Ken Imoto
WebRTC & Voice AI Engineer
LLMO · WebRTC · Real-time AI · Context Engineering
Building AI-native organizations powered by LLMs, automation, and distributed agents.
123K+
PV on Qiita
14
Books
3
Research Papers
Now
- Building the LLMO Framework, LLMO Checker, and the Open LLMO Research Initiative
- Documenting AI-Native MEO as the local-business implementation of LLMO
- Applying Harness Engineering to products — dev, ops, and marketing with AI agents
- Publishing books & articles from hands-on practice
Updated April 2026
Publications
🔧 AI DEVELOPMENT
🔍 LLMO / AI SEARCH OPTIMIZATION
🛡️ SECURITY & QUALITY
📊 KNOWLEDGE & DATA
💡 ENGINEERING CULTURE
Latest Articles
- 📝 Harness Engineering — Everyone Says Something Different: 5 Interpretations Compared Zenn 12,000PV
- 📝 Why ChatGPT Doesn't Know Your Product DEV.to
- 📝 Swiss Cheese Model × AI Security DEV.to
- 📝 Squash Merge in the Age of AI DEV.to
- 📝 Voice AI: 3 Cliffs at 300ms, 500ms, 800ms Zenn
- 📝 Stop Using HTTPS Connections Right Now Qiita 42,000PV
Papers
📊 Excess Vocabulary in Japanese AI-Generated Text: A Cross-Model Quantitative Analysis
350 samples from 7 LLMs vs. 977 human articles. 651 statistically significant excess words identified. Newer Claude generations show increasing vocabulary bias (TTR: 0.52→0.29). Cross-domain classifier achieves AUC=0.946 in-domain but fails completely across genres. First Japanese excess vocabulary study with coevolution evidence.
📝 AI Text Slop: Stylistic Convergence Across Six LLMs in Japanese Technical Writing
180 samples (6 models × 10 topics × 3 trials) measured with 16 pattern indicators. RLHF-aligned commercial models score significantly higher than OSS (Cohen's d = 1.01). Vocabulary and structural patterns dissociate ("Swallow Paradox"). Human Qiita articles score higher than AI on structural metrics, revealing cultural confounding.
🔵 AI Blue: Systematic Color Recognition Bias in Vision-Language Models
We tested 4 VLMs × 40 colors × 480 observations. Commercial models miss intermediate hues. 95% of AI-generated UI colors land on blue-purple. First quantitative link between VLM color bias and the "AI Slop" phenomenon.
Research
🔬 LLMO Framework
Optimizing content visibility in AI search engines
🔬 Voice AI 300ms
Breaking the latency barrier for human-AI conversation
🔬 Context Engineering
CLAUDE.md, multi-agent architecture, AI dev workflows
🔬 Generative Agent Simulation
LLM multi-agent social simulation
🔬 Fragrance × AI
AI-powered perfumery and personality-based scent design
Projects & Sites
Propel-Lab — company
AI Systems consultancy focused on LLM development environments and harness engineering for enterprises.
LLMO Framework — open methodology
A practical framework for LLM Optimization (AEO + GEO). Self-hosted documentation site as the canonical source.
mypcrig — PC selection (JA)
A use-case-driven guide for choosing the right PC: AI dev rigs, gaming, laptops, and parts. Bench-driven recommendations.
legacydram — whisky × engineering eyes
A curation media reading every bottle as somebody's commit history. People · Craft · Tasting.
ainativemeo — AI-Native MEO playbook
Local-business optimization for the era when customers ask AI, not search engines, for recommendations. Engineering-layer reading of Google Business Profile, JSON-LD survivability, and per-engine citation behavior. Bilingual EN / JA.
Open Source
🔍 LLMO / AI SEARCH
open-llmo — GitHub organization · MIT
The Open LLMO Research Initiative. Specs, validators, and tooling for measuring how AI-retrievable a URL is — beyond traditional SEO signals.
llmo-checker — TypeScript CLI · MIT
A Lighthouse-style CLI that scores how AI-retrievable a URL is. Runs static checks for llms.txt, JSON-LD, robots policy, and semantic structure, and outputs a 0–100 LLMO Score as JSON. v0.1 draft.
🎙️ VOICE & SPEECH
voice-clone — Python · MIT
A voice-cloning tool built on Qwen3-TTS. Clone a voice from ~3 seconds of audio and have any text read in that voice. WSLg recording, multi-language.
speech-habit-lens — Python CLI · MIT
A 1-minute speech habit analyzer combining AmiVoice ESAS (20 acoustic emotion parameters) with an LLM. Three layers — acoustic, textual, and the cross-layer that links body and language — rendered as a Markdown report.
🛠️ DEV UTILITIES
🧬 PERSONA & IDENTITY