Yeah, I'd caution against taking experience of even 1 year old in this field. In a year, models both shrink and improve performance at the same time. At least in local AI space, the best we got going last July was something like gpt-oss-120b and you couldn't do anything real with it. Today, you run Qwen3.8-Flash-Next at the same computer and it's amazingly good.
In my experience, AI is better at disproving hypothesis and hunting bugs than it is at coming up with valid solutions and implementing them. I think the scientific approach of producing testable hypothesis which are invalidated and then converted into verifiable fact works the best. It can read your code and point out the ways it fails, and it can write tests that prove existence of a bug and when it has these tests, then it can also prove that the solution bites. AI can write code, but it is even better at reading code, in my experience. Possibly the closed AIs are much better than the things I can run on a computer myself, but at least so far, the tests indicate that they are only a few months ahead, in practice. They are big and good, no doubt, but they are not mine, and I don't like depending on what I can't run myself.
In my experience, AI is better at disproving hypothesis and hunting bugs than it is at coming up with valid solutions and implementing them.
Exactly the same here. It's weird because hardly anyone else seems to have this experience. I think I actually lose time correcting an AI's mistakes than if just wrote the code myself, but AI always finds my mistakes and I reckon I save a lot of time overall not getting bitten by them later.
Yeah, this. When you see the efficacy gains from something like the evolution of the DeepSeek line of models from V3 through V4 Flash/Pro early to release versions through to V4.1 Flash now, you see how LLM-powered Agentic systems start to compete skill-wise with above average (but not top15%) human labor and absolutely win in terms of cost.
Further on from that, when you approach Agent utilization from a scientific method approach where you start with testable hypotheses (aka test-driven design) and you build up a project such that it can only function in a way that is testable and verifiable, you find that you've created an environment where, often but not always, the Agent must build a functional product.
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u/audioen 2d ago
Yeah, I'd caution against taking experience of even 1 year old in this field. In a year, models both shrink and improve performance at the same time. At least in local AI space, the best we got going last July was something like gpt-oss-120b and you couldn't do anything real with it. Today, you run Qwen3.8-Flash-Next at the same computer and it's amazingly good.
In my experience, AI is better at disproving hypothesis and hunting bugs than it is at coming up with valid solutions and implementing them. I think the scientific approach of producing testable hypothesis which are invalidated and then converted into verifiable fact works the best. It can read your code and point out the ways it fails, and it can write tests that prove existence of a bug and when it has these tests, then it can also prove that the solution bites. AI can write code, but it is even better at reading code, in my experience. Possibly the closed AIs are much better than the things I can run on a computer myself, but at least so far, the tests indicate that they are only a few months ahead, in practice. They are big and good, no doubt, but they are not mine, and I don't like depending on what I can't run myself.