I’ve spent exactly zero hours reading React documentation cover-to-cover. Zero hours on comprehensive Next.js courses. Zero hours memorizing Python’s standard library.
Yet I’ve built multiple production applications, trained LLM models, and ran an EdTech startup that served thousands of students. How? Fast Food Learning.
And before you judge me, hear me out. This isn’t about cutting corners. It’s about recognizing that in 2025, the way we learn needs to match the speed at which we need to build.
The Problem with Traditional Learning
Remember when you decided to learn that new framework? You probably did what I used to do:
Find the “definitive guide.” Bookmark 47 articles. Queue up a 12-hour Udemy course. Promise yourself you’ll finish it before writing any code.
Three weeks later, you’re still on module 4, and you haven’t built anything.
This is what I call the Documentation Trap, and it’s killing your momentum.
Here’s the truth nobody wants to admit: most of what you learn upfront, you’ll forget before you need it.
I once spent two weeks learning Redux from first principles. Read the docs. Built the todo app. Understood the philosophy. Felt great about myself.
Then I didn’t touch Redux for three months while working on other features. When I finally needed it, I’d forgotten 80% of what I learned. Had to relearn it anyway, but this time with an actual problem to solve.
That two-week investment? Mostly wasted.
The myth of “proper” learning says you need to understand everything before you start. You need the fundamentals. The theory. The best practices. The design patterns.
But here’s what actually happens: you spend so much time learning that you never start building. Or worse, you finish the tutorial and freeze when facing a real problem that doesn’t match the examples.
I’ve watched countless developers get stuck in tutorial hell—endlessly consuming content, always “almost ready” to build something real, but never quite confident enough to start.
They’re learning, sure. But they’re not shipping.
And in the world of startups and real projects, shipping beats learning every single time.
What is Fast Food Learning?
Fast Food Learning is exactly what it sounds like: grab what you need, consume it quickly, and get back to building.
It’s not gourmet. It’s not comprehensive. But it gets the job done when you’re hungry and have places to be.
Here are the core principles:
Just-in-time knowledge acquisition. Learn exactly what you need, exactly when you need it. No sooner, no later.
Iteration-first approach. Start building with incomplete knowledge. Let the problems teach you what you need to learn next.
Build-to-learn, not learn-to-build. The building IS the learning. Every bug you encounter is a lesson. Every error message is a pointer to what you need to know.
Think about actual fast food. Is it the healthiest option? No. Is it a Michelin-star experience? Definitely not. But when you’re road-tripping at midnight and need to eat, it’s perfect. It’s fit for purpose.
The same applies to learning. Sometimes you don’t need a deep understanding of computer science theory. You need to know how to make this API call work by 5 PM.
Fast Food Learning says: that’s okay. Google it. Ask ChatGPT. Copy from Stack Overflow. Get it working. Learn the deeper concepts later if (and only if) you need them.
The Framework
Here’s how I actually learn new technologies now:
Step 1: Identify the Exact Problem
Don’t start with “I want to learn React.” That’s too broad and leads to tutorial hell.
Instead: “I need to build a user dashboard that updates in real-time without page refreshes.”
See the difference? One is a vague learning goal. The other is a concrete problem with a clear success criterion.
When I needed to add authentication to a side project, I didn’t learn “authentication theory.” I asked: “How do I verify a JWT token in this specific Express.js route?”
Much narrower. Much faster.
Step 2: Find the Minimum Information Needed
This is where Fast Food Learning really shines in 2025.
I don’t open the official docs and start at Chapter 1. I do this:
- Ask ChatGPT or Claude: “Show me how to [specific thing] in [specific context]”
- Search GitHub for real-world examples
- Check Stack Overflow for the exact error I’m getting
- Skim (not read) the relevant docs section
I’m looking for the minimum viable understanding to attempt an implementation.
When I needed to implement rate limiting, I didn’t read about algorithms and distributed systems. I asked: “What’s the simplest way to add rate limiting to an Express API?”
Got an answer. Tried it. Moved on.
Step 3: Implement Immediately (Even if Imperfect)
This is the step most people skip, and it’s the most important one.
Write the code NOW. Not after you “fully understand it.” Not after you’ve read three more articles. Now.
It will be messy. It might be wrong. That’s the point.
When I was building a feature to export user data to CSV, I found a code snippet, copied it, and tried to run it. It broke immediately. Got an error about file encodings.
Perfect. Now I had something real to debug.
If I’d spent an hour reading about CSV libraries first, I wouldn’t have learned about encoding issues until I hit them in production. By building first, I encountered the problem in a safe environment where I could learn from it.
Step 4: Debug and Iterate - THIS is Where Real Learning Happens
Here’s the secret: debugging teaches you more than documentation ever will.
