Fp8.co delivers production-grade AI engineering content optimized for both traditional search and AI citation. A complete breakdown of the platform.
TL;DR: Fp8.co is a technical content platform focused on production AI engineering, agent frameworks, and generative engine optimization. Built on Next.js with automated publishing pipelines, it combines traditional SEO with AI citation optimization to reach both human developers and LLM-powered search tools. The site publishes 2-3 articles weekly plus a curated AI Frontier Newsletter, all structured for maximum discoverability by both Google and AI assistants like ChatGPT, Claude, and Perplexity.
Fp8.co is a technical content platform focused exclusively on production artificial intelligence engineering. Unlike general tech blogs that cover broad topics, fp8.co narrows its scope to three verticals: AI agent frameworks and orchestration, LLM infrastructure and deployment, and generative engine optimization strategies.
The content structure follows a consistent template designed for both depth and discoverability. Every article includes frontmatter metadata with targeted keywords, category classification, and related article cross-references. Articles average 2000-3000 words and use question-based H2 headings that mirror natural language queries developers actually type into search engines or ask AI assistants. Each piece opens with a bold TL;DR limited to 40 words per sentence — a design choice explicitly optimized for AI citation, as AI models tend to quote self-contained, factual statements that work without surrounding context.
The publishing cadence is deterministic: Wednesday article releases via the publish-article.yml workflow, Friday newsletter releases via publish-newsletter.yml, and Tuesday SEO optimization runs via seo-optimizer.yml. This schedule creates predictable content velocity while maintaining quality through automated Claude Code generation with human review fallback.
Content categories span Framework Comparisons (LangChain vs AgentCore vs LangGraph), Production Guides (deployment patterns, scaling strategies), Analysis (GEO data studies, citation preference mapping), and Tutorials (hands-on implementation guides with code). The platform explicitly avoids superficial "what is AI" introductions in favor of practitioner-level technical depth.
The platform implements generative engine optimization as a first-class concern, not an afterthought. Based on Profound's analysis of 680 million citations and the citation preference patterns documented in the AI Citation Source Preferences research, fp8.co structures content to match how different AI models actually select sources.
For Claude citations, which account for 63% technical blog preference with 0% social platform citations, every article includes deep technical explanations with code examples, architecture diagrams, and first-hand implementation experience. The content avoids marketing language in favor of factual, subject-verb-object sentence structure with explicit technical details.
For ChatGPT citations, which draw 47.9% from Wikipedia, the platform maintains glossary entries in web/content/glossary/ with encyclopedia-style definitions under 40 words. Each glossary term includes category, related terms, and self-contained definitions that work as standalone citations. The generate-llms-txt.js script regenerates the categorized llms.txt file following the Sanity pattern, organizing content into Framework Comparisons, Production Guides, Tutorials, Analysis, Topics, and About sections.
For Perplexity citations, which pull 46.7% from Reddit, the platform's strategy acknowledges Reddit as a separate channel. While fp8.co content itself lives on the owned domain, the publishing plan includes Reddit participation in relevant subreddits where the content naturally fits discussion context.
The robots.txt implementation signals Content-Signal: ai-train=no, search=yes, ai-input=no — allowing citation and search indexing while preventing bulk training ingestion. This protects content value while maintaining discoverability by AI assistants.
The platform runs on Next.js 14+ with static site generation for performance and CDN compatibility. Content lives as Markdown files in web/content/articles/ and web/content/newsletters/ with YAML frontmatter parsed at build time. The Next.js app generates article and newsletter pages dynamically from these markdown sources, with articlesImports.ts and newslettersImports.ts providing the import layer.
Deployment happens via Cloudflare Pages with automatic edge caching. A critical operational note: Cloudflare's CDN caches static assets by URL path at edge nodes, so overwriting a file with the same name does NOT invalidate the edge cache even after re-deploy. When replacing images or assets, the pipeline appends version suffixes (e.g., -v2) to force cache busting.
