### **Image Generation Prompt for "Memory Me Objects Ka Behavior Kya Hoga?"** **Prompt:** Create a detailed and visually intuitive diagram to explain the behavior of objects in memory during a `while` loop that iterates over a database `ResultSet`. The diagram should include the following elements: 1. **Heap Memory Section:** - Show how new `User` objects are created in the heap memory during each iteration of the loop. - Highlight that each object corresponds to a row from the `ResultSet`. 2. **Garbage Collection:** - Illustrate how objects lose their reference after the loop ends and become eligible for garbage collection. - Use an arrow or icon to represent the Java Garbage Collector cleaning up unused objects. 3. **Permanent Storage (Optional):** - Show a scenario where objects are stored in a `List<User>` for permanent use. - Highlight the difference between temporary objects (eligible for garbage collection) and permanently stored objects. 4. **Flow of Execution:** - Include a flowchart-like representation of the `while` loop: - Start with `rs.next()` moving the cursor to the next row. - Show the creation of a new `User` object for each row. - End with either garbage collection or storage in a `List`. 5. **Annotations:** - Add labels and arrows to explain each step clearly. - Use Hindi/Hinglish annotations like: - "Har row ke liye naya object banega." - "Object ka reference lost ho jayega, to Garbage Collector clean kar dega." - "Agar List mei store kiya, to object permanent rehta hai." 6. **Color Coding:** - Use different colors for: - Heap memory (e.g., light blue). - Garbage-collected objects (e.g., grayed out). - Permanently stored objects (e.g., green). 7. **Database Table Example:** - Include a small table representation (e.g., `users` table with columns `id` and `name`) to show the source of data. --- ### **Expected Output:** The image should look like this: 1. **Top Section:** - A small database table (`users`) with rows and columns. 2. **Middle Section:** - A heap memory area showing multiple `User` objects being created during each iteration of the loop. - Arrows pointing from the `ResultSet` rows to the corresponding `User` objects in heap memory. 3. **Bottom Section:** - Two paths: - Path 1: Objects losing reference and being garbage collected (grayed out). - Path 2: Objects being stored in a `List<User>` for permanent use (highlighted in green). 4. **Annotations:** - Clear Hindi/Hinglish explanations for each step. --- This prompt will help generate a visually rich and easy-to-understand diagram for explaining the behavior of objects in memory! 😊
1133
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.
015
Hero
Description You want support me for Updates / New Lora? Tested and trained on UberRealisticPornMerge12 - Other Models not tested Update 0.2: For same Pose use the Triggerword: clothcuncloth_samepose Works fine with 0.5 ~ 1.0 Check Sample Image 3-7 Prompt Example <lora:clothuncloth:1>, clotuncloth, 21yo blond woman, left wearing suit and stockings, right naked, (high detailed face) Prompt Explain <lora:clothuncloth:1> - Load Lora clotuncloth - triggerword 21yo blond woman - Explain your "Main Person" left wearing suit and stockings - Explain your left Image (Works on dressed Person) right naked - (Explain your right Image) (high detailed face) - (Low Res Face fix) For not UberRealisticPornMerge you can use under "Explain your "main Person" this prompt: 21yo blond identical woman If you has Ugly Face turn strengh down - 0.5~1 works well This is a early Version, its hard to train two identical person in the same Image.
014
Hero
As well as explaining where man came from and how we came to create civilisation, the Greeks also used their myths to explain the origins of natural phenomena, such as the seasons. Why do we have summer and winter? For the ancient Greeks, it was thanks to Persephone, the daughter of Zeus and Demeter. Persephone was abducted by Hades, god of the Underworld, and taken away with him; because she was connected to vegetation, Persephone’s absence from the land led to the failure of crops, and everyone began to starve. Hades was told by Zeus to return Persephone to Demeter above-ground, but (thanks to Hades’ trick which involved, effectively, drugging Persephone with some pomegranate seeds), eventually a compromise was reached, whereby Persephone would spend the winter months in Hades and the rest of the year with Demeter. And this explains the origins of the seasons.
