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Python, JavaScript, TypeScript? My no-BS guide to picking a language that lasts

#webdev #ai #programming #javascript

For juniors overwhelmed by choices and seniors re-evaluating their stack. Opinionated take on language tradeoffs, readability, team velocity, and ecosystem support.

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Table of contents

  1. Intro Welcome to the language warzone
  2. What “lasting” even means in 2025
  3. Python The comfy hoodie of programming
  4. JavaScript & TypeScript The unavoidable web hydra
  5. Choosing for team velocity, not just taste
  6. Conclusion There’s no best, but there is better for you

Intro: Welcome to the language warzone

Picking a programming language today feels like choosing a starter Pokémon while everyone’s yelling in your ear.

You’re on Reddit, someone says “Rust or bust”. You scroll Twitter, and a startup just rewrote their backend in Elixir and claims 1000x productivity. Your bootcamp says go full stack with JavaScript, but your senior dev friend whispers:

“Nah, use Go and never look back.”

Whether you’re a junior drowning in tutorials or a senior dev tired of fighting build tools and migrating codebases for the 8th time this article’s for you.

We’re not here to rehash Wikipedia-level pros and cons of every language. You can get that from ChatGPT (👋). This is a no-fluff guide about what actually matters when choosing a language that won’t screw you over long term.

That means readability, team velocity, hiring ecosystem, tooling, and maintainability. Not whether a language is “cool” in a YouTube thumbnail.

And yes, I’ll throw in some opinions, war stories, and code snippets. You won’t agree with all of it and that’s the point.

Here’s how it feels for most people today:

# junior-devs.sh
dev_goal="learn to code"
language_stack=$(shuf -n1 <<< "Python JavaScript TypeScript Rust Go Elixir Kotlin Dart")
echo "You should learn $language_stack — trust me bro."

Don’t worry by the end of this article, you’ll know when to ignore that advice... and when to steal it.

Let’s talk about what “lasting” really means when picking your weapon in 2025.

What “lasting” even means in 2025

Let’s clear this up: “lasting” doesn’t mean the language is old. It means it sticks around without becoming a liability.

Some languages stay relevant because they evolve without breaking everything. Others die not because they were bad, but because they became too annoying to deal with. (RIP CoffeeScript, you had vibes.)

So, what does “lasting” actually mean for a developer, a team, or a project in 2025?

Lasting = readable + maintainable + supported

It’s not about hype cycles. It’s about how long your code can survive before you hate it. A language “lasts” when:

  • You can onboard new devs without a PhD in framework archaeology.
  • It’s readable six months later even after caffeine withdrawal.
  • You’re not rebuilding the tooling every time a new version drops.
  • The ecosystem doesn’t feel like a museum or a junkyard.
  • StackOverflow still has answers. Real ones.

Key questions to ask before committing

  • Will people still be learning this 5 years from now?
  • Is there a strong ecosystem of frameworks, docs, and libraries?
  • How stable is the language’s evolution?
  • Can I find help when I’m stuck at 3 a.m. and Stack Overflow is half-down?
  • Do major companies or open-source projects rely on it?

Spoiler: Python, JavaScript, and TypeScript tick most of these boxes but in very different ways.

Real-world signals to check

Here’s how I evaluate if a language’s future is healthy:

def is_language_healthy(name, job_postings, github_repos, stackoverflow_qs):
    if job_postings > 10000 and github_repos > 50000 and stackoverflow_qs > 20000:
        return f"{name} is probably still a solid bet."
    return f"{name} might be niche or declining fast."

print(is_language_healthy("Python", 150000, 200000, 80000))

Output: "Python is probably still a solid bet."

For real numbers, check:

Python: The comfy hoodie of programming

Python isn’t the fastest, trendiest, or flashiest language out there. But you know what it is?

Comfortable. Reliable. Everywhere.

It’s the language equivalent of that worn-out hoodie you’ve had since college it doesn’t turn heads, but it gets the job done and keeps you warm when production’s on fire.

Why Python just won’t die

Python’s not surviving on hype. It’s thriving because it makes things simple to build and easy to read. That’s why you’ll see it everywhere from:

  • University courses
  • AI/ML research
  • Backend scripts
  • Automation tools
  • DevOps glue code
  • 10-year-old Django monoliths that just… keep working

Even the AI boom (read: OpenAI, Hugging Face, etc.) is dominated by Python. If you’re messing with anything from data pipelines to LLMs Python is home base.

Tradeoffs: clean syntax, loose rules

The readability of Python is its killer feature. But that freedom can backfire in large codebases without strict conventions or linters.

# Simple, clean, readable
def greet(name):
    print(f"Hello, {name}!")

greet("world")

Try doing that in Java or C++ without a dozen lines of ceremony.

But here’s the catch: Python’s dynamism can also make it unpredictable at scale. You’ll start missing types when your codebase crosses a few thousand lines. That’s where tools like mypy and pyright come in but it’s still duct-taping a dynamic language.

Where Python really works

Python thrives in:

  • Startups prototyping fast
  • Machine learning / AI teams
  • Internal tooling and automation
  • Backend APIs (Django, FastAPI)
  • Scripting in DevOps, data ETL pipelines

Companies like Instagram, Dropbox, and Reddit still run massive chunks of Python in production. And it’s the default scripting language in tools like Blender, Ansible, and even parts of Unreal Engine.

Where Python might hurt you

  • High-performance backends (see: global interpreter lock)
  • Realtime systems and low-latency services
  • Extremely large teams with no enforced typing discipline

TL;DR: Python will probably outlive your startup. Just don’t expect it to break speed records.

