[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I logging and monitoring best practices?

[Assistant] Structured logging with Pino/Winston. Example: pino({ level: 'info' }).info({ userId, action }, 'Action'). Correlation IDs across requests. Centralized: ELK or Datadog. Alert on error rate > threshold.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Logging and monitoring best practices

[Assistant] Structured logging with Pino/Winston. Example: pino({ level: 'info' }).info({ userId, action }, 'Action'). Correlation IDs across requests. Centralized: ELK or Datadog. Alert on error rate > threshold.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I environment config across dev/staging/prod?

[Assistant] dotenv: .env.development, .env.production. Validate with Zod: z.object({ DATABASE_URL: z.string().url() }).parse(process.env). Never commit .env files, use .env.example.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Environment config across dev/staging/prod

[Assistant] dotenv: .env.development, .env.production. Validate with Zod: z.object({ DATABASE_URL: z.string().url() }).parse(process.env). Never commit .env files, use .env.example.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I web security (cors, xss, csrf, sql injection)?

[Assistant] CORS: cors() with origin whitelist. XSS: sanitize HTML, CSP header. CSRF: SameSite=Strict, CSRF tokens. SQL injection: parameterized queries: db.query('SELECT * FROM users WHERE id = $1', [userId]).


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Web security (CORS, XSS, CSRF, SQL injection)

[Assistant] CORS: cors() with origin whitelist. XSS: sanitize HTML, CSP header. CSRF: SameSite=Strict, CSRF tokens. SQL injection: parameterized queries: db.query('SELECT * FROM users WHERE id = $1', [userId]).


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I accessibility implementation patterns?

[Assistant] Semantic HTML, aria-* attributes, keyboard nav, color contrast. Example: <button aria-label="Close" onClick={onClose}>X</button>. role='alert' for dynamic messages. Focus management. Test with axe-core.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Accessibility implementation patterns

[Assistant] Semantic HTML, aria-* attributes, keyboard nav, color contrast. Example: <button aria-label="Close" onClick={onClose}>X</button>. role='alert' for dynamic messages. Focus management. Test with axe-core.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I internationalization (i18n) setup?

[Assistant] react-i18next: i18n.use(initReactI18next).init({ resources: { en: { greeting: 'Hello' }, fr: { greeting: 'Bonjour' } }, lng: 'en' }); Usage: const { t } = useTranslation(); <h1>{t('greeting')}</h1>.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Internationalization (i18n) setup

[Assistant] react-i18next: i18n.use(initReactI18next).init({ resources: { en: { greeting: 'Hello' }, fr: { greeting: 'Bonjour' } }, lng: 'en' }); Usage: const { t } = useTranslation(); <h1>{t('greeting')}</h1>.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I mobile responsive design?

[Assistant] CSS Grid + Flexbox. Breakpoints: @media (max-width: 768px) tablet, 480px mobile. Mobile-first: min-width. Example: grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)). Viewport: <meta name="viewport" content="width=device-width, initial-scale=1">.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Mobile responsive design

[Assistant] CSS Grid + Flexbox. Breakpoints: @media (max-width: 768px) tablet, 480px mobile. Mobile-first: min-width. Example: grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)). Viewport: <meta name="viewport" content="width=device-width, initial-scale=1">.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I how image recognition works (cnns and vision transformers)?

[Assistant] Image recognition uses deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). CNNs apply convolution filters to extract hierarchical features from edges to complex shapes. ViTs split images into patches, embed them linearly, and process them through transformer encoder layers with multi-head self-attention. Both approaches learn to map raw pixel values to class probabilities through supervised training on labeled datasets.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about How Image Recognition Works (CNNs and Vision Transformers)

[Assistant] Image recognition uses deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). CNNs apply convolution filters to extract hierarchical features from edges to complex shapes. ViTs split images into patches, embed them linearly, and process them through transformer encoder layers with multi-head self-attention. Both approaches learn to map raw pixel values to class probabilities through supervised training on labeled datasets.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I image captioning?

