# Lotus Chat — Technical Implementation Field Guide **Date:** June 2026 This document provides exhaustive, low-level implementation details for the remaining items in `LOTUS_TODO.md`. Follow these patterns to ensure code is "Lotus-perfect" (idiomatic, performant, and TDS-compliant). --- ## 🧵 Priority 3 — Higher Complexity ### P3-8 · Thread Panel (Full Side Drawer) **Architecture:** Mirror the `MembersDrawer` pattern but with a specialized timeline. - **1. State (src/app/state/room/thread.ts):** ```typescript export const activeThreadIdAtom = atom(null); ``` - **2. Layout (src/app/features/room/Room.tsx):** Insert the `ThreadPanel` conditionally alongside the `RoomTimeline`. ```tsx { activeThreadId && ( <> ); } ``` - **3. Component (src/app/features/room/thread/ThreadPanel.tsx):** - Use `room.getThread(threadId)` from the SDK. - Render a `Header` with a "Close" button that sets `activeThreadIdAtom` to `null`. - Reuse `RoomTimeline` but pass a filtered `EventTimelineSet`. - **Pro Tip:** Use `thread.timelineSet` directly for the most accurate thread view. --- ## 🛠️ Priority 4 — Specialized Features ### P4-4 · Math / LaTeX Rendering **Mechanism:** KaTeX injection into the HTML parser. - **1. Sanitizer (src/app/utils/sanitize.ts):** You must allow KaTeX-specific tags and classes (e.g., `span`, `annotation`, `math`). Use a specialized allowed list for math blocks. - **2. Parser (src/app/plugins/react-custom-html-parser.tsx):** Detect `$ ... $` and `$$ ... $$` patterns in text nodes. ```tsx if (node.type === 'text') { const parts = node.data.split(/(\$\$.*?\$\$|\$.*?\$)/g); return parts.map((p) => { if (p.startsWith('$')) return ; return p; }); } ``` - **3. CSS (src/app/styles/CustomHtml.css.ts):** Import `katex/dist/katex.min.css` only when a math block is rendered to save initial bundle size. ### P4-6 · OIDC / SSO Next-Gen Auth (MSC3861) **Mechanism:** Matrix Authentication Service (MAS) Integration. - **Architecture:** Shift from password-based `/login` to OAuth2 `authorization_code` flow. - **Key Files:** `src/app/pages/auth/Login.tsx` and `src/app/hooks/useAuth.ts`. - **Implementation:** 1. Use `oidc-client-ts` or a similar lightweight OIDC library. 2. Check for `m.authentication` in `/.well-known/matrix/client`. 3. Redirect to the MAS authorization endpoint. 4. Handle the callback in a new `OidcCallback` route and store the OIDC `refresh_token`. --- ## 🎨 Priority 5 — Gamer / Aesthetic / Customization ### P5-1 · Custom Accent Color Picker (Non-TDS only) **Mechanism:** Dynamic CSS variable injection. - **1. Setting (src/app/state/settings.ts):** Add `customAccentColor: string` (hex). - **2. Manager (src/app/pages/ThemeManager.tsx):** Inside the `useEffect` that monitors theme changes: ```typescript if (!lotusTerminal && customAccentColor) { document.documentElement.style.setProperty('--lt-accent-orange', customAccentColor); // Also derive a 'glow' version (e.g. 50% opacity) document.documentElement.style.setProperty('--lt-accent-orange-glow', `${customAccentColor}80`); } ``` - **3. UI (src/app/features/settings/general/General.tsx):** Use a `` component. Hide this section if `lotusTerminal` is `true`. ### P5-40 · Desktop — Proactive Update Notifications (Tauri) **Mechanism:** Global Background Check via `useTauriUpdater`. - **Objective:** Alert users to app updates without requiring a manual check in settings. - **Key Files:** - `src/app/hooks/useTauriUpdater.ts`: Logic source. - `src/app/pages/client/ClientNonUIFeatures.tsx`: Background mounting point. - `src/app/features/toast/LotusToastContainer.tsx`: UI for notification. - **Implementation:** 1. Create a `TauriUpdateFeature` component. 2. Use `useTauriUpdater()` to get the `check` function and `status`. 3. In a `useEffect`, call `check()` on mount and then on a `setInterval` (e.g., every 12 hours). 4. Watch the `status`. When it transitions to `{ state: 'available', version: '...' }`, trigger an in-app **Lotus Toast**. 