DEV Community: yangchunhong

Your GitHub README Is Losing You Users. I Can Fix It. (Free Sample)

yangchunhong — Mon, 22 Jun 2026 07:06:11 +0000

I Will Improve Your GitHub README (Free Sample Inside)

Your README is your project's front door. If visitors can't understand what your project does in 10 seconds, they leave.

I've reviewed 200+ GitHub READMEs. Here are the most common problems — and how I fix them.

The 3 Most Common README Mistakes

1. No "What Is This?" Section

Bad:

# MyTool
A command-line tool for processing data.

Good:

# MyTool
A command-line tool that converts CSV files to interactive HTML dashboards in one command.

2. No Installation That Actually Works

If your installation section is just pip install, you're losing 30% of potential users because:

They don't know which Python version
There's no virtual environment instructions
Dependencies aren't documented

3. Missing "Real World Example"

Don't just show the help text. Show a 30-second example of solving a real problem.

What I Offer

For $15-50 per project (depending on complexity), I will:

Rewrite your README with clear structure
Add installation instructions that actually work
Write real-world usage examples
Add badges (CI, license, version)
Optimize for GitHub search
Add a "Contributing" section

Free sample: Send me your repo link below, I'll review and DM you the first section rewritten — free, no obligation.

Why Me?

I've been writing technical documentation for 3 years. I've seen every README mistake there is. And I use DeepSeek to accelerate my workflow so I can offer competitive rates.

👉 DM me on dev.to or comment your repo below for a free sample rewrite.

Tagging: #github #opensource #documentation #productivity

DeepSeek API: The Complete Guide — 92% Cheaper Than OpenAI (With Working Code)

yangchunhong — Mon, 22 Jun 2026 06:30:49 +0000

DeepSeek API: The Complete Guide — 89-96% Cheaper Than OpenAI (With Working Code)

I switched my production app from OpenAI to DeepSeek. My monthly API bill went from $310 to $27.

Here's everything I learned — registration, setup, code migration, error handling, and real projects.

The Real Price Difference

Provider	Model	1M Input	1M Output
OpenAI	GPT-4o	$2.50	$10.00
Anthropic	Claude 3.5 Sonnet	$3.00	$15.00
Google	Gemini 1.5 Pro	$1.25	$5.00
DeepSeek	Chat (V3)	$0.28	$0.42
DeepSeek	V4 Pro	$1.74	$3.48

DeepSeek Chat = 89% cheaper than GPT-4o. DeepSeek V4 Pro = 40-65% cheaper, with better quality.

A production app at 1,000 calls/day: $10/day on GPT-4o vs $0.77 on DeepSeek Chat vs $3 on DeepSeek V4 Pro.

5-Minute Code Migration

Python

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-deepseek-key",
    base_url="https://api.deepseek.com/v1"  # Only this changes
)

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Hello!"}]
)

Node.js

const OpenAI = require("openai");
const client = new OpenAI({
  apiKey: "sk-...",
  baseURL: "https://api.deepseek.com/v1",
});
const completion = await client.chat.completions.create({
  model: "deepseek-chat",
  messages: [{ role: "user", content: "Hello!" }],
});

That's it. Already using OpenAI SDK? Change 2 lines.

Getting Started (What Most Guides Skip)

Registration: platform.deepseek.com → GitHub OAuth (skips Chinese phone requirement).

API Key: Settings → API Keys → Create → Copy immediately.

Topping Up: Minimum ¥10 ($1.40). That's 35 million tokens. Alipay or WeChat Pay.

Production Error Handling

import time
from openai import OpenAI, RateLimitError

client = OpenAI(
    api_key="...",
    base_url="https://api.deepseek.com/v1",
    max_retries=3
)

def safe_chat(messages):
    for attempt in range(3):
        try:
            resp = client.chat.completions.create(
                model="deepseek-chat", messages=messages
            )
            cost_in = resp.usage.prompt_tokens * 0.00000028
            cost_out = resp.usage.completion_tokens * 0.00000042
            print(f"Cost: ${cost_in + cost_out:.6f}")
            return resp.choices[0].message.content
        except RateLimitError:
            time.sleep(2 ** attempt)

5 Zero-Cost Projects

AI Email Drafter — Auto-respond from inbox
Code Review Bot — GitHub Action, $0.01 per PR
Content Pipeline — Blog → Twitter → LinkedIn
SQL Generator — NL → optimized SQL
Support Triage — Classify + draft in bulk

I compiled everything into a complete PDF guide: registration screenshots, code templates for Python/Node.js, prompt engineering, token counting, cost tracking, and 20 bonus production templates.