[Assistant] Image captioning generates natural language descriptions of image content. Architectures typically combine a vision encoder (CNN or ViT) with a language decoder (LSTM or transformer). The encoder extracts visual features; the decoder autoregressively generates caption tokens. Models like BLIP, GIT, and Flamingo achieve state-of-the-art results. Beam search or nucleus sampling produces diverse captions. Evaluation metrics include BLEU, ROUGE, METEOR, and CIDEr.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Image Captioning

[Assistant] Image captioning generates natural language descriptions of image content. Architectures typically combine a vision encoder (CNN or ViT) with a language decoder (LSTM or transformer). The encoder extracts visual features; the decoder autoregressively generates caption tokens. Models like BLIP, GIT, and Flamingo achieve state-of-the-art results. Beam search or nucleus sampling produces diverse captions. Evaluation metrics include BLEU, ROUGE, METEOR, and CIDEr.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I object detection?

[Assistant] Object detection locates and classifies objects in images using bounding boxes. Two-stage detectors (Faster R-CNN, Mask R-CNN) first propose regions then classify each. One-stage detectors (YOLO, SSD, RetinaNet) directly predict boxes and classes in a single pass. Anchor boxes serve as reference priors. Non-maximum suppression removes duplicate detections. Common benchmarks: COCO, Pascal VOC, Open Images. Output format: [x1, y1, x2, y2, class, confidence].


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Object Detection

[Assistant] Object detection locates and classifies objects in images using bounding boxes. Two-stage detectors (Faster R-CNN, Mask R-CNN) first propose regions then classify each. One-stage detectors (YOLO, SSD, RetinaNet) directly predict boxes and classes in a single pass. Anchor boxes serve as reference priors. Non-maximum suppression removes duplicate detections. Common benchmarks: COCO, Pascal VOC, Open Images. Output format: [x1, y1, x2, y2, class, confidence].


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I optical character recognition (ocr)?

[Assistant] OCR extracts text from images. Traditional pipelines use binarization, line segmentation, and character recognition. Modern approaches use deep learning: CNN+RNN+CTC (CRNN) or transformer-based end-to-end models (TrOCR, PaddleOCR). Text detection finds text regions (EAST, CRAFT); text recognition reads the characters. Tesseract is a popular open-source engine. Challenges include handwriting, skewed text, variable lighting, and non-Latin scripts.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Optical Character Recognition (OCR)

[Assistant] OCR extracts text from images. Traditional pipelines use binarization, line segmentation, and character recognition. Modern approaches use deep learning: CNN+RNN+CTC (CRNN) or transformer-based end-to-end models (TrOCR, PaddleOCR). Text detection finds text regions (EAST, CRAFT); text recognition reads the characters. Tesseract is a popular open-source engine. Challenges include handwriting, skewed text, variable lighting, and non-Latin scripts.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I face detection and recognition basics?

[Assistant] Face detection finds faces in images (Haar cascades, MTCNN, RetinaFace). Face recognition identifies or verifies individuals. Modern systems use deep metric learning: a CNN maps faces to embedding vectors in a high-dimensional space. FaceNet, ArcFace, and CosFace optimize for cosine similarity between embeddings of the same identity. The embedding is compared against a gallery database. LFW (Labeled Faces in the Wild) is a standard benchmark.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Face Detection and Recognition Basics

[Assistant] Face detection finds faces in images (Haar cascades, MTCNN, RetinaFace). Face recognition identifies or verifies individuals. Modern systems use deep metric learning: a CNN maps faces to embedding vectors in a high-dimensional space. FaceNet, ArcFace, and CosFace optimize for cosine similarity between embeddings of the same identity. The embedding is compared against a gallery database. LFW (Labeled Faces in the Wild) is a standard benchmark.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I image classification?

