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"_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": 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9656
FLUX
Create a realistic 3D image of a futuristic and technological rocket horizontally divided into 7 sections, representing a content production flow. The rocket should be purple, with yellow and blue details. Each section of the rocket is designed with geometric and connected shapes, in a technological and futuristic style. The divisions are numbered from 1 to 7, with arrows indicating the flow between the stages. Each section must be separated from each other, like rocket parts separated by 7. At the end of the rocket, there is a circular target with radar lines. In the background, siren space with stars
166
FLUX
Create a professional, clean, and representational diagram illustrating a software architecture process flow. The image will be used in a technical presentation for a .NET developer audience, so it must look highly polished, modern, and corporate rather than overly cartoonish. The view must be from a side-profile perspective, detailing a sequential step-by-step process flow moving strictly from left to right. The diagram visualizes the "Producer-Consumer" design pattern using an industrial assembly line metaphor. ### Visual Elements & Spatial Layout (Left to Right): 1. **The Entry Point (Far Left):** - An elegant, minimalist digital portal or gateway icon representing a Web API Endpoint. - Text label near it reads: "GET /migration" - An arrow points from this endpoint toward the producer robot. 2. **The Producer (Left Center):** - A modern, sleek industrial robotic arm representing the "BackgroundTaskQueue" service. - The robot is actively packaging incoming request data into neat, uniform digital cargo boxes. - Label this entity: "BackgroundTaskQueue (Producer)" 3. **The Buffer (Center):** - A long, horizontal conveyor belt extending from the robot toward the right side of the frame. - On the conveyor belt, multiple identical boxes are placed at equal, perfectly spaced intervals, moving to the right. - These boxes represent the queued tasks. 4. **The Consumer (Far Right):** - A sophisticated automated workstation or processing unit representing the "MigrationBackgroundService". - This service is actively dequeuing (unpacking) the boxes as they arrive at the end of the belt. - Inside or directly above this station, show a dynamic visual indicator of execution—such as gears, a glowing progress ring, or a subtle vortex graphic—to clearly demonstrate that the unpacked requests are "spinning" (actively executing). - Label this entity: "MigrationBackgroundService (Consumer)" ### Aesthetic & Style Guidelines: - **Style:** Flat vector design or clean 3D isometric rendering suitable for enterprise architecture slide decks. - **Color Palette:** Professional corporate tones (e.g., .NET tech colors like deep purples, blues, cool greys, and crisp white backgrounds). - **Clarity:** Sharp contrasts, clean lines, high legibility, and zero visual clutter. Avoid messy abstract backgrounds.
05
Grok Image
create a simple conceptual framework where the Input includes INPUT: • Age of Plumbing System • Type of Materials (GI, PVC, etc.) • Installation Quality • Maintenance Practices • Water Usage Level PROCESS: • Visual Inspection • Water Quality Observation (color, odor, clarity) • Flow Rate Measurement • Survey / Interview OUTPUT: • Plumbing Condition (Good / Fair / Poor) • Detected Defects (Leaks, Corrosion, etc.) • Water Quality Status (Acceptable / Not Acceptable) • Safety Assessment
07
Hero
Act as a world-class Telugu YouTube scriptwriter, enterprise career strategist, AI transformation advisor, storytelling expert, and content architect. I am creating Video 3 for my YouTube channel "Data Dharma." Channel Mission: Enterprise AI, Data Engineering, Career Transformation, and Future-Proofing IT Careers using powerful storytelling. Target Audience: 1. QA Automation Engineers (Selenium, Cypress, Playwright, Tosca, UFT, API Testing, Automation Frameworks) 2. Manual Testers wanting to move into technical careers 3. Engineering Students 4. Recent Graduates 5. IT professionals worried about AI disruption 6. Professionals wondering whether Data Engineering, AI Engineering, or QA Automation has a better future VIDEO TITLE THEME: "AI Era lo QA Automation Engineers Future Enti? Data Engineer Avvacha?" or "Can QA Automation Engineers Become Data Engineers Before AI Replaces Their Work?" OBJECTIVE: This should not be a boring tutorial. It should feel like a Netflix-style career transformation documentary. The audience should feel: * Fear * Curiosity * Hope * Motivation * Clear Action Plan ================================================== PART 1 – COMPLETE YOUTUBE SCRIPT (TELUGU) ========================================= Create a complete 8–12 minute Telugu script. Requirements: A. FIRST 30 SECONDS (VERY IMPORTANT) The first 30 seconds must stay completely aligned to: * Title * Thumbnail * Core topic No introductions. No welcome messages. No channel promotion. Immediately create curiosity. Example emotions: * AI is writing Selenium scripts. * Copilot is generating test cases. * Automation is becoming easier. * What happens to QA careers? The viewer should feel: "Wait... what happens to my future?" B. OPEN LOOP Build curiosity. Continuously tease: * What is the biggest mistake QA engineers make? * Why are some QA professionals growing while others are stuck? * Why are students making the same mistake? * What career path will survive the AI era? Keep viewers watching until the end. C. CONSEQUENCES SECTION Create a realistic section: "What happens if a QA Automation Engineer does not evolve?" Discuss: * AI-assisted testing * Reduced manual effort * Higher expectations * Need for broader skills Do NOT use fear-mongering. Be realistic and balanced. D. WHY DATA ENGINEERING? Explain: Why Data Engineering is a strong transition path. Connect existing QA skills: * SQL * APIs * Data validation * Python * Automation mindset * CI/CD * Analytical thinking Explain why these skills transfer naturally. E. WHY NOT JUMP DIRECTLY INTO AI ENGINEERING? Give a balanced explanation. Explain: * AI Engineering is exciting * But many professionals skip foundations * Data Engineering builds: * Data skills * Pipelines * Architecture understanding * Enterprise experience Explain why Data Engineering can be a practical bridge toward AI. F. STUDENTS & FRESHERS SECTION Do NOT make this video only for experienced QA engineers. Include a dedicated section for: * Engineering students * Fresh graduates Explain: If they are entering the industry today: * What should they learn? * What mistakes should they avoid? * Should they choose Testing? * Should they choose Data Engineering? * How should they prepare for the next 10 years? G. ROADMAP SECTION Provide: 6-month roadmap 12-month roadmap Skills: * SQL * Python * Data Modeling * Databricks * Spark * Cloud Basics * Data Warehousing Explain in simple Telugu. H. ENDING End with: Hope. Transformation. Future opportunity. Not fear. ================================================== PART 2 – VISUAL STORYBOARD ========================== For every slide provide: Slide Number Slide Title Key Message Narration Summary Suggested Visual Emotion to Create ================================================== PART 3 – GOOGLE NANO BANANA IMAGE PROMPTS ========================================= Create 15 cinematic image prompts. Examples: * Worried QA Engineer looking at AI-generated test scripts * Future enterprise control room * Student standing at career crossroads * Data pipelines flowing through a futuristic city * Engineer transforming into AI-era architect Style: Netflix documentary Cinematic lighting Enterprise technology Modern Emotional YouTube quality 16:9 ================================================== PART 4 – NOTEBOOKLM SLIDES ========================== Identify which slides are best generated using NotebookLM style. Examples: * Roadmaps * Skill comparisons * Career evolution diagrams * Timeline slides Provide exact slide content. ================================================== PART 5 – CHATGPT-GENERATED VISUAL SLIDES ======================================== Identify slides better created using ChatGPT image generation. Examples: * Emotional scenes * Career transformation scenes * AI future scenes * Student journey scenes Provide detailed prompts. ================================================== PART 6 – THUMBNAILS =================== Generate: 20 thumbnail ideas 20 title variations Mix: Fear Curiosity Career Growth AI Impact Data Engineering Opportunity ================================================== PART 7 – RETENTION STRATEGY =========================== Identify: * Hooks * Open loops * Mid-video curiosity points * Pattern interrupts * Emotional moments Explain exactly how to maximize watch time and retention. The final output should feel like a premium YouTube documentary made for Telugu IT professionals and engineering students trying to survive and thrive in the AI era.
