Live
web design
citability.dev
AI Visibility Auditing
AI visibility auditing platform. 10 verified infrastructure checks measuring whether AI systems can find, recommend, and cite your website.
Next.jsTypeScriptTailwind CSS +2
Case study Flagship project: AI-visible web architecture
Issue 01 / Guided entry
I build websites that AI assistants can read, trust, and cite.
Humans read it. Search engines index it. AI assistants cite it. Agents query it. chudi.dev is a live, public case study in AI Visibility Readiness: one solo engineer building a single body of work for all four audiences, publishing exactly how, and measuring whether ChatGPT, Claude, Perplexity, and Google AI Overviews actually cite it. The methods are real. The infrastructure is public. The results are measured.
AI-visible web architecture is the practice of structuring a website so it serves three audiences simultaneously: human readers through editorial design, large language models through machine-readable content surfaces (llms.txt, structured data, FAQ schema), and AI agents through callable tool interfaces (WebMCP, ai.txt). This site is the reference implementation.
Flagship project: chudi.dev as a public working model Human-readable and agent-legible by design AAO / AEO / GEO stack: llms.txt, ai.txt, WebMCP, and schema Machine-readable identity through topic hubs and entity clarity Live case studies, not recycled summaries
Visibility stack
The flagship claim is architectural, not cosmetic.
chudi.dev is designed so readers, search engines, and AI systems can all navigate the same authority graph through interfaces tuned to how they retrieve information. Each surface (HTML for humans, schema for crawlers, WebMCP for agents) reads the same underlying content. The same audit framework powers all three.
AAO
Agent-facing interfaces
WebMCP tools, ai.txt, and callable content surfaces make chudi.dev usable by agents, not just readable by them.
AEO
Answer-ready article design
Definition blocks, FAQ structure, source sections, and reading paths turn posts into extractable answers instead of generic essays.
GEO
Machine-readable discovery
llms.txt, structured data, sitemaps, and entity clarity make the site legible as a coherent knowledge node instead of a generic content archive.
Three editorial journeys
Which reading track fits how you think and build?
Each journey is a topic hub, a recommended reading order, and a newsletter segment. The goal is faster orientation and stronger follow-through. Pick the track that matches the work you do today: builder, operator, or strategist. Each one ends in a concrete next action.
AI Visibility Engineering
SEO optimizes for rank. Answer engines optimize for citation-worthiness.
This track is the engineering playbook for the second game: entity coherence, schema specificity, originality signals, and the measurement loops that show whether ChatGPT, Perplexity, and Google AI Overviews are actually citing you.
Best for
content engineers and sub-DR-20 operators engineering for AI citations
Reading depth
16 foundational, tactical, and case-study notes
Start here
Answer Engine Optimization: 6 Factors That Decide If AI Cites You
Answer engine optimization (AEO) determines which sites AI search engines cite. The 6 factors driving citations in Perplexity, ChatGPT, and Google AI Overview.
Start, branch, continue Explore the track
Build with AI
Build AI products like systems, not demos.
This track covers Claude Code workflows, WebMCP agent interfaces, context management, evidence gates, RAG, and the operational decisions that move an AI idea into production.
Best for
builders, founders, and engineers shipping with AI
Reading depth
20 implementation notes, case studies, and security-automation architectures
Start here
Claude Code Best Practices: A Field Guide from 36K Lines Shipped
Field-tested Claude Code workflows from 36K lines of shipped production code: quality gates, multi-agent orchestration, and the patterns that actually work.
Start, branch, continue Explore the track
Think Better with ADHD
Treat ADHD cognition like a systems design input, not a flaw to hide.
This track reframes parallel thinking, novelty seeking, abstraction, and chaos tolerance as engineering leverage, then turns them into practical workflow scaffolding.
Best for
ADHD and neurodivergent developers, designers, and founders
Reading depth
7 core essays and workflow guides
Start here
ADHD Productivity: The System I Built After GTD Failed Me
GTD doesn't work for ADHD brains. The energy-aware productivity system I built instead, hyperfocus scheduling, AI processing, and the workflow I use to ship.
Start, branch, continue Explore the track
Cluster spotlights
Where should I start in each topic cluster?
Every cluster has one cornerstone post that frames the whole topic and several supporting reads that fill in the details. Start with the cornerstone, then branch into the adjacent system at your own pace. Each cluster reads as one stitched argument rather than scattered posts.
AI Visibility Engineering
AI Visibility Engineering
Featured entry
Answer Engine Optimization: 6 Factors That Decide If AI Cites You
Answer engine optimization (AEO) determines which sites AI search engines cite. The 6 factors driving citations in Perplexity, ChatGPT, and Google AI Overview.
Supporting reads
How to Get Perplexity and ChatGPT to Cite Your Website
How to optimize your website for ChatGPT and Perplexity: robots.txt, llms.txt, schema, and answer-first structure so AI search crawls and cites you.
I Audited 7 Websites for AI Citability. Here Is What Actually Predicts Citations.
Audit data from 7 websites shows domain authority does not predict AI citations. DA-10 sites outperform DA-92 sites. Here is what actually matters.
Build with AI
AI Product Development
Featured entry
Claude Code Best Practices: A Field Guide from 36K Lines Shipped
Field-tested Claude Code workflows from 36K lines of shipped production code: quality gates, multi-agent orchestration, and the patterns that actually work.
Supporting reads
Claude Was Eating My Tokens. This 3-Tier System Cut Usage 60%.