[Assistant] Image classification assigns a category label to an input image. CNNs like ResNet, EfficientNet, and ConvNeXt stack convolutional and pooling layers followed by a fully-connected classifier. Vision Transformers (ViT, DeiT) apply transformer encoders on image patches. Pre-training on ImageNet (1000 classes) is standard, with fine-tuning on downstream tasks. Top-1 and top-5 accuracy measure performance. Softmax produces a probability distribution over classes.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Image Classification

[Assistant] Image classification assigns a category label to an input image. CNNs like ResNet, EfficientNet, and ConvNeXt stack convolutional and pooling layers followed by a fully-connected classifier. Vision Transformers (ViT, DeiT) apply transformer encoders on image patches. Pre-training on ImageNet (1000 classes) is standard, with fine-tuning on downstream tasks. Top-1 and top-5 accuracy measure performance. Softmax produces a probability distribution over classes.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I using hugging face image models?

[Assistant] Hugging Face transformers provides a unified API for image models via pipeline('image-classification'), pipeline('object-detection'), pipeline('image-segmentation'), and pipeline('image-to-text'). Load models with AutoModelForImageClassification or AutoModelForObjectDetection. The Processor class handles image preprocessing (resize, normalize, tensor conversion). Over 1000 vision models are available on the Hub including Google's ViT, Microsoft's BEiT, and Meta's DINOv2.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Using Hugging Face Image Models

[Assistant] Hugging Face transformers provides a unified API for image models via pipeline('image-classification'), pipeline('object-detection'), pipeline('image-segmentation'), and pipeline('image-to-text'). Load models with AutoModelForImageClassification or AutoModelForObjectDetection. The Processor class handles image preprocessing (resize, normalize, tensor conversion). Over 1000 vision models are available on the Hub including Google's ViT, Microsoft's BEiT, and Meta's DINOv2.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I using openai vision api integration?

[Assistant] OpenAI's GPT-4V and GPT-4o accept images for analysis via the chat completions API. Images can be provided as base64-encoded strings or URLs. The API returns a text response describing or analyzing the image. Multi-modal prompts can combine text and image inputs. Use max_tokens to control response length. Cost scales with image resolution. Supported formats: PNG, JPEG, GIF, WebP. The gpt-4o-mini model offers a cheaper alternative for vision tasks.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Using OpenAI Vision API Integration

[Assistant] OpenAI's GPT-4V and GPT-4o accept images for analysis via the chat completions API. Images can be provided as base64-encoded strings or URLs. The API returns a text response describing or analyzing the image. Multi-modal prompts can combine text and image inputs. Use max_tokens to control response length. Cost scales with image resolution. Supported formats: PNG, JPEG, GIF, WebP. The gpt-4o-mini model offers a cheaper alternative for vision tasks.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I scene understanding?

[Assistant] Scene understanding interprets image context beyond individual objects. Tasks include scene classification, semantic segmentation, depth estimation, and relationship detection. Models like Places365 classify scene types (kitchen, highway, forest). Scene graphs represent objects and their pairwise relationships (person riding horse). Holistic understanding combines object detection, layout analysis, and contextual reasoning for applications like autonomous driving and robotics.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Scene Understanding

[Assistant] Scene understanding interprets image context beyond individual objects. Tasks include scene classification, semantic segmentation, depth estimation, and relationship detection. Models like Places365 classify scene types (kitchen, highway, forest). Scene graphs represent objects and their pairwise relationships (person riding horse). Holistic understanding combines object detection, layout analysis, and contextual reasoning for applications like autonomous driving and robotics.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I image preprocessing?

