How to Learn English Through Coding Projects: A Developer’s Guide to the English-level-up-tips Repository

You can learn English through coding projects by treating the byoungd/English-level-up-tips repository as a modular knowledge base—extracting technical vocabulary from language-specific word lists, applying AI-driven learning workflows, and embedding practice directly into your IDE workflow.

The byoungd/English-level-up-tips repository offers a structured approach that merges software development with language acquisition. By leveraging its curated word lists and AI integration strategies, developers can transform everyday coding tasks into immersive English learning opportunities.

Repository Architecture for Developers

The repository is organized as a modular learning engine designed specifically for technical professionals. Its structure aligns with six core language skills while providing programming-specific resources.

Core Learning Path

The docs/threads/part-1/ directory contains sequential markdown chapters covering understanding, vocabulary, listening, reading, speaking, and writing. Each chapter targets specific competencies:

• [1-understanding.md](https://github.com/byoungd/English-level-up-tips/blob/master/docs/threads/part-1/1-understanding.md) – Establishes the mindset for integrating language study with technical work
• [2-vocabulary.md](https://github.com/byoungd/English-level-up-tips/blob/master/docs/threads/part-1/2-vocabulary.md) – Details strategies for acquiring technical terminology
• [7-ai.md](https://github.com/byoungd/English-level-up-tips/blob/master/docs/threads/part-1/7-ai.md) – Outlines the AI-driven workflow using Gemini, ChatGPT, Claude, and Perplexity

Language-Specific Word Lists

Located in docs/threads/word-list/, these files contain curated technical terms essential for programmers. The [Python.md](https://github.com/byoungd/English-level-up-tips/blob/master/docs/threads/word-list/Python.md) file includes terms like "approximate," "async," and "dataclass," while similar lists exist for Java, Go, Rust, and JavaScript. These serve as direct input sources for flashcard generation and inline documentation.

AI-Driven Integration Resources

The AI chapter introduces a "training chain" methodology that combines Gem / Live / Guided Learning / Canvas / quiz / flashcards. This workflow enables developers to generate code-review prompts, documentation drafts, and conversational practice scripts directly within their development environment.

Practical Implementation Strategies

Transform the repository content into active learning tools using these three implementation patterns.

Extract Vocabulary for Spaced Repetition

Parse the word-list files to generate flashcard decks. The Python word list at docs/threads/word-list/Python.md contains raw terms suitable for Anki or similar spaced-repetition systems.

Integrate LLM Explanations

Apply the AI chapter’s workflow to generate learner-friendly explanations of programming jargon. Use API calls to Gemini or Claude to produce B1-level English descriptions of technical concepts like "dataclass" or "asynchronous execution."

Embed Documentation Practice

Leverage the [Common.md](https://github.com/byoungd/English-level-up-tips/blob/master/docs/threads/word-list/Common.md) word list to auto-generate vocabulary sections in your project README files. This turns repository documentation into a living English learning resource.

Code Examples for Integration

The following scripts demonstrate how to automate English learning within your coding workflow.

Generate a Flashcard CSV from the Python Word List

This script fetches the raw word list from docs/threads/word-list/Python.md and converts it into a CSV format suitable for Anki or any flashcard application:

import csv
import requests
from pathlib import Path

# Fetch the raw markdown file from GitHub

url = (
    "https://raw.githubusercontent.com/byoungd/English-level-up-tips/master/"
    "docs/threads/word-list/Python.md"
)
response = requests.get(url)
response.raise_for_status()

# Extract terms (skip headings and empty lines)

terms = [line.strip() for line in response.text.splitlines()
         if line and not line.startswith('#')]

# Write a simple CSV for Anki or any flashcard app

output_path = Path("python_terms.csv")
with output_path.open("w", newline="", encoding="utf-8") as f:
    writer = csv.writer(f)
    writer.writerow(["Term", "Definition"])
    for term in terms:
        writer.writerow([term, "← add your own definition"])

print(f"✅ Saved {len(terms)} terms to {output_path}")

Query an LLM for Technical Term Explanations

This example implements the AI-driven workflow from docs/threads/part-1/7-ai.md, using a Gemini-style API to explain programming concepts in plain English:

import json
import os
import requests

# Replace with your own Gemini endpoint; placeholders only

API_URL = "https://api.gemini.example/v1/completions"
API_KEY = os.getenv("GEMINI_API_KEY")   # <-- keep this secret!

