151–160 of 247 posts
Earlier quoted context omitted.
It sounds silly but lately I've been able to hit flow states doing exactly this.
While it can be productive, I never feel like I'm in a flow state doing this. The general context switching can actually be a bit draining to me.
I've stumbled into a couple of different ways to work with AI, each with their advantages and disadvantages:
* Ask it to solve the issue and trust the results. You're outsourcing your thinking, and lose understanding of the code. The result might work, or it might not. Chances are it will work, but your code slowly grows messier.
* Ask it to solve the issue and review the results. This should help you understand the resulting code, and give you a better chance of setting the AI straight when it messes up. But you're still outsourcing your thinking, and not thinking of the solution yourself still means you lose touch with the code. But more importantly, reviewing is the boring part of software development.
* You write the code, and let the AI review it. In a way, I think this should be the sweet spot. It doesn't make you faster, but your quality should go up. AI is very good at reviewing, and often finds issues that humans skip over. This is the quality over quantity solution. More than code, I think this is particularly important for writing high-stakes non-code documents, like financial reports for customers. Quality is really important there.
* You tell the AI how to solve the issue. The AI still writes the code, but it receives tighter guidance from you. This is what I usually end up doing. I like to think this results is better code. It certainly gives me the impression I understand better what the code does. And I do tackle much larger amounts of code, but I check what the AI does, and often push back on its suggestions and assumptions. I think this is a nice middle ground between speed and quality.
* Full agent mode. Let the AI do everything. Let multiple agents work simultaneously doing everything. You lose control and your mental model. You're going to have to trust whatever the AI is doing. Something it will be correct, sometimes not. Let's hope you never have to personally touch that code anymore. But it sure is fast.
I, like many nerds of the same stripe, have a dragon's hoard of every PC component I've owned in the last 20 years. I've attached as much of it to my homelab as is practical, but there's still a pile of GPUs from the last decade plus. So I decided to load up everything with more then 3GB of VRAM into various machines on the network. Anything that could conceivably run an LLM of any utility. I've been experimenting wi…
Too many people are using LLMs to shortcut knowledge completely. I have more work than ever fixing the security issues on vibe coded apps, and I don't think it will slow down any time soon.
As Boris said, you shouldn't be manually prompting anymore but asking the AI to prompt itself, in the form of workflows. I usually have 3 to 5 different sessions running at once all autonomously.
If I had to pay the API rate to have one LLM rewrite what I just told it to another one, then have the main one get busy or start waiting for subagents rather than be something I actively steer, and come back to the subagent being either gone or left hanging for hours blocking another one from doing the thing I actually asked it to do, I would never do it through Claude Code. It costs me only a few seconds to ask it do something and I almost never hit my usage limits without them, so I basically only use them because they're free.
For my own bulk workloads I just put codex and my own harness in container and built an API dispatcher for the repeatable workloads I care about. You can just pull from a queue or click a button or run a script, or use LLMs to launch them or review them, but it doesn't make any sense to me to have them "monitor" or manage each other passively because you just end up doing it anyway without a real API to control it.
"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
there are so many stupid little changes (e.g., rewording or deleting their numerous comments) that would be done better and faster by me but it's usually just less cumbersome to keep asking the machine to tweak it instead.
I, like many nerds of the same stripe, have a dragon's hoard of every PC component I've owned in the last 20 years. I've attached as much of it to my homelab as is practical, but there's still a pile of GPUs from the last decade plus. So I decided to load up everything with more then 3GB of VRAM into various machines on the network. Anything that could conceivably run an LLM of any utility. I've been experimenting wi…
That's how I code nowadays: 1. Start a session. 2. Grill my requirements. 3. Write an ADR, then either start implementing or separate into pieces. 4. Review the code on pyor.review, compared to Github, Pyor allows me to categorize the files and changes then review the important stuff and skim the noise it identifies. 5. Since I can do local reviews with Pyor, I can do that with Claude and feed back my comments to be…
If so, have you compared it to obra’s superpowers?
"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