[Assistant] Image preprocessing transforms raw images for model input. Steps: resize to model input size (224x224, 384x384), normalize pixel values to [0,1] or [-1,1] using dataset mean/std, convert color space (RGB vs BGR), apply data augmentation (random crop, flip, rotation, color jitter). Libraries: torchvision.transforms, tensorflow.keras.preprocessing, OpenCV. For transformers, the image processor handles these steps automatically via the `image_processor` or `feature_extractor` API.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Image Preprocessing

[Assistant] Image preprocessing transforms raw images for model input. Steps: resize to model input size (224x224, 384x384), normalize pixel values to [0,1] or [-1,1] using dataset mean/std, convert color space (RGB vs BGR), apply data augmentation (random crop, flip, rotation, color jitter). Libraries: torchvision.transforms, tensorflow.keras.preprocessing, OpenCV. For transformers, the image processor handles these steps automatically via the `image_processor` or `feature_extractor` API.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I handling different image formats?

[Assistant] Common image formats: JPEG (lossy, best for photographs), PNG (lossless, supports transparency), WebP (Google's modern format, better compression than JPEG), GIF (supports animation, limited colors), BMP (uncompressed), SVG (vector, not raster). Use PIL/Pillow (Image.open(), Image.save()) for format conversion. OpenCV imread/imwrite handles most formats. Detect format via file header bytes (magic numbers) or extension. WebP requires libwebp; SVG requires rasterization for pixel-based models.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Handling Different Image Formats

[Assistant] Common image formats: JPEG (lossy, best for photographs), PNG (lossless, supports transparency), WebP (Google's modern format, better compression than JPEG), GIF (supports animation, limited colors), BMP (uncompressed), SVG (vector, not raster). Use PIL/Pillow (Image.open(), Image.save()) for format conversion. OpenCV imread/imwrite handles most formats. Detect format via file header bytes (magic numbers) or extension. WebP requires libwebp; SVG requires rasterization for pixel-based models.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I screenshot analysis?

[Assistant] Screenshot analysis extracts UI elements, text, and layout information from screen captures. Use OCR to read on-screen text, object detection to find buttons/icons, and layout analysis to understand structure. SSD-based UI detection models and Apple's Screen Recognition research show approaches. Applications include test automation, accessibility analysis, and intelligent assistants. Icon classification with specialized models improves accuracy. Coordinate mapping links detected elements to screen positions.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Screenshot Analysis

[Assistant] Screenshot analysis extracts UI elements, text, and layout information from screen captures. Use OCR to read on-screen text, object detection to find buttons/icons, and layout analysis to understand structure. SSD-based UI detection models and Apple's Screen Recognition research show approaches. Applications include test automation, accessibility analysis, and intelligent assistants. Icon classification with specialized models improves accuracy. Coordinate mapping links detected elements to screen positions.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I diagram and chart interpretation?

[Assistant] Chart interpretation extracts data from visualizations. Approaches: detect chart type first (bar, line, pie, scatter), then segment visual elements (bars, lines, points, axes), and finally recover data values from positions and scales. OCR reads axis labels and legends. FigureQA and PlotQA are benchmark datasets. Vision-language models (Pix2Struct, MatCha, DePlot) can answer questions about charts directly. SVG parsing offers a structured alternative for charts rendered as vector graphics.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Diagram and Chart Interpretation

[Assistant] Chart interpretation extracts data from visualizations. Approaches: detect chart type first (bar, line, pie, scatter), then segment visual elements (bars, lines, points, axes), and finally recover data values from positions and scales. OCR reads axis labels and legends. FigureQA and PlotQA are benchmark datasets. Vision-language models (Pix2Struct, MatCha, DePlot) can answer questions about charts directly. SVG parsing offers a structured alternative for charts rendered as vector graphics.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I handwriting recognition?

