How to Build Technical Programming Vocabulary in English for Go, Rust, JavaScript, and Python

Use the curated word-list files in the byoungd/English-level-up-tips repository to create spaced-repetition flashcards, then reinforce terms by writing code and verbalizing your technical decisions.

Building a robust technical vocabulary in English is essential for reading documentation, participating in code reviews, and contributing to open-source projects. The byoungd/English-level-up-tips repository provides structured word lists specifically designed for programmers learning English. By leveraging these plain-text Markdown files, you can create a systematic workflow that targets language-specific jargon alongside universal software engineering terms.

Why Targeted Vocabulary Lists Accelerate Learning

Generic English courses often skip terms like goroutine or borrow checker, leaving gaps in your technical communication skills. The repository solves this by organizing terminology into language-specific files that mirror how concepts actually cluster in real projects.

• Focused scope: Isolating Go, Rust, JavaScript, and Python terms prevents cognitive overload from unrelated jargon.
• Cross-language synergy: The Common.md file contains shared vocabulary—like asynchronous, refactor, and dependency—that appears across all codebases, reinforcing connections between languages.
• Pedagogical layering: Combining term study with active coding follows proven language-learning principles: input → output → feedback loops.

Step-by-Step Workflow to Build Technical Programming Vocabulary

Follow this five-stage process to convert static word lists into active, usable knowledge.

Identify Core Terminology

Start by extracting language-specific concepts from the dedicated files. In docs/threads/word-list/Go.md you will find terms like channel and goroutine; docs/threads/word-list/Rust.md covers ownership and borrow checker; docs/threads/word-list/JavaScript.md includes event loop and promise; and docs/threads/word-list/Python.md lists dataclass and coroutine.

Simultaneously, study docs/threads/word-list/Common.md to master cross-language engineering vocabulary that appears in every codebase.

Collect Definitions and Usage Examples

Look up each term in official documentation or reputable technical dictionaries. Write a short example sentence demonstrating the concept in context. For instance: "The garbage collector manages memory automatically," or "We need to refactor this function to reduce cyclomatic complexity."

The repository acts as a central index, allowing you to script a bulk extraction of terms before enriching them manually or via AI-assisted prompts.

Build Spaced-Repetition Flashcards

Import your curated terms into Anki, Quizlet, or any SRS tool. Each flashcard should contain:

• The technical term
• A concise definition
• A code snippet showing usage
• A real-world context sentence describing when you would use this concept

The plain-text format of the word-list files makes them trivial to parse into CSV or TSV formats required by flashcard applications.

Practice in Context

Write short programs or refactor existing snippets that intentionally incorporate new vocabulary. After implementing a feature using pattern matching or generics, record a short audio narration explaining your architectural decisions—this builds both coding and speaking fluency.

Include meta-vocabulary from docs/threads/word-list/VibeCoding.md, such as pair programming and rubber ducking, to practice describing your workflow in English.

Review and Iterate

After one week, revisit cards you struggled with, add new terms encountered during code review, and retire mastered items. Since the repository is actively maintained, periodically re-run your extraction scripts to fetch updates and keep your deck current.

How to Automate Flashcard Generation from the Repository

Because the word lists are stored as plain Markdown files, you can automate the creation of Anki decks. The following Python script reads the four language word-lists and produces a CSV file ready for import:

import csv
from pathlib import Path

# Base directory of the cloned repo

BASE = Path("/cache/repos/github.com/byoungd/English-level-up-tips/master/docs/threads/word-list")

# Languages we care about

langs = ["Go", "Rust", "JavaScript", "Python"]

def load_terms(file_path: Path) -> list[str]:
    """Read a word-list file and return the terms (skip headings & blanks)."""
    return [line.strip() for line in file_path.read_text().splitlines()
            if line and not line.startswith("#")]

def build_rows() -> list[dict]:
    rows = []
    for lang in langs:
        terms = load_terms(BASE / f"{lang}.md")
        for term in terms:
            rows.append({
                "Language": lang,
                "Term": term,
                "Definition": "",          # fill manually or via AI later

                "Example": ""              # add a short code snippet later

            })
    return rows

def write_csv(rows: list[dict], out_path: Path):
    fieldnames = ["Language", "Term", "Definition", "Example"]
    with out_path.open("w", newline="", encoding="utf-8") as fp:
        writer = csv.DictWriter(fp, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(rows)

if __name__ == "__main__":
    csv_path = Path("programming_vocab.csv")
    write_csv(build_rows(), csv_path)
    print(f"✔️  CSV generated at {csv_path}")

Running this script produces programming_vocab.csv containing rows for terms like goroutine, ownership, promise, and dataclass. You can then populate the Definition and Example columns using official documentation or LLM prompts for efficiency.

Key Repository Files for Programming English

The byoungd/English-level-up-tips repository contains the following critical vocabulary files:

• docs/threads/word-list/Go.md — Core Go terminology including goroutine, channel, and interface
• docs/threads/word-list/Rust.md — Rust-specific concepts like ownership, borrow checker, and lifetime
• docs/threads/word-list/JavaScript.md — JavaScript vocabulary including event loop, async/await, and closure
• docs/threads/word-list/Python.md — Python terms such as dataclass, coroutine, and decorator
• docs/threads/word-list/Common.md — Universal engineering vocabulary used across all languages
• docs/threads/word-list/VibeCoding.md — Meta-vocabulary for describing coding workflows like pair programming and refactoring

These files collectively form a ready-made vocabulary bank that integrates into any learning pipeline—manual flashcards, spaced-repetition software, or AI-assisted note generation.

Summary

• Use targeted lists: Focus on language-specific files (Go.md, Rust.md, etc.) rather than generic English dictionaries to learn relevant jargon fast.
• Automate extraction: Parse the plain-text Markdown files to generate CSV flashcards for Anki or similar SRS tools.
• Combine input and output: Study terms via flashcards, then reinforce them by writing code and verbalizing your technical decisions using the VibeCoding.md meta-vocabulary.
• Iterate regularly: Update your decks as you encounter new terms in documentation and pull periodic updates from the actively maintained repository.

Frequently Asked Questions

How long should I study technical vocabulary each day to see improvement in code reviews?

Aim for 15–20 minutes of spaced-repetition review combined with one short coding session (30 minutes) where you intentionally use new terms in variable names, comments, or documentation. According to the repository structure, focusing on one language file per week (e.g., Go.md) allows deep absorption of approximately 50–100 terms without overwhelming your working memory.

Can I use these word lists if I am not a native English speaker?

Yes. The byoungd/English-level-up-tips repository is specifically designed for non-native English speakers working in software development. The plain-text format allows you to easily translate terms into your native language in the Definition column of your flashcards while keeping the English term as the prompt.

What is the difference between the language-specific files and the Common.md file?

The language-specific files (Python.md, Rust.md, etc.) contain jargon unique to that ecosystem—terms like ownership (Rust) or goroutine (Go) that rarely appear in other contexts. Common.md contains cross-language vocabulary—such as asynchronous, refactor, and dependency—that appears in every modern codebase. You should study both in parallel to build comprehensive technical English proficiency.

How do I maintain my flashcard deck as the repository updates?

Since the word lists are maintained as Markdown files in an active GitHub repository, run your extraction script (like the Python example above) monthly to detect new terms. Compare the new output against your existing Anki deck, adding only the Diff (new terms) to avoid duplicate cards. This ensures your vocabulary stays current with evolving language features like Rust's async traits or JavaScript's iterator helpers.

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    "instagit": {
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