{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47403292","slug":"smart-glasses-that-tell-me-when-to-stop-pouring","name":"Smart glasses that tell me when to stop pouring","type":"repo","url":"https://github.com/RealComputer/GlassKit/tree/main/examples/rokid-overshoot-openai-realtime","page_url":"https://unfragile.ai/smart-glasses-that-tell-me-when-to-stop-pouring","categories":["automation"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47403292__cap_0","uri":"capability://image.visual.real.time.video.stream.processing.from.smart.glasses","name":"real-time video stream processing from smart glasses","description":"Captures continuous video feed from Rokid smart glasses hardware via native device APIs and streams frames to processing pipeline at 30fps. Uses hardware-accelerated video encoding to minimize latency between capture and analysis, enabling sub-100ms feedback loops for real-time visual tasks like pour detection.","intents":["I need to capture live video from AR glasses and process it with minimal latency","I want to build real-time computer vision applications that run on wearable hardware","I need to integrate smart glasses video input with cloud-based AI models"],"best_for":["hardware engineers building AR/VR applications","roboticists integrating wearable sensors with AI pipelines","developers creating real-time safety or assistance applications for smart glasses"],"limitations":["Rokid-specific implementation — not portable to other smart glasses brands without refactoring","Real-time processing adds 50-150ms latency depending on network conditions and model inference time","Video stream bandwidth requires stable WiFi or 5G connection; degrades on poor connectivity","Frame rate capped at device hardware capabilities (typically 30fps for Rokid)"],"requires":["Rokid smart glasses hardware (AR glasses device)","Rokid SDK or native device driver installed","Network connectivity (WiFi or cellular) for cloud inference","Python 3.8+ or Node.js 14+ depending on implementation"],"input_types":["video stream (H.264/H.265 encoded)","raw frame buffers (RGB/YUV format)"],"output_types":["frame metadata (timestamp, resolution, encoding)","processed frames with annotations","detection results (bounding boxes, confidence scores)"],"categories":["image-visual","hardware-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47403292__cap_1","uri":"capability://image.visual.real.time.object.detection.and.visual.reasoning.via.openai.vision.api","name":"real-time object detection and visual reasoning via openai vision api","description":"Sends captured video frames to OpenAI's real-time API for multimodal analysis, using GPT-4V or similar vision models to detect pouring actions, liquid levels, and container states. Implements streaming inference where frames are batched and sent asynchronously, with results returned as structured JSON predictions that trigger immediate feedback to the glasses display.","intents":["I need to identify when a liquid is being poured and detect when to stop based on visual cues","I want to use a powerful vision model without running inference locally on the glasses","I need to get semantic understanding of a scene (not just object detection) to make context-aware decisions"],"best_for":["developers building assistive AR applications without local GPU resources","teams prototyping vision-based safety systems that need high accuracy","builders creating real-time feedback loops that require semantic scene understanding"],"limitations":["Requires API calls to OpenAI — adds 200-500ms latency per frame depending on network and model load","OpenAI API costs scale with frame rate; continuous 30fps processing costs ~$0.50-1.00 per minute","Dependent on external service availability — network outages break the application","Rate limits on OpenAI API may throttle high-frequency frame submission","Privacy concern: video frames sent to external cloud service"],"requires":["OpenAI API key with access to vision models (GPT-4V or gpt-4-turbo-vision)","Network connectivity with sufficient bandwidth for frame uploads (~1-5 Mbps for 30fps)","Python 3.8+ with openai library (version 1.0+)","Account with sufficient API credits"],"input_types":["video frames (JPEG/PNG encoded)","frame metadata (timestamp, resolution)","optional context prompts (e.g., 'detect liquid level')"],"output_types":["structured JSON with detection results","confidence scores for predictions","semantic descriptions of scene state","recommended actions (e.g., 'stop pouring')"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47403292__cap_2","uri":"capability://text.generation.language.low.latency.audio.feedback.synthesis.and.playback.on.smart.glasses","name":"low-latency audio feedback synthesis and playback on smart glasses","description":"Converts detection results (e.g., 'stop pouring') into audio cues that are synthesized and played through smart glasses speakers with <200ms end-to-end latency. Uses text-to-speech synthesis (likely OpenAI TTS or similar) combined with audio buffering