[Assistant] Handwriting recognition (HTR) converts handwritten text to digital text. Offline HTR processes scanned images; online HTR captures pen strokes in real time. Deep learning models use CRNN (CNN + BiLSTM + CTC loss) or transformer-based architectures. IAM Dataset and RIMES are standard benchmarks. Challenges include cursive writing, varied styles, and unconstrained layouts. TrOCR from Microsoft shows strong results. Data augmentation (affine transforms, noise) improves generalization.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Handwriting Recognition

[Assistant] Handwriting recognition (HTR) converts handwritten text to digital text. Offline HTR processes scanned images; online HTR captures pen strokes in real time. Deep learning models use CRNN (CNN + BiLSTM + CTC loss) or transformer-based architectures. IAM Dataset and RIMES are standard benchmarks. Challenges include cursive writing, varied styles, and unconstrained layouts. TrOCR from Microsoft shows strong results. Data augmentation (affine transforms, noise) improves generalization.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I barcode and qr code reading?

[Assistant] Barcode and QR code detection decodes machine-readable patterns. 1D barcodes (UPC, EAN, Code 128) encode data in varying bar widths. 2D codes (QR, Data Matrix, PDF417) store more data using matrix patterns. Libraries: zbar, pyzbar, OpenCV with wechat_qrcode module, and boofcv. Detection involves finding the code region, sampling the pattern, and decoding. QR codes have error correction (L, M, Q, H levels). Real-time scanning uses video frame processing with region tracking.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Barcode and QR Code Reading

[Assistant] Barcode and QR code detection decodes machine-readable patterns. 1D barcodes (UPC, EAN, Code 128) encode data in varying bar widths. 2D codes (QR, Data Matrix, PDF417) store more data using matrix patterns. Libraries: zbar, pyzbar, OpenCV with wechat_qrcode module, and boofcv. Detection involves finding the code region, sampling the pattern, and decoding. QR codes have error correction (L, M, Q, H levels). Real-time scanning uses video frame processing with region tracking.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I image similarity and duplicate detection?

[Assistant] Image similarity finds visually similar images. Perceptual hashing (pHash, dHash, aHash) generates compact fingerprints for near-duplicate detection. Deep feature embeddings from CNNs produce semantic similarity vectors. Cosine similarity or Euclidean distance measures similarity. Approximate Nearest Neighbor search (FAISS, Annoy, ScaNN) enables fast retrieval from large databases. Use cases: deduplication, reverse image search, content-based recommendation. dHash compares grayscale pixel differences.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Image Similarity and Duplicate Detection

[Assistant] Image similarity finds visually similar images. Perceptual hashing (pHash, dHash, aHash) generates compact fingerprints for near-duplicate detection. Deep feature embeddings from CNNs produce semantic similarity vectors. Cosine similarity or Euclidean distance measures similarity. Approximate Nearest Neighbor search (FAISS, Annoy, ScaNN) enables fast retrieval from large databases. Use cases: deduplication, reverse image search, content-based recommendation. dHash compares grayscale pixel differences.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I color analysis and palette extraction?

[Assistant] Color analysis extracts dominant colors and palettes from images. K-means clustering on pixel values in RGB or LAB space finds representative colors. LAB space perceptually separates lightness from color. Palette extraction samples regions and merges similar colors. Dominant color ratios describe the distribution. Tools: color-thief, extcolors, OpenCV. Applications include UI design, branding analysis, and image categorization. HSV space helps filter by hue/saturation ranges for color-based segmentation.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Color Analysis and Palette Extraction

[Assistant] Color analysis extracts dominant colors and palettes from images. K-means clustering on pixel values in RGB or LAB space finds representative colors. LAB space perceptually separates lightness from color. Palette extraction samples regions and merges similar colors. Dominant color ratios describe the distribution. Tools: color-thief, extcolors, OpenCV. Applications include UI design, branding analysis, and image categorization. HSV space helps filter by hue/saturation ranges for color-based segmentation.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I image metadata reading (exif, dimensions)?

