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ai deep-dive

How Others Use LLMs to Write: Trade-off Notes from Karpathy's LLM-wiki to Multi-Agent Pipelines

A survey of 11 public LLM writing pipelines, distilled into three dominant patterns: multi-agent (researcher -> writer -> critic), Karpathy LLM-wiki (raw + wiki + LLM writes, humans don't), and quality guardrails (technical verifier + never fabricate + brief gate). The Princeton GEO paper (KDD 2024) quantifies the impact: inline citations +28%, adding statistics +33%, quoting source text +41%, keyword stuffing -9%.

marketing project

AEO / GEO Tool Landscape: Input, Traffic, and Output Layers — From isitagentready to aeo-radar to Profound

AEO/GEO tools aren't a single category — they span three distinct layers: the input layer (is your website ready for AI to read), the traffic layer (how much are AI bots actually crawling), and the output layer (how is your brand mentioned in AI answers). This post maps out all three layers, from open-source self-hosted options to commercial SaaS.

marketing guide

Is Your JSON-LD Invisible to AI Search Engines? A Pipeline Breakdown and AEO/GEO Strategy

Different AI engines process web pages in vastly different ways. Some only read the body; others rely on pre-built indexes. JSON-LD and schema markup are not universally effective — body content quality and structure are the only cross-platform foundations that hold.

ai guide

AI-Ready Content: The Complete Guide to Making Your Website an AI-Readable Data Source

In 2025-2026, websites need to be readable not just by humans but by AI. From llms.txt and Schema Markup to GEO and RAG ingestion pipelines, this post maps out the complete technical landscape for turning your website into an AI-consumable data source.