to ensure immediate auditory feedback without blocking the vision processing pipeline.","intents":["I need to alert the user immediately when the pouring threshold is reached","I want audio feedback that doesn't require looking at a screen or display","I need to generate dynamic audio messages based on real-time detection results"],"best_for":["developers building hands-free AR assistants","accessibility-focused applications requiring audio-primary interfaces","safety-critical systems where immediate auditory alerts are essential"],"limitations":["TTS synthesis adds 100-300ms latency; pre-synthesized audio cues are faster but less flexible","Smart glasses speaker quality may be poor; audio cues must be loud/distinct to be heard in noisy environments","Audio playback can be interrupted by system sounds or other applications","Continuous TTS API calls add cost (~$0.015-0.03 per 1000 characters)"],"requires":["Smart glasses with audio output capability (speaker or bone conduction)","Text-to-speech API access (OpenAI TTS, Google Cloud TTS, or local TTS engine)","Audio playback library (e.g., PyAudio, Web Audio API)","Python 3.8+ or Node.js 14+"],"input_types":["text strings (e.g., 'stop pouring')","detection confidence scores","optional audio parameters (pitch, speed, volume)"],"output_types":["audio stream (WAV/MP3 encoded)","playback status (playing, queued, completed)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47403292__cap_3","uri":"capability://image.visual.real.time.ar.display.overlay.rendering.on.smart.glasses","name":"real-time ar display overlay rendering on smart glasses","description":"Renders visual annotations (e.g., 'STOP' indicator, liquid level gauge, confidence scores) directly onto the smart glasses display using native Rokid rendering APIs. Implements frame-synchronized overlay composition where detection results are mapped to screen coordinates and rendered at the glasses' native refresh rate (typically 60Hz) without tearing or latency.","intents":["I need to show visual feedback on the glasses display synchronized with detection results","I want to overlay real-time metrics (liquid level, confidence) on the user's view","I need to render AR annotations that don't interfere with the user's natural vision"],"best_for":["AR application developers building visual feedback systems","teams creating assistive AR interfaces with real-time overlays","developers building spatial computing applications on smart glasses"],"limitations":["Rokid-specific rendering APIs — not portable to other AR glasses platforms","Display resolution and refresh rate limited by hardware (typically 1080p @ 60Hz)","Rendering performance degrades with complex overlays; limited to ~10-20 simultaneous annotations","Battery drain from continuous display rendering; typical smart glasses have 2-4 hour battery life"],"requires":["Rokid smart glasses with display capability","Rokik SDK with rendering/graphics libraries","Understanding of coordinate transformation (camera frame to display space)","C++ or Java for native rendering (or Rokid's proprietary scripting language)"],"input_types":["detection results (bounding boxes, labels, confidence scores)","frame metadata (resolution, camera intrinsics)","rendering parameters (color, size, position)"],"output_types":["rendered frames with overlays","display refresh status","performance metrics (FPS, latency)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47403292__cap_4","uri":"capability://automation.workflow.end.to.end.latency.optimization.and.frame.synchronization","name":"end-to-end latency optimization and frame synchronization","description":"Orchestrates the entire pipeline (video capture → inference → feedback) with explicit latency budgeting and frame synchronization. Implements timestamp tracking across all stages, adaptive frame skipping when inference falls behind, and priority queuing to ensure critical alerts (e.g., 'stop pouring') are never delayed. Uses a state machine to coordinate async operations without blocking.","intents":["I need to ensure feedback reaches the user within 200-300ms of the triggering event","I want to handle cases where inference is slower than video capture without dropping critical alerts","I need to monitor and optimize latency across the entire pipeline"],"best_for":["developers building real-time safety-critical AR applications","teams optimizing latency-sensitive computer vision pipelines","builders creating responsive AR experiences where user perception of lag matters"],"limitations":["Latency optimization is hardware-dependent; results vary across different smart glasses models","Frame skipping can miss important events if inference consistently lags; requires tuning per use case","Timestamp synchronization requires accurate system clocks; may drift on older hardware","Adds ~10-20% CPU overhead for latency tracking and coordination"],"requires":["Understanding of async/await patterns or event-driven programming","Ability to profile and measure latency at each pipeline stage","Python 3.8+ with