I really enjoyed this article. I think the author is precisely right and I've been saying this for a long time. There's a ton of extremely interesting low hanging fruit that can vastly improve the effectiveness of even currently existing models hiding in how we design our agent harnesses; enough to — at least until we hit diminishing returns — make as much or more of a difference than training new models!

I think one of the things that this confirms, for me at least, is that it's better to think of "the AI" as not just the LLM itself, but the whole cybernetic system of feedback loops joining the LLM and its harness. Because, if the harness can make as much if not more of a difference, when improved, as improvements to the model itself, then they have to be really considered equally important. Not to mention the fact that models are specifically reinforcement learned to use harnesses and harnesses are adapted to the needs of models in general or specific models. So they necessarily sort of develop together in a feedback loop. And then in practice, as they operate, it is a deeply intertwined feedback loop where the entity that actually performs the useful work, and which you interact with, is really the complete system of the two together.

I think thinking like this could not only unlock quantitative performance improvements like the ones discussed in this blog post, but also help us conceive of the generative AI project as actually a project of neurosymbolic AI, even if the most capital intensive and a novel aspect is a neural network; and once we begin to think like that, that unlocks a lot of new options and more holistic thinking and might increase research in the harness area.

My Weird Hill is that we should be building things with GPT-4.

I can say unironically that we haven't even tapped the full potential of GPT-4. The original one, from 2023. With no reasoning, no RL, no tool calling, no structured outputs, etc. (No MCP, ye gods!) Yes, it's possible to build coding agents with it!

I say this because I did!

Forcing yourself to make things work with older models forces you to keep things simple. You don't need 50KB of prompts. You can make a coding agent with GPT-4 and half a page of prompt.

Now, why would we do this? Well, these constraints force you to think differently about the problem. Context management becomes non-optional. Semantic compression (for Python it's as simple as `grep -r def .`) becomes non-optional. Bloating the prompt with infinite detail and noise... you couldn't if you wanted to!

Well, surely none of this is relevant today? Well, it turns out all of it still is! e.g. small fix, the "grep def" (or your language's equivalent) can be trivially added as a startup hook to Claude Code, and suddenly it doesn't have to spend half your token budget poking around the codebase, because -- get this -- it can just see where everything is... (What a concept, right?)

-- We can also get into "If you let the LLM design the API then you don't need a prompt because it already knows how it should work", but... we can talk about that later ;)

Ive been working on Peen, a CLI that lets local Ollama models call tools effectively. It’s quite amateur, but I’ve been surprised how spending a few hours on prompting, and code to handle responses, can improve the outputs of small local models.

https://github.com/codazoda/peen

If I remember, both Claude Code and OpenAI Codex "harnesses" improved themselves now.

OpenAI used early versions of GPT-5.3-Codex to: debug its own training process, manage its deployment and scaling and diagnose test results and evaluation data.

Claude Code have shipped 22 PRs in a single day and 27 the day before, with 100% of the code in each PR generated entirely by Claude Code.

Also, yes, I'm aware that I use a lot of "its not just X, its Y." I promise you this comment is entirely human written. I'm just really tired and tend to rely on more wrote rhetorical tropes when I am. Believe me, I wrote like this long before LLMs were a thing.

I was just looking at the SWE-bench docs and it seems like they use almost an arbitrary form of context engineering (loading in some arbitrary amount of files to saturate context). So in a way, the bench suites test how good a model is with little to no context engineering (I know ... it doesn't need to be said). We may not actually know which models are sensitive to good context-engineering, we're simply assuming all models are. I absolutely agree with you on one thing, there is definitely a ton of low hanging fruit.

2026 is the year of the harness.

Once you begin to see the “model” as only part of the stack, you begin to realize that you can draw the line of the system to include the user as well.

That’s when the future really starts hitting you.

So deep your comment. Asking for a friend, how did you manage to have the em dash — in your keyboard ?