Ask HN: How do you get into a flow state when using AI to code?

Here's the neat thing: you don't.

I've tried, and I feel like I've got closer with faster models, but ultimately the agentic loop excludes you. Even if you're asking the agent to do simple short tasks, it's still: prompt, wait, wait, wait, check, and you never really feel like you're the one in control.

The problem with faster models is also that they're more stupid, so that additionally breaks your flow when you have to fix something dumb it's done.

LLM-powered autocomplete is a bit more like it, but that tends to be either so dumb as to be a net negative, or slow enough to be useless. And autocomplete is pretty distracting for me.

I feel like I'm missing a mode that works more like a pair programmer. Perhaps a multimodal model that can talk to you about what you're writing, as you write it, and offer suggestions rather than trying to take over and do everything for you.

Short answer is I don’t and it adversely affects my job satisfaction.

I’m quite sure I’ve left money on the table over the years as a result of my reluctance to manage and mentor junior developers. Disappointing that I’ve ended up managing junior AI developers who won’t even grow as a result of the time I’m putting into them.

I genuinely have a lot of fun coding with AI, possibly more than I used to have coding normally. It’s true that I don’t spend hours on a single problem any more, but I instead spend a lot of time thinking and researching higher level issues like architecture and design. I find this work to be very rewarding because I very much enjoy learning new things. I don’t know if I would call it a flow state in the same way that coding was a flow state, but I definitely have a lot of fun learning. For instance, yesterday I was learning a lot about AWS infra, which I found very enjoyable. Before that I was learning about Fly Sprites, which it turns out are a little broken, but it was still interesting to learn about them. Pre-AI I think coding was slow enough that I would be able to learn a new thing like this once every couple of weeks, because then you'd have to go spend a ton of time on the implementation work. Now the implementation work has compressed and I get to learn new things more often, which is fun.

When I send one agent off to do work I usually begin thinking about some other unrelated problem I also want done, and then I try to spin up a parallel agent to do that as well. The thinking itself is where a lot of the deep work happens for me IMO. I probably spend like 80% of my time thinking, researching and reviewing plans. The other 20% is actually promoting.

I see a lot of people saying that agents trivialize work now - like you just push a button and an answer comes out. This is so far from my experience I actually don’t know how to bridge the gap. If you are not spending a lot of time researching you are likely going to be asking the agent to do things that don’t really make sense.

Never, has made my programming joyless and boring. If it wasn't a requirement to keep my job I'd never touch it.

I use the trust but verify method. Build in snippets, review what AI spits out and chive on if it meets my satisfaction. I cannot blindly write something with AI and utilize it.

Keeping build in steps keeps me as well as AI focused.

I use "comment-driven development" by building out the skeleton manually, writing comments instead of code, and letting the agent fill the code in (and then repeat, until done). It is a "lower level" of AI usage, compared say to full-vibe mode, spec-driven development, whatever. But I feel that it's even easier to stay in the flow, because I do not get bored by boilerplate or mundane implementation details.

"Higher levels" of AI usage are exhausting and flow-free endeavours.

(1) Spending time building a plan. I have lots of artifacts like diagrams, tables, web pages that help me think through all the details quickly. Get code snippets to reduce uncertainty, get options for architectural decisions, flesh out assumptions.

Main thing is (1) how do I verify the agent hits the happy path and (2) how can I elicit and clarify assumptions it might make.

Then follow up the build with exploring and refactoring.

(2) prioritized context switching (like playing an RTS) I have several tasks going at once, while one works I hop onto other tasks.

I usually have one or two “core” goals I’m trying to accomplish that take deeper thinking and get priority. The other tasks are smaller and require less thinking.

A lot of times I’ll have the secondary agents build research docs I can review in detail later.

Usually take mushrooms or gummies and see what happens.

This isn't even troll post. AI has killed the ability to reach flow for me, but I basically have to use it at work so <shrug>. But if I'm WFH or at night, a little help helps me stay focused and connected to my work, sometimes even with AI. Does my mind drift? sure. But that's as close as I get to flow now.

Instead of context switching to a different worktree, contemplate on next steps of the same problem. This keeps you in the loop.

I can not fathom context switching between multiple worktrees so that the PM can make JIRA graph look better.

Flow state relies on a constant information inflow that holds your attention perfectly, often hinging on competency and challenge. You can't enter it in AI coding because you need to wait for replies. It's incompatible.

I just watch YouTube in the downtime these days, or movies that I don't care too much about

I have this problem too. The only thing that has 10x'd is my boss's expectations and the number of draft PRs I have open...

For me the flow state has always come from a sensation of creative juices spawning ideas, a vision for an end result, and the deep focus that ensues in working towards that.

Such a cycle previously could take hours or days, resulting in long, deep flow states. But now I go through dozens such cycles a day.