0130
DreamShaper
Create a video from the subject image with the person and landscape remaining completely unchanged in Disney•Pixar-inspired cinematic 3D animation - A caring female teacher without glasses helping students, explaining Chinese lessons on the blackboard, a female teacher with glasses explaining English lessons on the blackboard, and a male teacher with glasses explaining Math lessons on the blackboard , classroom filled with warm sunlight, female Chinese lessons teacher marking homework after school. Teacher smiles while teaching. Close-up of homework being marked. Narration Prompt 中文 consistent voice of a 13-year-old girl - sync and display the texts of the narrative at the bottom of the screen：今天，我想代表所有同学，说三声“谢谢”： 第一声，谢谢我们的老师。 是你们把枯燥的拼音变成了有趣的游戏，把难懂的公式变成了生活的魔法。你们批改作业到很晚的背影，是我们心中最温暖的图画。 Music Warm piano Soft strings
012
Grok Imagine Video
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.
015
Hero
Description You want support me for Updates / New Lora? Tested and trained on UberRealisticPornMerge12 - Other Models not tested Update 0.2: For same Pose use the Triggerword: clothcuncloth_samepose Works fine with 0.5 ~ 1.0 Check Sample Image 3-7 Prompt Example <lora:clothuncloth:1>, clotuncloth, 21yo blond woman, left wearing suit and stockings, right naked, (high detailed face) Prompt Explain <lora:clothuncloth:1> - Load Lora clotuncloth - triggerword 21yo blond woman - Explain your "Main Person" left wearing suit and stockings - Explain your left Image (Works on dressed Person) right naked - (Explain your right Image) (high detailed face) - (Low Res Face fix) For not UberRealisticPornMerge you can use under "Explain your "main Person" this prompt: 21yo blond identical woman If you has Ugly Face turn strengh down - 0.5~1 works well This is a early Version, its hard to train two identical person in the same Image.
014
Hero
### **Image Generation Prompt for "Memory Me Objects Ka Behavior Kya Hoga?"** **Prompt:** Create a detailed and visually intuitive diagram to explain the behavior of objects in memory during a `while` loop that iterates over a database `ResultSet`. The diagram should include the following elements: 1. **Heap Memory Section:** - Show how new `User` objects are created in the heap memory during each iteration of the loop. - Highlight that each object corresponds to a row from the `ResultSet`. 2. **Garbage Collection:** - Illustrate how objects lose their reference after the loop ends and become eligible for garbage collection. - Use an arrow or icon to represent the Java Garbage Collector cleaning up unused objects. 3. **Permanent Storage (Optional):** - Show a scenario where objects are stored in a `List<User>` for permanent use. - Highlight the difference between temporary objects (eligible for garbage collection) and permanently stored objects. 4. **Flow of Execution:** - Include a flowchart-like representation of the `while` loop: - Start with `rs.next()` moving the cursor to the next row. - Show the creation of a new `User` object for each row. - End with either garbage collection or storage in a `List`. 5. **Annotations:** - Add labels and arrows to explain each step clearly. - Use Hindi/Hinglish annotations like: - "Har row ke liye naya object banega." - "Object ka reference lost ho jayega, to Garbage Collector clean kar dega." - "Agar List mei store kiya, to object permanent rehta hai." 6. **Color Coding:** - Use different colors for: - Heap memory (e.g., light blue). - Garbage-collected objects (e.g., grayed out). - Permanently stored objects (e.g., green). 7. **Database Table Example:** - Include a small table representation (e.g., `users` table with columns `id` and `name`) to show the source of data. --- ### **Expected Output:** The image should look like this: 1. **Top Section:** - A small database table (`users`) with rows and columns. 2. **Middle Section:** - A heap memory area showing multiple `User` objects being created during each iteration of the loop. - Arrows pointing from the `ResultSet` rows to the corresponding `User` objects in heap memory. 3. **Bottom Section:** - Two paths: - Path 1: Objects losing reference and being garbage collected (grayed out). - Path 2: Objects being stored in a `List<User>` for permanent use (highlighted in green). 4. **Annotations:** - Clear Hindi/Hinglish explanations for each step. --- This prompt will help generate a visually rich and easy-to-understand diagram for explaining the behavior of objects in memory! 😊
1133
FLUX
As well as explaining where man came from and how we came to create civilisation, the Greeks also used their myths to explain the origins of natural phenomena, such as the seasons. Why do we have summer and winter? For the ancient Greeks, it was thanks to Persephone, the daughter of Zeus and Demeter. Persephone was abducted by Hades, god of the Underworld, and taken away with him; because she was connected to vegetation, Persephone’s absence from the land led to the failure of crops, and everyone began to starve. Hades was told by Zeus to return Persephone to Demeter above-ground, but (thanks to Hades’ trick which involved, effectively, drugging Persephone with some pomegranate seeds), eventually a compromise was reached, whereby Persephone would spend the winter months in Hades and the rest of the year with Demeter. And this explains the origins of the seasons.