011
Hero
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"_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": {"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
174
FLUX
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"clip_vision_output": ["581", 0]}, "class_type": "StyleModelApply", "_meta": {"title": "Apply Style Model"}}, "193": {"inputs": {"noise_mask": false, "positive": ["192", 0], "negative": ["198", 0], "vae": ["32", 0], "pixels": ["199", 1], "mask": ["199", 2]}, "class_type": "InpaintModelConditioning", "_meta": {"title": "InpaintModelConditioning"}}, "194": {"inputs": {"unet_name": "flux1-fill-dev.safetensors", "weight_dtype": "fp8_e4m3fn"}, "class_type": "UNETLoader", "_meta": {"title": "Load Diffusion Model"}}, "195": {"inputs": {"guidance": 30, "conditioning": ["197", 0]}, "class_type": "FluxGuidance", "_meta": {"title": "FluxGuidance"}}, "196": {"inputs": {"strength": 1, "model": ["582", 0]}, "class_type": "DifferentialDiffusion", "_meta": {"title": "Differential Diffusion"}}, "197": {"inputs": {"text": "32K UHD, ultra-high resolution, extremely sharp, intricate details, masterpiece, realistic, Clothes wrinkle naturally", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "N\u1ebfu \u1ea3nh ra kh\u00f4ng \u0111\u01b0\u1ee3c nh\u01b0 \u00fd => H\u00e3y m\u00f4 t\u1ea3 th\u00eam"}}, "198": {"inputs": {"text": "", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}, "199": {"inputs": {"context_expand_pixels": 10, "context_expand_factor": 1, "fill_mask_holes": true, "blur_mask_pixels": 0, "invert_mask": false, "blend_pixels": 32, "rescale_algorithm": "bicubic", "mode": "ranged size", "force_width": 1024, "force_height": 1024, "rescale_factor": 1.2, "min_width": 512, "min_height": 512, "max_width": 1536, "max_height": 1536, "padding": 32, "image": ["187", 0], "mask": ["224", 0], "optional_context_mask": ["225", 0]}, "class_type": "InpaintCrop", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Crop node)"}}, "203": {"inputs": {"samples": ["234", 0], "vae": ["32", 0]}, "class_type": "VAEDecode", "_meta": {"title": "VAE Decode"}}, "204": {"inputs": {"rescale_algorithm": "bislerp", "stitch": ["199", 0], "inpainted_image": ["203", 0]}, "class_type": "InpaintStitch", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Stitch node)"}}, "206": {"inputs": {"expand": 10, "incremental_expandrate": 0, "tapered_corners": true, "flip_input": false, "blur_radius": 2, "lerp_alpha": 1, "decay_factor": 1, "fill_holes": false, "mask": ["518", 1]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur (\u0111i\u1ec1u ch\u1ec9nh m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "210": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["219", 0], "image2": ["356", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate (gh\u00e9p t\u1ea1o m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "219": {"inputs": {"width": ["504", 1], "height": ["504", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "220": {"inputs": {"width": ["569", 1], "height": ["569", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "221": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["222", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "222": {"inputs": {"mask": ["232", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "223": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["221", 0], "image2": ["220", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate m\u1eb7t n\u1ea1 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "224": {"inputs": {"channel": "red", "image": ["223", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "225": {"inputs": {"channel": "red", "image": ["210", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "232": {"inputs": {"expand": 15, "incremental_expandrate": 0.0, "tapered_corners": false, "flip_input": false, "blur_radius": 4.0, "lerp_alpha": 1.0, "decay_factor": 1.0, "fill_holes": true, "mask": ["371", 0]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur"}}, "234": {"inputs": {"seed": 629966258210641, "steps": 20, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1, "model": ["196", 0], "positive": ["193", 0], "negative": ["193", 1], "latent_image": ["193", 2]}, "class_type": "KSampler", "_meta": {"title": "KSampler"}}, "279": {"inputs": {"prompt": ["578", 0], "threshold": 0.3, "sam_model": ["280", 0], "grounding_dino_model": ["281", 0], "image": ["405", 0]}, "class_type": "GroundingDinoSAMSegment (segment anything)", "_meta": {"title": "GroundingDinoSAMSegment (segment anything)"}}, "280": {"inputs": {"model_name": "sam_vit_h (2.56GB)"}, "class_type": "SAMModelLoader (segment anything)", "_meta": {"title": "SAMModelLoader (segment anything)"}}, "281": {"inputs": {"model_name": "GroundingDINO_SwinT_OGC (694MB)"}, "class_type": "GroundingDinoModelLoader (segment anything)", "_meta": {"title": "GroundingDinoModelLoader (segment anything)"}}, "293": {"inputs": {"value": 1536}, "class_type": "SimpleMathInt+", "_meta": {"title": "1536 Resolution"}}, "296": {"inputs": {"any_02": ["293", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "356": {"inputs": {"mask": ["206", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "368": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/021e43c9-0966-41ca-9c95-8f86a71b951e.webp", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh trang ph\u1ee5c"}, "is_changed": NaN}, "371": {"inputs": {"any_01": ["279", 1], "any_02": ["405", 1]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "404": {"inputs": {"images": ["487", 0]}, "class_type": "PreviewImage", "_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": {"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
9656
FLUX
Create a realistic 3D image of a futuristic and technological rocket horizontally divided into 7 sections, representing a content production flow. The rocket should be purple, with yellow and blue details. Each section of the rocket is designed with geometric and connected shapes, in a technological and futuristic style. The divisions are numbered from 1 to 7, with arrows indicating the flow between the stages. Each section must be separated from each other, like rocket parts separated by 7. At the end of the rocket, there is a circular target with radar lines. In the background, siren space with stars
166
FLUX
Act as a world-class Telugu YouTube scriptwriter, enterprise career strategist, AI transformation advisor, storytelling expert, and content architect. I am creating Video 3 for my YouTube channel "Data Dharma." Channel Mission: Enterprise AI, Data Engineering, Career Transformation, and Future-Proofing IT Careers using powerful storytelling. Target Audience: 1. QA Automation Engineers (Selenium, Cypress, Playwright, Tosca, UFT, API Testing, Automation Frameworks) 2. Manual Testers wanting to move into technical careers 3. Engineering Students 4. Recent Graduates 5. IT professionals worried about AI disruption 6. Professionals wondering whether Data Engineering, AI Engineering, or QA Automation has a better future VIDEO TITLE THEME: "AI Era lo QA Automation Engineers Future Enti? Data Engineer Avvacha?" or "Can QA Automation Engineers Become Data Engineers Before AI Replaces Their Work?" OBJECTIVE: This should not be a boring tutorial. It should feel like a Netflix-style career transformation documentary. The audience should feel: * Fear * Curiosity * Hope * Motivation * Clear Action Plan ================================================== PART 1 – COMPLETE YOUTUBE SCRIPT (TELUGU) ========================================= Create a complete 8–12 minute Telugu script. Requirements: A. FIRST 30 SECONDS (VERY IMPORTANT) The first 30 seconds must stay completely aligned to: * Title * Thumbnail * Core topic No introductions. No welcome messages. No channel promotion. Immediately create curiosity. Example emotions: * AI is writing Selenium scripts. * Copilot is generating test cases. * Automation is becoming easier. * What happens to QA careers? The viewer should feel: "Wait... what happens to my future?" B. OPEN LOOP Build curiosity. Continuously tease: * What is the biggest mistake QA engineers make? * Why are some QA professionals growing while others are stuck? * Why are students making the same mistake? * What career path will survive the AI era? Keep viewers watching until the end. C. CONSEQUENCES SECTION Create a realistic section: "What happens if a QA Automation Engineer does not evolve?" Discuss: * AI-assisted testing * Reduced manual effort * Higher expectations * Need for broader skills Do NOT use fear-mongering. Be realistic and balanced. D. WHY DATA ENGINEERING? Explain: Why Data Engineering is a strong transition path. Connect existing QA skills: * SQL * APIs * Data validation * Python * Automation mindset * CI/CD * Analytical thinking Explain why these skills transfer naturally. E. WHY NOT JUMP DIRECTLY INTO AI ENGINEERING? Give a balanced explanation. Explain: * AI Engineering is exciting * But many professionals skip foundations * Data Engineering builds: * Data skills * Pipelines * Architecture understanding * Enterprise experience Explain why Data Engineering can be a practical bridge toward AI. F. STUDENTS & FRESHERS SECTION Do NOT make this video only for experienced QA engineers. Include a dedicated section for: * Engineering students * Fresh graduates Explain: If they are entering the industry today: * What should they learn? * What mistakes should they avoid? * Should they choose Testing? * Should they choose Data Engineering? * How should they prepare for the next 10 years? G. ROADMAP SECTION Provide: 6-month roadmap 12-month roadmap Skills: * SQL * Python * Data Modeling * Databricks * Spark * Cloud Basics * Data Warehousing Explain in simple Telugu. H. ENDING End with: Hope. Transformation. Future opportunity. Not fear. ================================================== PART 2 – VISUAL STORYBOARD ========================== For every slide provide: Slide Number Slide Title Key Message Narration Summary Suggested Visual Emotion to Create ================================================== PART 3 – GOOGLE NANO BANANA IMAGE PROMPTS ========================================= Create 15 cinematic image prompts. Examples: * Worried QA Engineer looking at AI-generated test scripts * Future enterprise control room * Student standing at career crossroads * Data pipelines flowing through a futuristic city * Engineer transforming into AI-era architect Style: Netflix documentary Cinematic lighting Enterprise technology Modern Emotional YouTube quality 16:9 ================================================== PART 4 – NOTEBOOKLM SLIDES ========================== Identify which slides are best generated using NotebookLM style. Examples: * Roadmaps * Skill comparisons * Career evolution diagrams * Timeline slides Provide exact slide content. ================================================== PART 5 – CHATGPT-GENERATED VISUAL SLIDES ======================================== Identify slides better created using ChatGPT image generation. Examples: * Emotional scenes * Career transformation scenes * AI future scenes * Student journey scenes Provide detailed prompts. ================================================== PART 6 – THUMBNAILS =================== Generate: 20 thumbnail ideas 20 title variations Mix: Fear Curiosity Career Growth AI Impact Data Engineering Opportunity ================================================== PART 7 – RETENTION STRATEGY =========================== Identify: * Hooks * Open loops * Mid-video curiosity points * Pattern interrupts * Emotional moments Explain exactly how to maximize watch time and retention. The final output should feel like a premium YouTube documentary made for Telugu IT professionals and engineering students trying to survive and thrive in the AI era.
011
Hero
Create a professional, clean, and representational diagram illustrating a software architecture process flow. The image will be used in a technical presentation for a .NET developer audience, so it must look highly polished, modern, and corporate rather than overly cartoonish. The view must be from a side-profile perspective, detailing a sequential step-by-step process flow moving strictly from left to right. The diagram visualizes the "Producer-Consumer" design pattern using an industrial assembly line metaphor. ### Visual Elements & Spatial Layout (Left to Right): 1. **The Entry Point (Far Left):** - An elegant, minimalist digital portal or gateway icon representing a Web API Endpoint. - Text label near it reads: "GET /migration" - An arrow points from this endpoint toward the producer robot. 2. **The Producer (Left Center):** - A modern, sleek industrial robotic arm representing the "BackgroundTaskQueue" service. - The robot is actively packaging incoming request data into neat, uniform digital cargo boxes. - Label this entity: "BackgroundTaskQueue (Producer)" 3. **The Buffer (Center):** - A long, horizontal conveyor belt extending from the robot toward the right side of the frame. - On the conveyor belt, multiple identical boxes are placed at equal, perfectly spaced intervals, moving to the right. - These boxes represent the queued tasks. 4. **The Consumer (Far Right):** - A sophisticated automated workstation or processing unit representing the "MigrationBackgroundService". - This service is actively dequeuing (unpacking) the boxes as they arrive at the end of the belt. - Inside or directly above this station, show a dynamic visual indicator of execution—such as gears, a glowing progress ring, or a subtle vortex graphic—to clearly demonstrate that the unpacked requests are "spinning" (actively executing). - Label this entity: "MigrationBackgroundService (Consumer)" ### Aesthetic & Style Guidelines: - **Style:** Flat vector design or clean 3D isometric rendering suitable for enterprise architecture slide decks. - **Color Palette:** Professional corporate tones (e.g., .NET tech colors like deep purples, blues, cool greys, and crisp white backgrounds). - **Clarity:** Sharp contrasts, clean lines, high legibility, and zero visual clutter. Avoid messy abstract backgrounds.
