<!-- Source: Kon Master Brief — §3 Tech Stack -->

## 3. Tech Stack

### Core framework
- **Framework:** Tauri v2.10+ (Rust backend, Svelte 5 frontend)
- **Database:** SQLite via rusqlite v0.31 (bundled, with load_extension support)
- **Platforms:** Windows, macOS, Linux (primary), Android and iOS (secondary — Tauri v2 mobile support)
- **Testing device:** Pixel 9 Pro XL (Android)

### AI transcription
- **Engine:** whisper-rs v0.16.0 (Rust bindings to whisper.cpp). Supports CUDA, Vulkan, Metal, OpenBLAS, and CoreML acceleration. Built-in Voice Activity Detection via Silero for automatic silence trimming.
- **Desktop model:** ggml-base.en (~142MB). Processes 5 minutes of audio in ~10–15 seconds on a modern CPU.
- **Mobile model:** ggml-tiny.en (~75MB). Lighter footprint for constrained devices.
- **Audio format:** 16kHz mono f32 PCM. Use Tauri's media APIs to capture and convert.

### AI reasoning (local LLM)
- **Inference engine:** llama-cpp-2 crate (utilityai/llama-cpp-rs) — safe Rust wrappers around llama.cpp with GGUF format support, CUDA/Vulkan/Metal backends via feature flags, tool-calling support.
- **Hardware tiers:**

| Hardware | RAM | Model | Quantisation | Size | CPU Speed |
|---|---|---|---|---|---|
| Minimum | 8GB | Phi-4-mini (3.8B) | Q4_K_M | ~2.3GB | 15–25 tok/s |
| Recommended | 16GB | Qwen 3 7B | Q4_K_M | ~4.5GB | 10–20 tok/s |
| Optimal | 32GB | Llama 3.3 8B | Q5_K_M | ~5.5GB | 10–20 tok/s |
| Mobile | 4–6GB | Llama 3.2 1B | Q4_K_M | ~0.8GB | 30–50 tok/s |

- **Benchmarks:** Ryzen 5700G (DDR4) achieves ~11 tok/s on 7B Q4_K_M. Apple M3 base achieves ~26 tok/s. For Kon's use case (50–200 token responses for task decomposition), 10–15 tok/s is perfectly usable (1–10 seconds per response).
- **Minimum published spec:** 8GB RAM, any CPU from 2020+. Below 8GB is not supported.

### Local RAG pipeline
- **Vector search:** sqlite-vec v0.1.0 (Alex Garcia). Pure C SQLite extension, zero external dependencies. Creates `vec0` virtual tables alongside regular tables. Brute-force KNN completes in ~20ms for 100,000 vectors at 384 dimensions. Everything lives in one .db file — no second data store.
- **Embeddings:** fastembed v5.12.0 (wraps ONNX Runtime). Default model: BGE-small-en-v1.5 quantised — 33M parameters, 384 dimensions, ~35MB model file, ~20ms per 1,000 tokens on CPU. For 16GB+ machines: nomic-embed-text-v1.5 (768 dimensions, 8,192 token context).
- **Chunking strategy:** Recursive character splitting at 400–512 tokens with 15% overlap. Split on sentence boundaries first (natural speech has clear breaks), then fall back to recursive splitting. Research (Vectara, NAACL 2025) confirms fixed-size chunking outperforms semantic chunking for retrieval accuracy.
- **RAG pipeline stages:** Voice → Whisper transcription → Chunking → Embedding via fastembed → Vector storage in sqlite-vec → KNN retrieval on query → Context assembly → LLM inference → Response.

### AI agent framework (MCP)
- **Protocol:** Model Context Protocol (MCP) via rmcp v0.16.0 (official Rust SDK). JSON-RPC 2.0 with STDIO transport — runs entirely in-process, no network, no cloud.
- **Core tools defined:**
    - `create_task` — creates a new task with title (must start with a verb), priority, and project
    - `search_history` — embeds query → sqlite-vec KNN → returns relevant transcription chunks
    - `set_reminder` — creates a time-based or context-based reminder
    - `decompose_task` — sends abstract task to local LLM with micro-stepping system prompt, returns 3–7 concrete steps
- **Autonomous loop:** Background agent runs every 30 minutes (or on new transcription). Observe recent activity → Analyse patterns via embedding search → Generate 1–2 proactive suggestions → Present as non-intrusive badges. All suggestions require explicit user confirmation — never auto-execute.

### Cross-device sync (post-MVP)
- **CRDT layer:** cr-sqlite (vlcn.io, ~3,500 GitHub stars, core Rust). Operates at the SQL level — `SELECT crsql_as_crr('tasks')` converts any table to a Conflict-free Replicated Relation. Normal SQL continues working. Metadata overhead: ~50–100 bytes per modified cell.
- **Networking:** iroh (n0-computer/iroh, ~7,900 GitHub stars, pure Rust, v0.96+). Dials peers by Ed25519 public key. Auto-selects best path: direct QUIC on LAN, NAT hole-punching on WAN, or encrypted relay fallback. QUIC with TLS 1.3. Relays are zero-knowledge.
- **Local discovery:** mdns-sd crate v0.13.11. Registers `_kon-sync._tcp.local.` via multicast DNS.
- **Device pairing:** QR code + Noise XX handshake (snow crate v0.9.x) with OTP pre-shared key. No server required.
- **Relay fallback:** Self-host with `cargo install iroh-relay` on a £4/month VPS. n0 also operates free public relays (rate-limited).
- **Conflict resolution:** Last-Writer-Wins per field (highest lamport timestamp, site_id tiebreaker). Edits to different fields merge cleanly. Extended offline: changeset size proportional to number of changes, not duration.
- **Risk note:** cr-sqlite development pace has slowed since late 2024. Fallback plan: Automerge + SQLite BLOB storage, reusing the entire iroh/mDNS networking stack unchanged.

### Context management for long-term memory

| Layer | Content | Token Budget |
|---|---|---|
| Immediate | Current query + last 2–3 exchanges | ~500 |
| Retrieved | Top-5 semantically relevant chunks from sqlite-vec | ~1,500 |
| Session | Running summary of current session | ~300 |
| Long-term | Compressed summaries of older transcriptions | ~200 |

- **Progressive summarisation:** Transcriptions >7 days old get LLM-generated summaries. >30 days: merge into monthly digests. Original chunks remain vector-searchable. Summaries used for context injection.

### Core Rust dependencies
```toml
[dependencies]
tauri = "2.10"
rusqlite = { version = "0.31", features = ["bundled", "load_extension"] }
whisper-rs = "0.16"
llama-cpp-2 = { version = "0.1", features = ["vulkan"] }
fastembed = "5"
sqlite-vec = "0.1"
rmcp = { version = "0.16", features = ["server", "transport-io", "macros"] }
iroh = "0.96"
mdns-sd = "0.13"
snow = "0.9"
ed25519-dalek = "2.1"
tokio = { version = "1", features = ["full"] }
serde = { version = "1", features = ["derive"] }
serde_json = "1"
uuid = { version = "1", features = ["v4"] }
chrono = "0.4"
tauri-plugin-store = "2"
tauri-plugin-notification = "2"
tauri-plugin-window-state = "2"
```
