). And what I’d be most interested in are big-picture questions and global analyses.
No, it's not. I've not seen any local models that can handle 1m+ tokens.
I haven't actually done many experiments with long context local models - I tend to hit the hosted API models for that kind of thing.
Re: Things we learned about LLMs in 2024
#364Re: Things we learned about LLMs in 2024
#365Earlier quoted context omitted.
> Don't use LLMs where accuracy is paramount. Then why do people keep pushing it for code related tasks? Accuracy and precision is paramount with code. It needs to express exactly what needs to be done and how.
Code is the best possible application of LLMs because you can TEST the output. If the LLM hallucinates something the code won't compile or run. If the LLM makes a logic error you'll catch it in the manual QA process. (If you don't have good personal manual QA habits, don't try using LLMs to write your code. And maybe don't hit "accept" on other developer's code reviews either?)
> Code is the best possible application of LLMs because you can TEST the output.
This is an overly simplistic view of software development.
Poorly made abstractions and functions will have knock on effects on future code that can be hard to predict.
Not to mention that code can have side effects that may not affect a given test case, or the code could be poorly optimized, etc.
Just because code compiles or passes a test does not mean it’s entirely correct. If it did, we wouldn’t have bugs anymore.
The usual response to this is something like “we can use the LLM to refactor LLM code if we need” but, in my experience, this leads to very complex, hard to reason about codebases.
Especially if the stack isn’t Python or JavaScript.
Re: Things we learned about LLMs in 2024
#366Earlier quoted context omitted.
I'm surprised at the description that it's "useless" as a programming / design partner. Even if it doesn't make "elegant" code (whatever that means), it's the difference between an app existing at all, or not. I built and shipped a Swift app to the App Store, currently generating $10,200 in MRR, exclusively using LLMs. I wouldn't describe myself as a programmer, and didn't plan to ever build an app, mostly because in…
May you expand how you did this? I'm seeing a number of apps that claim to do just this and there are number that are becoming super popular. Not just the development of the code but the entire the thing from the code, infra, auth, cc payments, etc.
Planning to write a lengthy blog post on this. Will reply here.
Re: Things we learned about LLMs in 2024
#367Earlier quoted context omitted.
Code is the best possible application of LLMs because you can TEST the output. If the LLM hallucinates something the code won't compile or run. If the LLM makes a logic error you'll catch it in the manual QA process. (If you don't have good personal manual QA habits, don't try using LLMs to write your code. And maybe don't hit "accept" on other developer's code reviews either?)
> Code is the best possible application of LLMs because you can TEST the output. This is an overly simplistic view of software development. Poorly made abstractions and functions will have knock on effects on future code that can be hard to predict. Not to mention that code can have side effects that may not affect a given test case, or the code could be poorly optimized, etc. Just because code compiles or passes a t…
So code review LLM-generated code and reject it (or require changes to it) if it doesn't fit your idea of what good code looks like.
Re: Things we learned about LLMs in 2024
#368Earlier quoted context omitted.
Like all stubborn anti-AI know-it-alls, you sound like you’ve tried a couple of times to do something and have decided to label all LLMs with the same brush. What models have you tried, and what are you trying to do with them? Give us an example prompt too so we can see how you’re coaxing it so we can rule out skill issue. And a big strength LLMs have is summarizing things - I’d like to see you summarize the latest 1…
> And a big strength LLMs have is summarizing things - I’d like to see you summarize the latest 10 arxiv papers relating to prompt engineering and produce a report geared towards non-techies. And do this every 30 mins please. Also produce social media threads with that info. Is this a task you could do yourself, better than LLMs? Right, but this is the part that is silly and sort of disingenuous and I think built upo…
Who said anything about value? I can argue the vast majority of human generated content is valueless - look at Quora and Medium even before ChatGPT blew up. Where else are humans producing this amazing content? Facebook? X? Don’t even get me started.
Being able to summarise multiple articles quicker than a human can read and digest a single one is obviously more productive. I’m not sure why you’re assuming I’m talking about rewriting the papers to produce slightly different variations? It’s a summary. Concerned about the lack of “insight” or something? Then add a workflow that takes the summaries and use your imagination - maybe ask it to find potential applications in completely different fields? You already have comprehensive summaries (or the full papers in a vector db). Am I missing something?
Also the quality of the summary will be linked to the prompts and the way you go about the process (one-shotting the full paper in the prompt, map reduce, semantically chunked summaries, what model you’re using, its context length etc) as well as your RAG setup. I’m still working on my implementation but it’s simple as fuck and pretty decent in giving me, well, summaries of papers.
I can’t articulate it well enough but your human curation argument sounds to me like someone dismissing Google because anyone can lie online, and the good old Yellow Pages book can never be wrong.
Re: Things we learned about LLMs in 2024
#369Earlier quoted context omitted.
I'm surprised at the description that it's "useless" as a programming / design partner. Even if it doesn't make "elegant" code (whatever that means), it's the difference between an app existing at all, or not. I built and shipped a Swift app to the App Store, currently generating $10,200 in MRR, exclusively using LLMs. I wouldn't describe myself as a programmer, and didn't plan to ever build an app, mostly because in…
Which service/LLM performed the best for you?
Sonnet-3.5 seemed to churn out the best code, so I would default to that. If it got stuck in circular reasoning, 4o would usually resolve it. Then back to Sonnet.
Re: Things we learned about LLMs in 2024
#370About "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 a lot of the confusion is in how we approach LLMs. Perhaps stemming from the over-broad term “AI”. There are certain classes of problems that LLMs are good at. Accurately regurgitating all accumulated world knowledge ever is not one, so don’t ask a language model to diagnose your medical condition or choose a political candidate. But do ask them to perform suitable tasks for a language model! Every day by aut…
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