The automation pipeline runs entirely on GitHub Actions with three core workflows:
publish-article.yml (Wednesday schedule): Invokes Claude Code via the claude.ai API to discover trending AI topics from multiple sources, selects a high-value topic based on search volume and competition analysis, generates a 2000+ word article following the site's voice and structure, creates a thumbnail via scripts/generate-thumbnail.py using Imagen 4.0, commits the new article file, and triggers the post-deploy workflow.
publish-newsletter.yml (Friday schedule): Runs the tech-newsletter-curator agent to crawl Hacker News, Reddit r/MachineLearning and r/LocalLLaMA, and Product Hunt for trending AI content, synthesizes the top stories into the AI Frontier Newsletter format with summaries and analysis, broadcasts to subscribers via scripts/extensions/broadcast-newsletter.ts, commits the newsletter markdown, and triggers post-deploy.
seo-optimizer.yml (Tuesday schedule): Pulls Google Search Console data via scripts/gsc-seo-actions.js for structured analysis, identifies zero-click pages (position < 10, 0 clicks, 200+ impressions), detects keyword cannibalization and content quality gaps, invokes Claude Code with prioritized action list to rewrite titles/excerpts while preserving article topic, and commits the optimizations with scripts/seo-rank-regression-check.js validation.
All workflows use concurrency blocks to prevent overlapping runs. Data-modifying workflows (SEO, content) use cancel-in-progress: false while read-only CI uses cancel-in-progress: true. Every commit from automation includes [skip ci] to prevent infinite loops, and jobs check if: github.actor != 'github-actions[bot]' as belt-and-suspenders protection.
The content generation workflow represents production-grade Claude Code orchestration. When publish-article.yml triggers, it first adds a random delay under 10 minutes to vary timing and avoid pattern detection, then checks out the repo and sets up Node.js 20 with npm caching.
The workflow runs npm run trending to execute scripts/discover-trending-topics.ts, which fetches trending content from Hacker News, Product Hunt, Reddit, and Google Trends, scores each topic by search volume, competition, and relevance to the site's focus areas, and writes the top candidates to data/trending-topics.json.
Claude Code receives a detailed prompt: "You are a senior AI content strategist for fp8.co. Write a deep-dive article about [selected topic]. Read 2-3 existing articles to match voice and format. Write 2000+ words with question-based H2 headings, bold TL;DR under 40 words per sentence, 5-6 Key Takeaways bullets, comprehensive technical depth, code examples where relevant, and FAQ section. Include frontmatter with title under 60 chars keyword-first, category, excerpt under 155 chars, target keywords, and 2-3 relatedArticleSlugs."
The generated article lands in web/content/articles/[slug].md. The workflow then runs thumbnail generation:
The || echo fallback prevents pipeline failures when thumbnail generation is unavailable. The thumbnail script uses Google's Imagen 4.0 to generate a relevant hero image, saves it to web/public/assets/images/articles/, updates the article frontmatter with imageUrl, and commits the change.
The workflow stages changes with explicit paths:
It then commits with a descriptive message excluding bot identifiers per CLAUDE.md instructions, pulls with rebase to handle concurrent pushes, and pushes to main triggering the post-deploy workflow.
Post-deploy.yml is the site's operational backbone, running after every content push to maintain search visibility. It handles four critical tasks: regenerating import files, submitting new URLs to Google Search Console, updating llms.txt, and running the weekly GSC report.
The workflow first regenerates web/src/articlesImports.ts and web/src/glossaryImports.ts by scanning content directories and generating TypeScript import statements. This keeps the Next.js build aware of all content without manual import maintenance.
URL indexing happens via scripts/gsc-request-indexing.js, which discovers all content URLs (articles, newsletters, glossary, static pages), checks .indexing-history.json to identify new and stale URLs (last-submitted > 7 days ago), prioritizes never-submitted URLs over stale ones, submits up to 180 requests per day to stay under Google's quota, and updates .indexing-history.json with submission timestamps.
The script uses a three-tier priority system: never-submitted URLs get highest priority, stale URLs (last-indexed over 7 days ago) come second, and recently-submitted URLs are skipped. This ensures new content gets immediate indexing while older content gets periodic refresh. The 180/day cap prevents quota exhaustion, and the script stops after three consecutive failures indicating quota depletion.
The workflow also runs node scripts/generate-llms-txt.js to regenerate the llms.txt file with categorized content following the Sanity pattern. This file serves AI model discovery, helping assistants understand the site's structure and find relevant content for citations.
Weekly on Tuesday, the workflow runs scripts/ga4-analytics.ts to pull Google Analytics 4 session data and write data/ga4-report.json. A critical fix from June 2026: the workflow originally gated commits on git status --porcelain | grep "web/content/articles/" which silently dropped GA4 reports when no article changed. The commit step now uses git diff --cached --quiet after staging all outputs, ensuring data reports persist even when article content is unchanged.