asyncio or equivalent async runtime","Optional: profiling tools (cProfile, py-spy) for latency analysis"],"input_types":["video frames with timestamps","inference results with processing time metadata","system clock readings"],"output_types":["latency metrics (per-stage and end-to-end)","frame drop statistics","alert delivery status (on-time, delayed, dropped)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47403292__cap_5","uri":"capability://image.visual.context.aware.pouring.detection.with.liquid.level.estimation","name":"context-aware pouring detection with liquid level estimation","description":"Combines object detection (identifying containers, liquids, pouring action) with semantic reasoning to estimate liquid level and predict when the container will be full. Uses vision model analysis to track liquid surface position across frames, applies geometric reasoning to estimate volume, and triggers 'stop pouring' alerts based on configurable thresholds. Handles multiple container types (cups, glasses, bottles) with adaptive detection logic.","intents":["I need to detect when a liquid is being poured and estimate how full the container is","I want to predict when the container will overflow and alert before it happens","I need to handle different container shapes and sizes without manual calibration"],"best_for":["developers building assistive technology for people with vision impairment or motor control issues","roboticists implementing autonomous pouring or liquid handling systems","teams creating smart kitchen appliances with overflow prevention"],"limitations":["Accuracy depends on lighting conditions; poor lighting (backlit containers) reduces detection reliability","Opaque containers prevent liquid level visibility; only works with transparent or translucent containers","Liquid color affects detection; dark liquids in dark containers are harder to detect","Geometric estimation assumes simple container shapes; complex or irregular containers reduce accuracy","Requires multiple frames to build confidence; single-frame detection is unreliable"],"requires":["OpenAI API key with vision model access","Video stream with sufficient resolution (720p minimum) to detect liquid surface","Calibration data for container types (optional, for improved accuracy)","Python 3.8+ with numpy for geometric calculations"],"input_types":["video frames showing pouring action","container metadata (type, approximate size)","optional: 3D container model for geometric reasoning"],"output_types":["liquid level percentage (0-100%)","pouring action confidence score","predicted time to overflow","alert trigger (boolean)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"low","permissions":["Rokid smart glasses hardware (AR glasses device)","Rokid SDK or native device driver installed","Network connectivity (WiFi or cellular) for cloud inference","Python 3.8+ or Node.js 14+ depending on implementation","OpenAI API key with access to vision models (GPT-4V or gpt-4-turbo-vision)","Network connectivity with sufficient bandwidth for frame uploads (~1-5 Mbps for 30fps)","Python 3.8+ with openai library (version 1.0+)","Account with sufficient API credits","Smart glasses with audio output capability (speaker or bone conduction)","Text-to-speech API access (OpenAI TTS, Google Cloud TTS, or local TTS engine)"],"failure_modes":["Rokid-specific implementation — not portable to other smart glasses brands without refactoring","Real-time processing adds 50-150ms latency depending on network conditions and model inference time","Video stream bandwidth requires stable WiFi or 5G connection; degrades on poor connectivity","Frame rate capped at device hardware capabilities (typically 30fps for Rokid)","Requires API calls to OpenAI — adds 200-500ms latency per frame depending on network and model load","OpenAI API costs scale with frame rate; continuous 30fps processing costs ~$0.50-1.00 per minute","Dependent on external service availability — network outages break the application","Rate limits on OpenAI API may throttle high-frequency frame submission","Privacy concern: video frames sent to external cloud service","TTS synthesis adds 100-300ms latency; pre-synthesized audio cues are faster but less flexible","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.28,"quality":0.22,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:10:08.734Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=smart-glasses-that-tell-me-when-to-stop-pouring","compare_url":"https://unfragile.ai/compare?artifact=smart-glasses-that-tell-me-when-to-stop-pouring"}},"signature":"iCxttVm/WTEH6rxPoljjoPgkXTjBHXJeV9yfc/Lds4/FDRcWoUR0Yfb/TSCEUBBDD1hVs4kCYI4WA78LpfDlDw==","signedAt":"2026-06-19T11:30:25.785Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/smart-glasses-that-tell-me-when-to-stop-pouring","artifact":"https://unfragile.ai/smart-glasses-that-tell-me-when-to-stop-pouring","verify":"https://unfragile.ai/api/v1/verify?slug=smart-glasses-that-tell-me-when-to-stop-pouring","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}