Word count is the most overrated SEO signal in 2026. Sites with 1,500+ word posts get demoted; sites with 800-word posts rank top three. Here is the quality rubric that replaces length as a signal.
A working Python + n8n pipeline that groups thousands of keywords into content topics by intent using embeddings, UMAP and HDBSCAN.
A 1,200-post site lost 99.6% of its traffic in a single day despite averaging 1,250+ words per post. The reason is not depth — it is signature similarity at scale. Here is what Google’s classifier is actually reading.
Build a Python pipeline that turns a target keyword into a data-grounded SEO content brief in under a minute: scrape the SERP, count what ranks, and let an LLM draft the outline from real evidence.
Stop writing a new script for every SEO question. Build a Model Context Protocol (MCP) server that exposes Google Search Console as tools an agent can drive live — with working Python code.
Build a pipeline that finds CTR-decayed pages in Search Console, rewrites their title tags and meta descriptions with an LLM, QA-gates the output, and ships the winners.
Choosing an embedding model for SEO isn’t a leaderboard question. A practitioner’s framework for OpenAI vs Voyage vs Cohere vs BGE, with a reproducible AUC benchmark, the dimension trade-off, and the cost math at scale.
Build an automated SEO regression testing pipeline in GitHub Actions that crawls a preview deployment, asserts SEO invariants with pytest, and fails the build before titles, canonicals, robots directives, or schema break in production.
Build an automated 301 redirect mapper that matches old URLs to new ones by meaning with sentence embeddings, confidence thresholds, and post-launch validation.
Raw-HTML crawlers miss the JavaScript-rendered web. A hands-on comparison of Playwright, Puppeteer, and Selenium for SEO automation — rendering JS pages, capturing Core Web Vitals, and crawling at scale, with working code.