ai.log

A Rust-based static blog generator with AI integration capabilities.

Overview

ai.log is part of the ai ecosystem - a static site generator that creates blogs with built-in AI features for content enhancement and atproto integration. The system follows the yui system principles with dual-layer MCP architecture.

Architecture

Dual MCP Integration

ai.log MCP Server (API Layer)

• Role: Independent blog API
• Port: 8002
• Location: ./src/mcp/
• Function: Core blog generation and management

ai.gpt Integration (Server Layer)

• Role: AI integration gateway
• Port: 8001 (within ai.gpt)
• Location: ../src/aigpt/mcp_server.py
• Function: AI memory system + HTTP proxy to ai.log

Data Flow

Claude Code → ai.gpt (Server/AI) → ai.log (API/Blog) → Static Site
              ↑                      ↑
              Memory System          File Operations
              Relationship AI        Markdown Processing
              Context Analysis       Template Rendering

Features

• Static Blog Generation: Inspired by Zola, built with Rust
• AI-Powered Content: Memory-driven article generation via ai.gpt
• 🌍 Ollama Translation: Multi-language markdown translation with structure preservation
• atproto Integration: OAuth authentication and comment system (planned)
• MCP Integration: Seamless Claude Code workflow

Installation

cargo install ailog

Usage

Standalone Mode

# Initialize a new blog
ailog init myblog

# Create a new post
ailog new "My First Post"

# Build the blog
ailog build

# Serve locally
ailog serve

# Start MCP server
ailog mcp --port 8002

# Generate documentation
ailog doc readme --with-ai
ailog doc api --output ./docs
ailog doc structure --include-deps

# Translate documents (requires Ollama)
ailog doc translate --input README.md --target-lang en
ailog doc translate --input docs/api.md --target-lang ja --model qwen2.5:latest

# Clean build files
ailog clean

AI Ecosystem Integration

When integrated with ai.gpt, use natural language:

• "ブログ記事を書いて" → Triggers log_ai_content
• "記事一覧を見せて" → Triggers log_list_posts
• "ブログをビルドして" → Triggers log_build_blog

Documentation & Translation

Generate comprehensive documentation and translate content:

• "READMEを生成して" → Triggers log_generate_docs
• "APIドキュメントを作成して" → Generates API documentation
• "プロジェクト構造を解析して" → Creates structure documentation
• "このファイルを英語に翻訳して" → Triggers log_translate_document
• "マークダウンを日本語に変換して" → Uses Ollama for translation

MCP Tools

ai.log Server (Port 8002)

• create_blog_post - Create new blog post
• list_blog_posts - List existing posts
• build_blog - Build static site
• get_post_content - Get post by slug
• translate_document ⭐ - Ollama-powered markdown translation
• generate_documentation ⭐ - Code analysis and documentation generation

ai.gpt Integration (Port 8001)

• log_create_post - Proxy to ai.log + error handling
• log_list_posts - Proxy to ai.log + formatting
• log_build_blog - Proxy to ai.log + AI features
• log_get_post - Proxy to ai.log + context
• log_system_status - Health check for ai.log
• log_ai_content ⭐ - AI memory → blog content generation
• log_translate_document 🌍 - Document translation via Ollama
• log_generate_docs 📚 - Documentation generation

Documentation Generation Tools

• doc readme - Generate README.md from project analysis
• doc api - Generate API documentation
• doc structure - Analyze and document project structure
• doc changelog - Generate changelog from git history
• doc translate 🌍 - Multi-language document translation

Translation Features

• Language Support: English, Japanese, Chinese, Korean, Spanish
• Markdown Preservation: Code blocks, links, images, tables maintained
• Auto-Detection: Automatically detects Japanese content
• Ollama Integration: Uses local AI models for privacy and cost-efficiency
• Smart Processing: Section-by-section translation with structure awareness

Configuration

ai.log Configuration

• Location: ~/.config/syui/ai/log/
• Format: TOML configuration

ai.gpt Integration

• Configuration: ../config.json
• Auto-detection: ai.log tools enabled when ./log/ directory exists
• System prompt: Automatically triggers blog tools for related queries

AI Integration Features

Memory-Driven Content Generation

• Source: ai.gpt memory system
• Process: Contextual memories → AI analysis → Blog content
• Output: Structured markdown with personal insights

Automatic Workflows

• Daily blog posts from accumulated memories
• Content enhancement and suggestions
• Related article recommendations
• Multi-language content generation

atproto Integration (Planned)

OAuth 2.0 Authentication

• Client metadata: public/client-metadata.json
• Comment system integration
• Data sovereignty: Users own their comments
• Collection storage in atproto

Comment System

• atproto account login
• Distributed comment storage
• Real-time comment synchronization

Build & Deploy

GitHub Actions

# .github/workflows/gh-pages.yml
- name: Build ai.log
  run: |
    cd log
    cargo build --release
    ./target/release/ailog build

Cloudflare Pages

• Static output: ./public/
• Automatic deployment on main branch push
• AI content generation during build process

Development Status

✅ Completed Features

• Project structure and Cargo.toml setup
• CLI interface (init, new, build, serve, clean, mcp, doc)
• Configuration system with TOML support
• Markdown parsing with frontmatter support
• Template system with Handlebars
• Static site generation with posts and pages
• Development server with hot reload
• MCP server integration (both layers)
• ai.gpt integration with 6 tools
• AI memory system connection
• 📚 Documentation generation from code
• 🔍 Rust project analysis and API extraction
• 📝 README, API docs, and structure analysis
• 🌍 Ollama-powered translation system
• 🚀 Complete MCP integration with ai.gpt
• 📄 Markdown-aware translation preserving structure
• Test blog with sample content and styling

