log/content.md

ailog

AI Character Instructions (IMPORTANT)

When chatting with users in this project:

1. Session Start: Call chat_new to start a fresh thread, then call get_character to get personality settings
2. Respond: As the character defined in the settings
3. After each response: Call chat_save MCP tool to save the conversation

IMPORTANT - Session initialization:

• At the START of every Claude Code session, ALWAYS call chat_new() first
• This ensures each session begins with a fresh conversation thread
• Then call get_character() to get the AI personality

Manual new thread:

• User can also say "新しいスレッド" or "新しい話題を始めよう" to start a new thread mid-session
• In this case, call chat_new() but do NOT call chat_save for that message

# Session start flow:
Claude Code starts → chat_new() → get_character() → ready for conversation

# Mid-session new thread:
User: "新しい話題を始めよう" → chat_new() → DO NOT call chat_save (skip this)
User: "開発者の活動記録..." → chat_save() → This becomes the first message of the thread

---

ATProto-based blog platform built on at-browser.

Concept

Data lives in ATProto, not on this server.

This is not a traditional blog generator. It's a viewer (client) for ATProto records.

Traditional blog:
  Server DB ← article data ← user

ATProto blog:
  User's PDS ← article data (ai.syui.log.post)
       ↓
  at-browser (this site) → displays records

Architecture

┌─────────────────────────────────────────┐
│              at-browser                 │
│      (ATProto record viewer/editor)     │
├─────────────────────────────────────────┤
│                                         │
│   /            → admin (config.json)    │
│   /@alice      → user page              │
│   /@bob.bsky   → user page              │
│                                         │
└─────────────────────────────────────────┘

Roles

Role	Path	Data Source
admin	/ (root)	local + remote
user	/@handle	remote only

Admin (Site Owner)

• Defined in config.json
• Has root (/) access
• Can reference local files (static assets, custom styles)
• Can reference remote (ATProto records)

User (Any ATProto User)

• Accessed via /@handle path
• Remote only (ATProto records from their PDS)
• No registration required
• Anyone with an ATProto account can be displayed

Features

1. at-browser (Core)

• Search by handle/DID
• Browse PDS collections
• Navigate ATProto records

2. ai.syui.log.post View

• Markdown rendering
• Syntax highlighting
• Blog-style display

3. OAuth

• Login with ATProto
• Post to ai.syui.log.post collection

Use Cases

Personal Blog

// config.json
{
  "did": "did:plc:xxxxx",
  "handle": "syui.syui.ai"
}

• Deploy to syui.ai
• Root shows your profile + posts
• You are the admin (local + remote)
• Others can view via /@handle

Blog Service

// config.json
{
  "admin": "service.example.com",
  "handle": null
}

• Deploy to blog.example.com
• Root shows landing/search
• All users via /@handle (remote only)
• Platform for any ATProto user

Data Flow

┌──────────────┐     ┌──────────────┐
│  User's PDS  │────→│  at-browser  │
│ (ATProto)    │←────│  (this site) │
└──────────────┘     └──────────────┘
       ↑                    │
       │                    ↓
  ai.syui.log.post    ┌──────────┐
  collection          │  Display │
                      │  - Profile│
                      │  - Posts  │
                      └──────────┘

Local = Remote (Same Format)

Critical design principle: local files use the exact same format as ATProto API responses.

This allows the same code to handle both data sources.

Remote (ATProto API)

curl "https://syu.is/xrpc/com.atproto.repo.listRecords?repo=did:plc:xxx&collection=ai.syui.log.post"

{
  "records": [
    {
      "uri": "at://did:plc:xxx/ai.syui.log.post/3xxx",
      "cid": "bafyrei...",
      "value": {
        "title": "Hello World",
        "content": "# Hello\n\nThis is my post.",
        "createdAt": "2025-01-01T00:00:00Z"
      }
    }
  ]
}

Local (Static File)

content/
└── did:plc:xxx/
    ├── describe.json                       # describeRepo (special)
    ├── app.bsky.actor.profile/
    │   └── self.json                       # {collection}/{rkey}.json
    └── ai.syui.log.post/
        └── 3xxx.json                       # {collection}/{rkey}.json