05
Grok Image
create a simple conceptual framework where the Input includes INPUT: • Age of Plumbing System • Type of Materials (GI, PVC, etc.) • Installation Quality • Maintenance Practices • Water Usage Level PROCESS: • Visual Inspection • Water Quality Observation (color, odor, clarity) • Flow Rate Measurement • Survey / Interview OUTPUT: • Plumbing Condition (Good / Fair / Poor) • Detected Defects (Leaks, Corrosion, etc.) • Water Quality Status (Acceptable / Not Acceptable) • Safety Assessment
07
Hero
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"clip_vision_output": ["581", 0]}, "class_type": "StyleModelApply", "_meta": {"title": "Apply Style Model"}}, "193": {"inputs": {"noise_mask": false, "positive": ["192", 0], "negative": ["198", 0], "vae": ["32", 0], "pixels": ["199", 1], "mask": ["199", 2]}, "class_type": "InpaintModelConditioning", "_meta": {"title": "InpaintModelConditioning"}}, "194": {"inputs": {"unet_name": "flux1-fill-dev.safetensors", "weight_dtype": "fp8_e4m3fn"}, "class_type": "UNETLoader", "_meta": {"title": "Load Diffusion Model"}}, "195": {"inputs": {"guidance": 30, "conditioning": ["197", 0]}, "class_type": "FluxGuidance", "_meta": {"title": "FluxGuidance"}}, "196": {"inputs": {"strength": 1, "model": ["582", 0]}, "class_type": "DifferentialDiffusion", "_meta": {"title": "Differential Diffusion"}}, "197": {"inputs": {"text": "32K UHD, ultra-high resolution, extremely sharp, intricate details, masterpiece, realistic, Clothes wrinkle naturally", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "N\u1ebfu \u1ea3nh ra kh\u00f4ng \u0111\u01b0\u1ee3c nh\u01b0 \u00fd => H\u00e3y m\u00f4 t\u1ea3 th\u00eam"}}, "198": {"inputs": {"text": "", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}, "199": {"inputs": {"context_expand_pixels": 10, "context_expand_factor": 1, "fill_mask_holes": true, "blur_mask_pixels": 0, "invert_mask": false, "blend_pixels": 32, "rescale_algorithm": "bicubic", "mode": "ranged size", "force_width": 1024, "force_height": 1024, "rescale_factor": 1.2, "min_width": 512, "min_height": 512, "max_width": 1536, "max_height": 1536, "padding": 32, "image": ["187", 0], "mask": ["224", 0], "optional_context_mask": ["225", 0]}, "class_type": "InpaintCrop", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Crop node)"}}, "203": {"inputs": {"samples": ["234", 0], "vae": ["32", 0]}, "class_type": "VAEDecode", "_meta": {"title": "VAE Decode"}}, "204": {"inputs": {"rescale_algorithm": "bislerp", "stitch": ["199", 0], "inpainted_image": ["203", 0]}, "class_type": "InpaintStitch", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Stitch node)"}}, "206": {"inputs": {"expand": 10, "incremental_expandrate": 0, "tapered_corners": true, "flip_input": false, "blur_radius": 2, "lerp_alpha": 1, "decay_factor": 1, "fill_holes": false, "mask": ["518", 1]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur (\u0111i\u1ec1u ch\u1ec9nh m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "210": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["219", 0], "image2": ["356", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate (gh\u00e9p t\u1ea1o m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "219": {"inputs": {"width": ["504", 1], "height": ["504", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "220": {"inputs": {"width": ["569", 1], "height": ["569", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "221": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["222", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "222": {"inputs": {"mask": ["232", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "223": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["221", 0], "image2": ["220", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate m\u1eb7t n\u1ea1 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "224": {"inputs": {"channel": "red", "image": ["223", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "225": {"inputs": {"channel": "red", "image": ["210", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "232": {"inputs": {"expand": 15, "incremental_expandrate": 0.0, "tapered_corners": false, "flip_input": false, "blur_radius": 4.0, "lerp_alpha": 1.0, "decay_factor": 1.0, "fill_holes": true, "mask": ["371", 0]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur"}}, "234": {"inputs": {"seed": 629966258210641, "steps": 20, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1, "model": ["196", 0], "positive": ["193", 0], "negative": ["193", 1], "latent_image": ["193", 2]}, "class_type": "KSampler", "_meta": {"title": "KSampler"}}, "279": {"inputs": {"prompt": ["578", 0], "threshold": 0.3, "sam_model": ["280", 0], "grounding_dino_model": ["281", 0], "image": ["405", 0]}, "class_type": "GroundingDinoSAMSegment (segment anything)", "_meta": {"title": "GroundingDinoSAMSegment (segment anything)"}}, "280": {"inputs": {"model_name": "sam_vit_h (2.56GB)"}, "class_type": "SAMModelLoader (segment anything)", "_meta": {"title": "SAMModelLoader (segment anything)"}}, "281": {"inputs": {"model_name": "GroundingDINO_SwinT_OGC (694MB)"}, "class_type": "GroundingDinoModelLoader (segment anything)", "_meta": {"title": "GroundingDinoModelLoader (segment anything)"}}, "293": {"inputs": {"value": 1536}, "class_type": "SimpleMathInt+", "_meta": {"title": "1536 Resolution"}}, "296": {"inputs": {"any_02": ["293", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "356": {"inputs": {"mask": ["206", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "368": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/021e43c9-0966-41ca-9c95-8f86a71b951e.webp", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh trang ph\u1ee5c"}, "is_changed": NaN}, "371": {"inputs": {"any_01": ["279", 1], "any_02": ["405", 1]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "404": {"inputs": {"images": ["487", 0]}, "class_type": "PreviewImage", "_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": {"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
174
FLUX
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"clip_vision_output": ["581", 0]}, "class_type": "StyleModelApply", "_meta": {"title": "Apply Style Model"}}, "193": {"inputs": {"noise_mask": false, "positive": ["192", 0], "negative": ["198", 0], "vae": ["32", 0], "pixels": ["199", 1], "mask": ["199", 2]}, "class_type": "InpaintModelConditioning", "_meta": {"title": "InpaintModelConditioning"}}, "194": {"inputs": {"unet_name": "flux1-fill-dev.safetensors", "weight_dtype": "fp8_e4m3fn"}, "class_type": "UNETLoader", "_meta": {"title": "Load Diffusion Model"}}, "195": {"inputs": {"guidance": 30, "conditioning": ["197", 0]}, "class_type": "FluxGuidance", "_meta": {"title": "FluxGuidance"}}, "196": {"inputs": {"strength": 1, "model": ["582", 0]}, "class_type": "DifferentialDiffusion", "_meta": {"title": "Differential Diffusion"}}, "197": {"inputs": {"text": "32K UHD, ultra-high resolution, extremely sharp, intricate details, masterpiece, realistic, Clothes wrinkle naturally", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "N\u1ebfu \u1ea3nh ra kh\u00f4ng \u0111\u01b0\u1ee3c nh\u01b0 \u00fd => H\u00e3y m\u00f4 t\u1ea3 th\u00eam"}}, "198": {"inputs": {"text": "", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}, "199": {"inputs": {"context_expand_pixels": 10, "context_expand_factor": 1, "fill_mask_holes": true, "blur_mask_pixels": 0, "invert_mask": false, "blend_pixels": 32, "rescale_algorithm": "bicubic", "mode": "ranged size", "force_width": 1024, "force_height": 1024, "rescale_factor": 1.2, "min_width": 512, "min_height": 512, "max_width": 1536, "max_height": 1536, "padding": 32, "image": ["187", 0], "mask": ["224", 0], "optional_context_mask": ["225", 0]}, "class_type": "InpaintCrop", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Crop node)"}}, "203": {"inputs": {"samples": ["234", 0], "vae": ["32", 0]}, "class_type": "VAEDecode", "_meta": {"title": "VAE Decode"}}, "204": {"inputs": {"rescale_algorithm": "bislerp", "stitch": ["199", 0], "inpainted_image": ["203", 0]}, "class_type": "InpaintStitch", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Stitch node)"}}, "206": {"inputs": {"expand": 10, "incremental_expandrate": 0, "tapered_corners": true, "flip_input": false, "blur_radius": 2, "lerp_alpha": 1, "decay_factor": 1, "fill_holes": false, "mask": ["518", 1]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur (\u0111i\u1ec1u ch\u1ec9nh m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "210": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["219", 0], "image2": ["356", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate (gh\u00e9p t\u1ea1o m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "219": {"inputs": {"width": ["504", 1], "height": ["504", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "220": {"inputs": {"width": ["569", 1], "height": ["569", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "221": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["222", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "222": {"inputs": {"mask": ["232", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "223": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["221", 0], "image2": ["220", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate m\u1eb7t n\u1ea1 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "224": {"inputs": {"channel": "red", "image": ["223", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "225": {"inputs": {"channel": "red", "image": ["210", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "232": {"inputs": {"expand": 15, "incremental_expandrate": 0.0, "tapered_corners": false, "flip_input": false, "blur_radius": 4.0, "lerp_alpha": 1.0, "decay_factor": 1.0, "fill_holes": true, "mask": ["371", 0]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur"}}, "234": {"inputs": {"seed": 629966258210641, "steps": 20, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1, "model": ["196", 0], "positive": ["193", 0], "negative": ["193", 1], "latent_image": ["193", 2]}, "class_type": "KSampler", "_meta": {"title": "KSampler"}}, "279": {"inputs": {"prompt": ["578", 0], "threshold": 0.3, "sam_model": ["280", 0], "grounding_dino_model": ["281", 0], "image": ["405", 0]}, "class_type": "GroundingDinoSAMSegment (segment anything)", "_meta": {"title": "GroundingDinoSAMSegment (segment anything)"}}, "280": {"inputs": {"model_name": "sam_vit_h (2.56GB)"}, "class_type": "SAMModelLoader (segment anything)", "_meta": {"title": "SAMModelLoader (segment anything)"}}, "281": {"inputs": {"model_name": "GroundingDINO_SwinT_OGC (694MB)"}, "class_type": "GroundingDinoModelLoader (segment anything)", "_meta": {"title": "GroundingDinoModelLoader (segment anything)"}}, "293": {"inputs": {"value": 1536}, "class_type": "SimpleMathInt+", "_meta": {"title": "1536 Resolution"}}, "296": {"inputs": {"any_02": ["293", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "356": {"inputs": {"mask": ["206", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "368": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/021e43c9-0966-41ca-9c95-8f86a71b951e.webp", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh trang ph\u1ee5c"}, "is_changed": NaN}, "371": {"inputs": {"any_01": ["279", 1], "any_02": ["405", 1]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "404": {"inputs": {"images": ["487", 0]}, "class_type": "PreviewImage", "_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": {"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
9656
FLUX
Create a realistic 3D image of a futuristic and technological rocket horizontally divided into 7 sections, representing a content production flow. The rocket should be purple, with yellow and blue details. Each section of the rocket is designed with geometric and connected shapes, in a technological and futuristic style. The divisions are numbered from 1 to 7, with arrows indicating the flow between the stages. Each section must be separated from each other, like rocket parts separated by 7. At the end of the rocket, there is a circular target with radar lines. In the background, siren space with stars
166
FLUX
Create a professional, clean, and representational diagram illustrating a software architecture process flow. The image will be used in a technical presentation for a .NET developer audience, so it must look highly polished, modern, and corporate rather than overly cartoonish. The view must be from a side-profile perspective, detailing a sequential step-by-step process flow moving strictly from left to right. The diagram visualizes the "Producer-Consumer" design pattern using an industrial assembly line metaphor. ### Visual Elements & Spatial Layout (Left to Right): 1. **The Entry Point (Far Left):** - An elegant, minimalist digital portal or gateway icon representing a Web API Endpoint. - Text label near it reads: "GET /migration" - An arrow points from this endpoint toward the producer robot. 2. **The Producer (Left Center):** - A modern, sleek industrial robotic arm representing the "BackgroundTaskQueue" service. - The robot is actively packaging incoming request data into neat, uniform digital cargo boxes. - Label this entity: "BackgroundTaskQueue (Producer)" 3. **The Buffer (Center):** - A long, horizontal conveyor belt extending from the robot toward the right side of the frame. - On the conveyor belt, multiple identical boxes are placed at equal, perfectly spaced intervals, moving to the right. - These boxes represent the queued tasks. 4. **The Consumer (Far Right):** - A sophisticated automated workstation or processing unit representing the "MigrationBackgroundService". - This service is actively dequeuing (unpacking) the boxes as they arrive at the end of the belt. - Inside or directly above this station, show a dynamic visual indicator of execution—such as gears, a glowing progress ring, or a subtle vortex graphic—to clearly demonstrate that the unpacked requests are "spinning" (actively executing). - Label this entity: "MigrationBackgroundService (Consumer)" ### Aesthetic & Style Guidelines: - **Style:** Flat vector design or clean 3D isometric rendering suitable for enterprise architecture slide decks. - **Color Palette:** Professional corporate tones (e.g., .NET tech colors like deep purples, blues, cool greys, and crisp white backgrounds). - **Clarity:** Sharp contrasts, clean lines, high legibility, and zero visual clutter. Avoid messy abstract backgrounds.