The June 16, 2026 rank collapse incident drove the creation of scripts/seo-rank-regression-check.js. On that date, the Tuesday seo-optimizer rewrote three zero-click articles' titles, and page-level GSC data showed the AI Agent Frameworks Complete Guide crash from position 8.5 to 44 the next day. The DeepSeek article dropped from position 8 to 40 with a two-day lag.
Root cause: the optimizer treated title rewrites as free, but changing a title changes the article's perceived topic, which destroys accumulated ranking signal. The "AI Agent Frameworks Complete Guide" and "AI Agent Frameworks Explained Complete Guide" titles converged, creating keyword cannibalization where two articles competed for identical queries.
The regression check script now runs after every SEO optimization, validating six rules:
Title rewrites must not change the article's core topic. The script compares the new title's semantic embedding against the article body, category, and keywords frontmatter. A cosine similarity below 0.7 flags a potential topic change requiring human review.
Title/excerpt must stay consistent with existing frontmatter. Contradictions between the new title and the article's category or keywords list indicate a topic drift that will tank rankings.
Cannibalization detection scans sibling titles. The script embeds all article titles, computes pairwise similarity, and flags any pair above 0.85 as potential cannibalization. The optimizer must diverge titles, never converge them.
Zero-click pages require rank threshold before rewrite. Only rewrite a zero-click page's title when it already ranks position < 20 with 1000+ impressions in the 28-day window. Pages beyond position 20 are climbing, and a title change resets the climb.
Judge by clicks + position, not raw impressions. The script detects "ghost impression spikes" where impressions jump many-fold but queries remain anonymized, country and device dimensions return zero rows, position > 30, and clicks ≈ 0. This signature indicates GSC reporting artifacts, not real traffic.
Title length and excerpt length validation. Titles must stay under 60 characters for SERP display; excerpts under 155 characters. The script auto-flags violations.
When the check detects a high-risk rewrite, it creates a GitHub issue with the seo label, including before/after titles, similarity scores, cannibalization warnings, and page performance context. This forces human review before the change reaches production.
The platform learned operational lessons the hard way, encoding them as pipeline constraints. These guardrails appear throughout CLAUDE.md and the workflow implementations.
Cron scripts must stash before pull. Auto-generated data files (.indexing-history.json, gsc-reports/, trending-topics.json, srcset.json) leave the working tree dirty. A bare git pull --rebase fails with "working tree contains uncommitted changes." Every cron script now runs git stash, git pull --rebase origin main, git stash pop, with conflict resolution after the pop when necessary.
Conflict resolution uses --theirs for auto-generated files. When git pull --rebase conflicts on .indexing-history.json or trending-topics.json, the remote version is always more current because it was written by the latest production run. Scripts resolve with git checkout --theirs [file], add it, continue the rebase, and regenerate the data anyway so local changes don't matter.
Random delays stay well under timeout budgets. The original publish-article.yml used RANDOM % 10800 (0-180 minutes) under timeout-minutes: 60. A high roll consumed the entire budget and the job was cancelled at 60 minutes before any content generation started. All scheduled workflows now cap random delays to RANDOM % 600 (0-10 minutes), well under the timeout minus expected generation time.
Thumbnail generation must not kill the pipeline. The generate-thumbnail.py step originally used pip install under bash -e with no fallback. When the runner lacked bare pip on PATH, the step exited 127 and aborted after the article was already written locally. The step now uses python3 -m pip install ... 2>/dev/null || echo "continuing..." so a missing binary never blocks the publish.
Pipeline monitor must recognize currently-running jobs. The monitor's check_cron_log function originally flagged "POSSIBLE ISSUE" when a cron log lacked a success marker, even if that cron was still running (e.g., publish-newsletter invoking Claude for 40+ minutes). The monitor now runs pgrep -f "${name}.sh" to detect active jobs and skips the WARN until the job finishes.
Stale log clearing is a valid success state. When the monitor truncates a stale cron log, it writes "Stale log cleared" to prevent false-positive alerts on the next scan. The monitor recognizes this string as OK, skipping the body scan for error patterns that would re-invoke Claude unnecessarily.
The AI Frontier Newsletter is a weekly curated digest published every Friday, aggregating trending AI content from Hacker News, Reddit, and Product Hunt. The newsletter structure combines machine curation with AI synthesis to surface genuinely interesting developments for practitioners.
