Engineering teams often blame AI tools when adoption fails to improve delivery speed or quality. This episode argues that the missing ingredient is usually process: explicit planning, codebase context, honest documentation of legacy problems, project-specific standards, and a feedback loop that converts engineering judgment into reusable instructions. Execution is getting easier, but experienced engineers remain essential because they define what good execution means.

Your personal operating system might be the reason you are not shipping.

AI makes it easy to brainstorm, research, compare tools, refine prompts, and build enormous backlogs. All of that can feel productive while quietly postponing the only feedback that matters: what happens after you release something.
In this episode, I explain why builders get trapped in tool-hopping and analysis paralysis, when research stops being useful, and how to turn an operating system back into a tool for action.

Today's challenge:
1. Delete one unnecessary part of your process.

2. Ship something before the end of the day.

3. Learn from what actually happens.

Stop perfecting the system. Build what you really want to build.
#StartupsAndCode #AI #BuildInPublic #Productivity #Startups

Want better results from ChatGPT, Claude, or your favorite AI tool? Stop trying to get everything right in one prompt.

Instead, ask the AI to interview you.

Have it ask about your goals, technical challenges, preferences, tone, and working style. Those questions create the context it needs to give you more useful—and more personalized—answers.

Try this prompt:

“Interview me about what I’m building. Ask any questions you need to understand my goals, constraints, and preferences before we begin.”

Give it a try and let me know what happened in the comments.

#AI #ChatGPT #ClaudeAI #PromptEngineering #Startups #BuildInPublic