0130
DreamShaper
Create a video from the subject image with the person and landscape remaining completely unchanged in Disney•Pixar-inspired cinematic 3D animation - A caring female teacher without glasses helping students, explaining Chinese lessons on the blackboard, a female teacher with glasses explaining English lessons on the blackboard, and a male teacher with glasses explaining Math lessons on the blackboard , classroom filled with warm sunlight, female Chinese lessons teacher marking homework after school. Teacher smiles while teaching. Close-up of homework being marked. Narration Prompt 中文 consistent voice of a 13-year-old girl - sync and display the texts of the narrative at the bottom of the screen：今天，我想代表所有同学，说三声“谢谢”： 第一声，谢谢我们的老师。 是你们把枯燥的拼音变成了有趣的游戏，把难懂的公式变成了生活的魔法。你们批改作业到很晚的背影，是我们心中最温暖的图画。 Music Warm piano Soft strings
012
Grok Imagine Video
As well as explaining where man came from and how we came to create civilisation, the Greeks also used their myths to explain the origins of natural phenomena, such as the seasons. Why do we have summer and winter? For the ancient Greeks, it was thanks to Persephone, the daughter of Zeus and Demeter. Persephone was abducted by Hades, god of the Underworld, and taken away with him; because she was connected to vegetation, Persephone’s absence from the land led to the failure of crops, and everyone began to starve. Hades was told by Zeus to return Persephone to Demeter above-ground, but (thanks to Hades’ trick which involved, effectively, drugging Persephone with some pomegranate seeds), eventually a compromise was reached, whereby Persephone would spend the winter months in Hades and the rest of the year with Demeter. And this explains the origins of the seasons.
0130
DreamShaper
### **Image Generation Prompt for "Memory Me Objects Ka Behavior Kya Hoga?"** **Prompt:** Create a detailed and visually intuitive diagram to explain the behavior of objects in memory during a `while` loop that iterates over a database `ResultSet`. The diagram should include the following elements: 1. **Heap Memory Section:** - Show how new `User` objects are created in the heap memory during each iteration of the loop. - Highlight that each object corresponds to a row from the `ResultSet`. 2. **Garbage Collection:** - Illustrate how objects lose their reference after the loop ends and become eligible for garbage collection. - Use an arrow or icon to represent the Java Garbage Collector cleaning up unused objects. 3. **Permanent Storage (Optional):** - Show a scenario where objects are stored in a `List<User>` for permanent use. - Highlight the difference between temporary objects (eligible for garbage collection) and permanently stored objects. 4. **Flow of Execution:** - Include a flowchart-like representation of the `while` loop: - Start with `rs.next()` moving the cursor to the next row. - Show the creation of a new `User` object for each row. - End with either garbage collection or storage in a `List`. 5. **Annotations:** - Add labels and arrows to explain each step clearly. - Use Hindi/Hinglish annotations like: - "Har row ke liye naya object banega." - "Object ka reference lost ho jayega, to Garbage Collector clean kar dega." - "Agar List mei store kiya, to object permanent rehta hai." 6. **Color Coding:** - Use different colors for: - Heap memory (e.g., light blue). - Garbage-collected objects (e.g., grayed out). - Permanently stored objects (e.g., green). 7. **Database Table Example:** - Include a small table representation (e.g., `users` table with columns `id` and `name`) to show the source of data. --- ### **Expected Output:** The image should look like this: 1. **Top Section:** - A small database table (`users`) with rows and columns. 2. **Middle Section:** - A heap memory area showing multiple `User` objects being created during each iteration of the loop. - Arrows pointing from the `ResultSet` rows to the corresponding `User` objects in heap memory. 3. **Bottom Section:** - Two paths: - Path 1: Objects losing reference and being garbage collected (grayed out). - Path 2: Objects being stored in a `List<User>` for permanent use (highlighted in green). 4. **Annotations:** - Clear Hindi/Hinglish explanations for each step. --- This prompt will help generate a visually rich and easy-to-understand diagram for explaining the behavior of objects in memory! 😊
1133
FLUX
Description You want support me for Updates / New Lora? Tested and trained on UberRealisticPornMerge12 - Other Models not tested Update 0.2: For same Pose use the Triggerword: clothcuncloth_samepose Works fine with 0.5 ~ 1.0 Check Sample Image 3-7 Prompt Example <lora:clothuncloth:1>, clotuncloth, 21yo blond woman, left wearing suit and stockings, right naked, (high detailed face) Prompt Explain <lora:clothuncloth:1> - Load Lora clotuncloth - triggerword 21yo blond woman - Explain your "Main Person" left wearing suit and stockings - Explain your left Image (Works on dressed Person) right naked - (Explain your right Image) (high detailed face) - (Low Res Face fix) For not UberRealisticPornMerge you can use under "Explain your "main Person" this prompt: 21yo blond identical woman If you has Ugly Face turn strengh down - 0.5~1 works well This is a early Version, its hard to train two identical person in the same Image.