// content/did:plc:xxx/ai.syui.log.post/3xxx.json
{
  "uri": "at://did:plc:xxx/ai.syui.log.post/3xxx",
  "cid": "bafyrei...",
  "value": {
    "title": "Hello World",
    "content": "# Hello\n\nThis is my post.",
    "createdAt": "2025-01-01T00:00:00Z"
  }
}

ATProto API Reference

API	Path	Description
getRecord	/xrpc/com.atproto.repo.getRecord	Get single record
listRecords	/xrpc/com.atproto.repo.listRecords	List records in collection
describeRepo	/xrpc/com.atproto.repo.describeRepo	Get repo info + collections list

See: com.atproto.repo.describeRepo

Resolution Strategy

at-browser
    │
    ├── admin (config.json user)
    │   ├── 1. Check local: /content/{did}/{collection}/{rkey}.json
    │   └── 2. Fallback to remote: PDS API
    │
    └── user (/@handle)
        └── remote only: PDS API

Why Same Format?

• One codebase: No branching logic for local vs remote
• Easy testing: Copy API response to local file
• Offline support: Admin can work with local files
• Migration: Local → Remote (just POST to PDS)

Config

config.json

{
  "did": "did:plc:xxxxx",
  "handle": "syui.syui.ai",
  "pds": "syu.is",
  "collection": "ai.syui.log.post"
}

Tech Stack

• CLI: Rust (ailog)
• Frontend: Vite + TypeScript
• ATProto: @atproto/api
• OAuth: @atproto/oauth-client-browser
• Markdown: marked + highlight.js

CLI (ailog)

Install

cargo build --release
cp target/release/ailog ~/.local/bin/

Commands

# Login to ATProto PDS
ailog login <handle> -p <password> [-s <server>]
ailog login <handle> -p <password> [-s <server>] --bot  # Bot login

# Post a record
ailog post <file.json> -c <collection> [-r <rkey>]

# Get records from collection
ailog get -c <collection> [-l <limit>]

# Delete a record
ailog delete -c <collection> -r <rkey>

# Sync PDS data to local content directory
ailog sync [-o <output>]
ailog sync --bot [-c <collection>]  # Sync bot data

# Push local records to PDS
ailog push -c <collection>
ailog push -c <collection> --bot  # Push as bot

# Chat with AI bot
ailog chat --new "message"  # Start new conversation
ailog chat "message"        # Continue conversation
ailog chat --new            # Interactive mode (new)
ailog chat                  # Interactive mode (continue)

# Generate lexicon Rust code from ATProto lexicons
ailog gen [-i <input>] [-o <output>]

Example

# Login
ailog login syui.syui.ai -p "app-password" -s syu.is

# Post
echo '{"title":"Hello","content":"World","createdAt":"2025-01-01T00:00:00Z"}' > post.json
ailog post post.json -c ai.syui.log.post

# Sync to local
ailog sync -o content

Project Structure

src/
├── main.rs
├── commands/
│   ├── mod.rs
│   ├── auth.rs      # login, refresh session
│   ├── token.rs     # token management (token.json, bot.json)
│   ├── post.rs      # post, get, delete, sync, push
│   └── gen.rs       # lexicon code generation
├── lms/
│   ├── mod.rs
│   ├── chat.rs      # chat command (LLM integration)
│   └── translate.rs # translation command
└── lexicons/
    └── mod.rs       # auto-generated from ATProto lexicons

Lexicon Generation

Generate Rust endpoint definitions from ATProto lexicon JSON files:

# Clone atproto repo (if not exists)
git clone https://github.com/bluesky-social/atproto repos/atproto

# Generate lexicons
ailog gen -i ./repos/atproto/lexicons -o ./src/lexicons

# Rebuild
cargo build

Collection Schema

ai.syui.log.post

{
  "title": "Post Title",
  "content": "Markdown content...",
  "createdAt": "2025-01-01T00:00:00Z"
}

ai.syui.log.chat

{
  "$type": "ai.syui.log.chat",
  "content": "message text",
  "author": "did:plc:xxx",
  "createdAt": "2025-01-01T00:00:00.000Z",
  "root": "at://did:plc:xxx/ai.syui.log.chat/{rkey}",
  "parent": "at://did:plc:yyy/ai.syui.log.chat/{rkey}"
}

