I see many AI proponents advising "Use it to build all of your own tooling". This falls apart when people and orgs depend on those tools. Total cost of ownership is:

TCO = Cost of creation + (Cost of annual maintenance * years)

Does AI make maintenance cheaper? Is it easy to maintain a big pile of AI generated code? GitHub's downtime chart would suggest the answer is "NO".

Project manager: "What's technical debt? Explain it to me like I'm 6 years old"

Devs:

(source: "Richard Scarry's Storybook Dictionary" : https://archive.org/details/1scarryRichardStorybookDictionary/page/n56/mode/1up )

A trap I see product teams fall into is confusing activity with progress. Progress is having a goal and hitting milestones towards that goal. Reducing app startup time by 5 seconds, growing active users to 50K or making $500 in sales are all goals you can make progress towards.

Activity is work that feels like it gets you towards that goal but actually doesn’t. How much code written or PRDs produced are examples of activity that people confuse with progress.

The best software engineers solve problems, not just write code.

They ask:
- What problem are we actually trying to solve?
- Is this the right problem to solve?
- What's the simplest solution that could work?
- What are the trade-offs?
- How will we know if it's working?

Writing code is the implementation detail. Understanding the problem space, considering alternatives, and thinking through implications—that's where the real value lies.

Code is just the tool. Problem-solving is the skill.

So many goodies in rails of extras — particularly measure_queries. A lot of overlap with pghero.

https://github.com/pawurb/rails-pg-extras