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ChatGPT, Perplexity, and Google AI Mode have fundamentally different citation patterns. A comparative analysis of platform-specific citation rate data and optimization strategies.
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Analyzing the concept and measurement methods of AI Shelf Share — the brand share within AI answers. From Answer Share and citation frequency to content velocity strategy, a practical framework for marketers.
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Dissecting Conductor's AEO/GEO benchmark report analyzing 13,770 domains and 3.3 billion sessions. AI referral traffic at 1.08%, platform citation rate gaps, and industry visibility differences — implications for brand strategy.
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Analyzing the structural tension in AI safety release strategy raised by Claude Mythos (codename Capybara) — a 10-trillion-parameter model and Opus super-tier leaked through an Anthropic CMS misconfiguration.
Read article![[Comparison] From Link Lists to Answer Engines: ChatGPT Search vs Google AI Mode vs Perplexity](/_next/image?url=%2Fimages%2Fblog%2Fcomparison-chatgpt-search-ai-mode-perplexity-2026-04-04.svg&w=1200&q=68)
How do the three major AI-search experiences differ in 2026? A practical comparison of source transparency, personalization depth, action connectivity, and real workflow fit.
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Is Musk’s claim pure hype or a valid directional signal? A reality check against coding-agent product signals and operational data as of April 2026.
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If AI outputs drift, rework repeats, and results vary every run, the root issue is usually operations. This practical guide shows how to improve consistency with requirement templates, test-first workflows, and checklist-based review.
Read article![[Road to AI 09] Pre-training, Fine-tuning, and RLHF: How Conversational LLMs Are Built](/_next/image?url=%2Fimages%2Fblog%2Fai-evolution-chronicle-09-pretraining-finetuning-rlhf-2026-04-01.svg&w=1200&q=68)
If the Transformer is the engine, pre-training, fine-tuning, and RLHF are the training process that makes it usable. A practical guide to how conversational AI systems like ChatGPT are actually built.
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In the coding-agent era, advantage is moving away from generating more code and toward validating and accumulating reliable change. This deep dive analyzes structural signals from OpenAI, Anthropic, and GitHub.
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A practical 2026 guide to combining Claude Code Skills, forked context, subagents, CLAUDE.md, hooks, and MCP. Focused on repeatable team operations, not one-off prompt tricks.
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Why do Korean pages get cited less often by ChatGPT, Claude, and Gemini? This guide explains the structural causes: sparse Korean RAG benchmarks, weak entity signals, missing structured data, and crawler-policy gaps.
Read article![[Weekly Signal] Why Verification Now Matters More Than Code Generation: Coding Agent Signals from Late March 2026](/_next/image?url=%2Fimages%2Fblog%2Fweekly-signal-code-verification-loop-2026-03-30.svg&w=1200&q=68)
This week’s coding-agent signals converged on one point: generation is no longer the hard part. Based on OpenAI harness engineering, Anthropic Claude Code advanced patterns, and GitHub agentic code review updates.
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