Live data from Hacker News

Don't trust large context windows

garrit.xyz

151–160 of 211 posts

Re: Don't trust large context windows

#151

Earlier quoted context omitted.

>what "sense of rigour"? it's way too soon to put those rose-tinted glasses on. I don't think OP is claiming that prior to LLM coding everything in the software development world was super rigorous (I assume that's effectively what you mean with the "rose-tinted glasses" comment). But rigor was actually possible and in a deterministic way too, which is fundamentally impossible with LLMs. You can build all kinds of gu…

Aren’t human coders non-deterministic? There’s no guarantee two people with otherwise identical levels of experience will always write identical code. Any software engineering practice that had enough review and feedback to work with humans should work more or less the same with AI coders. It’s when someone tries replacing an entire team or an entire process with a single prompt that they get in trouble.

>Aren’t human coders non-deterministic?

Sure, but LLMs are non-deterministic in ways that no sane human ever would be. See the "Is it better to drive or walk to the carwash" scenario from a few months ago as one of many, many examples. Or a personal example I encountered just a week ago: I asked Claude (Opus 4.8 in case any of the "you aren't using the latest model that totally fixes that issue" types are interested) to convert a bunch of DB calls that currently use raw ADO.NET calls to use Dapper instead.

The projects in this repo were on .NET 4.8.1 and were still using the older format for the .csproj file instead of the newer (and far better) "SDK-style" format that Microsoft introduced a few years ago. It tried to use the dotnet CLI to add references to Dapper, even though the older format of .csproj doesn't work with that. The dotnet CLI returned errors about trying to add the package references for Dapper, which Claude completely ignored while continuing to try and convert the ADO.NET calls to Dapper. And at the end it tried building the project, which of course failed, and then it confidently informed me that the conversion had been completed successfully and that the build completed successfully and all tests were passing successfully, even though the output from the build it had done immediately prior clearly told the LLM otherwise.

A real human, despite being non-deterministic, would have caught the issue at multiple stages. They would have seen the error when trying to add the reference. If they ignored that then they would have seen the red squiggly lines all over the (deterministic) IDE telling them there was something wrong, along with autocomplete for Dapper calls not working. And if they continued to ignore those and managed to keep going anyways, they would have clearly seen that the build failed, with tons of errors specifically about references to Dapper failing to resolve. An LLM keeps going on its merry way in ways that effectively 0 humans would.

Re: Don't trust large context windows

#152
post #74
post #40

I've been able to avoid context size issues by applying one simple constraint to my agent loop. What I do is prevent all tool calling in the user's top-level conversation thread. Anything that needs to tool call must happen in a recursive invoke of the agent, which returns whatever results to caller. I can keep the same high level conversation going for an entire day over a million LOC+ codebase without ever hitting…

This makes intuitive sense. Can I ask what harness you're using that allows you to configure the constraint and how?

It's a custom agent loop. There are no other parties involved here. Just vanilla C#/.NET and the OpenAI DLL.

Re: Don't trust large context windows

#153
post #123

I guess I am mostly enjoying learning the fundamentals of AI stuff, even though I disagree with the direction it is going. But I am struggling to put into words how alarming I find the comments on threads like this — all sorts of good-natured anecdotes about how XYZ works for them that are more like the suggestions in pet care or cookery threads on Facebook. (Or worse still, like any Facebook 3D printing group: anyon…

It's in the hype train's interest to keep the actual value unknowable. If you quantify what you're paying for then the FOMO is greatly reduced.

Re: Don't trust large context windows

#154
> the dumb zone, where attention drops off and the model starts forgetting what you told it five minutes ago

I use opus 1m context all day every day at work and I simply have never encountered this. I don’t even think about context windows anymore I just let it do what it wants re compaction. Hard for me to understand where this article is coming from.