So less of a single flow state, more so many short flow states. As for waiting, that’s when you can explore another idea in parallel. Double the flow states for me :)

Do not wait for replies, try to structure your workflow so that you are always either refining requirements for the future tasks that you are going to to give to the agent later or reviewing (sometimes also manually testing) the code that the agent has produced before.

I think that this is mostly a UI problem. Chat UI is just not a good UI for programming and the fact that the current "AI"-coding sphere has converged on it is incredibly silly.

One of of the first things that I did when I first seriously tried an LLM-based coding agent is making an ad-hoc task manager on skills and simple daemons.

So that I can interact with it using files instead of this stupid workflow of typing a prompt into the console and then just doing nothing while waiting for the response.

There is absolutely no reason not to do it asynchronously.

While I don't have the answer, I will leave my thoughts here, maybe my comment helps you or some of the responses might...

I was thinking about this when I tried a faster model (Cursor released something fast about a month ago?). It was such a joy to use (well, at least compared to other models, where you wait 5-10 mins for even simpler tasks), and I noticed I felt much closer to the problems, and I got closer to a "flow" state... ...but unfortunately, the models are faster for a reason, and the output got worse. While I did enjoy my job more, I was also left worried that the model missed important things (and it did when cross referenced with other models or just doing the thinking myself).

IMO we need much faster yet capable models to bring back a bit of a flow state.

Another approach worth trying is to get some agents researching 4-5 tasks thoroughly in the background, discovering all the relevant details, collecting all the files likely to be edited, their content etc..., then work on one thing at a time with a better focus for yourself, maybe use a faster model.

One thing I try to do is code manually if I know that I can be faster and better. It's convenient to stick to one tool, the agents, when editing code, but for smaller clean up tasks, they just never get it right, and sometimes it's better to do 1 min manual work over 5 mins of explaining what you want and the agents still not delivering it...

I have converged on a workflow that is just what I was doing before, but use LLMs just for boring or tedios parts. General guidelines are: only single agent at a time, small targeted queries, understand what you are building, if something would seem like a fun task, do it yourself. I use LLMs for bug hunting, to trace the flow, to build quick visualizatios (paste csv, ask to generate visualization), to search in the code of the dependencies using github mcp, to write 100 line scripts (deno + ts + zx was a game changer for me). Even "dumber" opensource models are good for this kind workflow, more tokens per second is generally more benefitial than plain intelligence. I would use LLMs more or less, sometimes even full vibecoding if the task is something like quick tooling web app and the flow is just firing of the next LLM query every 30 seconds. But, depending on the type of task or domain that you work in, YMMV

For working on a single project, I have a text document open where I’m working on future prompts. For working on tasks in a team setting, with prs and reviews, i built a tui that manages multiple tmux sessions. It creates a worktree for each task. It has status indicators for each session, hotkeys to quickly cycle through them, hotkeys to pull, annotate and paste pr comments, pull, annotate and paste linear tasks and comments, hotkeys to launch external tools and to jump directly to the pr url, and any time there’s a period when all the dots are busy i just start a new task. It’s a bit manic, but it’s manageable because it’s all in one terminal window. It’s also for AI as it existed 3 months ago, work slowed down and i haven’t tried to apply loops or whatever the latest crazy thing is.

As a Staff level engineer, a large part of my job is taking interruptions from others. I also do a lot of code reviews, design meetings and the like. Which is to say I have only rarely been in the flow anyway. I just more/better code written these days just because the AI can be in the flow while I'm answer those things. However I have even more code to review because the AI needs constant "this is a bad design" prompts.

Why would you try?

Just give it a zillion linters - including ones you wrote yourself - and make it write its own tests (red/green) so it doesn’t need to stop until it’s made working software with nothing dumb in it.

Then get into a flow state when you write your weekly update emails and respond to customers.

I've actually embraced the asynchronous element of AI programming in my personal projects.

Playing Star Citizen? There's pockets of 5 minutes all over the place traveling from A to B. I keep my laptop nearby and have a prepared todo list of items to work through. Those moments wasted on Reddit are now moments wasted on feature experiments!

Waiting for a cup of tea? Run an experiment. Waiting on wife? Run an experiment.

Piece by piece an app is coming together built from 5 minute increments of reclaimed time.

im finding it way way more "flowy" than pre-ai. all the boring trivia is gone, i can focus on what i actually care about - the shape of what i am building, tradeoffs, second- and third-order consequences of decisions.

the trick to get it uninterrupted is "selective multitasking". i don't like having too many Claudes / Codexes in parallel on auto-pilot; this way im finding i'm getting _something_ that is perfectly plausible, but rarely what i wanted. but I have N going at any given time, just enough to be basically non-stop reading. problems need to be related; within one project, ideally adjacent areas that are complementary. then my "flow" is just switching between reading and typing non-stop. never felt time flying by faster in my life, pure flow

You need to saturate your attention span with running so many agents at once that when you issue the last prompt to the last agent, the first one is already finished. This way you need to continuously concentrate, so flow is achievable.