[Assistant] Image metadata includes EXIF (Exchangeable Image File Format), IPTC, and XMP data. EXIF stores camera settings (aperture, shutter speed, ISO, focal length), date/time, GPS coordinates, and thumbnail. Tools: Pillow (Image._getexif()), exifread, piexif, and ExifTool. Image dimensions (width, height) are directly accessible from file headers. Color profiles (ICC) affect rendering. Orientation tags require rotation normalization. Metadata parsing is critical for forensics and photo management.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Image Metadata Reading (EXIF, Dimensions)

[Assistant] Image metadata includes EXIF (Exchangeable Image File Format), IPTC, and XMP data. EXIF stores camera settings (aperture, shutter speed, ISO, focal length), date/time, GPS coordinates, and thumbnail. Tools: Pillow (Image._getexif()), exifread, piexif, and ExifTool. Image dimensions (width, height) are directly accessible from file headers. Color profiles (ICC) affect rendering. Orientation tags require rotation normalization. Metadata parsing is critical for forensics and photo management.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I nsfw detection and safety filtering?

[Assistant] NSFW detection identifies adult or unsafe content. Models classify images into categories: safe, suggestive, explicit, or violent. Approaches: fine-tuned CNNs (ResNet, EfficientNet) trained on curated datasets, CLIP-based zero-shot classification, and multi-modal filtering. The NSFW JS library runs in-browser. APIs: AWS Rekognition, Google Vision API, Azure Content Moderator. Key design considerations: false positive tolerance, cultural nuance in acceptable content, and privacy preservation.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about NSFW Detection and Safety Filtering

[Assistant] NSFW detection identifies adult or unsafe content. Models classify images into categories: safe, suggestive, explicit, or violent. Approaches: fine-tuned CNNs (ResNet, EfficientNet) trained on curated datasets, CLIP-based zero-shot classification, and multi-modal filtering. The NSFW JS library runs in-browser. APIs: AWS Rekognition, Google Vision API, Azure Content Moderator. Key design considerations: false positive tolerance, cultural nuance in acceptable content, and privacy preservation.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I document scanning and enhancement?

[Assistant] Document scanning captures, deskews, and enhances document images. Pipeline: detect document corners with contour finding, apply perspective transform to obtain a top-down view, binarize with adaptive thresholding or Otsu's method, remove background noise, adjust contrast, and sharpen text edges. Libraries: OpenCV, Pillow, and specialized tools like OCRmyPDF and ScanTailor. Deep learning approaches (DocTr, DewarpNet) handle curled pages and complex lighting. Output is optimized for OCR or display.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Document Scanning and Enhancement

[Assistant] Document scanning captures, deskews, and enhances document images. Pipeline: detect document corners with contour finding, apply perspective transform to obtain a top-down view, binarize with adaptive thresholding or Otsu's method, remove background noise, adjust contrast, and sharpen text edges. Libraries: OpenCV, Pillow, and specialized tools like OCRmyPDF and ScanTailor. Deep learning approaches (DocTr, DewarpNet) handle curled pages and complex lighting. Output is optimized for OCR or display.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I real-time video frame analysis basics?

[Assistant] Real-time video analysis processes frames sequentially with low latency. Approaches: frame-by-frame detection with lightweight models (MobileNet, TinyYOLO), optical flow for motion tracking, and background subtraction for object segmentation. Key considerations: skip frames to maintain FPS, use model quantization (INT8) for speed, employ hardware acceleration (CUDA, TensorRT, OpenVINO). Sliding window of frames with temporal smoothing improves consistency over single-frame predictions.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Real-Time Video Frame Analysis Basics

[Assistant] Real-time video analysis processes frames sequentially with low latency. Approaches: frame-by-frame detection with lightweight models (MobileNet, TinyYOLO), optical flow for motion tracking, and background subtraction for object segmentation. Key considerations: skip frames to maintain FPS, use model quantization (INT8) for speed, employ hardware acceleration (CUDA, TensorRT, OpenVINO). Sliding window of frames with temporal smoothing improves consistency over single-frame predictions.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I emotion detection from facial expressions?

