Stop Treating AI Like a Chatbot
You're still typing questions and copy-pasting answers. Meanwhile, the builders winning right now have AI doing the work while they sleep.
Hey All, Real John here… 10 of diamonds, if you know you know… ok, going to babble a little bit before this. I did a lot this week and realized that I still fall back to my old patterns of using AI like a glorified Google Search. It is much more than that, so I pushed it this week, to change my workflow. Stop asking it questions, and instead I’ve been giving it directions. I answer the questions IT asks, not who won the Knicks game last night (Answer: THE KNICKS!). I moved to auto-mode in some cases, I’ve created a bot, created workflows, made websites, created presentations, created scripts and talking points for presentations, organized my inbox with a new labeling system, and generated promo videos of my dog dancing for Knicks game 4. It is not just a search engine, but a coworker, an employee, that starts as a new hire intern, and becomes a Staff Engineer in a week depending on how you interact with it.
The one-shot BS is annoying and watching silly youtube demos of the marketing crap from Anthropic or OpenAI is generally a lie of some sort. But the interactions and more is real. It is so real that it can help you build custom solutions for your use case today. Ok, let’s get into this week. I did the card thing earlier, so I’m just going to get into it. Shout out this week to the WAV Pod for the kindness they showed for the passing my dog recently. Thank you all, it really meant a lot.
A few months ago, I caught myself doing something embarrassing.
I had Claude open in one tab, my code editor in another, and I was doing this little dance: ask a question, read the answer, copy the code, paste it, test it, go back, ask another question. Over and over. For hours.
I’m a guy who’s been writing software since I was 9 years old on a Commodore 64. I’ve shipped Cash Critters and statussage almost entirely with AI tools. And I was still using AI like it was 2023 — like a really smart search engine that happens to write code.
Here’s the thing nobody wants to say out loud: most of you are still doing this too. You ask, you read, you copy, you paste. That’s not wrong, exactly. It’s just... small. It’s using a power drill to turn one screw and then setting it down.
The shift happening in AI right now isn’t about smarter chatbots. It’s about AI moving from answering to doing. From conversation to action. And if you’re still in question-and-answer mode, you’re leaving most of the value on the table.
So let’s fix that. Here’s how I actually use AI agents now — not in theory, but in the actual workflows I run for Cash Critters, statussage, and my day job.
Step 1: Stop asking “how do I” — start asking “do this”
The biggest mental shift is moving from questions to instructions with a defined outcome.
“How do I add authentication to my app?” is a chatbot question. You’ll get a tutorial. You still have to do the work.
“Add email/password auth to this app using Supabase, update the login page, and write a test that confirms a bad password gets rejected” is an agent instruction. The agent goes and does it — reads your code, makes the changes, runs the tests, and tells you what happened.
The difference is ownership of the outcome, not just the information. You’re not asking for help anymore. You’re delegating a task.
Step 2: Give the agent a way to check its own work
This is the part most people skip, and it’s the part that actually makes agentic workflows trustworthy.
When I’m working on statussage with Claude Code, I don’t just say “build this feature.” I make sure there’s something the agent can run to verify it worked — a test, a build command, a linter, a script that hits an endpoint and checks the response.
Why does this matter? Because an agent that can run code, see the error, and fix it is fundamentally different from one that just hands you code and hopes. The first one is doing real work. The second one is still a chatbot wearing a trench coat.
Practical tip: before you ask an agent to build something, ask yourself “how would I know if this worked?” If you can answer that in one sentence, you can probably hand that verification to the agent too.
Step 3: Let it run in the background while you do something else
This is the one that changes everything, and it’s the one almost nobody does.
I’ll kick off a task in Claude Code — “refactor this module to use the new API client, update all the call sites, and make sure the existing tests pass” — and then I go do something else. Answer emails. Walk Ella and Lego. Grab coffee with Susie.
Twenty minutes later I come back and either it’s done, or it hit a wall and explained why. Either way, I didn’t sit there babysitting a chat window.
If you’re tethered to your screen watching an AI “think,” you’re not using an agent. You’re using a very expensive loading spinner.
Step 4: Build a pipeline, not a one-off
Once you’ve got a task that an agent can do well — write tests, generate documentation, review a pull request, draft a status update — don’t just do it once. Make it repeatable.
For Cash Critters, I have a setup where new feature branches automatically get a basic test pass and a summary of what changed, generated by AI, before I even look at it. That’s not magic. It’s just: take the thing you did manually with an agent once, and wire it so it happens every time without you asking.
This is the “Series A average is $51.9 million” world we’re living in — most of you don’t have that money, and you don’t need it. A solo builder with two or three of these pipelines running is doing the work of a small team. That’s not hype. That’s just math.
Step 5: Keep a human checkpoint where it actually matters
I’m not telling you to YOLO everything to an agent and walk away forever. Production deploys, anything customer-facing, anything involving money or data — you review that. Always.
But the line between “needs a human” and “doesn’t” is way further out than most people think. Internal tooling, test writing, refactors, documentation, first-draft anything — let the agent run. Save your attention for the decisions that actually need a human brain behind them.
This Week’s Action Items
Pick one recurring task you currently do by asking AI a question and copy-pasting the result. Rewrite it as an instruction with a clear, checkable outcome.
Give it a way to verify itself — a test, a script, a build step. If it can’t check its own work, it’s still a chatbot.
Walk away while it runs. Set a timer for 15 minutes and do literally anything else. Resist the urge to babysit.
Turn it into a pipeline. If it worked once, automate it so it happens every time without you asking.
That’s it. Four steps. None of them require a $59 billion funding round or a quantum computer. They require you to stop treating AI like a search box and start treating it like what it actually is now: a teammate that can do the work, not just describe it.
Go build something amazing.
John Mann is the founder of Startups and Code LLC, a software engineering executive, and the guy who built Cash Critters for $50/month because constraints are a feature, not a bug. Subscribe for weekly takes on AI, startups, and building things that matter.