014
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.
015
Hero
Create a video from the subject image with the person and landscape remaining completely unchanged in Disney•Pixar-inspired cinematic 3D animation - A caring female teacher without glasses helping students, explaining Chinese lessons on the blackboard, a female teacher with glasses explaining English lessons on the blackboard, and a male teacher with glasses explaining Math lessons on the blackboard , classroom filled with warm sunlight, female Chinese lessons teacher marking homework after school. Teacher smiles while teaching. Close-up of homework being marked. Narration Prompt 中文 consistent voice of a 13-year-old girl - sync and display the texts of the narrative at the bottom of the screen：今天，我想代表所有同学，说三声“谢谢”： 第一声，谢谢我们的老师。 是你们把枯燥的拼音变成了有趣的游戏，把难懂的公式变成了生活的魔法。你们批改作业到很晚的背影，是我们心中最温暖的图画。 Music Warm piano Soft strings
012
Grok Imagine Video
Description You want support me for Updates / New Lora? Tested and trained on UberRealisticPornMerge12 - Other Models not tested Update 0.2: For same Pose use the Triggerword: clothcuncloth_samepose Works fine with 0.5 ~ 1.0 Check Sample Image 3-7 Prompt Example <lora:clothuncloth:1>, clotuncloth, 21yo blond woman, left wearing suit and stockings, right naked, (high detailed face) Prompt Explain <lora:clothuncloth:1> - Load Lora clotuncloth - triggerword 21yo blond woman - Explain your "Main Person" left wearing suit and stockings - Explain your left Image (Works on dressed Person) right naked - (Explain your right Image) (high detailed face) - (Low Res Face fix) For not UberRealisticPornMerge you can use under "Explain your "main Person" this prompt: 21yo blond identical woman If you has Ugly Face turn strengh down - 0.5~1 works well This is a early Version, its hard to train two identical person in the same Image.
