351–360 of 615 posts
> I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that. This 100%. “Agentic” especially as a buzzword can piss off
I find that Anthropic has a good, clarifying set of definitions with examples: https://www.anthropic.com/research/building-effective-agents
The money is still flowing, for now, to subsidize that fiasco but as soon as that starts to slow, even just a bit, things are gonna get bumpy real quick. Super excited about this tech but there are dark storm clouds building on the horizon and absent a major “moat” breakthrough it’s gonna get rough soon.
> I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that. This 100%. “Agentic” especially as a buzzword can piss off
The software "has agency"? That is, I can entrust it to carry out the task I've described, to completion, without telling it how to perform the task?
My problem is when people use that definition (or any other) without clarifying, because they assume it's THE obvious definition.
Earlier quoted context omitted.
Have you tried the latest open weight models? They're SO MUCH better today than they were even six months ago. If I was in an environment that didn't allow hosted API models I'd absolutely be looking into the various Llama 3 models or Qwen2.5-Coder-32B.
Legal does not even want us running offline models for reasons. I assume that comes down to not knowing what offline-only means, but such is life.
Earlier quoted context omitted.
Have you tried the latest open weight models? They're SO MUCH better today than they were even six months ago. If I was in an environment that didn't allow hosted API models I'd absolutely be looking into the various Llama 3 models or Qwen2.5-Coder-32B.
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).
files-to-prompt . -e py -e md -c | pbcopy
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'll pipe it straight into my own LLM CLI tool and ask questions that way:
files-to-prompt . -e py -e md -c | \
llm -m gemini-2.0-flash-exp 'which files handle JWT verification?'
llm chat -c
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'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…
Not just the development of the code but the entire the thing from the code, infra, auth, cc payments, etc.
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Imagine not needing Claude to do any of that.
This is one of those things I like about Claude. I’m hitting my 40th year as a professional software developer and architect. I’ve written thousands of blocks of code from scratch. It gets boring. But then in the 2000’s me (and everyone else) started building code generators, often from ERD structures, but also UML designs. These tools were massively useful and (initially) reduced costs. The future balls of mud probl…
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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…
Due to unexpected capacity constraints, Claude is unable to reply to this message.
P.S my script uses local models - no capacity constraints (apart from VRAM!)
RE: Slop: Having Slop generations from an LLM is a choice. There are so many tricks to make models genuinely creative just at the sampler level alone. https://github.com/sam-paech/antislop-sampler https://openreview.net/forum?id=FBkpCyujtS
This won't solve anything. There's a myriad of sampling strategies, and they all have the same issue: samplers are dumb. They have no access to the semantics of what they're sampling. As a result, things like min-p or XTC will either overshoot or undershoot as they can't differentiate between the situations. For the same reason, samplers like DRY can't solve repetition issues. Slop is over-representation of model's s…
DRY does in fact solve repetition issues. You're not using the right settings with it. Set the penalty sky high like 5+. Yes that means you're going to have to modify the ui_paramas in oobabooga cus they have stupid defaults on what limits you can set the knobs to.
There's several other excellent samplers which deserve high ranking papers and will get them in due time. Constrained beam search, tfs (oldie but goodie), mirostat, typicality, top_a, top-n0, and more coming soon. Don't count out sampler work. It's the next frontier and the least well appreciated.
Also, contrastive search is pretty great. Activation/attention engineering is pretty great, and models can in fact be made to choose their own sampling/decoding settings, even on the fly. We haven't even touched on the value of constrained/structured decoding. You'll probably link a similarly bad paper to the previous one claiming that this too harms creativity. Good thing that folks who actually know what they're doing, i.e. the developers of outlines, pre-bunked that paper already for me: https://blog.dottxt.co/say-what-you-mean.html
I'm so incredibly bullish on AI creativity and I will die on the hill that soon AI systems will be undeniably more creative, and better at extrapolation, than most humans.
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'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…