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Why Python is the Language of MVPs and POCs (Even if You Used to Hate It)

Introduction​

I used to think Python was a mess.

Between Anaconda, interpreter hell, and broken installs on Windows, it felt like more pain than it was worth. So I avoided it. Hard.

But things changed when I switched to a Linux-based CLI lifestyle. Suddenly, Python was clean. Minimal. Powerful.

Now it’s my go-to language for building MVPs and proof-of-concepts. Here's why.

The Core Shift​

“Python is the fastest way to go from mental model → functional system.”

The real shift wasn’t just in tooling — it was in mindset.

I stopped fighting the environment and started focusing on what Python is actually good at.

When Python Wins​

  • ✅ One-off scripts
  • ✅ CLI-based tools with a single entrypoint
  • ✅ Parsing and transforming local files (like Markdown or JSON)
  • ✅ Early prototypes where you're still exploring the idea

Example: I built a markdown spider to crawl my portfolio site and map internal link relationships. Took me 90 minutes in Python. It would’ve taken half a day in Go.

When You'll Rewrite Later​

  • ♻ Long-running systems
  • 📁 Binary deliverables
  • 🧵 Complex concurrency and parallelism
  • 🛠️ Places where Go’s strictness gives you leverage

And that’s okay. Rewrites aren’t failure — they’re part of the product lifecycle.

“Rewriting a script in Go is easier than writing it in Go the first time.”

Sidebar: DSA in Python Felt Wrong — Here's Why​

When I first saw Python used for algorithms, I hated it. Too much class boilerplate, not enough clarity.

What I realized later is that I don’t code that way.

My real-world flow is:​

  1. main()

  2. Sketch the dataflow

  3. Split into logical functions

  4. Only use classes for:

    • API clients
    • DB layers
    • Configuration objects

“In real-world coding, you don’t start with a class. You start with a goal and a flow.”

Python’s Subtle Advantages​

Python nudges you toward using dicts — and that’s a good thing.

Compared to languages like JavaScript or Go, where you might debate .map vs .forEach or get tripped up on of vs in, Python feels almost conversational.

for key, value in my_dict.items(): → That just reads like English.

Even weird bits like is not None or using elif instead of else if are quirks you adapt to fast.

“Every language has edge-case syntax. Python just happens to hide most of it until you need it.”

What’s more, Python quietly guides you toward clean, expressive code. Not by forcing discipline, but by making the obvious thing the easiest thing.

“Python leans hashmaps. JavaScript makes you debate syntax. Go makes you structure everything. Python just lets you move.”

Bonus: This Graph Tool Was the Turning Point​

That markdown link-mapper? It’s lightweight, but still a graph problem. It helped me internalize:

  • Node-based thinking
  • Real use cases for graphs (not just Leetcode)
  • The power of building for your own projects

What About Performance?​

I’ve written noCRUD — a fully parallelized api test runner — entirely in Python.

And not just “because I started there.” I ran a pretty deep performance assessment, down to the millisecond. After benchmarking and profiling:

  • Bottlenecks weren’t in the language
  • I/O-bound tasks and process-level concurrency worked fine

In fact, the vast majority of runtime didn’t come from the runner itself — it came from the processes noCRUD spawns, which are out of my control.

The biggest performance wins came from architectural choices, like changing the way I did DB provisioning. I wrote about it here: DB Provisioning.

Sure, Go or Rust might be faster on paper. But the gain wouldn’t justify the rewrite — not for this kind of app.

Closing Thought​

Don’t let your past experience with a tool blind you to its strengths.

Python is still a mess in some ways. But for MVPs?

It’s unbeatable.

Build in Python. Rewrite in Go. Move forward now.