Instead of reading AI-slop explanations full of creaky metaphors, I'd advise you to just build your own basic coding harness from scratch. You will learn so much:
2) Instead of relying on an api key like in the blog post in 1), you might want to use a local model that can do tool calls like Qwen 27B.
3) Use a frontier LLM for self-study and to help you write the harness boilerplace if you want, but ask it to force you to write the interesting bits by hand (e.g. the main loop structure, the tool call functions)
4) Amaze your friends and colleagues with your newfound understanding of how agents actually work under the hood
5) Go even deeper and get into Inference engineering, LLM architecture internals, etc.
6) Profit. Perhaps? (Or at least enjoy the learning journey.)
Harness optimization is essential. Doesn’t matter how smart the model is if the tools you give it are ineffective, or the task is under defined. It needs invent the solution from first principles every time.
The LLM is the oven, the harness is the kitchen. You prep all your ingredients and put it into the oven (flour, water, salt, sugar). It transforms those inputs into a finished product (cake, loaf of bread).
Nah, that's a single event ("bake"). Interaction of a harness and an LLM could potentially be a continuous process.
I don't think we've scratched the surface of how the harness can fold, spindle and mutilate the LLM. It just seems unlikely to me that we happened to stumble on the best possible interface a couple of years out of the gate.
I don’t understand why this article makes you ask that, can you elaborate? The simple answer to why we didn’t have LLMs in 2005 is that nobody had the requisite ideas that make them work effectively (independent of any agentic harnesses and Schmidhuber notwithstanding) and there wasn’t sufficient clarity in the direction to scale for anyone to build anything at scale (also a requisite for them working well).
Surely the only problem isn’t the knowledge of how to train and the confidence it would work. Wouldn’t it also have required a 1 or 2 order of magnitude increase in computing power over 2005-era supercomputers to be able to train a modern LLM in a reasonable time (“reasonable” meaning “finish training before GPT-3 comes out in 15 years”)?
I think it’s plausible that attention could have been invented earlier and GPT demonstrated at smaller scale on earlier hardware. Then hardware timelines might have accelerated (or alternative approaches explored) and we’d have gotten to where we are earlier.
Fair on the prose. I explained how it was written in my top-level comment. If any of the ideas themselves seem wrong to you, I'd like to hear which ones.
1) This old but still excellent blog post by Thorsten Ball is a good place to start (old in LLM years... written 18 months ago!): https://ampcode.com/notes/how-to-build-an-agent
2) Instead of relying on an api key like in the blog post in 1), you might want to use a local model that can do tool calls like Qwen 27B.
3) Use a frontier LLM for self-study and to help you write the harness boilerplace if you want, but ask it to force you to write the interesting bits by hand (e.g. the main loop structure, the tool call functions)
4) Amaze your friends and colleagues with your newfound understanding of how agents actually work under the hood
5) Go even deeper and get into Inference engineering, LLM architecture internals, etc.
6) Profit. Perhaps? (Or at least enjoy the learning journey.)
We did a harness optimization on Jev. Raw it was no where near as good as deekseek, but optimized it was significantly better and a tiny fraction of the cost. https://kiln.tech/blog/auto_optimizing_jev_with_autoresearch
I don't think we've scratched the surface of how the harness can fold, spindle and mutilate the LLM. It just seems unlikely to me that we happened to stumble on the best possible interface a couple of years out of the gate.
https://hindex.rensberger.xyz/
Why this spate of pretending, against all evidence, that LLMs are alive, or even remotely structured like biological brains?
Why do we have so many people pretending, or disingenuously claiming, that they're alive, have thoughts, feelings, etc?
And why, oh why, do we have to read yet more LLM propaganda written by LLMs?
You could view LLMs as a super-charged mutation function inside a genetic programming harness.
On the other hand, Google was openly collecting data with a view towards building AI.
You can make the argument that some eng aren't good at writing, to which I say — it's worthwhile to improve writing skills.
No? Where?