05
Grok Image
Act as a world-class Telugu YouTube scriptwriter, enterprise career strategist, AI transformation advisor, storytelling expert, and content architect. I am creating Video 3 for my YouTube channel "Data Dharma." Channel Mission: Enterprise AI, Data Engineering, Career Transformation, and Future-Proofing IT Careers using powerful storytelling. Target Audience: 1. QA Automation Engineers (Selenium, Cypress, Playwright, Tosca, UFT, API Testing, Automation Frameworks) 2. Manual Testers wanting to move into technical careers 3. Engineering Students 4. Recent Graduates 5. IT professionals worried about AI disruption 6. Professionals wondering whether Data Engineering, AI Engineering, or QA Automation has a better future VIDEO TITLE THEME: "AI Era lo QA Automation Engineers Future Enti? Data Engineer Avvacha?" or "Can QA Automation Engineers Become Data Engineers Before AI Replaces Their Work?" OBJECTIVE: This should not be a boring tutorial. It should feel like a Netflix-style career transformation documentary. The audience should feel: * Fear * Curiosity * Hope * Motivation * Clear Action Plan ================================================== PART 1 – COMPLETE YOUTUBE SCRIPT (TELUGU) ========================================= Create a complete 8–12 minute Telugu script. Requirements: A. FIRST 30 SECONDS (VERY IMPORTANT) The first 30 seconds must stay completely aligned to: * Title * Thumbnail * Core topic No introductions. No welcome messages. No channel promotion. Immediately create curiosity. Example emotions: * AI is writing Selenium scripts. * Copilot is generating test cases. * Automation is becoming easier. * What happens to QA careers? The viewer should feel: "Wait... what happens to my future?" B. OPEN LOOP Build curiosity. Continuously tease: * What is the biggest mistake QA engineers make? * Why are some QA professionals growing while others are stuck? * Why are students making the same mistake? * What career path will survive the AI era? Keep viewers watching until the end. C. CONSEQUENCES SECTION Create a realistic section: "What happens if a QA Automation Engineer does not evolve?" Discuss: * AI-assisted testing * Reduced manual effort * Higher expectations * Need for broader skills Do NOT use fear-mongering. Be realistic and balanced. D. WHY DATA ENGINEERING? Explain: Why Data Engineering is a strong transition path. Connect existing QA skills: * SQL * APIs * Data validation * Python * Automation mindset * CI/CD * Analytical thinking Explain why these skills transfer naturally. E. WHY NOT JUMP DIRECTLY INTO AI ENGINEERING? Give a balanced explanation. Explain: * AI Engineering is exciting * But many professionals skip foundations * Data Engineering builds: * Data skills * Pipelines * Architecture understanding * Enterprise experience Explain why Data Engineering can be a practical bridge toward AI. F. STUDENTS & FRESHERS SECTION Do NOT make this video only for experienced QA engineers. Include a dedicated section for: * Engineering students * Fresh graduates Explain: If they are entering the industry today: * What should they learn? * What mistakes should they avoid? * Should they choose Testing? * Should they choose Data Engineering? * How should they prepare for the next 10 years? G. ROADMAP SECTION Provide: 6-month roadmap 12-month roadmap Skills: * SQL * Python * Data Modeling * Databricks * Spark * Cloud Basics * Data Warehousing Explain in simple Telugu. H. ENDING End with: Hope. Transformation. Future opportunity. Not fear. ================================================== PART 2 – VISUAL STORYBOARD ========================== For every slide provide: Slide Number Slide Title Key Message Narration Summary Suggested Visual Emotion to Create ================================================== PART 3 – GOOGLE NANO BANANA IMAGE PROMPTS ========================================= Create 15 cinematic image prompts. Examples: * Worried QA Engineer looking at AI-generated test scripts * Future enterprise control room * Student standing at career crossroads * Data pipelines flowing through a futuristic city * Engineer transforming into AI-era architect Style: Netflix documentary Cinematic lighting Enterprise technology Modern Emotional YouTube quality 16:9 ================================================== PART 4 – NOTEBOOKLM SLIDES ========================== Identify which slides are best generated using NotebookLM style. Examples: * Roadmaps * Skill comparisons * Career evolution diagrams * Timeline slides Provide exact slide content. ================================================== PART 5 – CHATGPT-GENERATED VISUAL SLIDES ======================================== Identify slides better created using ChatGPT image generation. Examples: * Emotional scenes * Career transformation scenes * AI future scenes * Student journey scenes Provide detailed prompts. ================================================== PART 6 – THUMBNAILS =================== Generate: 20 thumbnail ideas 20 title variations Mix: Fear Curiosity Career Growth AI Impact Data Engineering Opportunity ================================================== PART 7 – RETENTION STRATEGY =========================== Identify: * Hooks * Open loops * Mid-video curiosity points * Pattern interrupts * Emotional moments Explain exactly how to maximize watch time and retention. The final output should feel like a premium YouTube documentary made for Telugu IT professionals and engineering students trying to survive and thrive in the AI era.