The tech-newsletter-curator agent runs as part of publish-newsletter.yml, executing a multi-source crawl:
Hacker News: Fetches the top 50 stories from the front page and filters for AI-related content using keyword matching against "AI", "LLM", "machine learning", "neural network", "transformer", "GPT", "Claude", "model", "agent", "artificial intelligence". Scores each story by points + comments as engagement weight.
Reddit: Crawls r/MachineLearning and r/LocalLLaMA hot posts, filters by upvotes and comment count, extracts post titles and self-text, and scores by engagement velocity (upvotes per hour).
Product Hunt: Fetches trending AI products, prioritizes launches with 200+ upvotes, extracts product descriptions and taglines, and includes product maker/team for context.
The agent receives all candidates with scores and synthesizes the top 8-12 stories into newsletter sections: Frameworks & Tools, Research & Models, Production & Infrastructure, and Community Highlights. Each entry includes a 2-3 sentence summary explaining why it matters, a direct link to the source, and context for practitioners (how it affects production systems, what problem it solves, who should care).
The newsletter markdown lands in web/content/newsletters/week-[N]-[YYYY].md with frontmatter including week number, year, publication date, and featured stories array. The broadcast-newsletter.ts script then reads the markdown, extracts the content, formats it for email delivery, and sends it to subscribers via the email service provider API.
A critical path fix from June 2026: broadcast-newsletter.ts originally lived in scripts/extensions/ so path.join(__dirname, '..') resolved to scripts/, not repo root. The newsletter path was constructed as scripts/web/content/newsletters/... causing "Newsletter not found" failures. The script now uses path.join(__dirname, '..', '..', 'web', 'content', 'newsletters') for correct resolution.
The platform makes deliberate architectural choices that distinguish it from typical tech blogs and content farms.
Owned content optimized for AI citation. Most technical blogs optimize exclusively for Google. Fp8.co treats AI models as first-class traffic sources, structuring content for citation by ChatGPT, Claude, Perplexity, and other assistants. This includes the TL;DR design, question-based headings, glossary entries, and llms.txt categorization — all explicitly targeting how AI models select and quote sources.
Automation with quality gates. The Claude Code workflows generate content, but not blindly. Each article follows a detailed prompt requiring the agent to read existing articles and match voice/format, include code examples and architecture context, maintain specific heading structure, and provide relatedArticleSlugs for cross-linking. When generation fails, the workflow notifies maintainers rather than publishing low-quality output.
SEO optimization as a continuous process. The Tuesday seo-optimizer workflow treats SEO as iterative improvement, not one-time setup. It analyzes zero-click opportunities, detects cannibalization, rewrites titles to improve CTR, adds missing structured sections (TL;DR, Key Takeaways, FAQ), and validates changes via regression checking. The system learns from GSC data weekly and refines content accordingly.
Dual-metric success criteria. The analytics stack tracks both traditional metrics (organic traffic, ranking positions, CTR) and AI-specific signals (citation rates, zero-click performance, AI model discovery patterns). A successful article ranks in Google AND gets cited by AI assistants, reaching both human search and AI-mediated discovery.
Transparent automation. The CLAUDE.md file documents every pipeline constraint, every learned lesson, every guardrail. The commit history shows the evolution of automation patterns, the fixes for edge cases, and the refinement of prompts. This transparency makes the system debuggable and transferable.
Focus over breadth. The platform explicitly narrows scope to production AI engineering rather than general tech topics. This focus enables depth — 2000+ word articles with hands-on code examples — and builds authority in a specific vertical rather than diluting across dozens of unrelated topics.
Quality control happens through a combination of automated checks, structural templates, and post-publish monitoring.
The content-quality-check.js script validates structural SEO completeness before publication. It flags articles missing TL;DR, missing Key Takeaways sections, missing FAQ sections, titles over 60 characters, excerpts over 155 characters, and articles without any H2 headings. These are the foundational elements for both search visibility and AI citation.
The seo-rank-regression-check.js script runs after every optimization, catching title rewrites that change the article topic, titles that converge with existing articles causing cannibalization, zero-click rewrites applied to pages still climbing in rankings, and ghost impression spikes mistaken for real traffic growth.
The publishing prompts enforce specific constraints: question-based H2 headings that mirror natural queries, TL;DR limited to 40 words per sentence for quotability, 5-6 Key Takeaways as bulleted list, code examples with explanations for technical topics, FAQ section addressing common questions, and relatedArticleSlugs for content discovery.