Re: Don't trust large context windows

#155
post #123

I guess I am mostly enjoying learning the fundamentals of AI stuff, even though I disagree with the direction it is going. But I am struggling to put into words how alarming I find the comments on threads like this — all sorts of good-natured anecdotes about how XYZ works for them that are more like the suggestions in pet care or cookery threads on Facebook. (Or worse still, like any Facebook 3D printing group: anyon…

What sense of rigour is going to be in a field (LLM usage as a user) where models, context sizes, tooling and broadly "rules" (scary quotes) change every few weeks? There is no literal change to have a scientific approach to anything, churn is too high, there are papers about model XYZ v 12345 from a few months ago that are already old because there is model ABC on version 54321 that addresses half of the issue shown in the paper and add 3 new problems though.

Re: Don't trust large context windows

#156
Working in the era of 200k context window meant I had to narrowly scope tasks to fit in the context window, forcing me to think about how to reduce complexity and naturally resulting in atomic work. 1M context windows and the promise that the latest models are "better at long running tasks" made me lazy in how I scope tasks and quality got worse. I now went back to narrow-scoping one session per task and zero compaction, trying not to go past 400k context window. If I end up with a long session, I was likely too ambitious and should have broken up the task.

Re: Don't trust large context windows

#157

Earlier quoted context omitted.

>what "sense of rigour"? it's way too soon to put those rose-tinted glasses on. I don't think OP is claiming that prior to LLM coding everything in the software development world was super rigorous (I assume that's effectively what you mean with the "rose-tinted glasses" comment). But rigor was actually possible and in a deterministic way too, which is fundamentally impossible with LLMs. You can build all kinds of gu…

Aren’t human coders non-deterministic? There’s no guarantee two people with otherwise identical levels of experience will always write identical code. Any software engineering practice that had enough review and feedback to work with humans should work more or less the same with AI coders. It’s when someone tries replacing an entire team or an entire process with a single prompt that they get in trouble.

TBD on if the calculator can properly review and participate in the feedback loop with itself.

They also don't learn, so they never get less unpredictable. You can't give the senior robot the production keys and expect it won't delete prod.

Re: Don't trust large context windows

#159
post #123

I guess I am mostly enjoying learning the fundamentals of AI stuff, even though I disagree with the direction it is going. But I am struggling to put into words how alarming I find the comments on threads like this — all sorts of good-natured anecdotes about how XYZ works for them that are more like the suggestions in pet care or cookery threads on Facebook. (Or worse still, like any Facebook 3D printing group: anyon…

The arbitrary and non-deterministic nature of LLM workflows gives me full on ick. As an old embedded/systems guy I have always prioritized determinism and repeatability in my workflows. But damn, agents are amazing and I'm enjoying being a "thought process designer". I'm not going back. Even if AI development stops today my career will never be the same.

I felt the same way about the non-determinism but realized it can be really beneficial to have a machine that can fairly reliably turn non-determinism into determinism.

I’m working on a tiny agent harness at home to learn and the process of taking human speech and turning it into agent tool calls that output something generally deterministic depending on how the tool is defined is so interesting.

One of the big takeaways is you really only have to rely on the non-determinismdeterminism translation layer once when you switch between the two domains. You can obviously rely on it more if you want, and that’s probably faster because determinism is hard, but you don’t need too do that.

Re: Don't trust large context windows

#160
post #74
post #40

I've been able to avoid context size issues by applying one simple constraint to my agent loop. What I do is prevent all tool calling in the user's top-level conversation thread. Anything that needs to tool call must happen in a recursive invoke of the agent, which returns whatever results to caller. I can keep the same high level conversation going for an entire day over a million LOC+ codebase without ever hitting…

This makes intuitive sense. Can I ask what harness you're using that allows you to configure the constraint and how?

You can do this in opencode and pi (haven't used), by defining your own agents or overriding the built-in ones, so in your primary agent you can disable all tools and give it good instructions for how to delegate

I imagine most harnesses should have a way to do this today, if they don't, get a new one. OpenCode i.e. is highly customizable, Claude and VS Code both support a ton as well including custom agents (though unclear if you can create custom top-level in claude-code)

https://opencode.ai/docs/agents/

https://code.claude.com/docs/en/sub-agents

https://code.visualstudio.com/docs/agent-customization/custo...

Post reply on HN