How I Turned a Passion for Coin Collecting into a Technical Book: From Idea to O’Reilly Publication

Ever stared at a rare coin and thought, “There’s got to be a better way to verify this?” That’s exactly where I started. As a data validation specialist by day and coin collector by night, I saw patterns others missed. The same rigor I applied to software systems could crack open coin authentication. This post is my real story of turning that spark into a real O’Reilly Media book — complete with late-night coding sessions, nervous proposal emails, and the surprising tech community that helped it grow.

Why Write a Technical Book?

I’ve spent years building systems that validate data, detect fraud, and automate decisions. But weekends were for coins — especially the kind that make collectors nervous: Is it real? Graded fairly? Worth the asking price?

That’s when it hit me: coin authentication isn’t just about gut feelings. It’s a technical problem.

Think about it. Authenticating a coin means:

• Measuring physical properties (weight, density, composition)

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• Analyzing surface patterns (doubling, die errors, post-mint damage)
• Tracing provenance (mint records, ownership history)
• Modeling market value (trends, demand, risk)

Sound familiar? It’s exactly like testing a distributed system or training an ML model.

And that’s why I wrote the book: Automated Coin Authentication: Tools, Techniques, and Valuation Algorithms. Not for casual collectors. For the engineers, data scientists, and product builders who see numismatics as a domain of signals, not stories.

The Value of a Technical Book in a Niche Domain

Skeptical? I was too. Writing a book felt like a luxury — until I realized it was a tool.

My book has been used by:

• Blockchain teams building provenance trackers for rare coins
• AI researchers training image models on NGC-graded datasets
• Fintech startups automating appraisal workflows

I didn’t write a “this coin is worth $10,000” guide. I wrote a manual for turning coin analysis into code. That’s what made it stick.

Structuring the Book: From Hobby to Technical Framework

Here’s the mistake most make: they write a memoir, not a manual.

I started with a question: How would I build a coin grading API? That mindset shift — from storytelling to system design — shaped the entire book.

Chapter Architecture: The 5-Tier Validation Model

Instead of listing “rare coins,” I built a five-layer technical pipeline:

1. Image Acquisition — How to capture coins under consistent lighting and angles (with code for OpenCV setup)

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2. Material Analysis — Using weight, diameter, and thickness to estimate density (and flag plating or filler)

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3. Surface & Strike Analysis — Detecting doubling, die cracks, and re-engraving via pattern recognition
4. Historical Context — Mining mint records, auction archives, and blockchain provenance logs
5. Market Modeling — Building valuation algorithms with sentiment, scarcity, and auction history

Each chapter followed the same rhythm:

• A real coin problem (e.g., “Is this 1966 half dollar real?”)
• A technical approach (e.g., “Let’s calculate expected density”)
• Code, diagrams, and a test case

Code Example: Weight-to-Composition Validation

The most shared chapter? The one that turned coin guessing into a function.

def validate_silver_coin(weight_g, diameter_mm, thickness_mm, expected_percentage=0.90):
    """Check if a coin matches expected silver composition by density."""
    volume_cm3 = (3.14159 * (diameter_mm / 2)**2 * thickness_mm) / 1000
    density_g_cm3 = weight_g / volume_cm3
    
    # 90% silver: ~10.36 g/cm3 | 40% silver (clad): ~9.00 g/cm3
    if 10.2 <= density_g_cm3 <= 10.5:
        return f"Likely {expected_percentage*100}% silver (density: {density_g_cm3:.2f})"
    elif 8.5 <= density_g_cm3 <= 9.2:
        return "Clad or low-silver (density: {:.2f})".format(density_g_cm3)
    else:
        return "Anomalous density: possible damage, plating, or error"

# Test: 1966 Kennedy Half Dollar
print(validate_silver_coin(11.5, 30.6, 2.15))  # Likely 90% silver (density: 10.31)

Suddenly, “feels like silver” became “objective density range.” Engineers loved it. Collectors started sharing it with their clubs.

Crafting a Winning Book Proposal

Got an idea? Great. Now prove it matters.

Publishers get hundreds of “I wrote a book about X” emails. I sent one that answered: “Why now? Who needs this? And what makes it technical?”

1. Market Analysis (The “Why Now?” Section)

I didn’t just say “coins are cool.” I showed:

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• AI image models (YOLO, COCO) being trained on coin datasets

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• Blockchain platforms selling rare coins as NFTs — with need for verification
• PCGS and NGC processing millions of coins annually — but little public automation

2. Competitive Landscape

Existing options?