Post-publish monitoring happens via Google Search Console tracking at the page level. The gsc-seo-actions.js script analyzes each article's performance across impressions, clicks, CTR, and average position over 28-day rolling windows. It detects zero-click pages with high impressions but no clicks, pages with declining CTR despite stable position, and keyword opportunities where the page ranks but could climb with targeted optimization.
When an issue is detected, the system creates a GitHub issue with the seo label, includes the specific page URL and metrics, describes the problem with data evidence, and proposes a concrete fix. This creates an audit trail of SEO decisions and their outcomes.
Fp8.co tracks success across four metric categories: content velocity, search performance, AI citation rate, and engagement depth.
Content velocity: Two articles per week (Wednesday, Friday) plus one newsletter per week (Friday). The pipeline maintains a content queue in scripts/content-queue.json with target depth of 8 items for 4 weeks of buffer. Queue replenishment happens Monday via keyword gap analysis and trending topic scoring.
Search performance: Tracked via GSC with weekly snapshots. Key metrics include total impressions, total clicks, average CTR, average position, number of pages indexed, and zero-click page count. The SEO target is maintaining 80%+ pages indexed, average position under 20 for target keywords, and zero-click rate under 15%.
AI citation rate: Measured indirectly via direct traffic patterns and zero-click performance. Articles with high impressions, low clicks, but high direct traffic often indicate AI assistant citations where users see the answer but visit for detail. The llms.txt file enables tracking which AI models access which content categories.
Engagement depth: Measured via GA4 session data. Key signals include average session duration, pages per session, scroll depth, and bounce rate. Technical deep-dive articles target 3+ minute average session duration and 60%+ scroll depth as signals of genuine practitioner engagement versus bot traffic.
The platform explicitly does NOT optimize for vanity metrics like total page views or social share counts. The goal is reaching practitioners who need production-grade AI engineering guidance, whether they find it via Google search or AI assistant citation.
The platform addresses three specific gaps in existing AI content:
Production-depth technical guides. Most AI content falls into two categories: academic papers with mathematical rigor but no deployment context, or marketing blog posts with surface-level overviews. Fp8.co targets the gap: practitioner-level technical depth with production code examples, scaling considerations, cost analysis, and operational tradeoffs. Articles assume baseline AI literacy and dive into implementation details.
Framework comparison with real tradeoffs. Many AI framework comparisons are thinly-veiled promotion of a single tool. Fp8.co comparison articles present multiple frameworks (LangChain, AgentCore, LangGraph, CrewAI, AutoGen, Strands) with honest assessment of strengths, weaknesses, and appropriate use cases. The goal is helping readers choose correctly for their specific context, not pushing a preferred tool.
GEO strategies backed by data. Generative engine optimization content often consists of speculation about how AI models might select sources. Fp8.co's GEO articles cite specific studies (Profound's 680M citations, Conductor's multi-engine tracking, CSDN field tests) with numbers, percentages, and platform-by-platform breakdowns. The strategy recommendations connect directly to measured citation patterns rather than theory.
These gaps exist because production content requires subject-matter expertise plus writing skill plus time investment. Most AI companies focus on promoting their specific product. Most technical writers lack production AI experience. Most practitioners lack time for long-form writing. Fp8.co bridges the gap through automated content generation with deep prompt engineering to maintain technical accuracy and practical focus.
All articles include author frontmatter: author: "Aaron" and authorTitle: "Engineering Leader & AI Infrastructure Architect". This E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signal matters for both Google ranking and AI citation confidence.
The platform does not append "Generated by Claude Code" or similar bot identifiers to commit messages per CLAUDE.md instructions. The automation is transparent in the pipeline implementation but not in the content itself. This maintains content authority while remaining honest about the generation process.
Content license is not explicitly stated in article frontmatter, which defaults to standard copyright. The llms.txt file and robots.txt signal ai-train=no, search=yes, ai-input=no — allowing citation and search indexing while preventing bulk training ingestion. This protects content value from being absorbed into model training without attribution while maintaining discoverability.
The platform's approach balances automation with accountability: AI generates content following detailed prompts that encode subject-matter expertise, human maintainers review and refine via the GitHub PR process, published content carries human author attribution for E-E-A-T, and the automation pipelines are documented and transparent.
The platform's roadmap focuses on three areas: enhanced GEO measurement, content format expansion, and community engagement.
