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-**Arrow Functions:** Werden als Methods erkannt (gewollt, etwas Noise)
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-**Keyword-Filter:** Pro Sprache in `src/parser/languages/`
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## LogHub Developer Guide
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### Übersicht
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LogHub ist ein universeller Log-Empfänger. Jedes Programm kann per HTTP POST Logs senden — keine Library, kein SDK nötig. Die KI kann die Logs abfragen, der User sieht sie live im AiDex Viewer.
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### Setup (durch die KI)
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```
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1. aidex_log({ action: "init" }) # Server starten (Port 3335)
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## [Unreleased]
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## [1.16.1] - 2026-03-20
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### Added
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-**Log Hub consume pattern**: New `consume` parameter on `aidex_log` query — returned entries are removed from the buffer, ideal for polling without duplicates
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-**Viewer: Clear Logs button**: "Clear" button in the Logs tab to reset the log display
|`limit`| number | - | Max entries to return (default: `50`, used with query) |
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|`consume`| boolean | - | If true, returned entries are removed from buffer — ideal for polling without duplicates (default: `false`, used with query) |
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|`message`| string | for write | Log message text |
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|`data`| string | - | Optional JSON data (write) |
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|`init`| Start HTTP server and ring buffer. Optional: `persist` + `path` for SQLite storage |
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|`free`| Stop server, free all resources, release port |
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|`status`| Show stats: entries, buffer usage, sources, level counts, port |
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|`query`| Search logs with filters (since, level, source, contains, limit). Newest first |
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|`query`| Search logs with filters (since, level, source, contains, limit, consume). Newest first |
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|`clear`| Clear the ring buffer (keep server running) |
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|`write`| Inject a log entry as source "claude" |
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Guidelines are stored in `~/.aidex/global.db` — available across all your projects without `aidex_init`. Ask your AI: *"Load the review guideline and apply it to this file."*
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## Log Hub — Universal Logging
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Turn any program into a log source for your AI assistant. Your app sends logs via HTTP POST, the AI queries them via MCP, and you see them live in the Viewer — zero dependencies, zero setup in your code.
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### How it works
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```
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Your Program ──HTTP POST──→ AiDex Log Hub (port 3335) ──→ Ring Buffer
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│ │
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│ WebSocket │ MCP query
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↓ ↓
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Viewer (Logs tab) AI Assistant
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(you see live) (queries & analyzes)
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```
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### Quick start
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1. AI starts the Log Hub: `aidex_log({ action: "init" })`
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2. AI opens the Viewer: `aidex_viewer({ path: "." })` — Logs tab shows live stream
-**Zero-cost**: No server, no buffer, no resources until `init` is called
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-**Persistence**: Optional SQLite storage with 7-day auto-cleanup (`persist: true`)
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-**Consume pattern**: `query` with `consume: true` removes returned entries — ideal for polling
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-**Viewer integration**: Logs tab with WebSocket live-stream, level/source/text filters, auto-scroll
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-**Fire & forget**: Just POST and go — if the server isn't running, the POST silently fails
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## Screenshots — LLM-Optimized
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Take screenshots and **reduce them up to 95%** for LLM context. A typical screenshot goes from ~100 KB to ~5 KB — that's thousands of tokens saved per image.
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"name": "aidex-mcp",
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"version": "1.16.0",
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"version": "1.16.1",
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"mcpName": "io.github.CSCSoftware/aidex",
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"description": "MCP Server for persistent code indexing. Gives AI assistants (Claude, Gemini, Copilot, Cursor) instant access to your codebase. 50x less context than grep.",
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