• Red Book: Great for collectors, zero code
• Academic journals: Deep, but no reusable tools
• Blogs “Top 10 Rare Coins” — more gossip than science

My book filled the gap: a technical playbook for reproducible authentication.

3. Target Audience

I defined three readers:

• Software engineers building authentication tools (e.g., for auction sites)
• Data scientists training models on coin images
• Finance pros evaluating numismatic investment platforms

4. Sample Chapter

I sent Chapter 3: “Automated Edge Analysis for Clad vs. Silver Coins” with:

• Python edge detection code
• Jupyter notebooks with test cases
• Images of real anomalies (like a 1981-D dime with incorrect weight)

It wasn’t just a chapter. It was a demo.

Pitching to Publishers: O’Reilly vs. Manning vs. Apress

O’Reilly: The Tech Leader

O’Reilly doesn’t want nostalgia. They want tools. I pitched:

• Integration with Jupyter, OpenCV, TensorFlow
• APIs for third-party grading services
• Ultrasonic testing for haptic feedback (yes, that’s in the book)

Manning: The Educator

Manning likes learning paths. So I proposed:

• “Grading Labs” — hands-on projects like building a chatbot grader
• Video walkthroughs using OBS (recorded in my garage, with coin trays and calipers)

Apress: The Pragmatic

Apress wants ROI. I highlighted:

• “5 scripts to automate grading” — fast, practical wins
• Case studies from real grading disputes (e.g., the 1975 SMS quarter controversy)

I picked O’Reilly — not just for the brand, but for their audience of builders.

Building an Audience: The Pre-Launch Strategy

Here’s the surprise: the book started growing before it was finished.

1. GitHub Repository

I launched coin-authentication-tools with:

• Source code for all scripts (density, edge detection, provenance scraping)
• Public datasets from PCGS/NGC auctions
• An issue tracker for collectors to submit coins for analysis

Within a week, someone asked: “Can your script detect a doubled die on a 1955 cent?” I added it — and credited them.

2. Tech Blog & Newsletter

Instead of “coin tips,” I wrote about:

• “How I Used ML to Detect a Fake 1966 Half Dollar”
• “Why Your Coin’s Weight Might Be Lying to You”
• “The 5-Step Checklist Before Submitting to PCGS”

Readers weren’t just collectors. They were engineers asking: “Can this work for art authentication?”

3. Speaking & Webinars

I spoke at:

• PyData: “Computer Vision for Numismatics”
• Blockchain Conferences: “Provenance Tracking for Rare Coins”
• O’Reilly Events: Hosted a “Coin Grading Lab” workshop

The result? A community of 10K+ readers — many pre-ordered the book. Some even sent me coins to “test the scripts.”

Navigating the Writing Process

Tools & Environment

• O’Reilly’s AsciiDoc — for clean, format-agnostic manuscripts
• Jupyter Notebooks — to embed runnable code in chapters
• GitHub Issues — to track feedback from editors (“Clarify this on page 142”)

Writing Schedule

I used a 90-day sprint:

• Weeks 1–30: Draft chapters (one per week)
• Weeks 31–60: Code, diagrams, and case studies
• Weeks 61–75: Sent to 10 experts for peer review
• Weeks 76–90: O’Reilly review and final edits

Handling Criticism

One reviewer said: “This chapter on fire-damaged coins is too subjective.”

My response? I didn’t argue. I added:

• A quantitative damage scale (e.g., heat deformation in microns)
• Spectral analysis code to detect material changes

That chapter became one of the most cited.

Conclusion: The ROI of a Technical Book

The book didn’t just sell. It changed things.

• Speaking gigs at tech and finance conferences
• Consulting work with grading services and auction platforms
• VC interest in building a coin authentication SaaS
• Named to O’Reilly’s “Top 10 Tech Books of 2023”

Here’s what I learned:

• Structure like an engineer — your book is a system, not a journal
• Publishers want evidence — show demand, not just passion
• Build the audience early — GitHub, blogs, and talks matter more than you think
• Code is credibility — readers trust a script more than a statement

Whether you’re writing about coin authentication, machine learning, or cloud security — treat it like a technical problem, not a memoir. That’s how you turn a passion into a book that matters.

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