Progressive disclosure cuts AI token costs by 40%. Learn the 3-tier system that reduced my Claude expenses while improving output quality.
RAG Explained: How to Stop LLMs From Making Things Up
RAG retrieves live data to fix LLM hallucinations. Build accurate AI apps with up-to-date knowledge sources without retraining or fine-tuning models.
Think Better with ADHD
Neurodivergent Systems
Featured entry
ADHD Productivity: The System I Built After GTD Failed Me
GTD doesn't work for ADHD brains. The energy-aware productivity system I built instead, hyperfocus scheduling, AI processing, and the workflow I use to ship.
Supporting reads
Reading paths
Where do new readers land first?
Three explicit starting points so no one lands on the site and wonders where to begin. Each starter post is a thirty-minute read that introduces one of the three journeys and leaves you with one concrete next action. Pick the one that matches the work you do.
AI Visibility Engineering
Answer Engine Optimization: 6 Factors That Decide If AI Cites You
Answer engine optimization (AEO) determines which sites AI search engines cite. The 6 factors driving citations in Perplexity, ChatGPT, and Google AI Overview.
Start here
Build with AI
Claude Code Best Practices: A Field Guide from 36K Lines Shipped
Field-tested Claude Code workflows from 36K lines of shipped production code: quality gates, multi-agent orchestration, and the patterns that actually work.
Start here
Think Better with ADHD
ADHD Productivity: The System I Built After GTD Failed Me
GTD doesn't work for ADHD brains. The energy-aware productivity system I built instead, hyperfocus scheduling, AI processing, and the workflow I use to ship.
Start here
Product bridge
The writing points back to real products, case studies, and operational artifacts.
Every essay on this site has a working product or case study underneath it. citability.dev implements the AVR Framework that chudi.dev demonstrates. avr-pipeline is the open-source repo behind both. The writing is downstream of operational artifacts, not theoretical.
Live
automation
AI Visibility Readiness (AVR) Framework v1.0
Open Source Framework
Open-source framework for measuring whether AI systems can discover, retrieve, and cite website content. 15 checks across crawlability, structured data, and AI-specific signals.
PythonClaude APIWeb Scraping +2
Case study Case Study
case study
Bulenox CME Crypto Futures Bot
Futures Trading System
CME Micro Bitcoin Futures (MBT) trading bot for Bulenox prop firm. Rithmic connectivity, fade strategy, quant factory for signal research.
PythonasyncioRithmic API +2
Case study Latest dispatches
Recent writing, still arranged like an editorial product instead of a dump.
The newest posts sit at the top, the cornerstone reads are pinned, and the supporting essays link back into the same topic clusters. The goal is an editorial product anyone can pick up at any entry point and find a path forward, not a reverse-chronological pile of unrelated drafts.
I Found 1,200 AI Citations Hiding in Bing Webmaster Tools
A freeCodeCamp guest post and one overlooked Bing dashboard surfaced ~1,200 Microsoft Copilot citations to my DR-25 site. Here is how to read your own.
· 9 min
ai building
Content Intent Signaling: The robots.txt Directive That Controls How AI Uses Your Content
robots.txt controls access. Content Intent Signaling controls usage. Three new directives separate training from citation permission.
· 11 min
ai building
I Audited My Own Site With AVR v1.1.0. Here Is What I Found.
The first comprehensive 8-section AVR v1.1.0 audit of chudi.dev produced AGENT-READY 3/3 on §2.7 but only 40/100 on Fact-Block Density. Here is the full audit, plus the 2026-06-03 update where remediation lifted the root and framework pages to a verified 100/100 EXTRACTABLE.
· 6 min
ai visibility-engineering
AVR v1.1.0 Full 13-Section Audit: Cloudflare Radar Data Meets AI Visibility
The first complete AVR v1.1.0 audit with all 13 sections, including 5 new Cloudflare Radar-derived checks. chudi.dev passes every Cloudflare-backed check. Here are the numbers.
· 8 min
Frequently asked
Frequently asked questions
Five questions that come up before someone commits to the reading order on this site. The short answers live here; the long ones live in the cornerstone posts each topic cluster pins at the top.
How do I run the AVR Framework on my own site?
Clone github.com/ChudiNnorukam/avr-pipeline, install scripts/requirements.txt, and run python3 scripts/run_audit.py YOUR_URL --skip-lighthouse --full-v11. The free audit covers infrastructure plus the three v1.1.0 sections at zero API cost.
Where is the full methodology documented?
The canonical AVR v1.1.0 framework page lives at /framework. It documents all eight audit sections, the three verdict tiers, the example output for chudi.dev, the v1.0 to v1.1.0 changelog, and the verified citation numbers behind every claim.
What is the difference between chudi.dev and citability.dev?
chudi.dev DEMONSTRATES AVR. citability.dev IMPLEMENTS AVR. The framework was authored on this site as a case study; the audit tool that scores any URL against the framework lives at citability.dev. Both sites land AGENT-READY 3 of 3 on Section 6 as of 2026-05-23.
Where do I see chudi.dev's own audit results?
The live JSON snapshot is at /api/webmcp/avr-self-audit?format=full. A walkthrough post sits at /blog/avr-v1-1-0-case-study-audit. The reproducible source files live in the avr-pipeline repo under sample-audits.
Who is the author and what is the operator stack?
Chudi Nnorukam is the AI Visible Web Architect. The operator stack is the AVR Framework on top of an AI-Visible Web Architecture pattern. The full bio with cross-platform sameAs links, affiliations, and Entity Authority Tier signals lives at /about.