TL;DR: I'm releasing a workshop today focused on helping people move through the "this sucks" stage to "OK this is kind of useful" stage. You can read more below. I'm also hoping you reply and let me know your horror stories, and also your biggest problems with AI as I want to create a video that addresses these things directly.
If you haven't read about this guy's experience with Replit... well it's kind of wild:
Long story short, he tweeted out his vibe coding session and every ridiculous problem involved with creating an application on Replit. If you don't know, Replit is an app builder that's powered by AI, so you basically build what you want using prompts, and then host it there.
Neat idea, but apparently things can go pear-shaped quickly and you can have your production database dropped. If I'm honest: I don't know how true any of this is. Dude added screenshots to support his claims, so there's that, and he also got loads of press from this, which I can't help but think was intentional.
All that aside, I kept wondering: WTF was this guy doing to get these results?
Keep the Barrel Pointed Away From You
The massive problem we're having as an industry is straightforward: so much hype, so little care. Programmers have lamented the garbage that AI will produce, and are continuing to do so very publicly and loudly. I think complaining like this is good because it's drawing attention to the issue. It's also bad because it amounts to standing back, pointing fingers and laughing as other people jump off the same sinking ship you're on.
It's time to work the lifeboats, don't you think?
There's no way we're going to get away from AI in our industry. The main value proposition is that it saves time due to efficiency gains, and many people are realizing these gains, which means more money. That's pretty much the end of the story on that: more money means you get to learn how to use AI on the daily.
Rather than be pessimistic about it and point fingers while laughing, I think we can do better, and figure out a way to help people like poor Jason not turn on autopilot and let the car drive you off a cliff.
The Careful Art of Vibing
AI is a computer, which is the first and most important rule. You're coding with words, not code, which is bound to be extremely tricky. Just like coding, you move in small, well-defined steps, and work that process until you have what you need.
For instance: if you're converting an old Backbone.js application to Next.js, that's not where you start in terms of a prompt. You would do what any programmer would do without AI:
- Write down what it is you're doing and what you hope to achieve (aka "what 'done' looks like).
- Gather your style guides, policies, and "guard rails" for your team.
- Lay out your sprints, iterations, or milestones.
- Divide each of those into a set of tasks.
- Set up a branch to work in.
- Confine each AI "session" to a single task, which also represents a single commit.
This is the process I've used and it works really well. Yes, I've had things go completely wrong before and I've had to completely erase a sprint as I've realized I went the wrong way. The good news about that is that these "sprints" are usually represented in hours and minutes, not days. Yes it could be time wasted, but usually you learn something in the process.
Translating this to the real world, your sprints might be:
- Installing Next.js and making sure it works and you can deploy it to wherever the site will live.
- Asking the LLM to review the Backbone site's layout, and applying that to the Next.js site, while mocking up the data-driven bits (that part comes later).
- Move on to the home page, pulling in components, using mock data. Follow that up with the rest of the "main" pages that sit outside the app.
- Pick one "app" page and bring in the components, once again mocking the data. Rinse, repeat for every other app page.
- Create the API calls for each page, one bit at a time, slowly. Get a game plan in place from your LLM before you execute anything.
- Implement authentication, making sure to ask your LLM what is the best way to do this, provide sources, and even give you a game plan before anything is done. Go through a code review if needed, then do it.
There would obviously be more steps to consider, and this is just a general guideline, and it's exactly what I did when I rolled this site off of Rails back in April of 2025.
Plan. Plan a bit more. Gather your resources, move in small steps. Profit.
Taking the Time to Figure It Out
I was lucky in that my job at Microsoft was to figure out exactly how to do these things, so I had years to dig in to processes and what's possible. These days people are figuring it out on their own!
But you don't have to. I mentioned this in previous emails that I wanted to put together an updated workshop that I've given over the years, make some videos, and gather resources to help people move past the "gun/foot" stage of working with AI, and today it's available!
I'm considering this a "presale" in that I'm probably going to add to it, or possibly edit a few things where there might be a few issues; your feedback is crucial in this regard.
If you're interested, I have a discount for you because you've signed up to my site: use coupon code RJYM9KDP at checkout. If you take me up on this, thank you!
Why a Workshop?
The problem with creating AI "courses" is that they're simply not repeatable. There's no way you're going to watch a video I make and then be able to follow along and replicate it. Your LLM is going to decide, on that day, to do something completely different than what you see.
The only thing you can do, at that point, is to embrace the change and "go with it" by either tweaking your prompt to be more precise, or corralling your LLM by providing more explicit instructions.
Other times things can be a bit more positive. Your LLM might come up with something you didn't think of, and it might spark your curiosity, leading you to add a feature or two to other part of your application. That's when this stuff gets very fun and I wanted to capture that.
In the workshop there are "jump off points", where you can feel free to ride the wave and see where it takes you (being sure to branch and commit your work first). That's the brilliance of "vibe coding" - when you make something that you wouldn't have if it was just you coding away.
Anyway: if you're looking to make the leap from "all I get is CRAP" to "oh, yeah, I can see how this is super useful" then I made this workshop for you.
The Teams Version
I also built it so it can scale beyond a single person, through to a team. If you get the Team Edition, you'll get an instructor manual plus a ton of other useful "extras" that will help you roll out AI at your company. I even threw in things like policy templates, PR templates for AI-generated code, rollout plans, and more.
Share Your Horror Stories
I would love to hear about your horror stories, OR a problem you consistently have using AI tools. If you're OK with it, I want to make a video that addresses those concerns directly.
For instance: you might have a hard time moving from "fix this CSS so it doesn't suck" to something more precise that delivers exactly what you want. You might also hate the Python that's being generated because the variable names are always horrible. Another big problem is that LLMs tend to use old versions of libraries when generating things (like Tailwind or React) and what it gives you is useless.
Let me know! I would love to use real world examples, and I'm happy to leave you completely anonymous if you like.
Thanks for reading! Hope to hear from you...
Rob