014
Hero
### **Image Generation Prompt for "Memory Me Objects Ka Behavior Kya Hoga?"** **Prompt:** Create a detailed and visually intuitive diagram to explain the behavior of objects in memory during a `while` loop that iterates over a database `ResultSet`. The diagram should include the following elements: 1. **Heap Memory Section:** - Show how new `User` objects are created in the heap memory during each iteration of the loop. - Highlight that each object corresponds to a row from the `ResultSet`. 2. **Garbage Collection:** - Illustrate how objects lose their reference after the loop ends and become eligible for garbage collection. - Use an arrow or icon to represent the Java Garbage Collector cleaning up unused objects. 3. **Permanent Storage (Optional):** - Show a scenario where objects are stored in a `List<User>` for permanent use. - Highlight the difference between temporary objects (eligible for garbage collection) and permanently stored objects. 4. **Flow of Execution:** - Include a flowchart-like representation of the `while` loop: - Start with `rs.next()` moving the cursor to the next row. - Show the creation of a new `User` object for each row. - End with either garbage collection or storage in a `List`. 5. **Annotations:** - Add labels and arrows to explain each step clearly. - Use Hindi/Hinglish annotations like: - "Har row ke liye naya object banega." - "Object ka reference lost ho jayega, to Garbage Collector clean kar dega." - "Agar List mei store kiya, to object permanent rehta hai." 6. **Color Coding:** - Use different colors for: - Heap memory (e.g., light blue). - Garbage-collected objects (e.g., grayed out). - Permanently stored objects (e.g., green). 7. **Database Table Example:** - Include a small table representation (e.g., `users` table with columns `id` and `name`) to show the source of data. --- ### **Expected Output:** The image should look like this: 1. **Top Section:** - A small database table (`users`) with rows and columns. 2. **Middle Section:** - A heap memory area showing multiple `User` objects being created during each iteration of the loop. - Arrows pointing from the `ResultSet` rows to the corresponding `User` objects in heap memory. 3. **Bottom Section:** - Two paths: - Path 1: Objects losing reference and being garbage collected (grayed out). - Path 2: Objects being stored in a `List<User>` for permanent use (highlighted in green). 4. **Annotations:** - Clear Hindi/Hinglish explanations for each step. --- This prompt will help generate a visually rich and easy-to-understand diagram for explaining the behavior of objects in memory! 😊
1133
FLUX
Create a video from the subject image with the person and landscape remaining completely unchanged in Disney•Pixar-inspired cinematic 3D animation - A caring female teacher without glasses helping students, explaining Chinese lessons on the blackboard, a female teacher with glasses explaining English lessons on the blackboard, and a male teacher with glasses explaining Math lessons on the blackboard , classroom filled with warm sunlight, female Chinese lessons teacher marking homework after school. Teacher smiles while teaching. Close-up of homework being marked. Narration Prompt 中文 consistent voice of a 13-year-old girl - sync and display the texts of the narrative at the bottom of the screen：今天，我想代表所有同学，说三声“谢谢”： 第一声，谢谢我们的老师。 是你们把枯燥的拼音变成了有趣的游戏，把难懂的公式变成了生活的魔法。你们批改作业到很晚的背影，是我们心中最温暖的图画。 Music Warm piano Soft strings
012
Grok Imagine Video
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.
015
Hero
As well as explaining where man came from and how we came to create civilisation, the Greeks also used their myths to explain the origins of natural phenomena, such as the seasons. Why do we have summer and winter? For the ancient Greeks, it was thanks to Persephone, the daughter of Zeus and Demeter. Persephone was abducted by Hades, god of the Underworld, and taken away with him; because she was connected to vegetation, Persephone’s absence from the land led to the failure of crops, and everyone began to starve. Hades was told by Zeus to return Persephone to Demeter above-ground, but (thanks to Hades’ trick which involved, effectively, drugging Persephone with some pomegranate seeds), eventually a compromise was reached, whereby Persephone would spend the winter months in Hades and the rest of the year with Demeter. And this explains the origins of the seasons.
0130
DreamShaper
### **Image Generation Prompt for "Memory Me Objects Ka Behavior Kya Hoga?"** **Prompt:** Create a detailed and visually intuitive diagram to explain the behavior of objects in memory during a `while` loop that iterates over a database `ResultSet`. The diagram should include the following elements: 1. **Heap Memory Section:** - Show how new `User` objects are created in the heap memory during each iteration of the loop. - Highlight that each object corresponds to a row from the `ResultSet`. 2. **Garbage Collection:** - Illustrate how objects lose their reference after the loop ends and become eligible for garbage collection. - Use an arrow or icon to represent the Java Garbage Collector cleaning up unused objects. 3. **Permanent Storage (Optional):** - Show a scenario where objects are stored in a `List<User>` for permanent use. - Highlight the difference between temporary objects (eligible for garbage collection) and permanently stored objects. 