141–150 of 247 posts
"I haven't been able to enter flow state like I can when I hand write code." new flow state is having 10 terminal tabs in diff worktrees and trying to remember what each bit is
It sounds silly but lately I've been able to hit flow states doing exactly this.
Then I pass that to another LLM provider to review and check for any bloat that can be cut or blindspots that need to be addressed.
Finally I get a test plan to help me test different components directly and in debug mode. I then ask LLMs to implement in stages where I can test them in small components as possible.
I think I end up spending more time (easily 2x) than hand rolling. But the upside is the design is more thought out compared to hand rolled code. It has fewer accidental complexities and I have a clear mental model of the entire design that can also be shared to others through the document
I've now use GPT-5.5 as the primary. Code quality was just higher. I tinker and do R&D with open models then come back and refactor my slop into usable code I can save for future use.
A more exciting attempt the other day: I fed CC a PRD and ui-spec I'd drafted with Fable 5 (no reason, I just happened to be on my phone), told it to auto-approve commands, and let it fully build and test the project in Chrome overnight. I woke up to a mostly-working app ready for me to QA. Skipping the whole build cycle and going straight to watching it come together during testing was genuinely great.
The one thing I'm still figuring out is the trust side of fully-unattended runs like that. Curious how others are handling auto-approval when nobody's watching.
LLMs have barely been around for a few years. People are addicted, seriously addicted, to the next shiny workflow. It’s like JavaScript frameworks all over again.
The way to get over this addiction, is to just stop talking about it. Stop reading another BS article about how someone used agents to do some 10x improvement already. Unsubscribe from company channels where people endlessly bikeshed how to use some new LLM or agent harness or whatever.
By now, everything you need to know to maximize productivity has already been discovered. There are no new tricks, and even if there were, you’re really not missing out, the old tricks still work just fine. Just get out there and work.
But at the same time if you do like this you can't do that insane multitasking I see a lot of devs doing where they juggle multiple agents doing separate tasks (maybe even completely separate tasks on different git branches). I _really_ hate working like this and only do it if I know on of my prompts will take 10+ min. Usually an initial prompt for a large task where I will move to my usual 1-file-per-prompt style later.
Of note that I am working on a ~10 year old codebase with a lot of custom instructions for agents and a lot of code to dig through to get things done. I feel a lot of people are conflating using LLMs to start hobby projects and extrapolating the workflow to real world large codebases.
In this way I spend most of my time building understanding of existing code and understanding the impact of my changes. My company is heavy into AI use and I find I am pushing out more code and much cleaner code than most. The gaps that appear during review are usually product understanding gaps and not code failures, and my LLM spend is somehow less then most.
I find this iterative process is much more inline with building flow than spending 3 hours writing a spec and wait a half hour for it to build a monolith PR.
Earlier quoted context omitted.
I miss feeling like I was "in the zone", but I haven't been able to achieve it in years. Between having kids and a work situation a few years back, it is like my brain expects to be interrupted at any moment, so won't get there.
Teaching your kids to have a calendar and focus blocks (once they're old enough) is as good a habit to teach them as it is for you.
1. In my experience, well-articulated prompts are the most important part. You need to tell the model exactly what and how to do to avoid hallucinations. Especially in system design, write what the end result should be, how and let the model reason and look at the existing infra first, then plan the implementation. In my experience, there is little to no coding that needs to be done after model is done implementing. Make sure to let it implement in phases, with extensive tests.
2. Model choice. It is obvious, but Claude models are the current SOTA. In my experience, Opus 4.7 extra high is the perfect balance of speed and cost-efficiency. In my experience, OpenAI models were worse in system design, but faster and better at understanding the end result. Mostly used them to verify the bigger picture. Also used composer in Cursor. Was surprised how easy it was to do web design with it.
3. Long horizon tasks. Make models build plans. Very thorough plans - for a feature or a product. It is much more aligned with a written plan.
There are more details, but this is what I noticed so far myself.