MoneyPrinterTurbo: 키워드만 입력하면 자막과 음성까지 갖춘 숏폼 영상을 자동 생성하는 도구
(by 9bow님)
d.ptln.kr/10515
#llm #whisper #tts #videogeneration #contentcreation #shortvideo #moneyprinterturbo
6
Faute d’accès à Fable 5... Je me suis amusé à remonter des tests faits avec Sora. Avec moi comme personnage 😅 Ils avaient trouvé la bonne recette pour la consistance de personnages et le réalisme. Dommage qu’ils l’aient annulé. J’aurais pu faire un bon film… 😏 @OpenAI #IA #VideoGeneration
1
41
Google just dropped a video tool nobody expected. It adds objects that weren't in your footage.
So this is Gemini Omni. Drop it on any video you've already shot.
Feed it text, images, or audio. It processes all three at once.
What comes out: perfect lighting. Perfect shading. You genuinely can't tell it's AI.
I've been testing it for a while. Doesn't nail it every time.
But when it does? Nothing else comes close.
Like adding a prop after the shoot's done. Lighting matches perfectly.
Doesn't land every shot. But the shots that land are wild. Test it yourself.
#geminiomni #google #gemini #aivideo #videogeneration
1
1
78
Jun 13
I typed one sentence. An AI wrote the script, hired the director, built the storyboard, cast the characters, and delivered a full multi-scene video. I paid $0.
Tools like Sora, Runway, and Kling give you a 5-second clip. You still have to figure out the story, the shots, the consistency between scenes, and the characters.
ViMax skips all of that.
It's a multi-agent system — 12 AI agents working like an actual film crew. You give it an idea. A Screenwriter drafts the script. A Director plans the shots. A Producer builds the storyboard. A Generator renders the video. All automatically.
Character consistency across scenes. Multi-camera angles. Long-form video from a novel. Even AutoCameo — put yourself in any AI video with one photo.
7,100 GitHub stars. MIT license. 100% free.
🔗 github.com/HKUDS/ViMax
Comment "Max" and I'll send you the setup guide.
#ViMax #AIVideo #AgenticAI #VideoGeneration #OpenSource #AITools #ContentCreators #TextToVideo #AIAgents #FreeAI
1
110
We've spent months building, testing, and improving the platform, and we'd love your feedback.
Stay tuned for the launch! 🔥
#ProductHunt #AI #VideoGeneration #StartupLaunch #BuildInPublic
1
10
Jun 12
Kling AI 3.0 models launch, offering multimodal AI for photorealistic video & image creation. Now everyone can be a director.
#multimodalai #videogeneration #imagegeneration #aimodels
11
Jun 12
India AI Mission Varya AI model launch: દેશનું પ્રથમ સ્વદેશી વિડિયો AI મોડલ લોન્ચ
'ઈન્ડિયા AI મિશન' હેઠળ 'વર્યા'નું લોન્ચિંગ
અન્ય વૈશ્વિક મોડલ્સ કરતા 10 ગણું વધુ ઝડપી
માત્ર 4 સ્ટેપમાં સચોટ રીતે વિડિયો બનાવાશે
સ્ટાર્ટઅપ્સ અને કન્ટેન્ટ ક્રિએટર્સને ઉપયોગી
સાંસ્કૃતિક સમજને ધ્યાને રાખીને કરાયું તૈયાર
વિડિયો જનરેશન ખર્ચ 48 પૈસા પ્રતિ સેકન્ડ
#IndiaAI #VaryaAI #ArtificialIntelligence #AIVideoGenerator #MadeInIndia #TechNews #Innovation #DigitalIndia #AIMission #StartupIndia #ContentCreators #VideoGeneration #GenerativeAI
50
Part 3: Recommendations
Recommendation 1
Investigate Japanese TTS Regression and Consistency
Background:
Repeated testing suggested that Japanese TTS quality, emotional delivery, and dialogue consistency appeared stronger in earlier Grok Imagine generations observed before April 19, 2026.
Recommendation:
Conduct an internal comparison between:
Earlier Japanese TTS generations
Current Japanese TTS generations
with particular attention to:
Emotional expression
Dialogue pacing
Character consistency
Lip synchronization accuracy
Reason:
For narrative Japanese-language content, these elements directly determine production usability.
Improving consistency would significantly increase creator productivity and reduce generation waste.
Recommendation 2
Improve Feedback Transparency
Background:
Users can currently receive confirmation that feedback was shared internally.
However, users cannot determine whether submitted materials have progressed beyond that stage.
Recommendation:
Provide a simple feedback status indicator.
Example:
Received
Shared Internally
Flagged
Reviewed
No detailed internal information is required.
Reason:
The goal is not to demand individual responses.
The goal is to reduce uncertainty and improve trust in the feedback process.