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JavaScript & TypeScript: The unavoidable web hydra

If Python is your comfy hoodie, JavaScript is that slightly chaotic multi-tool your team keeps duct-taping to everything. And TypeScript? It’s the armor you eventually bolt on when your multi-tool starts stabbing people.

You can hate it, meme it, rage-quit over undefined is not a function but you still can’t avoid it. If your app touches a browser, JavaScript (or TypeScript) will be involved. Like it or not.

The everywhere language

JavaScript was originally built in 10 days. And yet here we are it runs:

  • Every major front-end framework (React, Vue, Svelte, etc.)
  • Backend with Node.js (Express, NestJS, Fastify)
  • Mobile (React Native)
  • Desktop apps (Electron)
  • Serverless functions
  • Literally your smart fridge if someone was bored enough
// JS at its most chaotic
let result = [] + false - null + true;
console.log(result); // NaN. Of course.

That’s JavaScript: flexible, dangerous, and weirdly successful.

TypeScript: the grown-up version

When teams get tired of chasing runtime bugs, they reach for TypeScript.

TS adds static typing, intelligent tooling, and code confidence without throwing away JavaScript’s flexibility. It’s why massive projects like VS Code, Slack, Airbnb, and Discord use TypeScript in production.

Here’s a TypeScript sanity-saver moment:

function getUser(id: number): User {
  // Compiler screams if you mess up
  return db.findUserById(id);
}

That one line of type safety will save you hours of head-scratching in large codebases.

Tradeoffs and dev pain

Let’s be honest: the JavaScript ecosystem is exhausting. There’s constant framework churn, hundreds of abandoned packages, and the eternal battle between npm and your will to live.

Also: configuring TypeScript for a monorepo can feel like writing a dissertation.

But the payoff is a language ecosystem with:

  • The biggest OSS contributor base on GitHub
  • Endless UI libraries, frameworks, and tools
  • Devs available in every job market
  • A runtime (Node.js) fast enough for most backend needs
  • Cloud-native deployment options galore (Vercel, Netlify, Deno, etc.)

Why it “lasts”

  • JS is built into every browser it’s not going anywhere
  • TypeScript has been adopted by nearly every major dev team
  • It scales from solo devs to 500-person engineering orgs
  • It powers both MVPs and billion-dollar apps

TL;DR: You don’t pick JS/TS because it’s beautiful. You pick it because it’s inescapable and battle-tested.

Choosing for team velocity, not just taste

Let’s talk about something tutorials won’t teach you and Medium listicles love to skip:

The best language isn’t the one you love the most. It’s the one that helps your team ship faster, with fewer bugs, and less rage-quitting.

Sure, you like Rust. Your coworker swears by Go. Your junior dev wants to rewrite the whole thing in Elixir because they saw a YouTube video.

But here’s the real question: Can your whole team move fast and stay sane using it?

Why velocity matters more than elegance

There’s a dangerous trap many devs fall into: choosing a language because it’s “fun” or “clean,” then getting bogged down in team-wide bottlenecks, hiring pain, and weird tooling issues.

You want velocity? Here’s what actually matters:

  • Onboarding speed How fast can new devs get productive?
  • Tooling maturity Are the dev tools intuitive or rage-inducing?
  • Hiring pool Can you find devs who already know this language?
  • Debuggability Can you figure out WTF went wrong without crying?
  • Code longevity Can you hand this off in a year without shame?

Real-world story: the Ruby to Go migration

A friend’s company rewrote their API from Ruby to Go for performance. Cool, right? Except:

  • Half the team had never touched Go
  • Onboarding slowed to a crawl
  • The type system confused the juniors
  • Devs lost time fighting with interface{} and reflection
  • Productivity dropped for 3 months

Did Go perform better? Sure.
Did they regret it? Also yes.

Choosing a language that kills your team velocity is like picking the fastest race car… and giving it to people who can’t drive stick.

The syntax trap

Some devs pick languages like they pick themes in VS Code. But this isn’t about taste it’s about collaboration.

Here’s the difference in mindset:

// taste-driven dev
“I love how clean and expressive this syntax is.”

// velocity-driven dev
“Can three devs, a tester, and a new hire understand this code next week?”

Pick boring. Pick predictable. Pick something your whole team can wield without injury.

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Conclusion: There’s no best, but there is better for you

If you made it this far, congrats you’ve survived the language warzone without switching to COBOL out of spite.

Here’s the bottom line:

There is no perfect language. But there are better fits depending on your:

  • Team size and experience
  • Hiring pipeline
  • Long-term project goals
  • Tooling ecosystem
  • Tolerance for chaos

You don’t always need the “best” tool. Sometimes you need the one that makes onboarding fast, code readable, and bugs traceable. That’s what lasting really means in the real world.

TL;DR

  • Python: Use it when you want clarity, simplicity, and speed to prototype. Not ideal for high-performance needs, but great for ML, scripting, and APIs.
  • JavaScript: The web default. Chaotic but everywhere. Good for front-end and full-stack setups.
  • TypeScript: Use it if you like your JavaScript without panic attacks. Default for serious frontend/backend JS projects now.
  • Don’t pick a language just because you like how it looks in a tutorial.
  • Pick based on team velocity, tooling, support, and how long you’ll be stuck maintaining it.

Helpful resources

Final thought

Pick a language your team can grow with.
Not just one that looks good on your resume or compiles the fastest or wins Hacker News debates.
Codebases age. Your choice of language decides whether they rot or mature.

So choose wisely and maybe… keep that Python hoodie handy just in case.

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