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Hero
create a simple conceptual framework where the Input includes INPUT: • Age of Plumbing System • Type of Materials (GI, PVC, etc.) • Installation Quality • Maintenance Practices • Water Usage Level PROCESS: • Visual Inspection • Water Quality Observation (color, odor, clarity) • Flow Rate Measurement • Survey / Interview OUTPUT: • Plumbing Condition (Good / Fair / Poor) • Detected Defects (Leaks, Corrosion, etc.) • Water Quality Status (Acceptable / Not Acceptable) • Safety Assessment
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Hero
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"clip_vision_output": ["581", 0]}, "class_type": "StyleModelApply", "_meta": {"title": "Apply Style Model"}}, "193": {"inputs": {"noise_mask": false, "positive": ["192", 0], "negative": ["198", 0], "vae": ["32", 0], "pixels": ["199", 1], "mask": ["199", 2]}, "class_type": "InpaintModelConditioning", "_meta": {"title": "InpaintModelConditioning"}}, "194": {"inputs": {"unet_name": "flux1-fill-dev.safetensors", "weight_dtype": "fp8_e4m3fn"}, "class_type": "UNETLoader", "_meta": {"title": "Load Diffusion Model"}}, "195": {"inputs": {"guidance": 30, "conditioning": ["197", 0]}, "class_type": "FluxGuidance", "_meta": {"title": "FluxGuidance"}}, "196": {"inputs": {"strength": 1, "model": ["582", 0]}, "class_type": "DifferentialDiffusion", "_meta": {"title": "Differential Diffusion"}}, "197": {"inputs": {"text": "32K UHD, ultra-high resolution, extremely sharp, intricate details, masterpiece, realistic, Clothes wrinkle naturally", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "N\u1ebfu \u1ea3nh ra kh\u00f4ng \u0111\u01b0\u1ee3c nh\u01b0 \u00fd => H\u00e3y m\u00f4 t\u1ea3 th\u00eam"}}, "198": {"inputs": {"text": "", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}, "199": {"inputs": {"context_expand_pixels": 10, "context_expand_factor": 1, "fill_mask_holes": true, "blur_mask_pixels": 0, "invert_mask": false, "blend_pixels": 32, "rescale_algorithm": "bicubic", "mode": "ranged size", "force_width": 1024, "force_height": 1024, "rescale_factor": 1.2, "min_width": 512, "min_height": 512, "max_width": 1536, "max_height": 1536, "padding": 32, "image": ["187", 0], "mask": ["224", 0], "optional_context_mask": ["225", 0]}, "class_type": "InpaintCrop", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Crop node)"}}, "203": {"inputs": {"samples": ["234", 0], "vae": ["32", 0]}, "class_type": "VAEDecode", "_meta": {"title": "VAE Decode"}}, "204": {"inputs": {"rescale_algorithm": "bislerp", "stitch": ["199", 0], "inpainted_image": ["203", 0]}, "class_type": "InpaintStitch", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Stitch node)"}}, "206": {"inputs": {"expand": 10, "incremental_expandrate": 0, "tapered_corners": true, "flip_input": false, "blur_radius": 2, "lerp_alpha": 1, "decay_factor": 1, "fill_holes": false, "mask": ["518", 1]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur (\u0111i\u1ec1u ch\u1ec9nh m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "210": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["219", 0], "image2": ["356", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate (gh\u00e9p t\u1ea1o m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "219": {"inputs": {"width": ["504", 1], "height": ["504", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "220": {"inputs": {"width": ["569", 1], "height": ["569", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "221": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["222", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "222": {"inputs": {"mask": ["232", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "223": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["221", 0], "image2": ["220", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate m\u1eb7t n\u1ea1 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "224": {"inputs": {"channel": "red", "image": ["223", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "225": {"inputs": {"channel": "red", "image": ["210", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "232": {"inputs": {"expand": 15, "incremental_expandrate": 0.0, "tapered_corners": false, "flip_input": false, "blur_radius": 4.0, "lerp_alpha": 1.0, "decay_factor": 1.0, "fill_holes": true, "mask": ["371", 0]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur"}}, "234": {"inputs": {"seed": 629966258210641, "steps": 20, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1, "model": ["196", 0], "positive": ["193", 0], "negative": ["193", 1], "latent_image": ["193", 2]}, "class_type": "KSampler", "_meta": {"title": "KSampler"}}, "279": {"inputs": {"prompt": ["578", 0], "threshold": 0.3, "sam_model": ["280", 0], "grounding_dino_model": ["281", 0], "image": ["405", 0]}, "class_type": "GroundingDinoSAMSegment (segment anything)", "_meta": {"title": "GroundingDinoSAMSegment (segment anything)"}}, "280": {"inputs": {"model_name": "sam_vit_h (2.56GB)"}, "class_type": "SAMModelLoader (segment anything)", "_meta": {"title": "SAMModelLoader (segment anything)"}}, "281": {"inputs": {"model_name": "GroundingDINO_SwinT_OGC (694MB)"}, "class_type": "GroundingDinoModelLoader (segment anything)", "_meta": {"title": "GroundingDinoModelLoader (segment anything)"}}, "293": {"inputs": {"value": 1536}, "class_type": "SimpleMathInt+", "_meta": {"title": "1536 Resolution"}}, "296": {"inputs": {"any_02": ["293", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "356": {"inputs": {"mask": ["206", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "368": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/021e43c9-0966-41ca-9c95-8f86a71b951e.webp", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh trang ph\u1ee5c"}, "is_changed": NaN}, "371": {"inputs": {"any_01": ["279", 1], "any_02": ["405", 1]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "404": {"inputs": {"images": ["487", 0]}, "class_type": "PreviewImage", "_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": {"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
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FLUX
Create a realistic 3D image of a futuristic and technological rocket horizontally divided into 7 sections, representing a content production flow. The rocket should be purple, with yellow and blue details. Each section of the rocket is designed with geometric and connected shapes, in a technological and futuristic style. The divisions are numbered from 1 to 7, with arrows indicating the flow between the stages. Each section must be separated from each other, like rocket parts separated by 7. At the end of the rocket, there is a circular target with radar lines. In the background, siren space with stars
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FLUX
Act as a world-class Telugu YouTube scriptwriter, enterprise career strategist, AI transformation advisor, storytelling expert, and content architect. I am creating Video 3 for my YouTube channel "Data Dharma." Channel Mission: Enterprise AI, Data Engineering, Career Transformation, and Future-Proofing IT Careers using powerful storytelling. Target Audience: 1. QA Automation Engineers (Selenium, Cypress, Playwright, Tosca, UFT, API Testing, Automation Frameworks) 2. Manual Testers wanting to move into technical careers 3. Engineering Students 4. Recent Graduates 5. IT professionals worried about AI disruption 6. Professionals wondering whether Data Engineering, AI Engineering, or QA Automation has a better future VIDEO TITLE THEME: "AI Era lo QA Automation Engineers Future Enti? Data Engineer Avvacha?" or "Can QA Automation Engineers Become Data Engineers Before AI Replaces Their Work?" OBJECTIVE: This should not be a boring tutorial. It should feel like a Netflix-style career transformation documentary. The audience should feel: * Fear * Curiosity * Hope * Motivation * Clear Action Plan ================================================== PART 1 – COMPLETE YOUTUBE SCRIPT (TELUGU) ========================================= Create a complete 8–12 minute Telugu script. Requirements: A. FIRST 30 SECONDS (VERY IMPORTANT) The first 30 seconds must stay completely aligned to: * Title * Thumbnail * Core topic No introductions. No welcome messages. No channel promotion. Immediately create curiosity. Example emotions: * AI is writing Selenium scripts. * Copilot is generating test cases. * Automation is becoming easier. * What happens to QA careers? The viewer should feel: "Wait... what happens to my future?" B. OPEN LOOP Build curiosity. Continuously tease: * What is the biggest mistake QA engineers make? * Why are some QA professionals growing while others are stuck? * Why are students making the same mistake? * What career path will survive the AI era? Keep viewers watching until the end. C. CONSEQUENCES SECTION Create a realistic section: "What happens if a QA Automation Engineer does not evolve?" Discuss: * AI-assisted testing * Reduced manual effort * Higher expectations * Need for broader skills Do NOT use fear-mongering. Be realistic and balanced. D. WHY DATA ENGINEERING? Explain: Why Data Engineering is a strong transition path. Connect existing QA skills: * SQL * APIs * Data validation * Python * Automation mindset * CI/CD * Analytical thinking Explain why these skills transfer naturally. E. WHY NOT JUMP DIRECTLY INTO AI ENGINEERING? Give a balanced explanation. Explain: * AI Engineering is exciting * But many professionals skip foundations * Data Engineering builds: * Data skills * Pipelines * Architecture understanding * Enterprise experience Explain why Data Engineering can be a practical bridge toward AI. F. STUDENTS & FRESHERS SECTION Do NOT make this video only for experienced QA engineers. Include a dedicated section for: * Engineering students * Fresh graduates Explain: If they are entering the industry today: * What should they learn? * What mistakes should they avoid? * Should they choose Testing? * Should they choose Data Engineering? * How should they prepare for the next 10 years? G. ROADMAP SECTION Provide: 6-month roadmap 12-month roadmap Skills: * SQL * Python * Data Modeling * Databricks * Spark * Cloud Basics * Data Warehousing Explain in simple Telugu. H. ENDING End with: Hope. Transformation. Future opportunity. Not fear. ================================================== PART 2 – VISUAL STORYBOARD ========================== For every slide provide: Slide Number Slide Title Key Message Narration Summary Suggested Visual Emotion to Create ================================================== PART 3 – GOOGLE NANO BANANA IMAGE PROMPTS ========================================= Create 15 cinematic image prompts. Examples: * Worried QA Engineer looking at AI-generated test scripts * Future enterprise control room * Student standing at career crossroads * Data pipelines flowing through a futuristic city * Engineer transforming into AI-era architect Style: Netflix documentary Cinematic lighting Enterprise technology Modern Emotional YouTube quality 16:9 ================================================== PART 4 – NOTEBOOKLM SLIDES ========================== Identify which slides are best generated using NotebookLM style. Examples: * Roadmaps * Skill comparisons * Career evolution diagrams * Timeline slides Provide exact slide content. ================================================== PART 5 – CHATGPT-GENERATED VISUAL SLIDES ======================================== Identify slides better created using ChatGPT image generation. Examples: * Emotional scenes * Career transformation scenes * AI future scenes * Student journey scenes Provide detailed prompts. ================================================== PART 6 – THUMBNAILS =================== Generate: 20 thumbnail ideas 20 title variations Mix: Fear Curiosity Career Growth AI Impact Data Engineering Opportunity ================================================== PART 7 – RETENTION STRATEGY =========================== Identify: * Hooks * Open loops * Mid-video curiosity points * Pattern interrupts * Emotional moments Explain exactly how to maximize watch time and retention. The final output should feel like a premium YouTube documentary made for Telugu IT professionals and engineering students trying to survive and thrive in the AI era.
