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625 points lukebennett | 2 comments | | HN request time: 0s | source
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LASR ◴[] No.42140045[source]
Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs?

I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go.

GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential applications.

For example, combining a human-moderated knowledge graph with an LLM with RAG allows you to build "expert bots" that understand your business context / your codebase / your specific processes and act almost human-like similar to a coworker in your team.

If you now give it some predictive / simulation capability - eg: simulate the execution of a task or project like creating a github PR code change, and test against an expert bot above for code review, you can have LLMs create reasonable code changes, with automatic review / iteration etc.

Similarly there are many more capabilities that you can ladder on and expose into LLMs to give you increasingly productive outputs from them.

Chasing after model improvements and "GPT-5 will be PHD-level" is moot imo. When did you hire a PHD coworker and they were productive on day-0 ? You need to onboard them with human expertise, and then give them execution space / long-term memories etc to be productive.

Model vendors might struggle to build something more intelligent. But my point is that we already have so much intelligence and we don't know what to do with that. There is a LOT you can do with high-schooler level intelligence at super-human scale.

Take a naive example. 200k context windows are now available. Most people, through ChatGPT, type out maybe 1500 tokens. That's a huge amount of untapped capacity. No human is going to type out 200k of context. Hence why we need RAG, and additional forms of input (eg: simulation outcomes) to fully leverage that.

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machiaweliczny ◴[] No.42140970[source]
Long context is a scam. Claude is best but it’s still gets lost with longer context
replies(2): >>42141050 #>>42141690 #
cruffle_duffle ◴[] No.42141690[source]
In my experience, the reality of long context windows doesn’t live up to the hype. When you’re iterating on something, whether it's code, text, or any document, you end up with multiple versions layered in the context. Every time you revise, those earlier versions stick around, even though only the latest one is the "most correct".

What gets pushed out isn’t the last version of the document itself (since it’s FIFO), but the important parts of the conversation—things like the rationale, requirements, or any context the model needs to understand why it’s making changes. So, instead of being helpful, that extra capacity just gets filled with old, repetitive chunks that have to be processed every time, muddying up the output. This isn’t just an issue with code; it happens with any kind of document editing where you’re going back and forth, trying to refine the result.

Sometimes I feel the way to "resolve" this is to instead go back and edit some earlier portion of the chat to update it with the "new requirements" that I didn't even know I had until I walked down some rabbit hole. What I end up with is almost like a threaded conversation with the LLM. Like, I sometimes wish these LLM chatbots explicitly treated the conversion as if it were threaded. They do support basically my use case by letting you toggle between different edits to your prompts, but it is pretty limited and you cannot go back and edit things if you do some operations (eg: attach a file).

Speaking of context, it's also hard to know what things like ChatGPT add to it's context in the first place. Many of times I'll attach a file or something and discover it didn't "read" the file into it's context. Or I'll watch it fire up a python program it writes that does nothing but echo the file into it's context.

I think there is still a lot of untapped potential in strategically manipulating what gets placed into the context window at all. For example only present the LLM with the latest and greatest of a document and not all the previous revisions in the thread.

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1. dr_kiszonka ◴[] No.42142946[source]
I like the idea of context editing and threaded conversations. I think I have seen some alternative UIs on HN that support branching.
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2. kreyenborgi ◴[] No.42144810[source]
gptel does this: https://github.com/karthink/gptel/?tab=readme-ov-file#extra-...

Here are the docs for an example of how it can look: https://news.ycombinator.com/item?id=42039895