Bolt has been amazing at design, but it's a token hog - one instruction can drain a million tokens on a fairly small codebase. But if I were prototyping a loose idea with no idea where to start on UI/UX, the most efficient way would be via Bolt.
Bolt will do app-wide changes. Like recently I'm working on a character generator. I want it to include personality traits after gender but before clothing. It adds the screen, increments the steps, and changes the final screen to reflect this new data type.
Cursor does the engineering very well. I think it's become common recently to have a shared markdown file that different agents can access.
Gemini Pro has the huge context window but tends to make edits you didn't ask for, like simplifying your instructions and removing some of the important ones. Claude sonnet 4 is just nice for most tasks. o3 is the most precise by far, but low creativity. Visually GPT-4.1 seems to be the best. So if you have a Figma design, you can just screenshot and paste it.
Do have a cheat sheet ready for the AI. Tell it how to open modals, where to get the fonts, colors, icons.
I know some people are able to make it fully automated, but that is something I don't trust it to do. It would be an interesting exercise to see how well we can write a PRD then, and it becomes an exercise in commanding genies.
3 weeks ago pair programming with Cursor in "Ask" mode and ignoring all agents was the most productive I could get with these tools.
Since then I've noticed this triple working really well:
1. Ask mode Cursor for my main track of development
2. o3 for learning about or checking up on approaches when more rigor is needed
3. Delegating parallel tasks to claude code on a separate machine
* small work I can describe in <3 sentences
* parallel debugging / explaining running at the same time I'm trying to figure something out to cross check work or preemptively give me some new ideas on debugging
* starting on bigger work that I'll mostly need to throw away and do myself but I'm going to procrastinate otherwise
Putting delegations somewhere else is key because I don't want my local environment to be polluted with a separate task.
1. Write a fully-defined specification file as markdown in technical-enough but business-enough language, without repeating anything, and including all requirements and context, being sure to emphasize what is important, how to make decisions if needed, and, most critically, what elements must exist and how they must behave in the outcome. This is logically outlined. A junior engineer could understand what the output needs to do without help, a mid engineer could implement it sufficiently well without help, and a senior engineer could implement it gracefully without help.
2. Submit the fully-defined requirements .md file to the LLM by drag-and-drop. If there are starting files to be modified, drag and drop those files as well (as a zip if hierarchical, or as unpackaged files if a flat structure is suitable).
3. Wait for LLM to be done. This can take as long as it needs to, since this process supports specification files with requirements of arbitrary complexity. Receive the correct, full output fileset as a downloadable file from the LLM. (If I asked for instructions on how to use the files that the LLM provides, that is included in the output fileset, too.)
Oh wait, this does not exist. Sorry! But it is what I want, so it is still my "ultimate AI-assisted coding setup".
We're not there. "AI" is not Intelligent. It's "Artificial" generated content based on an out-of-date snapshot of a specific scope which can be useful but not intelligent.
> anything else?
anonymous, local-only please. ie. Llama-3.2-3B-Instruct.Q6_K.llamafile or whatever the latest version is.
> And what kind of project are you building/maintaining with it (framework, language, deployment infra, aprox # of users)?
I use it as not much more than a "Use It At Your Own Risk" stackoverflow / google search. It is often wrong, but it is "good enough." I treat any generated code as "Sample Code" which requires review and is merely a sample.
Ask HN: What is your ultimate AI-assisted coding setup?
6 points by nico 21 hours ago | 13 comments
Comments
Bolt will do app-wide changes. Like recently I'm working on a character generator. I want it to include personality traits after gender but before clothing. It adds the screen, increments the steps, and changes the final screen to reflect this new data type.
Cursor does the engineering very well. I think it's become common recently to have a shared markdown file that different agents can access.
Gemini Pro has the huge context window but tends to make edits you didn't ask for, like simplifying your instructions and removing some of the important ones. Claude sonnet 4 is just nice for most tasks. o3 is the most precise by far, but low creativity. Visually GPT-4.1 seems to be the best. So if you have a Figma design, you can just screenshot and paste it.
Do have a cheat sheet ready for the AI. Tell it how to open modals, where to get the fonts, colors, icons.
I know some people are able to make it fully automated, but that is something I don't trust it to do. It would be an interesting exercise to see how well we can write a PRD then, and it becomes an exercise in commanding genies.
Since then I've noticed this triple working really well:
1. Ask mode Cursor for my main track of development
2. o3 for learning about or checking up on approaches when more rigor is needed
3. Delegating parallel tasks to claude code on a separate machine
* small work I can describe in <3 sentences
* parallel debugging / explaining running at the same time I'm trying to figure something out to cross check work or preemptively give me some new ideas on debugging
* starting on bigger work that I'll mostly need to throw away and do myself but I'm going to procrastinate otherwise
Putting delegations somewhere else is key because I don't want my local environment to be polluted with a separate task.
2. Submit the fully-defined requirements .md file to the LLM by drag-and-drop. If there are starting files to be modified, drag and drop those files as well (as a zip if hierarchical, or as unpackaged files if a flat structure is suitable).
3. Wait for LLM to be done. This can take as long as it needs to, since this process supports specification files with requirements of arbitrary complexity. Receive the correct, full output fileset as a downloadable file from the LLM. (If I asked for instructions on how to use the files that the LLM provides, that is included in the output fileset, too.)
Oh wait, this does not exist. Sorry! But it is what I want, so it is still my "ultimate AI-assisted coding setup".
We're not there. "AI" is not Intelligent. It's "Artificial" generated content based on an out-of-date snapshot of a specific scope which can be useful but not intelligent.
> anything else?
anonymous, local-only please. ie. Llama-3.2-3B-Instruct.Q6_K.llamafile or whatever the latest version is.
> And what kind of project are you building/maintaining with it (framework, language, deployment infra, aprox # of users)?
I use it as not much more than a "Use It At Your Own Risk" stackoverflow / google search. It is often wrong, but it is "good enough." I treat any generated code as "Sample Code" which requires review and is merely a sample.