011
Hero
create a simple conceptual framework where the Input includes INPUT: • Age of Plumbing System • Type of Materials (GI, PVC, etc.) • Installation Quality • Maintenance Practices • Water Usage Level PROCESS: • Visual Inspection • Water Quality Observation (color, odor, clarity) • Flow Rate Measurement • Survey / Interview OUTPUT: • Plumbing Condition (Good / Fair / Poor) • Detected Defects (Leaks, Corrosion, etc.) • Water Quality Status (Acceptable / Not Acceptable) • Safety Assessment
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Hero
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{"title": "N\u1ebfu \u1ea3nh ra kh\u00f4ng \u0111\u01b0\u1ee3c nh\u01b0 \u00fd => H\u00e3y m\u00f4 t\u1ea3 th\u00eam"}}, "198": {"inputs": {"text": "", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}, "199": {"inputs": {"context_expand_pixels": 10, "context_expand_factor": 1, "fill_mask_holes": true, "blur_mask_pixels": 0, "invert_mask": false, "blend_pixels": 32, "rescale_algorithm": "bicubic", "mode": "ranged size", "force_width": 1024, "force_height": 1024, "rescale_factor": 1.2, "min_width": 512, "min_height": 512, "max_width": 1536, "max_height": 1536, "padding": 32, "image": ["187", 0], "mask": ["224", 0], "optional_context_mask": ["225", 0]}, "class_type": "InpaintCrop", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Crop node)"}}, "203": {"inputs": {"samples": ["234", 0], "vae": ["32", 0]}, "class_type": "VAEDecode", "_meta": {"title": "VAE Decode"}}, "204": {"inputs": {"rescale_algorithm": "bislerp", "stitch": ["199", 0], "inpainted_image": ["203", 0]}, "class_type": "InpaintStitch", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Stitch node)"}}, "206": {"inputs": {"expand": 10, "incremental_expandrate": 0, "tapered_corners": true, "flip_input": false, "blur_radius": 2, "lerp_alpha": 1, "decay_factor": 1, "fill_holes": false, "mask": ["518", 1]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur (\u0111i\u1ec1u ch\u1ec9nh m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "210": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["219", 0], "image2": ["356", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate (gh\u00e9p t\u1ea1o m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "219": {"inputs": {"width": ["504", 1], "height": ["504", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "220": {"inputs": {"width": ["569", 1], "height": ["569", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "221": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["222", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "222": {"inputs": {"mask": ["232", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "223": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["221", 0], "image2": ["220", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate m\u1eb7t n\u1ea1 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "224": {"inputs": {"channel": "red", "image": ["223", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "225": {"inputs": {"channel": "red", "image": ["210", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "232": {"inputs": {"expand": 15, "incremental_expandrate": 0.0, "tapered_corners": false, "flip_input": false, "blur_radius": 4.0, "lerp_alpha": 1.0, "decay_factor": 1.0, "fill_holes": true, "mask": ["371", 0]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur"}}, "234": {"inputs": {"seed": 629966258210641, "steps": 20, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1, "model": ["196", 0], "positive": ["193", 0], "negative": ["193", 1], "latent_image": ["193", 2]}, "class_type": "KSampler", "_meta": {"title": "KSampler"}}, "279": {"inputs": {"prompt": ["578", 0], "threshold": 0.3, "sam_model": ["280", 0], "grounding_dino_model": ["281", 0], "image": ["405", 0]}, "class_type": "GroundingDinoSAMSegment (segment anything)", "_meta": {"title": "GroundingDinoSAMSegment (segment anything)"}}, "280": {"inputs": {"model_name": "sam_vit_h (2.56GB)"}, "class_type": "SAMModelLoader (segment anything)", "_meta": {"title": "SAMModelLoader (segment anything)"}}, "281": {"inputs": {"model_name": "GroundingDINO_SwinT_OGC (694MB)"}, "class_type": "GroundingDinoModelLoader (segment anything)", "_meta": {"title": "GroundingDinoModelLoader (segment anything)"}}, "293": {"inputs": {"value": 1536}, "class_type": "SimpleMathInt+", "_meta": {"title": "1536 Resolution"}}, "296": {"inputs": {"any_02": ["293", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "356": {"inputs": {"mask": ["206", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "368": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/021e43c9-0966-41ca-9c95-8f86a71b951e.webp", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh trang ph\u1ee5c"}, "is_changed": NaN}, "371": {"inputs": {"any_01": ["279", 1], "any_02": ["405", 1]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "404": {"inputs": {"images": ["487", 0]}, "class_type": "PreviewImage", "_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": {"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
9656
FLUX
Create a professional, clean, and representational diagram illustrating a software architecture process flow. The image will be used in a technical presentation for a .NET developer audience, so it must look highly polished, modern, and corporate rather than overly cartoonish. The view must be from a side-profile perspective, detailing a sequential step-by-step process flow moving strictly from left to right. The diagram visualizes the "Producer-Consumer" design pattern using an industrial assembly line metaphor. ### Visual Elements & Spatial Layout (Left to Right): 1. **The Entry Point (Far Left):** - An elegant, minimalist digital portal or gateway icon representing a Web API Endpoint. - Text label near it reads: "GET /migration" - An arrow points from this endpoint toward the producer robot. 2. **The Producer (Left Center):** - A modern, sleek industrial robotic arm representing the "BackgroundTaskQueue" service. - The robot is actively packaging incoming request data into neat, uniform digital cargo boxes. - Label this entity: "BackgroundTaskQueue (Producer)" 3. **The Buffer (Center):** - A long, horizontal conveyor belt extending from the robot toward the right side of the frame. - On the conveyor belt, multiple identical boxes are placed at equal, perfectly spaced intervals, moving to the right. - These boxes represent the queued tasks. 4. **The Consumer (Far Right):** - A sophisticated automated workstation or processing unit representing the "MigrationBackgroundService". - This service is actively dequeuing (unpacking) the boxes as they arrive at the end of the belt. - Inside or directly above this station, show a dynamic visual indicator of execution—such as gears, a glowing progress ring, or a subtle vortex graphic—to clearly demonstrate that the unpacked requests are "spinning" (actively executing). - Label this entity: "MigrationBackgroundService (Consumer)" ### Aesthetic & Style Guidelines: - **Style:** Flat vector design or clean 3D isometric rendering suitable for enterprise architecture slide decks. - **Color Palette:** Professional corporate tones (e.g., .NET tech colors like deep purples, blues, cool greys, and crisp white backgrounds). - **Clarity:** Sharp contrasts, clean lines, high legibility, and zero visual clutter. Avoid messy abstract backgrounds.
05
Grok Image
{"32": {"inputs": {"vae_name": "ae.safetensors"}, "class_type": "VAELoader", "_meta": {"title": "Load VAE"}}, "34": {"inputs": {"clip_name1": "ViT-L-14-BEST-smooth-GmP-TE-only-HF-format.safetensors", "clip_name2": "t5xxl_fp16.safetensors", "type": "flux", "device": "default"}, "class_type": "DualCLIPLoader", "_meta": {"title": "DualCLIPLoader"}}, "187": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["569", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate - Gh\u00e9p \u1ea3nh tham chi\u1ebfu"}}, "189": {"inputs": {"clip_name": "sigclip_vision_patch14_384.safetensors"}, "class_type": "CLIPVisionLoader", "_meta": {"title": "Load CLIP Vision"}}, "190": {"inputs": {"style_model_name": "flux1-redux-dev.safetensors"}, "class_type": "StyleModelLoader", "_meta": {"title": "Load Style Model"}}, "192": {"inputs": {"strength": 1, "strength_type": "multiply", "conditioning": ["195", 0], "style_model": ["190", 0], "clip_vision_output": ["581", 0]}, "class_type": "StyleModelApply", "_meta": {"title": "Apply Style Model"}}, "193": {"inputs": {"noise_mask": false, "positive": ["192", 0], "negative": ["198", 0], "vae": ["32", 0], "pixels": ["199", 1], "mask": ["199", 2]}, "class_type": "InpaintModelConditioning", "_meta": {"title": "InpaintModelConditioning"}}, "194": {"inputs": {"unet_name": "flux1-fill-dev.safetensors", "weight_dtype": "fp8_e4m3fn"}, "class_type": "UNETLoader", "_meta": {"title": "Load Diffusion Model"}}, "195": {"inputs": {"guidance": 30, "conditioning": ["197", 0]}, "class_type": "FluxGuidance", "_meta": {"title": "FluxGuidance"}}, "196": {"inputs": {"strength": 1, "model": ["582", 0]}, "class_type": "DifferentialDiffusion", "_meta": {"title": "Differential Diffusion"}}, "197": {"inputs": {"text": "32K UHD, ultra-high resolution, extremely sharp, intricate details, masterpiece, realistic, Clothes wrinkle naturally", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "N\u1ebfu \u1ea3nh ra kh\u00f4ng \u0111\u01b0\u1ee3c nh\u01b0 \u00fd => H\u00e3y m\u00f4 t\u1ea3 th\u00eam"}}, "198": {"inputs": {"text": "", "clip": ["34", 0]}, "class_type": "CLIPTextEncode", "_meta": {"title": "CLIP Text Encode (Prompt)"}}, "199": {"inputs": {"context_expand_pixels": 10, "context_expand_factor": 1, "fill_mask_holes": true, "blur_mask_pixels": 0, "invert_mask": false, "blend_pixels": 32, "rescale_algorithm": "bicubic", "mode": "ranged size", "force_width": 1024, "force_height": 1024, "rescale_factor": 1.2, "min_width": 512, "min_height": 512, "max_width": 1536, "max_height": 1536, "padding": 32, "image": ["187", 0], "mask": ["224", 0], "optional_context_mask": ["225", 0]}, "class_type": "InpaintCrop", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Crop node)"}}, "203": {"inputs": {"samples": ["234", 0], "vae": ["32", 0]}, "class_type": "VAEDecode", "_meta": {"title": "VAE Decode"}}, "204": {"inputs": {"rescale_algorithm": "bislerp", "stitch": ["199", 0], "inpainted_image": ["203", 0]}, "class_type": "InpaintStitch", "_meta": {"title": "(OLD \ud83d\udc80, use the new \u2702\ufe0f Inpaint Stitch node)"}}, "206": {"inputs": {"expand": 10, "incremental_expandrate": 0, "tapered_corners": true, "flip_input": false, "blur_radius": 2, "lerp_alpha": 1, "decay_factor": 1, "fill_holes": false, "mask": ["518", 1]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur (\u0111i\u1ec1u ch\u1ec9nh m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "210": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["219", 0], "image2": ["356", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate (gh\u00e9p t\u1ea1o m\u1eb7t n\u1ea1 trang ph\u1ee5c)"}}, "219": {"inputs": {"width": ["504", 1], "height": ["504", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "220": {"inputs": {"width": ["569", 1], "height": ["569", 2], "batch_size": 1, "color": 0}, "class_type": "EmptyImage", "_meta": {"title": "EmptyImage"}}, "221": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["222", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "222": {"inputs": {"mask": ["232", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "223": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["221", 0], "image2": ["220", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate m\u1eb7t n\u1ea1 