> The LLM makes this easier but the improvement drops to about 2-3x b/c there is a lot of back and forth + me reading the code to confirm etc
This makes sense when you stop viewing the LLM as a "vending machine" for apps and start seeing it as a repository of software deltas.
LLMs aren't just trained on final code; they are trained on the entire history of pull requests, review comments, and issue discussions that move a project from one version to the next.
When I use an LLM now, my workflow has shifted entirely. I’ve stopped trying to be the "coder" and have instead stepped into the role of PR Reviewer and Power User. My job is to point out edge cases, define the spec, and catch regressions—effectively managing a "virtual team" that handles the boilerplate and feature implementation.
Expecting a one-shot 1.0 release is unrealistic because it bypasses the thousand micro-decisions that happen in a real dev cycle. By embracing the "review and refine" loop, I’m becoming a better maintainer, even if that 100-hour gap to a polished product still exists.
This isn’t surprising when you consider how LLMs are actually trained to write code.
Expecting a one-shot 1.0 release is unrealistic because the sheer volume of context and decision-making required for a finished product is enormous.
Instead, I think of LLMs as being trained on the "delta" of software development: the pull requests, review comments, and issue discussions that move a project from one version to the next.
When you use an LLM for coding, you are effectively tapping into the collective output of a team of developers and a crowd of users. My mental model has shifted accordingly: I no longer try to be the "coder." Instead, I act as the PR reviewer and the passionate power user. My job is to point out edge cases and refine the output, rather than expecting a finished product in one go.
It’s making me a better maintainer and a more precise communicator, even if the "100-hour gap" to production remains a reality.
This makes sense when you stop viewing the LLM as a "vending machine" for apps and start seeing it as a repository of software deltas.
LLMs aren't just trained on final code; they are trained on the entire history of pull requests, review comments, and issue discussions that move a project from one version to the next.
When I use an LLM now, my workflow has shifted entirely. I’ve stopped trying to be the "coder" and have instead stepped into the role of PR Reviewer and Power User. My job is to point out edge cases, define the spec, and catch regressions—effectively managing a "virtual team" that handles the boilerplate and feature implementation.
Expecting a one-shot 1.0 release is unrealistic because it bypasses the thousand micro-decisions that happen in a real dev cycle. By embracing the "review and refine" loop, I’m becoming a better maintainer, even if that 100-hour gap to a polished product still exists.
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