How I Wrote a Technical Book on Auction Provenance Research: From Niche Idea to O’Reilly Publication

Writing a technical book changed how I see expertise. It’s not enough to know something deeply—you need to show others how to do it. Here’s how I took a passion for rare coin provenance from a weekend hobby to an O’Reilly-published book. Spoiler: It wasn’t about writing ability. It was about building systems, finding the right audience, and proving demand before the first chapter even existed.

Why Auction Provenance Research Is a Goldmine for Technical Authors

I got into coin collecting by accident. A buddy dragged me to an auction. I left obsessed—not with the coins, but with their stories. Who owned them? When did they last sell? Who graded them?

Then I hit a wall. The research process was a mess. No one had written it down. Just tribal knowledge, scattered PDFs, and sketchy databases. Sound familiar?

That’s when I saw the opportunity: this wasn’t just numismatics. It was data engineering. And technical books thrive on real engineering problems.

The Core Problem Worth Solving

Researching a coin’s history isn’t about luck. It’s about solving four problems:

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• Fusing disparate sources (Heritage, Stack’s Bowers, NNP, PCGS, Greysheet)

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• Breaking down data silos (scanned catalogs with no OCR, missing images, typos in lot descriptions)

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• Automating matches across regraded, cracked-out, or crossover coins

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• Creating repeatable workflows anyone can follow

These are the kind of challenges engineers love. And publishers pay attention.

Structuring a Technical Book Around a Niche Topic

I almost made the classic mistake: trying to cover all of numismatics. Then I paused. Who was I really writing for? Not general collectors. Tech-savvy researchers who wanted systems, not encyclopedias.

So I flipped the script. Instead of a broad survey, I built the book around one core process: tracking a single coin’s journey across 50+ years of auction data.

My Chapter Framework

1. Chapter 1: The Provenance Stack – Mapping every data source (and which ones actually matter)

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2. Chapter 2: OCR & Image Extraction from Legacy Catalogs – Tesseract, ImageMagick, and why you need custom thresholds
3. Chapter 3: Web Scraping Auction Archives – Handling JavaScript, login walls, and rate limits (without getting banned)
4. Chapter 4: AI for Provenance Matching – Using LLMs to parse descriptions, catch errors, and link records
5. Chapter 5: Building a Private Provenance Database – SQLite schema design, indexing, and query patterns
6. Chapter 6: Case Studies – How I traced a 1905-O Dime and an 1846-O Seated Dollar (and why it took months)
7. Chapter 7: Publishing Your Research – Static sites, APIs, and turning data into a service

Why This Structure Works

Each chapter is self-contained. No fluff. Just:

• A clear problem
• Code that runs (Python, Bash, SQL)
• Tool recommendations (Scrapy, Playwright, Hugging Face models)
• What doesn’t work—and how to fix it

Like in Chapter 4. I included a prompt I actually use to match coin descriptions:

You are a numismatic provenance expert. Given a coin slab image and description:
- Extract: date, mint, denomination, grade, certification number, notable varieties
- Search Heritage and Stack's archives for matching records
- Prioritize results with the same certification number or plate match
- Return a JSON list with lot numbers, auction dates, and provenance chain

This is the stuff that gets book deals. Not theory. Working code and real prompts.

Building an Audience Before You Write

Publishers don’t bet on manuscripts. They bet on markets.

I didn’t wait. Three months before pitching, I started:

• GitHub Repo: provenance-tracker with Jupyter notebooks—actual coin research, warts and all
• Twitter Threads: “Found a 1954 Stack’s catalog match for my 1905-O dime using AI OCR. Here’s how it fell apart (and how I fixed it).”
• Blog Series: 8 posts on Medium, each dissecting one coin’s provenance, ending with “This is the book I wish existed”
• Substack: Built a 2,000-subscriber list by offering free automation templates

By the time I pitched, I had proof people wanted this. O’Reilly’s editor said it directly: “We knew we could sell this because you already had readers.”

The Book Proposal That Won O’Reilly

I sent proposals to O’Reilly, Manning, and Apress. O’Reilly said yes. Not because my writing was perfect. Because my proposal answered three questions:

1. Is the Topic Timely?

• Yes. AI in archival research is booming
• Collectibles are assets now (NFTs, fractional ownership)
• Provenance is data integrity for blockchain-backed collectibles

2. Is the Author Credible?

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• Linked to my GitHub, blog, and email list
• Included testimonials from coin dealers using my tools
• Named my technical advisor: a former PCGS grader turned data scientist

3. Is the Content Actionable?

• Included 3 sample chapters with full code
• Added a “Tools & Services” appendix (e.g., “Use NNP’s Image Collections—sort by first name”)
• Proposed a companion site with a live provenance lookup demo

I used O’Reilly’s 10-page template. The sections that mattered: Market Analysis, Chapter Outline, Competitors, Marketing Plan.

Navigating the Writing Process (and Staying Sane)

Writing 300 pages while working full-time? Brutal. My survival system:

Write in Modules, Not Sequences

I didn’t write front-to-back. Each chapter was a mini-project:

• Week 1: Research (scraped Heritage for 1905-O dime data)
• Week 2: Code (built scraper + matching script)
• Week 3: Write (1,200 words + 2 code blocks)
• Week 4: Test (ran the script on 5 coins, fixed bugs)

Use Technical Editors Early

Before O’Reilly’s formal review, I sent drafts to 3 beta readers:

• A numismatist (for accuracy)
• A Python dev (for code clarity)
• A technical editor (for structure)

Automate the Grind

Repetitive tasks? Script them. Like this PDF-to-text converter:

# Convert PDF catalogs to searchable text
def pdf_to_text(catalog_pdf):
  !pdftoppm -png -r 300 {catalog_pdf} temp
  images = glob('temp-*.png')
  text = ""
  for img in images:
    text += pytesseract.image_to_string(Image.open(img))
  return text

Why This Book Resonates with Technical Readers

This isn’t about coins. It’s about data archaeology. The techniques apply to:

• Genealogy (old census records, handwritten forms)
• Historical artifact tracing (paintings, manuscripts)
• Patent litigation (prior art searches)
• AI training for domain experts

That’s why it works for CTOs (data pipelines), freelancers (automating research), and VCs (evaluating founder expertise in niche domains).

Conclusion: Your Turn to Publish

Here’s what I learned: specialization + systems = authority. To replicate this:

• Pick a narrow, technical problem (e.g., “automating provenance matching”)
• Build a modular, code-driven structure
• Grow an audience before pitching publishers
• Pitch to O’Reilly first—but keep Manning and Apress in the rotation
• Write in reproducible, tool-focused chapters

The best part? I now get emails from developers building provenance trackers for watches, cars, and art. That’s the power of a technical book: it turns your niche expertise into a global toolkit.

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