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https://github.com/backnotprop/plannotator Plannotator does this really effectively and natively through hooks
This makes no sense to my intuition of how an LLM works. It's not that I don't believe this works, but my mental model doesn't capture why asking the model to read the content "more deeply" will have any impact on whatever output the LLM generates.
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Please don't be knee-jerk dismissive of posts. Absolute nothing about this article looks "LLM-generated style" to me.
Here’s an older article from the author from 2024: https://boristane.com/blog/learnings-from-starting-building-...
This all looks fine for someone who can't code, but for anyone with even a moderate amount of experience as a developer all this planning and checking and prompting and orchestrating is far more work than just writing the code yourself. There's no winner for "least amount of code written regardless of productivity outcomes.", except for maybe Anthropic's bank account.
That said, if you're on a serious team writing professional software there is still tons of value in always telling AI to plan first, unless it's a small quick task. This post just takes it a few steps further and formalizes it.
I find Cursor works much more reliably using plan mode, reviewing/revising output in markdown, then pressing build. Which isn't a ton of overhead but often leads to lots of context switching as it definitely adds more time.
1. Use brainstorming to come up with the plan using the Socratic method
2. Write a high level design plan to file
3. I review the design plan
4. Write an implementation plan to file. We've already discussed this in detail, so usually it just needs skimming.
5. Use the worktree skill with subagent driven development skill
6. Agent does the work using subagents that for each task:
a. Implements the task
b. Spec reviews the completed task
c. Code reviews the completed task
8. Go back to the agent with any comments
9. If finished, delete the plan files and merge the PR
One thing for me has been the ability to iterate over plans - with a better visual of them as well as ability to annotate feedback about the plan. https://github.com/backnotprop/plannotator Plannotator does this really effectively and natively through hooks
Really nice ui based on the demo.
> Notice the language: “deeply”, “in great details”, “intricacies”, “go through everything”. This isn’t fluff. Without these words, Claude will skim. It’ll read a file, see what a function does at the signature level, and move on. You need to signal that surface-level reading is not acceptable. This makes no sense to my intuition of how an LLM works. It's not that I don't believe this works, but my mental model doesn…
This all looks fine for someone who can't code, but for anyone with even a moderate amount of experience as a developer all this planning and checking and prompting and orchestrating is far more work than just writing the code yourself. There's no winner for "least amount of code written regardless of productivity outcomes.", except for maybe Anthropic's bank account.
Yesterday I had Claude write an audit logging feature to track all changes made to entities in my app. Yeah you get this for free with many frameworks, but my company's custom setup doesn't have it.
It took maybe 5-10 minutes of wall-time to come up with a good plan, and then ~20-30 min for Claude implement, test, etc.
That would've taken me at least a day, maybe two. I had 4-5 other tasks going on in other tabs while I waited the 20-30 min for Claude to generate the feature.
After Claude generated, I needed to manually test that it worked, and it did. I then needed to review the code before making a PR. In all, maybe 30-45 minutes of my actual time to add a small feature.
All I can really say is... are you sure you're using it right? Have you _really_ invested time into learning how to use AI tools?
This is what I do with the obra/superpowers[0] set of skills. 1. Use brainstorming to come up with the plan using the Socratic method 2. Write a high level design plan to file 3. I review the design plan 4. Write an implementation plan to file. We've already discussed this in detail, so usually it just needs skimming. 5. Use the worktree skill with subagent driven development skill 6. Agent does the work using subage…