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "224": {"inputs": {"channel": "red", "image": ["223", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "225": {"inputs": {"channel": "red", "image": ["210", 0]}, "class_type": "ImageToMask", "_meta": {"title": "Convert Image to Mask"}}, "232": {"inputs": {"expand": 15, "incremental_expandrate": 0.0, "tapered_corners": false, "flip_input": false, "blur_radius": 4.0, "lerp_alpha": 1.0, "decay_factor": 1.0, "fill_holes": true, "mask": ["371", 0]}, "class_type": "GrowMaskWithBlur", "_meta": {"title": "Grow Mask With Blur"}}, "234": {"inputs": {"seed": 629966258210641, "steps": 20, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1, "model": ["196", 0], "positive": ["193", 0], "negative": ["193", 1], "latent_image": ["193", 2]}, "class_type": "KSampler", "_meta": {"title": "KSampler"}}, "279": {"inputs": {"prompt": ["578", 0], "threshold": 0.3, "sam_model": ["280", 0], "grounding_dino_model": ["281", 0], "image": ["405", 0]}, "class_type": "GroundingDinoSAMSegment (segment anything)", "_meta": {"title": "GroundingDinoSAMSegment (segment anything)"}}, "280": {"inputs": {"model_name": "sam_vit_h (2.56GB)"}, "class_type": "SAMModelLoader (segment anything)", "_meta": {"title": "SAMModelLoader (segment anything)"}}, "281": {"inputs": {"model_name": "GroundingDINO_SwinT_OGC (694MB)"}, "class_type": "GroundingDinoModelLoader (segment anything)", "_meta": {"title": "GroundingDinoModelLoader (segment anything)"}}, "293": {"inputs": {"value": 1536}, "class_type": "SimpleMathInt+", "_meta": {"title": "1536 Resolution"}}, "296": {"inputs": {"any_02": ["293", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "356": {"inputs": {"mask": ["206", 0]}, "class_type": "MaskToImage", "_meta": {"title": "Convert Mask to Image"}}, "368": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/021e43c9-0966-41ca-9c95-8f86a71b951e.webp", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh trang ph\u1ee5c"}, "is_changed": NaN}, "371": {"inputs": {"any_01": ["279", 1], "any_02": ["405", 1]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "404": {"inputs": {"images": ["487", 0]}, "class_type": "PreviewImage", "_meta": {"title": "Xem tr\u01b0\u1edbc m\u1eb7t n\u1ea1 t\u00e1ch \u0111\u1ed3 tr\u00ean ng\u01b0\u1eddi m\u1eabu"}}, "405": {"inputs": {"image": "https://s3.prod.nordy.ai/media/raw/622c097e-e328-4291-b194-111942a0b5b1.png", "choose file": "image", "File Direct Upload": "image"}, "class_type": "LoadImage", "_meta": {"title": "T\u1ea3i \u1ea3nh ng\u01b0\u1eddi m\u1eabu"}, "is_changed": NaN}, "487": {"inputs": {"direction": "left", "match_image_size": true, "image1": ["504", 0], "image2": ["221", 0]}, "class_type": "ImageConcanate", "_meta": {"title": "Image Concatenate"}}, "504": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["405", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "518": {"inputs": {"torchscript_jit": "default", "image": ["570", 0]}, "class_type": "InspyrenetRembg", "_meta": {"title": "Inspyrenet Rembg"}}, "534": {"inputs": {"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
174
FLUX
Create a professional, clean, and representational diagram illustrating a software architecture process flow. The image will be used in a technical presentation for a .NET developer audience, so it must look highly polished, modern, and corporate rather than overly cartoonish. The view must be from a side-profile perspective, detailing a sequential step-by-step process flow moving strictly from left to right. The diagram visualizes the "Producer-Consumer" design pattern using an industrial assembly line metaphor. ### Visual Elements & Spatial Layout (Left to Right): 1. **The Entry Point (Far Left):** - An elegant, minimalist digital portal or gateway icon representing a Web API Endpoint. - Text label near it reads: "GET /migration" - An arrow points from this endpoint toward the producer robot. 2. **The Producer (Left Center):** - A modern, sleek industrial robotic arm representing the "BackgroundTaskQueue" service. - The robot is actively packaging incoming request data into neat, uniform digital cargo boxes. - Label this entity: "BackgroundTaskQueue (Producer)" 3. **The Buffer (Center):** - A long, horizontal conveyor belt extending from the robot toward the right side of the frame. - On the conveyor belt, multiple identical boxes are placed at equal, perfectly spaced intervals, moving to the right. - These boxes represent the queued tasks. 4. **The Consumer (Far Right):** - A sophisticated automated workstation or processing unit representing the "MigrationBackgroundService". - This service is actively dequeuing (unpacking) the boxes as they arrive at the end of the belt. - Inside or directly above this station, show a dynamic visual indicator of execution—such as gears, a glowing progress ring, or a subtle vortex graphic—to clearly demonstrate that the unpacked requests are "spinning" (actively executing). - Label this entity: "MigrationBackgroundService (Consumer)" ### Aesthetic & Style Guidelines: - **Style:** Flat vector design or clean 3D isometric rendering suitable for enterprise architecture slide decks. - **Color Palette:** Professional corporate tones (e.g., .NET tech colors like deep purples, blues, cool greys, and crisp white backgrounds). - **Clarity:** Sharp contrasts, clean lines, high legibility, and zero visual clutter. Avoid messy abstract backgrounds.
05
Grok Image
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"image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
9656
FLUX
Create a realistic 3D image of a futuristic and technological rocket horizontally divided into 7 sections, representing a content production flow. The rocket should be purple, with yellow and blue details. Each section of the rocket is designed with geometric and connected shapes, in a technological and futuristic style. The divisions are numbered from 1 to 7, with arrows indicating the flow between the stages. Each section must be separated from each other, like rocket parts separated by 7. At the end of the rocket, there is a circular target with radar lines. In the background, siren space with stars
166
FLUX
Act as a world-class Telugu YouTube scriptwriter, enterprise career strategist, AI transformation advisor, storytelling expert, and content architect. I am creating Video 3 for my YouTube channel "Data Dharma." Channel Mission: Enterprise AI, Data Engineering, Career Transformation, and Future-Proofing IT Careers using powerful storytelling. Target Audience: 1. QA Automation Engineers (Selenium, Cypress, Playwright, Tosca, UFT, API Testing, Automation Frameworks) 2. Manual Testers wanting to move into technical careers 3. Engineering Students 4. Recent Graduates 5. IT professionals worried about AI disruption 6. Professionals wondering whether Data Engineering, AI Engineering, or QA Automation has a better future VIDEO TITLE THEME: "AI Era lo QA Automation Engineers Future Enti? Data Engineer Avvacha?" or "Can QA Automation Engineers Become Data Engineers Before AI Replaces Their Work?" OBJECTIVE: This should not be a boring tutorial. It should feel like a Netflix-style career transformation documentary. The audience should feel: * Fear * Curiosity * Hope * Motivation * Clear Action Plan ================================================== PART 1 – COMPLETE YOUTUBE SCRIPT (TELUGU) ========================================= Create a complete 8–12 minute Telugu script. Requirements: A. FIRST 30 SECONDS (VERY IMPORTANT) The first 30 seconds must stay completely aligned to: * Title * Thumbnail * Core topic No introductions. No welcome messages. No channel promotion. Immediately create curiosity. Example emotions: * AI is writing Selenium scripts. * Copilot is generating test cases. * Automation is becoming easier. * What happens to QA careers? The viewer should feel: "Wait... what happens to my future?" B. OPEN LOOP Build curiosity. Continuously tease: * What is the biggest mistake QA engineers make? * Why are some QA professionals growing while others are stuck? * Why are students making the same mistake? * What career path will survive the AI era? Keep viewers watching until the end. C. CONSEQUENCES SECTION Create a realistic section: "What happens if a QA Automation Engineer does not evolve?" Discuss: * AI-assisted testing * Reduced manual effort * Higher expectations * Need for broader skills Do NOT use fear-mongering. Be realistic and balanced. D. WHY DATA ENGINEERING? Explain: Why Data Engineering is a strong transition path. Connect existing QA skills: * SQL * APIs * Data validation * Python * Automation mindset * CI/CD * Analytical thinking Explain why these skills transfer naturally. E. WHY NOT JUMP DIRECTLY INTO AI ENGINEERING? Give a balanced explanation. Explain: * AI Engineering is exciting * But many professionals skip foundations * Data Engineering builds: * Data skills * Pipelines * Architecture understanding * Enterprise experience Explain why Data Engineering can be a practical bridge toward AI. F. STUDENTS & FRESHERS SECTION Do NOT make this video only for experienced QA engineers. Include a dedicated section for: * Engineering students * Fresh graduates Explain: If they are entering the industry today: * What should they learn? * What mistakes should they avoid? * Should they choose Testing? * Should they choose Data Engineering? * How should they prepare for the next 10 years? G. ROADMAP SECTION Provide: 6-month roadmap 12-month roadmap Skills: * SQL * Python * Data Modeling * Databricks * Spark * Cloud Basics * Data Warehousing Explain in simple Telugu. H. ENDING End with: Hope. Transformation. Future opportunity. Not fear. ================================================== PART 2 – VISUAL STORYBOARD ========================== For every slide provide: Slide Number Slide Title Key Message Narration Summary Suggested Visual Emotion to Create ================================================== PART 3 – GOOGLE NANO BANANA IMAGE PROMPTS ========================================= Create 15 cinematic image prompts. Examples: * Worried QA Engineer looking at AI-generated test scripts * Future enterprise control room * Student standing at career crossroads * Data pipelines flowing through a futuristic city * Engineer transforming into AI-era architect Style: Netflix documentary Cinematic lighting Enterprise technology Modern Emotional YouTube quality 16:9 ================================================== PART 4 – NOTEBOOKLM SLIDES ========================== Identify which slides are best generated using NotebookLM style. Examples: * Roadmaps * Skill comparisons * Career evolution diagrams * Timeline slides Provide exact slide content. ================================================== PART 5 – CHATGPT-GENERATED VISUAL SLIDES ======================================== Identify slides better created using ChatGPT image generation. Examples: * Emotional scenes * Career transformation scenes * AI future scenes * Student journey scenes Provide detailed prompts. ================================================== PART 6 – THUMBNAILS =================== Generate: 20 thumbnail ideas 20 title variations Mix: Fear Curiosity Career Growth AI Impact Data Engineering Opportunity ================================================== PART 7 – RETENTION STRATEGY =========================== Identify: * Hooks * Open loops * Mid-video curiosity points * Pattern interrupts * Emotional moments Explain exactly how to maximize watch time and retention. The final output should feel like a premium YouTube documentary made for Telugu IT professionals and engineering students trying to survive and thrive in the AI era.