4. **Flow of Execution:** - Include a flowchart-like representation of the `while` loop: - Start with `rs.next()` moving the cursor to the next row. - Show the creation of a new `User` object for each row. - End with either garbage collection or storage in a `List`. 5. **Annotations:** - Add labels and arrows to explain each step clearly. - Use Hindi/Hinglish annotations like: - "Har row ke liye naya object banega." - "Object ka reference lost ho jayega, to Garbage Collector clean kar dega." - "Agar List mei store kiya, to object permanent rehta hai." 6. **Color Coding:** - Use different colors for: - Heap memory (e.g., light blue). - Garbage-collected objects (e.g., grayed out). - Permanently stored objects (e.g., green). 7. **Database Table Example:** - Include a small table representation (e.g., `users` table with columns `id` and `name`) to show the source of data. --- ### **Expected Output:** The image should look like this: 1. **Top Section:** - A small database table (`users`) with rows and columns. 2. **Middle Section:** - A heap memory area showing multiple `User` objects being created during each iteration of the loop. - Arrows pointing from the `ResultSet` rows to the corresponding `User` objects in heap memory. 3. **Bottom Section:** - Two paths: - Path 1: Objects losing reference and being garbage collected (grayed out). - Path 2: Objects being stored in a `List<User>` for permanent use (highlighted in green). 4. **Annotations:** - Clear Hindi/Hinglish explanations for each step. --- This prompt will help generate a visually rich and easy-to-understand diagram for explaining the behavior of objects in memory! 😊
1133
FLUX
As well as explaining where man came from and how we came to create civilisation, the Greeks also used their myths to explain the origins of natural phenomena, such as the seasons. Why do we have summer and winter? For the ancient Greeks, it was thanks to Persephone, the daughter of Zeus and Demeter. Persephone was abducted by Hades, god of the Underworld, and taken away with him; because she was connected to vegetation, Persephone’s absence from the land led to the failure of crops, and everyone began to starve. Hades was told by Zeus to return Persephone to Demeter above-ground, but (thanks to Hades’ trick which involved, effectively, drugging Persephone with some pomegranate seeds), eventually a compromise was reached, whereby Persephone would spend the winter months in Hades and the rest of the year with Demeter. And this explains the origins of the seasons.
0130
DreamShaper
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.
015
Hero
Create a video from the subject image with the person and landscape remaining completely unchanged in Disney•Pixar-inspired cinematic 3D animation - A caring female teacher without glasses helping students, explaining Chinese lessons on the blackboard, a female teacher with glasses explaining English lessons on the blackboard, and a male teacher with glasses explaining Math lessons on the blackboard , classroom filled with warm sunlight, female Chinese lessons teacher marking homework after school. Teacher smiles while teaching. Close-up of homework being marked. Narration Prompt 中文 consistent voice of a 13-year-old girl - sync and display the texts of the narrative at the bottom of the screen：今天，我想代表所有同学，说三声“谢谢”： 第一声，谢谢我们的老师。 是你们把枯燥的拼音变成了有趣的游戏，把难懂的公式变成了生活的魔法。你们批改作业到很晚的背影，是我们心中最温暖的图画。 Music Warm piano Soft strings
012
Grok Imagine Video
Description You want support me for Updates / New Lora? Tested and trained on UberRealisticPornMerge12 - Other Models not tested Update 0.2: For same Pose use the Triggerword: clothcuncloth_samepose Works fine with 0.5 ~ 1.0 Check Sample Image 3-7 Prompt Example <lora:clothuncloth:1>, clotuncloth, 21yo blond woman, left wearing suit and stockings, right naked, (high detailed face) Prompt Explain <lora:clothuncloth:1> - Load Lora clotuncloth - triggerword 21yo blond woman - Explain your "Main Person" left wearing suit and stockings - Explain your left Image (Works on dressed Person) right naked - (Explain your right Image) (high detailed face) - (Low Res Face fix) For not UberRealisticPornMerge you can use under "Explain your "main Person" this prompt: 21yo blond identical woman If you has Ugly Face turn strengh down - 0.5~1 works well This is a early Version, its hard to train two identical person in the same Image.
014
Hero
As well as explaining where man came from and how we came to create civilisation, the Greeks also used their myths to explain the origins of natural phenomena, such as the seasons. Why do we have summer and winter? For the ancient Greeks, it was thanks to Persephone, the daughter of Zeus and Demeter. Persephone was abducted by Hades, god of the Underworld, and taken away with him; because she was connected to vegetation, Persephone’s absence from the land led to the failure of crops, and everyone began to starve. Hades was told by Zeus to return Persephone to Demeter above-ground, but (thanks to Hades’ trick which involved, effectively, drugging Persephone with some pomegranate seeds), eventually a compromise was reached, whereby Persephone would spend the winter months in Hades and the rest of the year with Demeter. And this explains the origins of the seasons.