When your code breaks, you’re forced to understand what’s actually happening. You can’t just passively read. You have to actively investigate.
Every error message is a breadcrumb leading you to understanding.
That CSV encoding error? It forced me to learn about UTF-8, BOM markers, and how different systems handle text files. I didn’t read about it abstractly—I learned because I had a broken thing I needed to fix.
This is contextual learning. The context of your specific problem makes the knowledge stick in a way that abstract learning never does.
When I was implementing WebSocket connections and kept getting disconnects, I learned more about connection lifecycle, heartbeat mechanisms, and browser behavior in 2 hours of debugging than I would have in a week of reading docs.
Step 5: Document Only What You’ll Need Again
After you solve the problem, write down the solution for future you.
Not a comprehensive guide. Just the specific thing that worked.
I keep a personal knowledge base (just markdown files) with entries like:
# How to handle file uploads in Next.js API routes
Used multer middleware. Key gotcha: need to disable Next.js body parsing.
Code: pages/api/upload.js
That’s it. No theory. No alternatives. Just what worked for this specific case.
If I need deeper knowledge later, I’ll learn it then. But for now, I’ve captured the solution and can move on.
Real-World Examples
Example 1: Learning Next.js Under Deadline
When I needed to rebuild a landing page in Next.js (I’d only used React before), traditional learning would have meant:
- Read Next.js docs (4-6 hours)
- Take a course on server-side rendering (8-12 hours)
- Understand file-based routing in depth (2 hours)
- Learn about static generation vs SSR (3 hours)
Total: 17-23 hours before writing a line of code.
Here’s what I actually did:
- Cloned a Next.js starter template (5 minutes)
- Started replacing components with my content (30 minutes)
- Hit an error about
getServerSideProps(didn’t know what it was) - Googled the error, found Stack Overflow answer (10 minutes)
- Copied the pattern, adapted to my needs (20 minutes)
- Deployed to Vercel (15 minutes)
Total: ~90 minutes to working deployment.
Did I understand Next.js deeply? No. Did I ship a working product? Yes.
I learned the other concepts later, as I needed them, by encountering actual problems.
Example 2: Implementing Stripe Webhooks
Traditional approach: Read Stripe’s entire webhooks documentation, understand signature verification theory, study idempotency, learn about webhook retry logic.
Time investment: 4-6 hours.
Fast Food Learning approach:
- Googled “Stripe webhook Next.js example”
- Found an official Stripe guide with code
- Copied the webhook handler
- Tested with Stripe CLI
- It worked
When I encountered signature verification failing, I learned about raw body parsing. When a webhook fired twice, I learned about idempotency keys.
Time to working implementation: 45 minutes. Depth of understanding: Exactly what I needed for this use case.
The difference? Traditional learning frontloads everything. Fast Food Learning distributes learning across actual usage.
When NOT to Use Fast Food Learning
Let me be clear: Fast Food Learning isn’t appropriate for everything.
Don’t use this approach for security implementations. When you’re handling authentication, authorization, or encryption, read the docs. Understand the theory. Know what you’re doing. Copy-pasting crypto code is how you create vulnerabilities.
Don’t use this for system architecture decisions. Choosing between SQL and NoSQL, monolith vs microservices, or system design patterns requires deeper understanding. The wrong choice here compounds over time.
Don’t skip core fundamentals. You should actually understand how databases work, what Big O notation means, and how HTTP requests function. These fundamentals pay dividends forever.
Don’t use this when the stakes are high. Production systems, financial transactions, healthcare applications—these deserve comprehensive learning and careful implementation.
Fast Food Learning is for experimentation, prototyping, and learning new tools. It’s for when you need to move fast and the cost of being wrong is low.
Know the difference.
Getting Started with Fast Food Learning
Here’s your challenge: Stop learning your next technology. Start building with it instead.
Pick a small project you’ve been postponing because you “don’t know the tech yet.”
Then follow this process:
- Define one specific feature to build (not “learn the framework”)
- Google/ChatGPT your way to a starting point
- Copy working code shamelessly
- Run it and watch it break
- Debug until it works
- Ship it
Tools that enable Fast Food Learning:
- ChatGPT/Claude for instant, context-specific answers
- GitHub search for real-world implementations
- Stack Overflow for specific problems
- Official “Quick Start” guides (ignore everything else)
The goal isn’t to never do deep learning. It’s to default to building first, and learn deeply only when necessary.
I still read papers on LLM training techniques. I still study system design. But I do it when I need that knowledge, not preemptively.
Because here’s what I’ve learned after building multiple startups and training AI models: the best way to learn is to have a problem that forces you to learn.
Create the problem by building. Let the learning follow.
Now stop reading this blog post and go build something.