def explain(term: str) -> str:
    payload = {
        "model": "gemini-1.0-pro",
        "prompt": f"Explain the programming concept \"{term}\" in plain English, suitable for a B1 learner.",
        "temperature": 0.7,
        "max_tokens": 150,
    }
    headers = {"Authorization": f"Bearer {API_KEY}"}
    resp = requests.post(API_URL, json=payload, headers=headers)
    resp.raise_for_status()
    data = resp.json()
    return data["choices"][0]["text"].strip()

# Example usage

print(explain("dataclass"))

Auto-Update Project README with Vocabulary Sections

This script embeds English learning into your project documentation by appending a vocabulary section derived from the common word list:

import pathlib
import requests

README_PATH = pathlib.Path("README.md")
WORD_LIST_URL = (
    "https://raw.githubusercontent.com/byoungd/English-level-up-tips/master/"
    "docs/threads/word-list/Common.md"
)

def insert_vocab_section():
    vocab = requests.get(WORD_LIST_URL).text
    section = "\n## Vocabulary for This Project\n\n" + "\n".join(

        f"- **{w}** – _definition pending_"
        for w in vocab.splitlines()
        if w and not w.startswith('#')
    )
    content = README_PATH.read_text(encoding="utf-8")
    if "## Vocabulary for This Project" not in content:

        README_PATH.write_text(content + section, encoding="utf-8")
        print("✅ Vocabulary section added.")
    else:
        print("ℹ️ Vocabulary section already exists.")

insert_vocab_section()

Key Source Files Reference

These files constitute the backbone of the English-level-up-tips repository:

• README.md – Landing page outlining the guide structure and AI resources
• docs/threads/part-1/1-understanding.md – Learning mindset and approach fundamentals
• docs/threads/part-1/2-vocabulary.md – Technical vocabulary acquisition strategies
• docs/threads/part-1/7-ai.md – LLM integration workflow for language practice
• docs/threads/word-list/Python.md – Curated Python terminology for flashcards
• docs/threads/word-list/JavaScript.md – JavaScript-specific technical terms
• docs/threads/part-2/my-story.md – Motivational narrative providing real-world context

Summary

• Treat the byoungd/English-level-up-tips repository as a queryable knowledge base for embedding English practice into development workflows
• Extract technical terms from docs/threads/word-list/ files to build personalized flashcard decks
• Implement the AI-driven workflow from docs/threads/part-1/7-ai.md to generate code explanations and documentation in learner-friendly English
• Automate vocabulary embedding using CLI scripts that pull from the common word list and update project documentation
• Cycle through the six-skill learning path (understanding, vocabulary, listening, reading, speaking, writing) while coding to build parallel programming and linguistic competence

Frequently Asked Questions

How do I start using the English-level-up-tips repository with my current project?

Begin by reading the Understanding and Vocabulary chapters to establish your learning framework. Then run the flashcard generation script against the word list matching your primary programming language (e.g., docs/threads/word-list/Python.md for Python developers) to create immediate study material relevant to your codebase.

Can I use these resources without an AI API subscription?

Yes. The repository functions perfectly as a static reference. The word lists in docs/threads/word-list/ are plain markdown files containing thousands of technical terms you can manually extract into Anki or physical flashcards. The AI chapter simply offers an optional acceleration layer for those wanting automated explanations and conversational practice.

Which word list should I use if I work with multiple languages?

Start with docs/threads/word-list/Common.md for universal programming terminology, then supplement with language-specific files like Python.md or JavaScript.md based on your current project. The scripts provided can concatenate multiple word lists into a single study deck, allowing you to cycle through relevant terminology as you switch between codebases.

How long does it take to see improvement in technical English?

According to the repository structure in docs/threads/part-1/, consistent daily practice across the six skill areas yields measurable improvement within 8-12 weeks. By embedding the vocabulary lists into your IDE workflow and writing documentation using the AI-assisted explanations, you transform passive reading time into active language production, typically accelerating vocabulary acquisition compared to traditional study methods.

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  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
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}