We Shipped a Production App in One Waymo Ride. 25 Minutes. Zero Lines of Code I Wrote Myself.

We were in a Waymo heading to SFO to get to Coachella. About 25 minutes to the terminal. And I had this idea rattling around: what if every SaaStr AI Annual 2026 attendee could generate a custom “I’ll Be There” card for LinkedIn — with their own headshot, their own message, the SaaStr AI 2026 branding — and share it right from the website?

Not a mockup. Not a prototype. A production app, live on saastrannual.com, that real attendees would use to promote the event to their networks.

So I opened Replit on my phone, described what I wanted to an AI agent, and started building.

By the time I walked into the terminal, it was deployed. Live. In production. Generating cards.

This isn’t a story about building something complex. It’s a story about what’s now normal if you’re willing to vibe code your way through a real problem.

The Idea: Attendee-Generated Social Proof

We’ve run SaaStr Annual for over a decade. One thing that always moves registrations is social proof — people posting that they’re going. But most attendees aren’t going to fire up Canva and design a branded card. They need it to be effortless.

So the spec was simple:

• Upload your headshot
• Pick a background
• Add your custom message (“Come find me at SaaStr Annual!” or whatever you want)
• Download a clean 1080×1080 PNG, ready for LinkedIn or Twitter

No login. No account. Just generate and go.

The First Approach Failed Immediately

The obvious move is to use something like html2canvas — render a styled HTML element and screenshot it. We tried that first.

It broke instantly. html2canvas doesn’t reliably support object-fit, aspect-ratio, or CSS backgroundImage. The output looked nothing like what the user saw on screen. Cropping was wrong. Positioning was wrong. The whole thing was unusable.

So we threw it out mid-Waymo and went straight to the Canvas 2D API. Draw everything programmatically: background image, gradient overlay, headshot circle with a glowing cyan border, headline text, subtitle. Every element placed at exact pixel coordinates, rendered at 1080×1080, exported as a clean PNG.

This is a pattern I’ve seen over and over with vibe coding. The AI’s first approach is often the “textbook” answer. It doesn’t quite work. You say “that’s broken, try something else,” and the second or third approach is the one that ships.

But bear in mind, I actually didn’t know what was failing. I was on my phone. I just tested it, shared the bug with the AI Agent … and it just fixed it.

The Headshot Problem

Cropping a portrait photo into a circle sounds trivial. It’s not.

The issue: if you center-crop most selfies or headshots, you get someone’s chin or neck. Faces are almost always in the top third of a photo, not the center.

The fix was a custom drawHeadshot function that anchors to the top of the uploaded image rather than the geometric center. We use canvas.clip() to mask to a circle path, then draw the image offset upward. The result: faces land in the circle naturally, without manual adjustment.

The CORS Problem

We wanted the SaaStr logo overlaid on each card. Simple, right? Fetch the logo from our CDN, draw it on the canvas, done.

Except browsers block cross-origin images from being drawn onto a canvas. The moment you draw a cross-origin image, the canvas becomes “tainted” and you can no longer export it. No download. No PNG. Nothing.

The fix: a small server-side proxy endpoint. The browser hits /api/proxy-image, which fetches the logo from the CDN and returns it with proper headers. The browser creates a local blob URL from the response and draws that onto the canvas. Clean export, no CORS issues.

This is the kind of thing that takes a junior dev two hours to debug and an AI agent about 45 seconds to solve once you describe the symptom. In fact, I didn’t need to understand this problem at all. I just told the Replit Agent to fix it.

What I Actually Did vs. What the AI Did

Let me be honest about the division of labor here.

The AI wrote all the code. The Canvas rendering logic, the headshot cropping algorithm, the CORS proxy endpoint, the background picker component, the download flow, the live preview — all AI-generated.

What I did:

• Described the product. Not in technical terms. I said things like “I want attendees to upload a headshot and get a branded LinkedIn card.”
• Caught edge cases. The first version didn’t handle the logo CORS issue. I saw the broken output and the AI diagnosed the tainted canvas problem.
• Made design calls. Which backgrounds to include. Where the text should sit. The cyan glow effect on the border.
• Hit deploy. Replit makes this trivially easy.

Total code I personally typed: zero. Everything was AI-generated, AI-debugged, and AI-deployed.

Why This Matters More Than the App Itself

The app itself is small. But think about what just happened. A CEO in the back of an autonomous car, shipped a production feature to a website that tens of thousands of people will visit — in the time it takes to get to the airport.

Just 12 months ago, this would have been a ticket in Jira. A designer would mock it up. An engineer would estimate it at 3-5 days. It would go into a sprint. Maybe it ships in two weeks. Now it’s a Waymo ride.

That’s the real story of AI and building software in 2026. It’s not about replacing engineers. It’s about the sheer volume of ideas that can go from “what if…” to “it’s live” before you’ve finished your commute. The bottleneck was never intelligence. It was time between idea and execution. That gap is now approaching zero.