Jake
26c7307607
Phase 9 of the rebrand cascade. Sweep covers everything the Phase 8
frontend pass deliberately skipped: docs/, root markdown, scripts,
Cargo.toml descriptions, code comments that survived earlier
word-boundary sed, plus a handful of identifiers caught on the final
verify pass.
transcription-app changes:
- README.md, HANDOVER.md, KNOWN-ISSUES.md, run.sh — magnotia/Magnotia
-> lumotia/Lumotia.
- docs/ — sweep across all subdirs except docs/handovers/ (preserved
as immutable audit trail). Includes architecture-map references
to magnotia_core::*, magnotia_storage::*, etc. now pointing at
lumotia_*; dev-setup.md tracing output examples (lumotia_startup
target); brief/ + superpowers/ + issues/ + whisper-ecosystem/ +
audit/.
- Cargo.toml descriptions on 9 crates (core, audio, cloud-providers,
hotkey, llm, mcp, plus referenced others).
- crates/core/src/{error,hardware,recommendation,paths}.rs +
crates/audio/src/wav.rs + crates/llm/src/model_manager.rs +
crates/cloud-providers/src/keystore.rs + crates/mcp/src/lib.rs —
doc comments and a model-manager user-agent string.
- Caught on final pass: BroadcastChannel("magnotia_task_sync") -> ...
("lumotia_task_sync"); magnotia_locale i18n localStorage key
renamed + migration shim added; CSS keyframe names
magnotiaPulse / magnotiaBar / magnotiaFade renamed in the design-
system kit; magnotia_viewer_item / magnotia_viewer_mode handoff
keys renamed in HistoryPage + viewer/+page.svelte; src/assets/
wordmark.svg text.
- src-tauri/src/lib.rs comment cleanup ("magnotia era" was sed'd
to "lumotia era" earlier — restored).
Preserved (intentional):
- crates/core/src/paths.rs — keeps "magnotia" / "Magnotia" / ".magnotia"
legacy detection strings in legacy_and_target_paths() so the
migration shim can still find user data from the magnotia era.
- src/lib/stores/{page,focusTimer}.svelte.ts + src/lib/i18n/index.ts
— migration call sites reference the legacy magnotia keys
deliberately.
- docs/handovers/ — historical audit trail.
cargo build --workspace passes. npm run check: 0 errors / 0 warnings
(3958 files). cargo test --workspace: 339 pass / 0 fail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
89 lines
6.3 KiB
Markdown
89 lines
6.3 KiB
Markdown
<!-- Source: Lumotia Master Brief — §3 Tech Stack -->
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## 3. Tech Stack
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### Core framework
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- **Framework:** Tauri v2.10+ (Rust backend, Svelte 5 frontend)
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- **Database:** SQLite via rusqlite v0.31 (bundled, with load_extension support)
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- **Platforms:** Windows, macOS, Linux (primary), Android and iOS (secondary — Tauri v2 mobile support)
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- **Testing device:** Pixel 9 Pro XL (Android)
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### AI transcription
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- **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.
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- **Desktop model:** ggml-base.en (~142MB). Processes 5 minutes of audio in ~10–15 seconds on a modern CPU.
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- **Mobile model:** ggml-tiny.en (~75MB). Lighter footprint for constrained devices.
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- **Audio format:** 16kHz mono f32 PCM. Use Tauri's media APIs to capture and convert.
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### AI reasoning (local LLM)
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- **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.
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- **Hardware tiers:**
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| Hardware | RAM | Model | Quantisation | Size | CPU Speed |
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| Minimum | 8GB | Phi-4-mini (3.8B) | Q4_K_M | ~2.3GB | 15–25 tok/s |
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| Recommended | 16GB | Qwen 3 7B | Q4_K_M | ~4.5GB | 10–20 tok/s |
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| Optimal | 32GB | Llama 3.3 8B | Q5_K_M | ~5.5GB | 10–20 tok/s |
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| Mobile | 4–6GB | Llama 3.2 1B | Q4_K_M | ~0.8GB | 30–50 tok/s |
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- **Benchmarks:** Ryzen 5700G (DDR4) achieves ~11 tok/s on 7B Q4_K_M. Apple M3 base achieves ~26 tok/s. For Lumotia's use case (50–200 token responses for task decomposition), 10–15 tok/s is perfectly usable (1–10 seconds per response).
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- **Minimum published spec:** 8GB RAM, any CPU from 2020+. Below 8GB is not supported.
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### Local RAG pipeline
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- **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.
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- **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).
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- **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.
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- **RAG pipeline stages:** Voice → Whisper transcription → Chunking → Embedding via fastembed → Vector storage in sqlite-vec → KNN retrieval on query → Context assembly → LLM inference → Response.
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### AI agent framework (MCP)
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- **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.
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- **Core tools defined:**
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- `create_task` — creates a new task with title (must start with a verb), priority, and project
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- `search_history` — embeds query → sqlite-vec KNN → returns relevant transcription chunks
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- `set_reminder` — creates a time-based or context-based reminder
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- `decompose_task` — sends abstract task to local LLM with micro-stepping system prompt, returns 3–7 concrete steps
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- **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.
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### Cross-device sync (post-MVP)
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- **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.
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- **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.
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- **Local discovery:** mdns-sd crate v0.13.11. Registers `_magnotia-sync._tcp.local.` via multicast DNS.
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- **Device pairing:** QR code + Noise XX handshake (snow crate v0.9.x) with OTP pre-shared key. No server required.
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- **Relay fallback:** Self-host with `cargo install iroh-relay` on a £4/month VPS. n0 also operates free public relays (rate-limited).
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- **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.
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- **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.
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### Context management for long-term memory
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| Layer | Content | Token Budget |
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|---|---|---|
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| Immediate | Current query + last 2–3 exchanges | ~500 |
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| Retrieved | Top-5 semantically relevant chunks from sqlite-vec | ~1,500 |
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| Session | Running summary of current session | ~300 |
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| Long-term | Compressed summaries of older transcriptions | ~200 |
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- **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.
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### Core Rust dependencies
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```toml
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[dependencies]
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tauri = "2.10"
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rusqlite = { version = "0.31", features = ["bundled", "load_extension"] }
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whisper-rs = "0.16"
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llama-cpp-2 = { version = "0.1", features = ["vulkan"] }
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fastembed = "5"
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sqlite-vec = "0.1"
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rmcp = { version = "0.16", features = ["server", "transport-io", "macros"] }
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iroh = "0.96"
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mdns-sd = "0.13"
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snow = "0.9"
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ed25519-dalek = "2.1"
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tokio = { version = "1", features = ["full"] }
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serde = { version = "1", features = ["derive"] }
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serde_json = "1"
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uuid = { version = "1", features = ["v4"] }
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chrono = "0.4"
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tauri-plugin-store = "2"
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tauri-plugin-notification = "2"
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tauri-plugin-window-state = "2"
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```
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