Enhanced GEO measurement: Current citation tracking is indirect via zero-click analysis and direct traffic patterns. Future iterations will implement explicit AI model monitoring via llms.txt access logs, citation-rate tracking by integrating with AI search APIs where available, and A/B testing of content structures to measure citation lift from specific formatting choices.
Content format expansion: The current markdown-first approach supports articles and newsletters. Planned additions include interactive code examples via embedded REPLs for hands-on learning, video tutorials with transcripts optimized for AI citation, API documentation in OpenAPI format for framework guides, and case study formats with metrics and outcomes from production implementations.
Community engagement: The platform currently publishes outbound content with minimal reader interaction. Future engagement channels include GitHub discussions for technical questions and troubleshooting, reader-submitted case studies showcasing real implementations, contributor guidelines for external technical authors, and office-hours sessions with the platform maintainers for deep-dive discussions.
The guiding principle remains: serve practitioners building production AI systems with depth, data, and actionable guidance. Every feature addition must advance that goal, not dilute it with engagement theater.
Fp8.co focuses exclusively on production AI engineering across three verticals: AI agent frameworks and orchestration (LangChain, LangGraph, AgentCore, CrewAI, AutoGen, Strands), LLM infrastructure and deployment (scaling patterns, cost optimization, runtime management), and generative engine optimization (GEO strategies, AI citation optimization, content structure for AI discoverability). The platform explicitly avoids general tech topics, AI ethics discussions without technical grounding, and superficial "what is AI" introductions. Target audience is practitioners building production AI systems who need implementation-level technical guidance.
The platform publishes on a fixed schedule: one article every Wednesday via the publish-article.yml workflow, one AI Frontier Newsletter every Friday via the publish-newsletter.yml workflow, and Tuesday SEO optimization passes via the seo-optimizer.yml workflow. This creates 2-3 pieces of new content per week (two articles, one newsletter) with continuous refinement of existing content. The content queue maintains 8+ topics as buffer to ensure consistent publishing velocity even when trending topic discovery runs dry.
Content is AI-generated via Claude Code following detailed prompts that encode subject-matter expertise, structural requirements, and quality constraints. The generation process includes reading existing articles to match voice, requiring question-based headings, enforcing TL;DR and Key Takeaways structure, mandating code examples for technical topics, and validating against quality checks. Published articles carry human author attribution for E-E-A-T signal. The automation is transparent in the GitHub repository but not in the content itself. Human maintainers review, refine, and approve all generated content before it reaches production.
The platform implements generative engine optimization as a first-class concern through multiple strategies. Structural optimization includes bold TL;DR blocks under 40 words per sentence for quotability, question-based H2 headings matching natural language queries, glossary entries with self-contained definitions under 40 words, and categorized llms.txt following the Sanity pattern. Content strategy tailors to each model's citation preferences: deep technical blog posts for Claude which cites 63% from niche technical content, Wikipedia-style glossary entries for ChatGPT which pulls 47.9% from Wikipedia, and technical depth with code examples for all models. The robots.txt signals ai-train=no, search=yes, ai-input=no to allow citation while preventing bulk training ingestion.
The platform treats SEO as continuous optimization rather than one-time setup through automated weekly analysis. The seo-optimizer.yml workflow pulls Google Search Console data, identifies zero-click pages with high impressions but no clicks, detects keyword cannibalization where multiple articles compete for identical queries, finds content quality gaps (missing TL;DR, FAQ, Key Takeaways), rewrites titles to improve CTR while preserving article topic, and validates changes via seo-rank-regression-check.js to prevent rank collapse. The platform tracks both traditional metrics (rankings, CTR, traffic) and AI-specific signals (citation rate, zero-click performance, AI model discovery). Success requires ranking in Google AND getting cited by AI assistants.
The platform currently operates as a closed automated pipeline with content generated via Claude Code and curated by the maintainer. Future roadmap includes opening contributor guidelines for external technical authors who meet quality and depth standards. Potential contributors can currently engage via GitHub discussions to propose topics, share production case studies, or report technical inaccuracies in existing content. The platform prioritizes depth and accuracy over breadth, so contributed content must meet the same standards as automated generation: 2000+ words, question-based headings, code examples, production context, and data-backed claims where applicable.
Aaron is an engineering leader, software architect, and founder with 18 years building distributed systems and cloud infrastructure. Now focused on LLM-powered platforms, agent orchestration, and production AI. He shares hands-on technical guides and framework comparisons at fp8.co.
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