Do you have any ideas on how to harness AI to only change specific parts of a system or workpiece? Like "I consider this part 80/100 done and only make 'meaningful' or 'new contributions' here" ...?
My AI Adoption Journey
61–70 of 420 posts
Re: My AI Adoption Journey
#62This is such a lovely balanced thoughtful refreshingly hype-free post to read. 2025 really was the year when things shifted and many first-rate developers (often previously AI skeptics, as Mitchell was) found the tools had actually got good enough that they could incorporate AI agents into their workflows. It's a shame that AI coding tools have become such a polarizing issue among developers. I understand the reasons…
but annoying hype is exactly the issue with AI in my eyes. I get it's a useful tool in moderation and all, but I also experience that management values speed and quantity of delivery above all else, and hype-driven as they are I fear they will run this industry to the ground and we as users and customers will have to deal with the world where software is permanently broken as a giant pile of unmaintainable vibe code and no experienced junior developers to boot.
Re: My AI Adoption Journey
#63> Break down sessions into separate clear, actionable tasks. Don't try to "draw the owl" in one mega session. This is the key one I think. At one extreme you can tell an agent "write a for loop that iterates over the variable `numbers` and computes the sum" and they'll do this successfully, but the scope is so small there's not much point in using an LLM. On the other extreme you can tell an agent "make me an app tha…
And lately, the sweet spot has been moving upwards every 6-8 weeks with the model release cycle.
Re: My AI Adoption Journey
#64This is such a lovely balanced thoughtful refreshingly hype-free post to read. 2025 really was the year when things shifted and many first-rate developers (often previously AI skeptics, as Mitchell was) found the tools had actually got good enough that they could incorporate AI agents into their workflows. It's a shame that AI coding tools have become such a polarizing issue among developers. I understand the reasons…
Your sentiment resonates with me a lot. I wonder what we’ll consider the inflection point 10 years from now. It seemed like the zeitgeist was screaming about scaling limits and running out of training data, then we got Claude code, sonnet 4.5, then Opus 4.5 and no ones looked back since.
Re: My AI Adoption Journey
#65> Break down sessions into separate clear, actionable tasks. Don't try to "draw the owl" in one mega session. This is the key one I think. At one extreme you can tell an agent "write a for loop that iterates over the variable `numbers` and computes the sum" and they'll do this successfully, but the scope is so small there's not much point in using an LLM. On the other extreme you can tell an agent "make me an app tha…
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Re: My AI Adoption Journey
#66Earlier quoted context omitted.
This is actually an aspect of using AI tools I really enjoy: Forming an educated intuition about what the tool is good at, and tastefully framing and scoping the tasks I give it to get better results. It cognitively feels very similar to other classic programming activities, like modularization at any level from architecture to code units/functions, thoughtfully choosing how to lay out and chunk things. It's always b…
I agree that framing and scoping tasks is becoming a real joy. The great thing about this strategy is there's a point at which you can scope something small enough that it's hard for the AI to get it wrong and it's easy enough for you as a human to comprehend what it's done and verify that it's correct. I'm starting to think of projects now as a tree structure where the overall architecture of the system is the main…
[Edit: I may have been replying to another comment in my head as now I re-read it and I'm not sure I've said the same thing as you have. Oh well.]
I agree. This is how I see it too. It's more like a shortcut to an end result that's very similar (or much better) than I would've reached through typing it myself.
The other day I did realise that I'm using my experience to steer it away from bad decisions a lot more than I noticed. It feels like it does all the real work, but I have to remember it's my/our (decades of) experience writing code playing a part also.
I'm genuinely confused when people come in at this point and say that it's impossible to do this and produce good output and end results.
Re: My AI Adoption Journey
#67For the AI skeptics reading this, there is an overwhelming probability that Mitchell is a better developer than you. If he gets value out of these tools you should think about why you can't.
The AI skeptics instead stick to hard data, which so far shows a 19% reduction in productivity when using AI.
There is no such hard data. It's just research done on 16 developers using Cursor and Sonnet 3.5.
Re: My AI Adoption Journey
#68It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever. I constantly have to tell it what I don't like or what can be improved or need to request clarifications or alternative solutions. This is what's so annoying about it. It's like a child that does the same errors again and again. But couldn't it adjust itself with the goal of reducing the error bit by bit? Woul…
> It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever.
Your improvement is someone else's code smell. There's no absolute right or wrong way to write code, and that's coming from someone who definitely thinks there's a right way. But it's my right way.
Anyway, I don't know why you'd expect it to write code the way you like after it's been trained on the whole of the Internet & the the RLHF labelers' preferences and the reward model.
Putting some words in AGENTS.md hardly seems like the most annoying thing.
tip: Add a /fix command that tells it to fix $1 and then update AGENTS.md with the text that'd stop it from making that mistake in the future. Use your nearest LLM to tweak that prompt. It's a good timesaver.
Re: My AI Adoption Journey
#69> At a bare minimum, the agent must have the ability to: read files, execute programs, and make HTTP requests. That's one very short step removed from Simon Willison's lethal trifecta.
I'm definitely not running that on my machine.
The way this is generally implemented is that agents have the ability to request a tool use. Then you confirm "yes, you may run this grep".
Re: My AI Adoption Journey
#70Earlier quoted context omitted.
This is the most common answer from people that are rocking and rolling with AI tools but I cannot help but wonder how is this different from how we should have built software all along. I know I have been (after 10+ years…)
I think you are right, the secret is that there is no secret. The projects I have been involved with thats been most successful was using these techniques. I also think experience helps because you develop a sense that very quickly knows if the model wants to go in a wonky direction and how a good spec looks like. With where the models are right now you still need a human in the loop to make sure you end up with code…
> The bottle neck has gone from writing code to reading code.
This has always been the bottleneck. Reviewing code is much harder and gets worse results than writing it, which is why reviewing AI code is not very efficient. The time required to understand code far outstrips the time to type it.
Most devs don’t do thorough reviews. Check the variable names seem ok, make sure there’s no obvious typos, ask for a comment and call it good. For a trusted teammate this is actually ok and why they’re so valuable! For an AI, it’s a slot machine and trusting it is equivalent to letting your coworkers/users do your job so you can personally move faster.