A lightweight transparency system would help users understand whether significant reports have advanced beyond initial submission
Alternative User-Focused Recommendations
Recommendation 1
Provide Access to the Previous Japanese TTS Version Until Current Quality Issues Are Resolved
Background:
Repeated testing suggested that Japanese TTS generations observed before April 19, 2026 delivered stronger:
Emotional expression
Character consistency
Dialogue pacing
Narrative performance
For creators producing story-driven Japanese content, these differences significantly affect production quality.
Recommendation:
Allow users to choose between:
Current Japanese TTS
Legacy Japanese TTS
until quality parity is achieved.
Reason:
This would allow creators to continue producing content while improvements are being developed.
The goal is not to block progress, but to provide users with a temporary fallback option.
Recommendation 2
Increase Video Generation Capacity for Premium Users
Background:
Observed generation availability appeared to decrease significantly compared with previous usage levels.
Example observation:
Previous usage level: approximately 50 generations
Current usage level: approximately 5 generations
Observed reduction:
Approximately 90%
Recommendation:
If restoring previous capacity is not possible, consider providing:
Approximately 25 generations or
A mid-tier allocation between previous and current limits
Reason:
Narrative video creation often requires multiple attempts due to:
Character consistency
TTS quality
Lip synchronization
Acting performance
A moderate increase would significantly improve production feasibility while remaining below historical allocation levels.
My goal is not to win an argument.
My goal is to create stories.
Please help creators keep creating.
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望
#拡散希望ＲPご協力お願い致します
1
90
Part 3: Recommendations
Recommendation 1
Investigate Japanese TTS Regression and Consistency
Background:
Repeated testing suggested that Japanese TTS quality, emotional delivery, and dialogue consistency appeared stronger in earlier Grok Imagine generations observed before April 19, 2026.
Recommendation:
Conduct an internal comparison between:
Earlier Japanese TTS generations
Current Japanese TTS generations
with particular attention to:
Emotional expression
Dialogue pacing
Character consistency
Lip synchronization accuracy
Reason:
For narrative Japanese-language content, these elements directly determine production usability.
Improving consistency would significantly increase creator productivity and reduce generation waste.
Recommendation 2
Improve Feedback Transparency
Background:
Users can currently receive confirmation that feedback was shared internally.
However, users cannot determine whether submitted materials have progressed beyond that stage.
Recommendation:
Provide a simple feedback status indicator.
Example:
Received
Shared Internally
Flagged
Reviewed
No detailed internal information is required.
Reason:
The goal is not to demand individual responses.
The goal is to reduce uncertainty and improve trust in the feedback process.
A lightweight transparency system would help users understand whether significant reports have advanced beyond initial submission
Alternative User-Focused Recommendations
Recommendation 1
Provide Access to the Previous Japanese TTS Version Until Current Quality Issues Are Resolved
Background:
Repeated testing suggested that Japanese TTS generations observed before April 19, 2026 delivered stronger:
Emotional expression
Character consistency
Dialogue pacing
Narrative performance
For creators producing story-driven Japanese content, these differences significantly affect production quality.
Recommendation:
Allow users to choose between:
Current Japanese TTS
Legacy Japanese TTS
until quality parity is achieved.
Reason:
This would allow creators to continue producing content while improvements are being developed.
The goal is not to block progress, but to provide users with a temporary fallback option.
Recommendation 2
Increase Video Generation Capacity for Premium Users
Background:
Observed generation availability appeared to decrease significantly compared with previous usage levels.
Example observation:
Previous usage level: approximately 50 generations
Current usage level: approximately 5 generations
Observed reduction:
Approximately 90%
Recommendation:
If restoring previous capacity is not possible, consider providing:
Approximately 25 generations or
A mid-tier allocation between previous and current limits
Reason:
Narrative video creation often requires multiple attempts due to:
Character consistency
TTS quality
Lip synchronization
Acting performance
A moderate increase would significantly improve production feasibility while remaining below historical allocation levels.
My goal is not to win an argument.
My goal is to create stories.
Please help creators keep creating.
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望
103
Supplementary Metrics
The following figures are included for context and transparency.
Video Generation Availability
Observed usage pattern:
- Previous observed limit: approximately 50 generations
- Current observed limit: approximately 5 generations
Observed reduction:
- 45 fewer generations
- Approximately 90% reduction
Calculation:
(50 - 5) / 50 = 0.90
Observed reduction rate: 90%
---
Support Communication
Support contacts submitted:
- 5
Responses received:
- 0
Observed response rate:
0 / 5 = 0%
Observed response rate: 0%
---
Verification Effort
Testing period:
- Approximately 50 days
Generation attempts:
- Approximately 200
Average tests per day:
200 / 50 = 4
Average:
Approximately 4 tests per day
---
Production Efficiency Comparison
Narrative Video Project
- Approximately 50 days
- Approximately 200 tests
- Final output: 36 seconds
Music-Based Projects
- Approximately 30 minutes production time
- Three completed releases
- Approximately 150–160 hours total playback
These figures are provided as observational data only.