0130
DreamShaper
### **Image Generation Prompt for "Memory Me Objects Ka Behavior Kya Hoga?"** **Prompt:** Create a detailed and visually intuitive diagram to explain the behavior of objects in memory during a `while` loop that iterates over a database `ResultSet`. The diagram should include the following elements: 1. **Heap Memory Section:** - Show how new `User` objects are created in the heap memory during each iteration of the loop. - Highlight that each object corresponds to a row from the `ResultSet`. 2. **Garbage Collection:** - Illustrate how objects lose their reference after the loop ends and become eligible for garbage collection. - Use an arrow or icon to represent the Java Garbage Collector cleaning up unused objects. 3. **Permanent Storage (Optional):** - Show a scenario where objects are stored in a `List<User>` for permanent use. - Highlight the difference between temporary objects (eligible for garbage collection) and permanently stored objects. 4. **Flow of Execution:** - Include a flowchart-like representation of the `while` loop: - Start with `rs.next()` moving the cursor to the next row. - Show the creation of a new `User` object for each row. - End with either garbage collection or storage in a `List`. 5. **Annotations:** - Add labels and arrows to explain each step clearly. - Use Hindi/Hinglish annotations like: - "Har row ke liye naya object banega." - "Object ka reference lost ho jayega, to Garbage Collector clean kar dega." - "Agar List mei store kiya, to object permanent rehta hai." 6. **Color Coding:** - Use different colors for: - Heap memory (e.g., light blue). - Garbage-collected objects (e.g., grayed out). - Permanently stored objects (e.g., green). 7. **Database Table Example:** - Include a small table representation (e.g., `users` table with columns `id` and `name`) to show the source of data. --- ### **Expected Output:** The image should look like this: 1. **Top Section:** - A small database table (`users`) with rows and columns. 2. **Middle Section:** - A heap memory area showing multiple `User` objects being created during each iteration of the loop. - Arrows pointing from the `ResultSet` rows to the corresponding `User` objects in heap memory. 3. **Bottom Section:** - Two paths: - Path 1: Objects losing reference and being garbage collected (grayed out). - Path 2: Objects being stored in a `List<User>` for permanent use (highlighted in green). 4. **Annotations:** - Clear Hindi/Hinglish explanations for each step. --- This prompt will help generate a visually rich and easy-to-understand diagram for explaining the behavior of objects in memory! 😊
1133
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.
015
Hero
Description You want support me for Updates / New Lora? Tested and trained on UberRealisticPornMerge12 - Other Models not tested Update 0.2: For same Pose use the Triggerword: clothcuncloth_samepose Works fine with 0.5 ~ 1.0 Check Sample Image 3-7 Prompt Example <lora:clothuncloth:1>, clotuncloth, 21yo blond woman, left wearing suit and stockings, right naked, (high detailed face) Prompt Explain <lora:clothuncloth:1> - Load Lora clotuncloth - triggerword 21yo blond woman - Explain your "Main Person" left wearing suit and stockings - Explain your left Image (Works on dressed Person) right naked - (Explain your right Image) (high detailed face) - (Low Res Face fix) For not UberRealisticPornMerge you can use under "Explain your "main Person" this prompt: 21yo blond identical woman If you has Ugly Face turn strengh down - 0.5~1 works well This is a early Version, its hard to train two identical person in the same Image.
014
Hero
Create a video from the subject image with the person and landscape remaining completely unchanged in Disney•Pixar-inspired cinematic 3D animation - A caring female teacher without glasses helping students, explaining Chinese lessons on the blackboard, a female teacher with glasses explaining English lessons on the blackboard, and a male teacher with glasses explaining Math lessons on the blackboard , classroom filled with warm sunlight, female Chinese lessons teacher marking homework after school. Teacher smiles while teaching. Close-up of homework being marked. Narration Prompt 中文 consistent voice of a 13-year-old girl - sync and display the texts of the narrative at the bottom of the screen：今天，我想代表所有同学，说三声“谢谢”： 第一声，谢谢我们的老师。 是你们把枯燥的拼音变成了有趣的游戏，把难懂的公式变成了生活的魔法。你们批改作业到很晚的背影，是我们心中最温暖的图画。 Music Warm piano Soft strings
012
Grok Imagine Video