Who actually uses these products,
and what made them stay.

The Vercel Frontend Developer

A frontend or full-stack developer with 2–8 years of experience who discovered Vercel and decided that deploy-on-push preview URLs should be table stakes for every project. They've tried to describe the Vercel experience to developers still using other deployment pipelines and can't fully convey it. They use Vercel for personal projects, client work, and have advocated for it at their company — sometimes successfully. Their bar for deployment infrastructure is now set by Vercel, which makes everything else feel like a step backward.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about build times that climb as the project grows — especially Next.”

The Clerk Authentication Developer

A full-stack developer or indie hacker building a SaaS product who has decided that authentication is not a competitive advantage and has no interest in building it. They chose Clerk because it ships the full auth experience — sign in, sign up, user profile, MFA, social providers, and organization management — as components they can drop in and style to match their product. They were building on NextJS and Clerk was the obvious answer. It took them four hours to integrate. They've never looked back and have never thought about auth again unless a customer asked for a feature.

“It happened mid-workflow — it's Saturday.”

The Fly.io Edge Deployer

A backend developer or DevOps engineer who deploys applications on Fly.io because they need their app running close to users globally — not just served from a CDN, but actually computing at the edge. They've outgrown Heroku's simplicity, don't want AWS's complexity, and find Vercel too opinionated for non-Next.js workloads. Fly.io hits the sweet spot: Docker containers deployed globally with a CLI that feels developer-first. They're comfortable with infrastructure but don't want it to be their full-time job.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about stateful workloads at the edge (databases, volumes) have limitations that aren't always clear until production in two weeks.”

The Vercel Agency Deployer

A developer at a web agency or a freelancer who deploys and manages 20–100 client projects on Vercel. They chose Vercel because the developer experience is excellent and Next.js deploys are zero-config. But managing 50 projects across 15 clients has turned deployment into project management. They spend as much time in the Vercel dashboard organizing teams and domains as they do writing code. They know every deployment preview URL is a demo link, and they've sent the wrong preview to the wrong client exactly once.

“A client emails asking why their site is showing an old version.”

The Spline 3D Web Designer

A web designer or creative developer who uses Spline to add 3D to their web projects without the learning curve of Blender or Cinema 4D. They create 3D hero sections, interactive product visualizations, animated icons, and immersive landing pages. They are a designer who crossed into the third dimension. They appreciate that Spline runs in the browser, exports to the web natively, and feels like a design tool rather than a 3D modeling application. They are the person making websites feel like they have depth.

“A SaaS company wants a landing page that stands out.”

The Notion AI Knowledge Worker

A product manager, writer, or operations lead who already uses Notion as their primary workspace and added Notion AI to make their existing workflows faster. They were already in Notion 4–6 hours a day. Notion AI is not a new tool to them — it's a capability inside the tool they already trust. They use it to summarize meeting notes, draft first versions of documents, and ask questions of their existing workspace. The context is already there. The AI can work with it. This is the part that makes Notion AI different from a separate AI tool to them.

“It happened mid-workflow — they've just finished a 90-minute discovery call.”

The Figma-to-Code Developer

A frontend or full-stack developer who didn't choose Figma but lives in it three hours a week. They open Figma to inspect designs, grab spacing values, export assets, and try to understand what the designer intended for edge cases that weren't mocked up. They've learned enough about auto-layout to know when a design will be painful to implement. They have opinions about design tokens that the design team doesn't want to hear yet.

“A teammate asked how they managed extract exact spacing, color, and typography values without guessing.”

The FullStory Digital Experience Analyst

A product analyst or UX researcher at a digital product company who uses FullStory as their lens into the user experience. They don't just look at funnels and conversion rates — they watch sessions, identify frustration signals (rage clicks, dead clicks, error clicks), and correlate behavioral patterns with business outcomes. They've learned to find the story in the data: why conversions dropped, where users get confused, what makes the checkout feel broken. They are the translator between raw user behavior and product decisions.

“The product team sees a 15% drop in checkout completion after a recent redesign.”

The Typeform Survey Designer

A UX researcher, product manager, or marketer who chooses Typeform over Google Forms because the survey experience matters. They've learned that completion rate is the most important metric for a survey, and completion rate is a design problem. They craft surveys that feel like conversations: one question at a time, conditional logic, thoughtful copy. They spend as much time on the question experience as they do on the question content. They are the person who says "we can't just send a Google Form — that sends a message about how much we value their feedback."

“The shift was quiet.”

The Sentry Error Wrangler

A developer — usually mid-level to senior — who has become the de facto owner of error tracking on their team. They set up Sentry, configured the alerts, and now they're the person who triages the error feed every morning. They know the difference between a real bug and a noisy exception. They've learned to read stack traces the way a doctor reads X-rays — quickly, looking for the thing that's actually wrong. They carry the mental burden of knowing exactly how many errors are happening in production at any given moment.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about grouping algorithms that split one bug into multiple issues or merge different bugs into one in two weeks.”