i use AI wrong (and you should too)
Lars Faye explains why he deliberately rejects the "AI-native, agents-first" workflow and instead treats models as a delegation layer rather than an abstraction layer: he states the intent, the model handles the how, and he still reads and owns the code. He delegates along four buckets — rote work, refactors, research, and reinforcement (self-taught tutorials) — and demos each with Zed as his editor and OpenCode's cheap open-weight models (GLM, Kimi, MiMo) on a $10/month plan. Examples include pulling formulas out of an Excel spreadsheet into PHP, swapping a browser alert for a custom modal in a years-old codebase, and using a model as an interactive Laravel tutor. His closing point: code generation solved the typing problem, not the thinking problem.
- He frames AI models as a delegation layer, not an abstraction layer — with abstraction you start with the how and get a result; with delegation you start with intent and the model figures out the how, like handing work to another developer.
- His delegation criteria are four buckets: rote work, refactors, research, and reinforcement (learning/tutorials).
- He moved from Cursor to Zed because Cursor swapped heavily toward an agent-first interface; he still wants to see directories, files, and code, with agent chat available on the side.
- The most valuable use of these models to him isn't code generation but "interactive documentation" — conversing with your own codebase and learning from it, like a dynamic tutorial generator.
- He uses OpenCode's $10/month Go plan with open-weight models (GLM, Kimi, MiMo), says they handle everything he throws at them because his requests are isolated and surgical, and calls open-weight models badly slept on.
- Demo 1: MiMo 2.5 Pro extracted solar-calculator formulas from an Excel sheet into PHP functions, kept the original calculations as comments, and wrote verification scripts against the spreadsheet values — but put them in the wrong file because he wasn't explicit enough.
- Demo 2: replacing a browser alert with his existing custom modal component in his years-old Wallaby platform; he gave the model the file locations up front to reduce wandering and burned tokens, then hand-tweaked the copy and Tailwind classes himself rather than re-prompting.
- Demo 3: he had a model generate a structured Laravel crash course that quizzes him and tells him where to write code, warning that you are both tutor and student, so "what you ask for, you will certainly receive."
- His conclusion: code generation solved the typing problem that snippets, autocomplete, and frameworks have chipped at for decades, but logic, creativity, strategy, and critical thinking are all still required.
This is the conceptual spine of the whole video and the clearest articulation of his alternative to agents-first workflows.
I do not feel that these models are an abstraction layer. I think that they are a delegation layer. With an abstraction layer, you begin with the how and you end up with your intended result. With a delegation layer, you start with your intent and the model handles the how.
A counterintuitive take that reframes what these tools are actually for, and it sets up his Laravel tutoring example later.
Ironically, it's the code generation aspect of these models that is not even the most useful and impressive to me, but it's this almost interactive documentation nature of them and the ability to converse with your code that I find to be by far the most transformative and useful aspect.
I'm not trying to get away from looking at code. I'm not trying to get out from under just writing code.
You are both the tutor and the student at the same time. You are generating your own lesson plan. So what you ask for, you will certainly receive.
Code generation has certainly solved the typing problem... The part that required all the logic, creativity, strategy, problem-solving, critical thinking, all of that is still there just with less typing.