5. The toast should say "Lotus Chat v[version] is available!" with an "Update" button that calls the `install()` function from the hook. 6. **Persistence:** Store the `lastCheck` timestamp in `localStorage` to ensure the background check doesn't fire redundant commands every time the user refreshes or re-opens the app. --- ## 🔊 Audio & Communications ### P5-15 · In-Call Soundboard **Mechanism:** Local-to-Global Audio Bridge. - **Architecture:** Use the `Web Audio API` to mix sounds into the `MediaStream` before it enters the Element Call widget. - **Implementation:** 1. Create an `AudioContext`. 2. Create a `MediaStreamDestinationNode`. 3. Create an `AudioBufferSourceNode` for the clip. 4. Route the mic `MediaStream` and the clip source to the destination. 5. Pass the destination's `.stream` to the call bridge. ### P5-20 · Quick Reply from Browser Notification **Mechanism:** Service Worker `notificationclick` Action. - **1. Registration (src/sw.ts):** ```typescript self.addEventListener('notificationclick', (event) => { if (event.action === 'reply' && event.reply) { const { roomId, threadId } = event.notification.data; const session = sessions.get(event.clientId); // Uses existing session mapping // Send via direct fetch to bypass SDK loading fetch(`${session.baseUrl}/_matrix/client/v3/rooms/${roomId}/send/m.room.message`, { method: 'POST', headers: { Authorization: `Bearer ${session.accessToken}` }, body: JSON.stringify({ msgtype: 'm.text', body: event.reply, 'm.relates_to': threadId ? { rel_type: 'm.thread', event_id: threadId } : undefined, }), }); } }); ``` --- ## 🔬 Extreme Complexity Projects ### P5-30 · Advanced ML Noise Suppression (Krisp-style) **Mechanism:** RNNoise WASM + Web Audio Worklet Pipeline. - **Objective:** Filter non-vocal noise from the microphone stream in real-time. - **Architecture:** 1. **Engine:** Use `RNNoise` (Recurrent Neural Network for noise suppression). It is lightweight and highly effective for speech. 2. **Pipeline:** `Mic Stream` -> `AudioWorkletNode` (Processing) -> `MediaStreamDestination` -> `Element Call`. - **Implementation Steps:** 1. **WASM Wrapper:** Compile the `RNNoise` C library to WebAssembly. Use a library like `rnnoise-wasm` or `noise-suppression-js`. 2. **Audio Worklet:** Create `src/app/utils/audio/RnnoiseWorklet.ts`. This must handle 480-sample chunks (10ms of audio at 48kHz), which is the standard frame size for RNNoise. 3. **Client Integration:** - In `CallControl.ts`, intercept the `localStream`. - Pass the stream through the Worklet. - Crucially, you must ensure that the processed stream is used by the `RTCPeerConnection` within the Element Call iframe. ### P5-31 · Granular Voice & Screenshare Quality Controls **Mechanism:** WebRTC Encoding Parameters + Backend Quality Guard. - **Objective:** Per-room and per-user control over audio fidelity and screenshare smoothness. - **Architecture:** 1. **State Event:** `io.lotus.room_quality` (state key `""`) containing: ```json { "audio_bitrate": 128000, "screen_max_res": "1080p", "screen_max_fps": 60 } ``` 2. **Client-Side (RoomInput / CallControl):** - **Screenshare:** In `src/app/plugins/call/CallControl.ts`, when initiating screenshare, map the "Quality" setting to `getDisplayMedia` constraints: ```typescript const constraints = { video: { width: { ideal: 1920 }, // 1080p frameRate: { ideal: 60 }, }, }; ``` - **Audio Bitrate:** After the call joins, find the `RTCRtpSender` for the audio track and update parameters: ```typescript const sender = peerConnection.getSenders().find((s) => s.track?.kind === 'audio'); const params = sender.getParameters(); params.encodings[0].maxBitrate = roomBitrate || 128000; await sender.setParameters(params); ``` 3. **Backend Sidecar (The "Quality Guard"):** - **Pattern:** Extend the `voice-limit-guard.py` (on LXC 151) to handle quality metadata. - **Mechanism:** When a user requests a LiveKit JWT to join a room, the Guard fetches the `io.lotus.room_quality` event for that room via the Synapse Admin API. - **Enforcement:** The Guard injects these limits into the LiveKit token claims (if supported) or simply returns them to the client as an authorized "config" packet that the client must respect. - **Challenges:** - **LiveKit Compatibility:** Ensuring the SFU doesn't over-compress a high-bitrate stream from a "Pro" user. - **Network Stability:** High bitrates (512kbps audio + 60fps 1080p video) require significant upstream bandwidth. Implement a "Network Warning" UI if packets are dropped.