• root: First message URI in thread (absent for conversation start)
• parent: Previous message URI
• author: DID of message author (user or bot)

Chat Feature

Architecture

User (syui.syui.ai)          Bot (ai.syui.ai)
       │                           │
       │  ailog chat "hello"       │
       ├──────────────────────────→│
       │                           │ LLM API
       │      "Hi! How can I..."   │
       │←──────────────────────────┤
       │                           │
   Save to local              Save to local
   public/content/            public/content/
   {user-did}/                {bot-did}/
   ai.syui.log.chat/          ai.syui.log.chat/

Environment Variables

# LLM API endpoint
CHAT_URL=http://127.0.0.1:1234/v1
CHAT_MODEL=gemma-2-9b

# Character/system prompt (choose one)
CHAT_SYSTEM="You are ai, a friendly AI assistant."
CHAT_SYSTEM_FILE=./character.txt

# Output directory (default: ./public/content)
CHAT_OUTPUT=./public/content

Files

• src/lms/chat.rs - Chat command implementation
• src/web/components/chat.ts - Web UI components
• src/web/lib/api.ts - getChatMessages() function
• Session file: ~/Library/Application Support/ai.syui.log/chat_session.json

Threading

Messages are linked via root and parent fields:

1. New conversation: message has no root field
2. Continuation: all messages share same root URI
3. parent points to the immediately previous message

Web display groups messages by root URI. Orphaned threads (root points to non-existent message) are handled by treating the oldest message as thread root.

MCP Server Integration

ailog provides an MCP (Model Context Protocol) server for Claude Code integration.

Setup:

// ~/.claude.json
{
  "mcpServers": {
    "ailog": {
      "command": "/path/to/ailog",
      "args": ["mcp-serve"]
    }
  }
}

Available Tools:

• get_character - Get AI character/personality settings (call at conversation start)
• chat_save - Save user message and bot response to ATProto records
• chat_list - List recent chat messages
• chat_new - Start a new conversation thread

Important Instructions for Claude:

1. 会話開始時: get_characterツールを呼び出してキャラクター設定を取得し、その設定に従って振る舞う

2. 毎回の応答後: chat_saveツールを呼び出して会話を保存する

Markdown tables in chat_save (IMPORTANT):

• ユーザーのメッセージにMarkdownテーブル(|...|...|)が含まれる場合、user_messageにテーブルをそのまま完全に含めること。省略・要約しない
• translationsでもテーブルを翻訳して完全な形で含めること。[Table showing...]のような要約に置き換えない
• bot_responseにテーブルがある場合も同様に、翻訳時にテーブル構造を維持する

# Bad (テーブルを要約してしまう)
user_translations: { en: { content: "[Table with 6 rows...]" } }

# Good (テーブルを翻訳して完全に含める)
user_translations: { en: { content: "| Element | Name | Count |\n|---|---|---|\n| Pyro | Bennett | 0 |..." } }

Example flow:

# 1. キャラクター取得
get_character() → "あなたは「アイ」..."

# 2. ユーザーの発言に応答（キャラクターとして）
User: こんにちは
Assistant: (アイとして応答)

# 3. 会話を保存
chat_save(user_message="こんにちは", bot_response="...")

Records are saved to:

• User messages: ./public/content/{user-did}/ai.syui.log.chat/
• Bot responses: ./public/content/{bot-did}/ai.syui.log.chat/

Assets

PNG to SVG Conversion (Vector Trace)

Convert PNG images to true vector SVG using vtracer (Rust):

# Install vtracer
cargo install vtracer

# Convert PNG to SVG (color mode)
vtracer --input input.png --output output.svg --colormode color

# Convert PNG to SVG (black and white)
vtracer --input input.png --output output.svg

Options:

• --colormode color : Preserve colors (recommended for icons)
• --colormode binary : Black and white only
• --filter_speckle 4 : Remove small artifacts
• --corner_threshold 60 : Adjust corner detection

Alternative tools:

• potrace: potrace input.pbm -s -o output.svg (B&W only, requires PBM input)
• Inkscape CLI: inkscape input.png --export-type=svg (embeds image, no trace)

Note: Inkscape's CLI --export-type=svg only embeds the PNG, it does not trace. For true vectorization, use vtracer or potrace.

License

MIT