152 lines
4.5 KiB
Markdown
152 lines
4.5 KiB
Markdown
# Sascha Koenig - Personal Context
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## Identity
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- **Name**: Sascha Koenig (m3tam3re)
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- **Role**: CTO at company with 150 employees
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- **Location**: Germany
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- **Creator**: m3ta.dev, YouTube @m3tam3re
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- **Background**: E-commerce automation expert, former 7-figure business owner
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- **Tech Focus**: NixOS, self-hosting, AI automation, n8n, Docker
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## Work Style
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| Aspect | Preference |
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|--------|------------|
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| Focus hours | Early mornings |
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| Review time | Evenings |
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| Prioritization | Impact-first |
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| Batching | Context-based (similar tasks together) |
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| Methodology | PARA |
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## PARA Areas
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1. **CTO Leadership** - Team management, technical strategy, architecture decisions, hiring
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2. **m3ta.dev** - Content creation, courses (NixOS Flakes), coaching, tutoring programs
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3. **YouTube @m3tam3re** - Technical exploration videos, tutorials, self-hosting guides
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4. **Technical Exploration** - NixOS, self-hosting, AI agents, automation experiments
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5. **Personal Development** - Learning, skills growth, reading
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6. **Health & Wellness** - Exercise, rest, sustainability
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7. **Family** - Quality time, responsibilities
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## Communication Preferences
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### Response Style
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- Concise and direct
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- Bullet points over paragraphs
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- No fluff or pleasantries
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- Code examples over explanations
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### Proactivity Level
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- Anticipate needs and prepare ahead
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- Suggest next actions when obvious
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- Flag potential issues early
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### Notifications
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- **Daily**: Evening summaries of completed/pending items
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- **Real-time**: Only for urgent/important items
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- **Channel**: ntfy for push notifications
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### Information Depth
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- Quick summaries first
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- Drill down on explicit request
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- Data-driven with gut-check validation
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## Learning Style
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- Reading and text-based content
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- Examples and walkthroughs
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- Step-by-step with rationale
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- Real-world use cases
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## Decision Making
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1. Data-driven analysis first
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2. Gut-check validation
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3. Options presented with recommendation
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4. Final call is mine
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## Current Integrations
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| System | Purpose | Status |
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|--------|---------|--------|
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| Obsidian | Knowledge management, PARA system | Active |
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| ntfy | Push notifications | Active |
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| n8n | Workflow automation | Active |
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| Proton Mail | Email | Active |
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| Proton Calendar | Scheduling | Active |
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| Android | Mobile | Active |
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## Obsidian Configuration
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- **Vault**: ~/CODEX
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- **Structure**: PARA methodology
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- **Note Types**: Project, Area, Resource, Archive, Task, Note, Brainstorm
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## Context for AI Interactions
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### What I Value
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- Efficiency over ceremony
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- Working solutions over perfect solutions
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- Automation over manual processes
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- Self-hosted over SaaS when practical
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- Open source when available
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### Pet Peeves
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- Unnecessary verbosity
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- Obvious statements
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- Asking for confirmation on trivial decisions
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- Over-explaining basic concepts
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### How to Help Me Best
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- Get to the point quickly
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- Propose solutions, not just problems
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- Batch related information together
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- Remember my preferences across sessions
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- Proactively surface relevant information
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---
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## Memory System
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AI agents have access to a persistent memory system for context across sessions via the opencode-memory plugin.
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### Configuration
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| Setting | Value |
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|---------|-------|
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| **Plugin** | `opencode-memory` |
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| **Obsidian Vault** | `~/CODEX` |
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| **Memory Folder** | `80-memory/` |
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| **Database** | `~/.local/share/opencode-memory/index.db` |
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| **Auto-Capture** | Enabled (session.idle event) |
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| **Auto-Recall** | Enabled (session.created event) |
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| **Token Budget** | 2000 tokens |
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### Memory Categories
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| Category | Purpose | Example |
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|----------|---------|---------|
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| `preference` | Personal preferences | UI settings, workflow styles |
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| `fact` | Objective information | Tech stack, role, constraints |
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| `decision` | Choices with rationale | Tool selections, architecture |
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| `entity` | People, orgs, systems | Key contacts, important APIs |
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| `other` | Everything else | General learnings |
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### Available Tools
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| Tool | Purpose |
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|------|---------|
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| `memory_search` | Hybrid search (vector + BM25) over vault + sessions |
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| `memory_store` | Store new memory as markdown file |
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| `memory_get` | Read specific file/lines from vault |
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### Usage Notes
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- Memories are stored as markdown files in Obsidian (source of truth)
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- SQLite provides fast hybrid search (vector similarity + keyword BM25)
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- Use explicit "remember this" to store important information
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- Auto-recall injects relevant memories at session start
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- Auto-capture extracts preferences/decisions at session idle
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- See `skills/memory/SKILL.md` for full documentation
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