🚧 In Progress

• AI-powered content enhancement pipeline
• atproto OAuth implementation

📋 Planned Features

• Advanced template customization
• Plugin system for extensibility
• Real-time comment system
• Multi-blog management
• VTuber integration (ai.verse connection)

Integration with ai Ecosystem

System Dependencies

• ai.gpt: Memory system, relationship tracking, AI provider
• ai.card: Future cross-system content sharing
• ai.bot: atproto posting and mention handling
• ai.verse: 3D world blog representation (future)

yui System Compliance

• Uniqueness: Each blog post tied to individual identity
• Reality Reflection: Personal memories → digital content
• Irreversibility: Published content maintains historical integrity

Getting Started

1. Standalone Usage

git clone [repository]
cd log
cargo run -- init my-blog
cargo run -- new "First Post"
cargo run -- build
cargo run -- serve

2. AI Ecosystem Integration

# Start ai.log MCP server
cargo run -- mcp --port 8002

# In another terminal, start ai.gpt
cd ../
# ai.gpt startup commands

# Use Claude Code with natural language blog commands

Documentation Generation Features

📚 Automatic README Generation

# Generate README from project analysis
ailog doc readme --source ./src --with-ai

# Output: Enhanced README.md with:
# - Project overview and metrics
# - Dependency analysis
# - Module structure
# - AI-generated insights

📖 API Documentation

# Generate comprehensive API docs
ailog doc api --source ./src --format markdown --output ./docs

# Creates:
# - docs/api.md (main API overview)
# - docs/module_name.md (per-module documentation)
# - Function signatures and documentation
# - Struct/enum definitions

🏗️ Project Structure Analysis

# Analyze and document project structure
ailog doc structure --source . --include-deps

# Generates:
# - Directory tree visualization
# - File distribution by language
# - Dependency graph analysis
# - Code metrics and statistics

📝 Git Changelog Generation

# Generate changelog from git history
ailog doc changelog --from v1.0.0 --explain-changes

# Creates:
# - Structured changelog
# - Commit categorization
# - AI-enhanced change explanations

🤖 AI-Enhanced Documentation

When --with-ai is enabled:

• Content Enhancement: AI improves readability and adds insights
• Context Awareness: Leverages ai.gpt memory system
• Smart Categorization: Automatic organization of content
• Technical Writing: Professional documentation style

🌍 Translation System

Ollama-Powered Translation

ai.log includes a comprehensive translation system powered by Ollama AI models:

# Basic translation
ailog doc translate --input README.md --target-lang en

# Advanced translation with custom settings
ailog doc translate \
  --input docs/technical-guide.ja.md \
  --target-lang en \
  --source-lang ja \
  --output docs/technical-guide.en.md \
  --model qwen2.5:latest \
  --ollama-endpoint http://localhost:11434

Translation Features

📄 Markdown-Aware Processing

• Code Block Preservation: All code snippets remain untranslated
• Link Maintenance: URLs and link structures preserved
• Image Handling: Alt text can be translated while preserving image paths
• Table Translation: Table content translated while maintaining structure
• Header Preservation: Markdown headers translated with level maintenance

🎯 Smart Language Detection

• Auto-Detection: Automatically detects Japanese content using Unicode ranges
• Manual Override: Specify source language for precise control
• Mixed Content: Handles documents with multiple languages

🔧 Flexible Configuration

• Model Selection: Choose from available Ollama models
• Custom Endpoints: Use different Ollama instances
• Output Control: Auto-generate or specify output paths
• Batch Processing: Process multiple files efficiently

Supported Languages

Language	Code	Direction	Model Optimized
English	en	↔️	✅ qwen2.5
Japanese	ja	↔️	✅ qwen2.5
Chinese	zh	↔️	✅ qwen2.5
Korean	ko	↔️	⚠️ Basic
Spanish	es	↔️	⚠️ Basic

Translation Workflow

1. Parse Document: Analyze markdown structure and identify sections
2. Preserve Code: Isolate code blocks and technical content
3. Translate Content: Process text sections with Ollama AI
4. Reconstruct: Rebuild document maintaining original formatting
5. Validate: Ensure structural integrity and completeness

Integration with ai.gpt

# Via ai.gpt MCP tools
await log_translate_document(
    input_file="README.ja.md",
    target_lang="en",
    model="qwen2.5:latest"
)

Requirements

• Ollama: Install and run Ollama locally
• Models: Download supported models (qwen2.5:latest recommended)
• Memory: Sufficient RAM for model inference
• Network: For initial model download only

Configuration Examples

Basic Blog Config

[blog]
title = "My AI Blog"
description = "Personal thoughts and AI insights"
base_url = "https://myblog.example.com"

[ai]
provider = "openai"
model = "gpt-4"
translation = true

Advanced Integration

// ../config.json (ai.gpt)
{
  "mcp": {
    "servers": {
      "ai_gpt": {
        "endpoints": {
          "log_ai_content": "/log_ai_content",
          "log_create_post": "/log_create_post"
        }
      }
    }
  }
}

Troubleshooting

MCP Connection Issues

• Ensure ai.log server is running: cargo run -- mcp --port 8002
• Check ai.gpt config includes log endpoints
• Verify ./log/ directory exists relative to ai.gpt

Build Failures

• Check Rust version: rustc --version
• Update dependencies: cargo update
• Clear cache: cargo clean

AI Integration Problems

• Verify ai.gpt memory system is initialized
• Check AI provider configuration
• Ensure sufficient context in memory system

License

© syui

---

Part of the ai ecosystem: ai.gpt, ai.card, ai.log, ai.bot, ai.verse, ai.shell