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望
#拡散希望ＲPご協力お願い致します
1
66
Supplementary Metrics
The following figures are included for context and transparency.
Video Generation Availability
Observed usage pattern:
- Previous observed limit: approximately 50 generations
- Current observed limit: approximately 5 generations
Observed reduction:
- 45 fewer generations
- Approximately 90% reduction
Calculation:
(50 - 5) / 50 = 0.90
Observed reduction rate: 90%
---
Support Communication
Support contacts submitted:
- 5
Responses received:
- 0
Observed response rate:
0 / 5 = 0%
Observed response rate: 0%
---
Verification Effort
Testing period:
- Approximately 50 days
Generation attempts:
- Approximately 200
Average tests per day:
200 / 50 = 4
Average:
Approximately 4 tests per day
---
Production Efficiency Comparison
Narrative Video Project
- Approximately 50 days
- Approximately 200 tests
- Final output: 36 seconds
Music-Based Projects
- Approximately 30 minutes production time
- Three completed releases
- Approximately 150–160 hours total playback
These figures are provided as observational data only.
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望
#拡散希望ＲPご協力お願い致します
44
Part 2: Observations and Findings
Overview
The following observations are based on approximately 50 days of testing and roughly 200 generation attempts.
These observations represent repeated user-side testing results and should be interpreted as observational findings rather than internal product conclusions.
Observation 1: Japanese TTS Quality Consistency
Repeated testing suggested that Japanese TTS quality appeared more stable in earlier Grok Imagine generations observed before April 19, 2026.
Observed differences included:
Emotional expression
Natural pacing
Intonation
Character personality consistency
Dialogue delivery
For story-driven content, these elements significantly affected final production quality.
Observation 2: Integrated Generation Success Rate
Video generation currently requires multiple independent elements to succeed simultaneously:
Character appearance
Character consistency
Acting performance
Camera behavior
Japanese TTS
Lip synchronization
Emotional delivery
In many tests, some elements succeeded while others failed.
Examples frequently observed:
Excellent visuals with degraded TTS
Excellent TTS with degraded acting
Correct acting with incorrect lip synchronization
Correct lip synchronization with inconsistent character behavior
As a result, complete success required all conditions to align simultaneously.
Observation 3: Lip Synchronization Dependency
Because video generation includes native lip synchronization, replacing audio after generation is difficult.
For Japanese dialogue-focused content, this creates a dependency between:
Visual Performance Voice Performance Lip Synchronization
A failure in one component often prevents practical reuse of otherwise successful footage.
Observation 4: Production Efficiency Gap
A significant efficiency difference was observed between:
A. Narrative video production using Grok Imagine
and
B. Music-focused content production
Observed example:
Narrative Video
Approximately 50 days of work
Roughly 200 tests
Final output: 36 seconds
Music Content
Approximately 30 minutes production time
Multiple completed releases
Approximately 150–160 hours total playback
This suggests that current generation reliability has a major impact on production efficiency for story-driven video projects.
Observation 5: Storytelling Requirements
The target project focuses on emotional storytelling rather than visual spectacle.
Key priorities include:
Character emotions
Dialogue delivery
Subtle pauses
Voice tone
Emotional authenticity
For this reason, Japanese TTS quality has a disproportionately large impact on final project quality.
Even small changes in emotional delivery can significantly affect the effectiveness of a completed scene.
Observation 6: Feedback Transparency
The distinction between
shared internally
and
reviewed
became a major topic during the verification process.
Through official responses, the following was understood:
shared internally indicates internal recording and routing
reviewed indicates actual evaluation and judgment
However, the verification process was unable to determine:
whether submitted materials were flagged
whether they were reviewed
whether engineers evaluated them
As a result, transparency after internal submission remains difficult for users to observe.
Summary
The primary issue observed was not the absence of features.
The primary issue observed was the difficulty of achieving consistent simultaneous success across:
Visual Quality Japanese TTS Quality Lip Synchronization Emotional Performance
for narrative Japanese-language video production
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望
#拡散希望ＲPご協力お願い致します
1
41
Part 2: Observations and Findings
Overview
The following observations are based on approximately 50 days of testing and roughly 200 generation attempts.
These observations represent repeated user-side testing results and should be interpreted as observational findings rather than internal product conclusions.
Observation 1: Japanese TTS Quality Consistency
Repeated testing suggested that Japanese TTS quality appeared more stable in earlier Grok Imagine generations observed before April 19, 2026.