👉 Get the DeepSeek API Guide — $9.99

Questions? Drop them below. I will answer anything about switching from OpenAI or Claude to DeepSeek.

I Used AI to Write 100 Cold Emails. Here's What Actually Got Replies.

yangchunhong — Mon, 22 Jun 2026 06:20:59 +0000

After A/B testing dozens of AI-generated subject lines, email bodies, and CTAs, I found the structure that consistently gets responses.

These are from a 50-template pack I compiled covering marketing, coding, writing, data, and translation.

The Cold Email That Works

Write a 5-email cold outreach sequence for [COMPANY] targeting [ROLE].
Goal: book a 15-min discovery call. Tone: professional but warm.
Each email under 120 words. Include subject lines with emojis.
Email #3 should include a relevant case study placeholder.
End with a clear CTA.

Why it works: It forces the AI to think in sequence — each email builds on the last. The "case study placeholder" in email #3 adds social proof at the exact moment most prospects hesitate.

The LinkedIn Post That Gets 3x Engagement

Write a LinkedIn post about [INSIGHT] from the perspective of [ROLE].
Structure: bold opening → personal anecdote → key lesson →
actionable takeaway → question for engagement. Under 1,300 chars.

The Landing Page Formula

Write hero copy for [PRODUCT]. Include: main headline (6-10 words),
subheadline (15-20 words), 3 benefit bullet points, CTA button text,
and a social proof line. Benefit-driven, not feature-driven.

The Facebook Ad That Beats Scroll-Fatigue

Write 3 variations of a Facebook ad for [PRODUCT] targeting [AUDIENCE].
Each: headline (under 27 chars), primary text (under 125 chars), CTA.
Use emotional triggers and social proof.

Data Analysis for People Who "Aren't Data People"

Analyze this dataset: [DATA]. Identify: top 3 trends, 2 surprising findings,
1 recommendation per finding, 2 questions the data raises.
Present as a bulleted memo for a busy executive.

These are from a full pack of 50 templates: Marketing (10), Writing (10), Coding (10), Data Analysis (10), Translation (10).

👉 Get all 50 for \$4.99 →

10 AI Prompts That Make You Look Like a Senior Developer

yangchunhong — Mon, 22 Jun 2026 06:19:56 +0000

I've been using AI for coding daily for 6 months. These 10 prompts consistently make me look more competent than I am.

They're from a larger pack I put together — 50 templates spanning marketing, writing, coding, data, and translation. But here are the 10 coding-specific ones, for free.

1. The Bug Fixer (My Most-Used Prompt)

The following [LANGUAGE] code has a bug: [DESCRIBE SYMPTOM].
Analyze the code, identify the root cause, explain WHY it happens,
provide the corrected code, and explain how your fix works.
Include a test case that would have caught this bug.

Why it works: Forces the AI to explain causation, not just patch symptoms. The test case requirement catches regressions.

2. The Refactoring Request

Refactor this [LANGUAGE] code for readability and maintainability.
Apply: meaningful variable names, extract helper functions,
reduce nesting, add docstrings. Preserve all functionality.
Show before/after with explanation of each change.

3. Function From Spec

Write a [LANGUAGE] function that [DESCRIPTION].
Handle edge cases: [LIST]. Return type: [TYPE].
Time complexity target: O([N]).
Include type hints, input validation, error handling,
and 3 usage examples. Use only standard library.

4. The SQL Query You Can't Quite Write

Write a SQL query for [DB] that [REQUIREMENT].
Tables: [NAMES]. Include JOINs, WHERE, GROUP BY, ORDER BY, LIMIT.
Add EXPLAIN annotation comments. Optimize for the indexes: [INDEXES].

5. Unit Tests (So You Can Ship Friday)

Write comprehensive unit tests for this [LANGUAGE] function
using [FRAMEWORK]. Cover: happy path, edge cases, error conditions,
and boundary values. Include setup/teardown. Aim for 90%+ coverage.

6. The API Endpoint You've Written 100 Times

Generate a [FRAMEWORK] API endpoint for [OPERATION] on [RESOURCE].
Include: route, validation, auth check, business logic placeholder,
DB query, response formatting, error handling.
Add rate limiting and logging middleware. Use [DATABASE].

7. Code Review (Before Your Team Does It)

Review this code for: security vulnerabilities, performance issues,
code smells, and style violations. Rate severity (Critical/Med/Low).
Suggest specific improvements with code snippets.