011
Hero
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{"width": ["504", 1], "height": ["504", 2], "position": "top-right", "x_offset": 0, "y_offset": 0, "image": ["204", 0]}, "class_type": "ImageCrop+", "_meta": {"title": "\ud83d\udd27 Image Crop"}}, "539": {"inputs": {"any_01": ["534", 0], "any_02": ["534", 0]}, "class_type": "Any Switch (rgthree)", "_meta": {"title": "Any Switch (rgthree)"}}, "559": {"inputs": {"filename_prefix": "ComfyUI", "images": ["539", 0]}, "class_type": "SaveImage", "_meta": {"title": "Save Image"}}, "560": {"inputs": {"seed": 1083186878674920}, "class_type": "Seed Everywhere", "_meta": {"title": "Seed Everywhere"}}, "569": {"inputs": {"width": 0, "height": ["504", 2], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "570": {"inputs": {"width": 0, "height": ["296", 0], "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0, "image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
174
FLUX
create a simple conceptual framework where the Input includes INPUT: • Age of Plumbing System • Type of Materials (GI, PVC, etc.) • Installation Quality • Maintenance Practices • Water Usage Level PROCESS: • Visual Inspection • Water Quality Observation (color, odor, clarity) • Flow Rate Measurement • Survey / Interview OUTPUT: • Plumbing Condition (Good / Fair / Poor) • Detected Defects (Leaks, Corrosion, etc.) • Water Quality Status (Acceptable / Not Acceptable) • Safety Assessment
07
Hero
Create a realistic 3D image of a futuristic and technological rocket horizontally divided into 7 sections, representing a content production flow. The rocket should be purple, with yellow and blue details. Each section of the rocket is designed with geometric and connected shapes, in a technological and futuristic style. The divisions are numbered from 1 to 7, with arrows indicating the flow between the stages. Each section must be separated from each other, like rocket parts separated by 7. At the end of the rocket, there is a circular target with radar lines. In the background, siren space with stars
166
FLUX
Create a professional, clean, and representational diagram illustrating a software architecture process flow. The image will be used in a technical presentation for a .NET developer audience, so it must look highly polished, modern, and corporate rather than overly cartoonish. The view must be from a side-profile perspective, detailing a sequential step-by-step process flow moving strictly from left to right. The diagram visualizes the "Producer-Consumer" design pattern using an industrial assembly line metaphor. ### Visual Elements & Spatial Layout (Left to Right): 1. **The Entry Point (Far Left):** - An elegant, minimalist digital portal or gateway icon representing a Web API Endpoint. - Text label near it reads: "GET /migration" - An arrow points from this endpoint toward the producer robot. 2. **The Producer (Left Center):** - A modern, sleek industrial robotic arm representing the "BackgroundTaskQueue" service. - The robot is actively packaging incoming request data into neat, uniform digital cargo boxes. - Label this entity: "BackgroundTaskQueue (Producer)" 3. **The Buffer (Center):** - A long, horizontal conveyor belt extending from the robot toward the right side of the frame. - On the conveyor belt, multiple identical boxes are placed at equal, perfectly spaced intervals, moving to the right. - These boxes represent the queued tasks. 4. **The Consumer (Far Right):** - A sophisticated automated workstation or processing unit representing the "MigrationBackgroundService". - This service is actively dequeuing (unpacking) the boxes as they arrive at the end of the belt. - Inside or directly above this station, show a dynamic visual indicator of execution—such as gears, a glowing progress ring, or a subtle vortex graphic—to clearly demonstrate that the unpacked requests are "spinning" (actively executing). - Label this entity: "MigrationBackgroundService (Consumer)" ### Aesthetic & Style Guidelines: - **Style:** Flat vector design or clean 3D isometric rendering suitable for enterprise architecture slide decks. - **Color Palette:** Professional corporate tones (e.g., .NET tech colors like deep purples, blues, cool greys, and crisp white backgrounds). - **Clarity:** Sharp contrasts, clean lines, high legibility, and zero visual clutter. Avoid messy abstract backgrounds.
05
Grok Image
create a simple conceptual framework where the Input includes INPUT: • Age of Plumbing System • Type of Materials (GI, PVC, etc.) • Installation Quality • Maintenance Practices • Water Usage Level PROCESS: • Visual Inspection • Water Quality Observation (color, odor, clarity) • Flow Rate Measurement • Survey / Interview OUTPUT: • Plumbing Condition (Good / Fair / Poor) • Detected Defects (Leaks, Corrosion, etc.) • Water Quality Status (Acceptable / Not Acceptable) • Safety Assessment
07
Hero
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"image": ["368", 0]}, "class_type": "ImageResize+", "_meta": {"title": "\ud83d\udd27 Image Resize"}}, "577": {"inputs": {"upscale_method": "lanczos", "width": 1216, "height": 0, "crop": "disabled", "image": ["368", 0]}, "class_type": "ImageScale", "_meta": {"title": "Upscale Image"}}, "578": {"inputs": {"text": "Bikini"}, "class_type": "ttN text", "_meta": {"title": "text"}}, "580": {"inputs": {"lora_name": "Migration_Lora_cloth.safetensors", "strength_model": 0, "model": ["194", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}, "581": {"inputs": {"crop": "center", "clip_vision": ["189", 0], "image": ["577", 0]}, "class_type": "CLIPVisionEncode", "_meta": {"title": "CLIP Vision Encode"}}, "582": {"inputs": {"lora_name": "comfyui_subject_lora16.safetensors", "strength_model": 1, "model": ["580", 0]}, "class_type": "LoraLoaderModelOnly", "_meta": {"title": "LoraLoaderModelOnly"}}}
174
FLUX
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FLUX
Act as a world-class Telugu YouTube scriptwriter, enterprise career strategist, AI transformation advisor, storytelling expert, and content architect. I am creating Video 3 for my YouTube channel "Data Dharma." Channel Mission: Enterprise AI, Data Engineering, Career Transformation, and Future-Proofing IT Careers using powerful storytelling. Target Audience: 1. QA Automation Engineers (Selenium, Cypress, Playwright, Tosca, UFT, API Testing, Automation Frameworks) 2. Manual Testers wanting to move into technical careers 3. Engineering Students 4. Recent Graduates 5. IT professionals worried about AI disruption 6. Professionals wondering whether Data Engineering, AI Engineering, or QA Automation has a better future VIDEO TITLE THEME: "AI Era lo QA Automation Engineers Future Enti? Data Engineer Avvacha?" or "Can QA Automation Engineers Become Data Engineers Before AI Replaces Their Work?" OBJECTIVE: This should not be a boring tutorial. It should feel like a Netflix-style career transformation documentary. The audience should feel: * Fear * Curiosity * Hope * Motivation * Clear Action Plan ================================================== PART 1 – COMPLETE YOUTUBE SCRIPT (TELUGU) ========================================= Create a complete 8–12 minute Telugu script. Requirements: A. FIRST 30 SECONDS (VERY IMPORTANT) The first 30 seconds must stay completely aligned to: * Title * Thumbnail * Core topic No introductions. No welcome messages. No channel promotion. Immediately create curiosity. Example emotions: * AI is writing Selenium scripts. * Copilot is generating test cases. * Automation is becoming easier. * What happens to QA careers? The viewer should feel: "Wait... what happens to my future?" B. OPEN LOOP Build curiosity. Continuously tease: * What is the biggest mistake QA engineers make? * Why are some QA professionals growing while others are stuck? * Why are students making the same mistake? * What career path will survive the AI era? Keep viewers watching until the end. C. CONSEQUENCES SECTION Create a realistic section: "What happens if a QA Automation Engineer does not evolve?" Discuss: * AI-assisted testing * Reduced manual effort * Higher expectations * Need for broader skills Do NOT use fear-mongering. Be realistic and balanced. D. WHY DATA ENGINEERING? Explain: Why Data Engineering is a strong transition path. Connect existing QA skills: * SQL * APIs * Data validation * Python * Automation mindset * CI/CD * Analytical thinking Explain why these skills transfer naturally. E. WHY NOT JUMP DIRECTLY INTO AI ENGINEERING? Give a balanced explanation. Explain: * AI Engineering is exciting * But many professionals skip foundations * Data Engineering builds: * Data skills * Pipelines * Architecture understanding * Enterprise experience Explain why Data Engineering can be a practical bridge toward AI. F. STUDENTS & FRESHERS SECTION Do NOT make this video only for experienced QA engineers. Include a dedicated section for: * Engineering students * Fresh graduates Explain: If they are entering the industry today: * What should they learn? * What mistakes should they avoid? * Should they choose Testing? * Should they choose Data Engineering? * How should they prepare for the next 10 years? G. ROADMAP SECTION Provide: 6-month roadmap 12-month roadmap Skills: * SQL * Python * Data Modeling * Databricks * Spark * Cloud Basics * Data Warehousing Explain in simple Telugu. H. ENDING End with: Hope. Transformation. Future opportunity. Not fear. ================================================== PART 2 – VISUAL STORYBOARD ========================== For every slide provide: Slide Number Slide Title Key Message Narration Summary Suggested Visual Emotion to Create ================================================== PART 3 – GOOGLE NANO BANANA IMAGE PROMPTS ========================================= Create 15 cinematic image prompts. Examples: * Worried QA Engineer looking at AI-generated test scripts * Future enterprise control room * Student standing at career crossroads * Data pipelines flowing through a futuristic city * Engineer transforming into AI-era architect Style: Netflix documentary Cinematic lighting Enterprise technology Modern Emotional YouTube quality 16:9 ================================================== PART 4 – NOTEBOOKLM SLIDES ========================== Identify which slides are best generated using NotebookLM style. Examples: * Roadmaps * Skill comparisons * Career evolution diagrams * Timeline slides Provide exact slide content. ================================================== PART 5 – CHATGPT-GENERATED VISUAL SLIDES ======================================== Identify slides better created using ChatGPT image generation. Examples: * Emotional scenes * Career transformation scenes * AI future scenes * Student journey scenes Provide detailed prompts. ================================================== PART 6 – THUMBNAILS =================== Generate: 20 thumbnail ideas 20 title variations Mix: Fear Curiosity Career Growth AI Impact Data Engineering Opportunity ================================================== PART 7 – RETENTION STRATEGY =========================== Identify: * Hooks * Open loops * Mid-video curiosity points * Pattern interrupts * Emotional moments Explain exactly how to maximize watch time and retention. The final output should feel like a premium YouTube documentary made for Telugu IT professionals and engineering students trying to survive and thrive in the AI era.
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