I Shared My SEO Strategy

Last week I told you I'd come back this week with concrete side project ideas for technologists in 2026. I had the research underway.

Then something happened that changed my plan, and it's worth telling you why, because the reason is the Building Loop in action.

Quick update before I explain why: the site is now at 210 indexed pages across Google and Bing. Three weeks ago it was at 35.

After publishing last week's issue about the SEO results (67 pages indexed on Google, 72 on Bing, first AI citations), I shared a condensed version of the journey on Reddit (to the r/beehiiv community that sparked this, this one's for you.).

I expected a few comments. What I got was a thread full of senior engineers and solopreneurs asking the same question:

"Can you share the actual strategy you used?"

"If you do put that .md together, I'd be super curious how you structure the keyword → template mapping logic."

"This would be amazing to share. Let me know when it's there."

What I understood then is that people were asking for the system. The technical spec with implementation details that turned "I should fix my SEO" into measurable results in a few weeks.

That's a demand signal I realized I shouldn't ignore. The Building Loop's Validate Step is explicit about this: when your audience tells you what they want, you don't stick to the plan. You respond to what's real.

So the side project ideas issue moves to next week. This week, I'm giving you the strategy and actual playbook that you can feed into your AI assistant and progress with your SEO journey too.

The full strategy is a 9-phase system built specifically for a Next.js content site, but the principles apply to any tech stack. Here's what it covers, and why each phase matters.

Phase 1: Technical Health Foundation. Before writing a single piece of content, we fixed what was broken. Title tags clamped to 60 characters. Meta descriptions tuned to 150–160 characters (below 150, Google rewrites them; above 160, they get truncated). Canonical tags set explicitly on every page, including paginated ones, which most people miss. One H1 per page enforced, even on pages with embedded third-party HTML. This phase alone eliminated over 200 audit findings.

Phase 2: Structured Data. Schema.org markup across 10 different schema types: Article, FAQPage, HowTo, BreadcrumbList, and more. This was done because it enables rich results, improves E-E-A-T signals and, increasingly, is how AI systems disambiguate entities. Every blog post carries computed word count, proper author/publisher objects, and deploy-time freshness signals.

Phase 3: Sitemap and Crawl Budget. The sitemap was rebuilt from scratch to include every page that should be indexed and exclude everything that shouldn't. Paginated listing pages got their own entries and canonical tags. Priority values were assigned deliberately, not left at defaults. The result: Google went from seeing ~35 pages to seeing all of them (as of today more than 200).

Phase 4: robots.txt Strategy. Two objectives. First, maximise useful crawling by disallowing internal infrastructure routes. Second and this is where most guides stop explicitly allow AI crawlers. GPTBot, PerplexityBot, Claude-Web, Google-Extended, and others. If you want to be cited by AI assistants, you need to let them in. One critical detail: Cloudflare's "AI Crawl Control" feature blocks AI crawlers by default. If you use Cloudflare, check this setting. It was silently undermining our AI discoverability until we disabled it.

Phase 5: IndexNow. Rather than waiting weeks for Bing to discover new pages, we built an API route that pushes all sitemap URLs to Bing immediately after every deploy, using a three-tier fallback (authenticated Bing API → unauthenticated IndexNow → api.indexnow.org). This is why Bing indexed 72 pages before Google hit 67. Most builders don't realise Bing can be faster than Google if you submit proactively. And bonus points: Bing Webmasters comes with a beta AI citation page to visualise how you’re doing with AI searches.

Phase 6: Social Metadata. Complete Open Graph and Twitter Card tags on every page. Absolute URLs enforced via a utility function with unit tests. A dedicated 1200×630 OG image. This sounds basic, but when I audited the site initially, 114 pages had no OG tags at all, meaning every LinkedIn or Twitter share was showing a broken preview card.

Phase 7: Programmatic SEO. This is where the scale came from. 110 keyword-cluster topic pages generated from a typed spec, each targeting a specific long-tail phrase. Plus 100 additional pages using the Jake Ward methodology (a methodology for scaling pages by crossing niches with content types) crossing niches (frontend engineer, data scientist, tech lead) with content types (side project ideas, career checklist, sovereignty roadmap). Each page has structured sections, pro tips, FAQs, and appropriate schema. This single phase accounted for the largest jump in indexed page count.

Phase 8: AI Discoverability (GAIO). An llms.txt file following the llmstxt.org specification, submitted to AI crawler directories. Key pages available at .md routes for AI crawlers that prefer Markdown over rendered HTML. An RSS feed served with noindex so it feeds AI systems without competing with canonical pages. This is why we got our first LLM citations within days.

Phase 9: Audit Cycle. Google Search Console weekly, Bing Webmaster Tools for issues Google misses, Ahrefs for deep crawl audits. Every finding is prioritized as blocking → high priority → backlog. Git commits prefixed with fix(seo): so the SEO history is traceable.

I've packaged the complete strategy: all 9 phases, implementation details, code patterns, the per-page launch checklist, environment variables, and a ranked list of what actually moved the needle into a single downloadable Markdown document.

→ Download the SEO Strategy Playbook (free, .md format readable in any text editor, IDE, or note-taking app and easily feedable into your AI agent)

This is the exact document I used to execute every change. Nothing removed, nothing polished out of it.

Two reasons.

First, this is the kind of resource I looked for when I started and couldn't find. Plenty of "SEO tips" articles. Almost no one shares their full working strategy as a technical spec with implementation details. If I can save you the weeks of trial and error I went through, that's worth more than gating it behind an email.

Second, this is the Building Loop in practice. The community told me what they needed. I validated that the demand was real (multiple independent signals from Reddit, not just one comment). And now I'm shipping the minimum useful artefact: a clean, complete document. If the signal continues, I'll expand it. If it doesn't, I'll have learned something about what this audience actually values.

Got the SEO strategy resource page live on thesovereigntechnologist.com. Additionally I am monitoring whether the download gets traction (another validation signal). And starting the research for next week's side project ideas issue, cross-referencing the search queries from Google Search Console with real market signals to find ideas worth your sovereign hours.

P.S. The toolkit is growing: Visibility Audit (where you stand) → Business Model Canvas (is your idea viable) → Building Loop (how to execute) → SEO Strategy Playbook (make your work findable). All free, all designed for builders with limited time.

P.P.S. Know a technologist who built something great but nobody can find it? This week's issue is specifically for them. Forward it. They can subscribe at thesovereigntechnologist.com.

That’s all for this week.

See you next Thursday.