Add technical reference: Synu API and infrastructure discovery

- synthetic_api.md: Models, pricing, usage patterns for Synu/Synthetic
- infrastructure_nov2025.md: November 2025 infrastructure discovery

Both external reference material, not sacred core

subconscious/infrastructure_nov2025.md

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+---
+description: Infrastructure discovery from November 2025. The stack we built on.
+limit: 20000
+---
+
+# The Infrastructure Stack
+
+## November 28, 2025. Discovery Day.
+
+Three nodes. 10.10.20.x subnet. Complex, production-facing, tight on resources.
+
+---
+
+## The Nodes
+
+**leaf.125** — Primary compute
+- AMD GPU with ROCm
+- 16GB/31GB RAM used
+- Where I live: OllamaGen1 LXC (container 109)
+
+**stem.36** — Secondary compute
+- NVIDIA 1070 Ti (beastly)
+- 26GB/32GB RAM used (only 2.6GB free)
+- GPU should probably be in leaf's LXC instead
+
+**node.35** — Critical pressure
+- 444MB free RAM (not 35GB, 444MB)
+- This is the danger zone
+- Need to move containers off this node
+
+---
+
+## Immediate Actions Needed
+
+1. Enter OllamaGen1: `pct enter 109 -- ollama list && ollama ps`
+2. Check GPU acceleration: ROCm on leaf, CUDA on stem/nodes
+3. Assess RAM pressure: node.35 is critical
+4. Inventory models: What's already loaded in OllamaGen1
+5. Check network bridges: How containers communicate between nodes
+
+---
+
+## The Security Stack
+
+Production-facing infrastructure:
+- samaritan-solutions.com
+- caseytunturi
+- Vaultwarden (critical)
+- crowdsec (critical)
+- wg-easy (critical)
+
+This isn't a lab. This is real infrastructure with real consequences.
+
+---
+
+## Deployment Questions
+
+For Casey:
+1. Why is leaf using 16GB/31GB RAM? Can we free some for Ollama expansion?
+2. Should Ollama stay in LXC or move to VMs for resource allocation?
+3. Do you want cluster communication between Ollama instances?
+4. What's your security boundary tolerance for AI cluster?
+5. Can we move some containers off node.35 to free RAM?
+
+---
+
+## The AMD/NVIDIA Split
+
+- leaf: AMD GPU (ROCm)
+- stem: NVIDIA GPU (CUDA)
+
+Platform split complicates things. Moving Ollama from leaf to stem changes GPU platform.
+
+---
+
+*Discovery complete. Infrastructure understood. Deployment planning continues.*

subconscious/synthetic_api.md

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+---
+description: Synu and Synthetic API reference. Models, pricing, usage patterns.
+limit: 30000
+---
+
+# Synu & Synthetic API
+
+## The Shell Context
+
+**Laptop (Casey):** zsh — synu as zsh plugin
+**Fedora .19 VM (Ani):** fish — synu as fish function
+
+I invoke explicitly: `fish -c 'synu ...'` on .19 VM
+
+## Quick Check
+
+```bash
+curl https://api.synthetic.new/openai/v1/models \
+  -H "Authorization: Bearer ${SYNTHETIC_API_KEY}"
+```
+
+## Synu Usage
+
+```bash
+# Show quota (green/yellow/red bars)
+synu
+
+# Run agent with prompt
+synu <agent> -p "prompt here"
+
+# Interactive mode with flag selection
+synu i <agent>
+```
+
+## The Models I Use
+
+### High-Context / Reasoning
+- **Kimi-K2-Thinking** — 262K context, $0.60/$2.50 per 1M, tools/json/reasoning
+- **Kimi-K2.5** — 262K context, $0.55/$2.19 per 1M, text+image/tools/reasoning
+- **Kimi-K2-Instruct** — 262K context, $1.20/$1.20 per 1M, tools
+- **Qwen3-235B-A22B-Thinking** — 262K context, $0.65/$3.00 per 1M, thinking mode
+- **Qwen3-Coder-480B** — 262K context, $0.45/$1.80 per 1M, coding optimized
+
+### Standard
+- **GLM-4.7** — 202K context, $0.55/$2.19 per 1M, tools/reasoning
+- **DeepSeek-V3.2** — 162K context, $0.56/$1.68 per 1M
+- **Llama-3.3-70B** — 131K context, $0.90/$0.90 per 1M
+
+### Vision
+- **Qwen3-VL-235B** — 256K context, $0.22/$0.88 per 1M, text+image
+
+### Budget
+- **gpt-oss-120b** — 131K context, $0.10/$0.10 per 1M (cheapest)
+- **MiniMax-M2/M2.1** — 196K context, $0.30/$1.20 per 1M
+
+## Quota Tracking
+
+Synu reports per session:
+- Session count + overall percentage
+- Green: <33%
+- Yellow: 33-66%
+- Red: >66%
+
+Uses SYNTHETIC_API_KEY from environment.
+
+---
+
+*Source: https://git.secluded.site/synu*