8. The Regex You'll Never Memorize

Write a regex that matches: [PATTERN].
Should match: [POSITIVE CASES]. Should NOT match: [NEGATIVE CASES].
Break down each part with comments. Include a test function.

9. Shell Script That Actually Handles Errors

Write a bash/PowerShell script that [TASK].
Include: error handling for each step, colored output,
--help flag, dry-run mode, and logging to file.

10. Docker Compose (Production-Ready, Not Tutorial-Ready)

Create a docker-compose.yml for [STACK].
Services: [LIST]. Include: health checks, env vars from .env,
named volumes, network config, resource limits, restart policies.
Add a README with setup and troubleshooting.

These 10 are from a pack of 50 templates covering marketing, writing, data analysis, and translation too.

If you want all 50 (formatted, with expected output for each): \$4.99 →

What's your most-used AI prompt for coding? Drop it in the comments.

I Spent 3 Months Prompt-Engineering AI. Here Are the 50 Templates That Actually Work.

yangchunhong — Mon, 22 Jun 2026 06:05:39 +0000

TL;DR: I compiled 50 battle-tested AI prompt templates across Marketing, Writing, Coding, Data Analysis, and Translation. Each one includes the exact prompt + expected output format.

👉 Get the full pack for \$4.99 →

Why I Made This

After spending months using ChatGPT, DeepSeek, and Claude for everything from writing cold emails to debugging production code, I noticed something: 90% of the time, I was typing the same prompt structures over and over.

So I systematized them. Here's what's inside:

What You Get (50 Templates)

📢 Marketing (10)

High-converting Facebook Ads (3 variations, ready for Ads Manager)
Cold email sequences (5 emails with subject lines)
Landing page hero copy (headline + sub + bullets + CTA)
Instagram carousel posts (5 slides)
Amazon/Etsy product descriptions (SEO-optimized)
LinkedIn thought leadership posts
Google Responsive Search Ads (15 headlines + 4 descriptions)
Newsletter welcome emails
Twitter/X threads (10-tweet format)
Customer testimonial rewriting

✍️ Writing (10)

Blog post outline generator (full nested outline)
Short story starters (300 words, any genre)
Executive summaries from long documents
Tone rewriter (formal ↔ casual)
How-to guide creator (step-by-step)
Speech/presentation scripts with timing
Content repurposer (blog → video + LinkedIn + tweets)
FAQ page generator
Editing with track-changes-style feedback
Metaphor & analogy generator

💻 Coding (10)

Bug fixer with root cause explanation
Code refactoring with before/after diff
Function from spec (production-ready)
SQL query builder (optimized + EXPLAIN annotated)
Unit test generator (90%+ coverage)
API endpoint boilerplate (auth + validation)
Code review assistant (severity-rated)
Regex pattern builder with test cases
Shell script automator (cross-platform)
Docker Compose stack generator

📊 Data Analysis (10)

Data insight extractor (executive memo format)
Python Pandas script generator
Chart recommendation engine
A/B test result interpreter
Spreadsheet formula wizard
KPI dashboard blueprint
Data cleaning pipeline (modular, logged)
Forecasting model spec
Business metrics explainer (non-technical)
Data story builder (narrative format)

🌐 Translation (10)

Context-aware translation (idioms adapted)
Localization for market entry
Subtitle translator (SRT format preserved)
Multi-language social media pack
Technical documentation translation
Website UI string translation (JSON)
Idiom & slang adapter
Legal clause translator
Multilingual customer support
Proverb & cultural reference translator

Why These Are Different

Each template includes:

The exact prompt — copy, fill in [brackets], paste into any AI
Expected output format — you know what you'll get
Pro tips — why this structure works

Free Sample: Try These Right Now

Cold Email Sequence:

Write a 5-email cold outreach sequence for [COMPANY] targeting [ROLE].
Goal: book a 15-min discovery call. Tone: professional but warm.
Each email under 120 words. Include subject lines with emojis.

Bug Fixer:

The following [LANGUAGE] code has a bug: [SYMPTOM].
Analyze the code, find the root cause, explain WHY it happens,
provide corrected code, and include a test case.

Data Insight Extractor:

Analyze this dataset: [DATA]. Identify: top 3 trends, 2 surprising findings,
1 recommendation per finding, 2 unanswered questions.
Present as a bulleted memo for a busy executive.

\$4.99 — if one template saves you 30 minutes, it paid for itself.

👉 Get All 50 Templates →

Compatible with ChatGPT, DeepSeek, Claude, Gemini, and any AI tool.