Depth Over Breadth: Why I Focus on Java and Python

PhilosophyCareerLearning

Context

As a software engineer, there's constant pressure to know every language, framework, and tool. My resume could list C, JavaScript, Go, Rust, and a dozen others I've touched. But listing languages doesn't make you a better engineer.

Decision

Focus deeply on Java and Python rather than maintaining superficial knowledge of many languages.

Alternatives Considered

Become a polyglot developer

Pros
  • Impressive-looking resume
  • Can jump between projects in different languages
  • Broader theoretical knowledge
Cons
  • Surface-level understanding of each language
  • Miss language-specific optimization techniques
  • Slower development in any given language
  • Can't leverage ecosystem depth

Learn one language only

Pros
  • Maximum depth
  • True expertise
Cons
  • Limited problem-solving toolkit
  • Some problems don't fit your language well

Reasoning

Two languages give me the right balance. Java for performance-critical, type-safe systems. Python for rapid development, scripting, and exploration. I'd rather be an expert in two languages than mediocre in ten. Deep knowledge means understanding the JVM internals, knowing when Python's GIL matters, recognizing performance footguns, and leveraging ecosystem strengths. That expertise compounds over time.

The Polyglot Pressure

The tech industry loves polyglots. Job postings list 10+ languages. Developers flex by showing GitHub repos in 15 different languages.

“Learn a new language every year!” “Good engineers can pick up any language quickly!”

Both statements are true. But they miss something important.

The Difference Between Knowing and Mastering

I can write working code in:

  • C: Systems programming, pointer manipulation
  • JavaScript: Frontend work, Node.js servers
  • Go: Concurrent services, CLI tools
  • Shell scripting: Automation, system administration

But there’s a huge gap between “can write working code” and “can write excellent code”.

What “Knowing” a Language Means

  • Syntax and basic idioms
  • Standard library awareness
  • Can build features
  • Can read documentation

What “Mastering” a Language Means

  • Performance characteristics and optimization
  • Ecosystem depth and best practices
  • Language-specific design patterns
  • Tooling expertise
  • When NOT to use the language

Why Java and Python

Java: My Performance Language

I don’t just “know Java”. I understand:

  • JVM internals: JIT compilation, escape analysis, inlining
  • Memory management: Object layout, GC tuning, allocation patterns
  • Performance: Profiling, benchmarking, optimization strategies
  • Concurrency: Thread models, memory visibility, lock-free structures
  • Ecosystem: Build tools, testing frameworks, production libraries

This depth let me build Aspira, a chess engine that reaches 20-22M nodes/sec. That requires understanding:

  • How to avoid allocations in hot loops
  • When the JIT will inline methods
  • How to structure code for CPU cache efficiency
  • GC pause management at high throughput

You can’t get that from “knowing” Java.

Python: My Productivity Language

For Python, I understand:

  • Performance: When to use PyPy, Cython, or C extensions
  • Async: Event loops, coroutines, concurrency patterns
  • Ecosystem: Django, Flask, pytest, mypy, packaging
  • Deployment: WSGI, ASGI, production best practices
  • Profiling: cProfile, memory_profiler, finding bottlenecks

At Alemca, this depth meant:

  • Building scalable Flask APIs quickly
  • Debugging production issues efficiently
  • Optimizing IoT data pipelines
  • Choosing the right libraries for each problem

Why Two Languages

Two languages give me balance:

Java = Type safety, performance, large-scale systems Python = Rapid development, exploration, scripting

Most problems fit cleanly into one of these. When they don’t, I pick the best tool and learn what I need - but I don’t pretend to have deep expertise.

What I’m NOT Saying

I’m not saying:

  • ❌ “Never learn new languages”
  • ❌ “Two languages are enough for everyone”
  • ❌ “Other languages are bad”

I’m saying:

  • ✅ Deep expertise beats shallow breadth
  • ✅ Specialization compounds over time
  • ✅ Honest assessment beats resume padding

The Compounding Effect

Here’s what happens when you go deep:

Year 1

  • Learn syntax and basics
  • Build working features
  • Read documentation constantly

Year 2

  • Understand idioms and patterns
  • Recognize common pitfalls
  • Start optimizing code

Year 3

  • Internals make sense
  • Performance intuition develops
  • Ecosystem expertise builds

Year 4+

  • You see solutions others miss
  • Optimization becomes natural
  • You contribute back to ecosystem

This doesn’t happen when you switch languages every year.

How I Decide to Learn Something New

I’ll invest time learning a new language when:

  1. Projects demand it: If I’m building a systems tool, I’ll learn Rust or Go
  2. Significant advantages: If a language solves problems my current stack can’t
  3. Long-term commitment: I’m willing to go deep, not just dabble

I won’t learn a language because:

  • ❌ It’s trending on HackerNews
  • ❌ It looks good on a resume
  • ❌ Someone said I “should know it”

The Resume Reality

My resume doesn’t list 10 languages. It lists:

  • Expert: Java, Python
  • Proficient: SQL, Bash
  • Familiar: C, JavaScript

Some recruiters pass me over. That’s fine. I’d rather work somewhere that values depth over breadth.

Advice for Others

If you’re early in your career:

  1. Learn broadly at first - Find what fits your brain
  2. Pick 1-2 languages to master - Where your projects live
  3. Go deep - Learn internals, performance, ecosystem
  4. Stay there for years - Let expertise compound
  5. Learn new languages sparingly - When truly needed

Depth beats breadth. Every. Single. Time.

All decisions