The difficulty is to break down the task in a way that multiple agents can work on it.

I usually spin two or three major issues with 10-12 agents in total.

I reach the same flow state by multitasking across multiple tasks in multiple repos. I can hold my own context across 5-10 different terminal tabs each running their own Claude code or pi. Watching Claude especially with sub agents is like watching paint dry.

I know it isn't the original flow state but I get lost in conversations with Claude even when slow.

I have a couple of terminals open and work on at max 3 things.

A main task, an exploration task and another prompt/skill improvement or documenting an issue (or a proposal)

I try and make very small and deterministic steps. I do the planning (in flow state) and let the model to the execution. When the model is executing, I follow the "thinking" and keep seeing the diff like I would when I was coding.

I think if you find that your coding style is disrupting your productivity or job satisfaction then you're misapplying the tool to your particular working style. We're all different in how we approach problems and tools should support our preferred approach (assuming that approach isn't unacceptable from a working performance perspective which, to defeat your imposter syndrome, I absolutely guarantee you isn't the case if your performance hasn't majorly shifted since the introduction of AI).

What has worked for me…

Pair programming. I call it pilot / copilot / autopilot. Two real people plus one or two agents working together. Classic XP stuff, the copilot can help remind what we are focusing on, file follow up issues, give instant code reviews.

Bake offs. Do the same task but in two different chats or agents or approaches (TDD vs vibe or legacy app vs next app).

I don’t do these all the time, and they don’t guarantee ROI, but it keeps me focused on one thing to completion intend of getting distracted

Welcome to management!!!

What works for me is:

- Use a fast model like DeepSeek Flash V4 on high (it's Sonnet level, but fast and cheap).

- While the LLM is working, start writting your next prompt. A good prompt usually takes between 1 and 10 minutes to write anyway.

Doing this should keep you busy enough to never leave flow.

But it is intense and demanding when the LLM is fast, I'll tell you that.

Flow comes from continuous engagement with the problem. Agentic coding often turns programming into project management.

The more time I spend waiting for an AI to think, the less flow I experience. Fast autocomplete-style AI boosts flow. Slow autonomous agents usually break it.

My workaround is to stay in the loop: AI handles the typing, I handle the thinking.

Read the blog posts from Peter Steinberget blog steipete.me about his setup. Many AI builders converged on using the terminal with multiple panes. When a prompt is running if you don't have anything to do add another pane and start another prompt. Little by little you might have 1-6 prompts in parallel at some point. The flow state with AI is managing productively several prompts in parallel.

I do 3-4 things at the same time, and in between waiting periods, keep pushing on something else.

It's more taxing because I'm switching problems but at least these are all libraries within the same ecosystem so eventually, they line up.

I've half-joked a few times that ADHD with hyperfocus is a perk in this agentic coding era.

I've never been surfing the web as much as now. I get sidetracked really often, I forget that a prompt was running, I lose trains of thought. It's horrible.

Built my own orchestration layer, to allow me to optimize the one thing that is most precious when working with AI. My attention and my time.

I don’t, nor I want to. If I’m forced to train my replacement by using these tools, I might as well enjoy the time it takes the agent to do work by doing something I like and enjoy in the meantime.

You accept that what made this career rewarding (besides money) is gone and pick up a hobby that gets you into flow state. That's why I've picked up drawing again :)

I just work on multiple projects at the same time. This way, the time it takes for one prompt to complete does not disturb my productivity

Use composer 2.5 fast in cursor (or cursor cli).

YMMW but I find it fast enough to maintain focus on one task (if that's what you're going for given a particular problem

I don't think you can. The joy is gone. But that also makes me wonder, what other careers had a "flow state"? Maybe art?

Never. It is like babysitting a special needs child while trying to accomplish actual work.

How do you get in the flow state while waiting for a junior dev to submit their changes that you will have to spend the rest of the day reviewing? You don't, you get annoyed the rest of the day is going to suck.

Could you not just, you know, not use Claude? Or if you must, delegate some tasks to the agent and go work on something yourself, the agent doesn't mind waiting for you to get back.

mkfs.ext4

I code by hand asking AI specific questions via emacs gptel and claude-code-ide. The ai works in the background as I write. When the answer is complete I context switch back

Keeping an IDEAS.md or SCRATCH.md, which I work on while reviewing changes / code / docs, and it is working on other things.

I tend to focus on on project at a time with multiple agents, rather than agents on multiple project, and then time slice myself across projects

Related:

Ask HN: Do you struggle with flow state when using AI assisted coding tools?

https://news.ycombinator.com/item?id=44811457

try more difficult tasks

Do something better with your life. Honestly, not /s. Your brain is trying to tell you something.

[flagged]