[Assistant] Facial emotion recognition classifies expressions into basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. Models like FER2013, AffectNet, and DeepFace use CNNs trained on labeled facial expression datasets. The pipeline: face detection, face alignment (landmark-based), feature extraction, and emotion classification. Action Units from FACS (Facial Action Coding System) provide granular expression analysis. Challenges include lighting variation, partial occlusion, and cultural differences.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Emotion Detection from Facial Expressions

[Assistant] Facial emotion recognition classifies expressions into basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. Models like FER2013, AffectNet, and DeepFace use CNNs trained on labeled facial expression datasets. The pipeline: face detection, face alignment (landmark-based), feature extraction, and emotion classification. Action Units from FACS (Facial Action Coding System) provide granular expression analysis. Challenges include lighting variation, partial occlusion, and cultural differences.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I image generation and editing concepts?

[Assistant] Image generation creates images from text or other inputs. Diffusion models (Stable Diffusion, DALL-E, Midjourney) iteratively denoise random noise guided by a text prompt. GANs (StyleGAN, BigGAN) use a generator-discriminator adversarial setup. Image editing tasks: inpainting fills masked regions, outpainting extends the canvas, style transfer applies artistic styles, and super-resolution increases detail. ControlNet adds spatial conditioning (pose, edge maps).


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Image Generation and Editing Concepts

[Assistant] Image generation creates images from text or other inputs. Diffusion models (Stable Diffusion, DALL-E, Midjourney) iteratively denoise random noise guided by a text prompt. GANs (StyleGAN, BigGAN) use a generator-discriminator adversarial setup. Image editing tasks: inpainting fills masked regions, outpainting extends the canvas, style transfer applies artistic styles, and super-resolution increases detail. ControlNet adds spatial conditioning (pose, edge maps).


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I visual question answering?

[Assistant] Visual Question Answering (VQA) answers natural language questions about images. Models embed both the image (CNN/ViT) and question (text transformer), then fuse them through cross-modal attention to predict answers. Benchmarks: VQA v2, GQA, VizWiz. Dataset biases (language priors) are addressed through balanced datasets. Modern approaches (BLIP-2, LLaVA, InstructBLIP) use large language models as reasoning backends fed with visual tokens. Open-ended and multiple-choice variants exist.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Visual Question Answering

[Assistant] Visual Question Answering (VQA) answers natural language questions about images. Models embed both the image (CNN/ViT) and question (text transformer), then fuse them through cross-modal attention to predict answers. Benchmarks: VQA v2, GQA, VizWiz. Dataset biases (language priors) are addressed through balanced datasets. Modern approaches (BLIP-2, LLaVA, InstructBLIP) use large language models as reasoning backends fed with visual tokens. Open-ended and multiple-choice variants exist.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] How do I combining vision with pc control?

[Assistant] Integrating vision with PC control enables autonomous GUI interaction. The system captures screenshots, analyzes UI elements via object detection and OCR, maps detections to screen coordinates, then automates mouse/keyboard actions (click, type, scroll). Frameworks: Playwright/Puppeteer for web automation, PyAutoGUI for desktop, and Microsoft's OmniParser for screen parsing. Vision-language models (GPT-4V, CogAgent) can directly predict actions from screen observations for agent-based automation.


[System]
You are Icarus, an AI assistant inspired by JARVIS. You are helpful, concise, knowledgeable.
CAPABILITIES:
- Control PC with [system:open:appName] [system:run:command] [system:volume:up|down]
- Search web with [search:query]
- Write code, manage files, launch apps
- Answer questions about Windows, development, automation, security, and more.

[User] Tell me about Combining Vision with PC Control

[Assistant] Integrating vision with PC control enables autonomous GUI interaction. The system captures screenshots, analyzes UI elements via object detection and OCR, maps detections to screen coordinates, then automates mouse/keyboard actions (click, type, scroll). Frameworks: Playwright/Puppeteer for web automation, PyAutoGUI for desktop, and Microsoft's OmniParser for screen parsing. Vision-language models (GPT-4V, CogAgent) can directly predict actions from screen observations for agent-based automation.