Observed differences included:
Emotional expression
Natural pacing
Intonation
Character personality consistency
Dialogue delivery
For story-driven content, these elements significantly affected final production quality.
Observation 2: Integrated Generation Success Rate
Video generation currently requires multiple independent elements to succeed simultaneously:
Character appearance
Character consistency
Acting performance
Camera behavior
Japanese TTS
Lip synchronization
Emotional delivery
In many tests, some elements succeeded while others failed.
Examples frequently observed:
Excellent visuals with degraded TTS
Excellent TTS with degraded acting
Correct acting with incorrect lip synchronization
Correct lip synchronization with inconsistent character behavior
As a result, complete success required all conditions to align simultaneously.
Observation 3: Lip Synchronization Dependency
Because video generation includes native lip synchronization, replacing audio after generation is difficult.
For Japanese dialogue-focused content, this creates a dependency between:
Visual Performance Voice Performance Lip Synchronization
A failure in one component often prevents practical reuse of otherwise successful footage.
Observation 4: Production Efficiency Gap
A significant efficiency difference was observed between:
A. Narrative video production using Grok Imagine
and
B. Music-focused content production
Observed example:
Narrative Video
Approximately 50 days of work
Roughly 200 tests
Final output: 36 seconds
Music Content
Approximately 30 minutes production time
Multiple completed releases
Approximately 150–160 hours total playback
This suggests that current generation reliability has a major impact on production efficiency for story-driven video projects.
Observation 5: Storytelling Requirements
The target project focuses on emotional storytelling rather than visual spectacle.
Key priorities include:
Character emotions
Dialogue delivery
Subtle pauses
Voice tone
Emotional authenticity
For this reason, Japanese TTS quality has a disproportionately large impact on final project quality.
Even small changes in emotional delivery can significantly affect the effectiveness of a completed scene.
Observation 6: Feedback Transparency
The distinction between
shared internally
and
reviewed
became a major topic during the verification process.
Through official responses, the following was understood:
shared internally indicates internal recording and routing
reviewed indicates actual evaluation and judgment
However, the verification process was unable to determine:
whether submitted materials were flagged
whether they were reviewed
whether engineers evaluated them
As a result, transparency after internal submission remains difficult for users to observe.
Summary
The primary issue observed was not the absence of features.
The primary issue observed was the difficulty of achieving consistent simultaneous success across:
Visual Quality Japanese TTS Quality Lip Synchronization Emotional Performance
for narrative Japanese-language video production
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望
34
Part 1: Objective Timeline and Verification Summary
Project: Grok Imagine Japanese TTS and Video Generation Verification
Verification Period: March 20, 2026 – June 10, 2026 Approximately 50 days
Account Type: Grok Premium Subscriber
Purpose: Creation of original Japanese video works using Grok Imagine native video generation and Japanese TTS.
Main Production Project: 『最幸のしあわせ🌸みち咲き』 Character Series: 👸👼👿🐻 and 👿ツンデレ処方箋
Verification Scale:
Approximately 200 video generation tests
Comparison tables created
Evaluation sheets created
Chronological records created
Support contact history recorded
Grok official Q&A history recorded
Observed Production Results:
Video Project A
Production period: approximately 50 days
Extensive generation and editing process
Final completed video length: 36 seconds
Approximate views: 100
Music-Based Projects
Production time: approximately 30 minutes
Three works released
Total playback time: approximately 150–160 hours
Support Activity:
Support submissions: 5
Additional feedback submitted through official channels
Questions asked regarding:
shared internally
flagged
reviewed
internal review process
feedback routing process
Confirmed Information Through Official Responses:
Feedback can be shared internally
Feedback may be used for pattern analysis
Premium user history can be associated with account records
shared internally and reviewed are different statuses
Unconfirmed Information:
Whether the submitted materials were flagged
Whether the submitted materials were reviewed
Whether an engineer reviewed the materials
Whether the materials influenced product decisions
Whether the materials entered roadmap discussions
Current Status:
The objective of this report is not to submit a new bug report.
The objective is to document a 50-day verification project and provide factual observations regarding Japanese TTS quality, video generation consistency, and feedback transparency.
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望ＲPご協力お願い致します
1
51
Part 1: Objective Timeline and Verification Summary
Project: Grok Imagine Japanese TTS and Video Generation Verification
Verification Period: March 20, 2026 – June 10, 2026 Approximately 50 days
Account Type: Grok Premium Subscriber
Purpose: Creation of original Japanese video works using Grok Imagine native video generation and Japanese TTS.
Main Production Project: 『最幸のしあわせ🌸みち咲き』 Character Series: 👸👼👿🐻 and 👿ツンデレ処方箋
Verification Scale:
Approximately 200 video generation tests
Comparison tables created
Evaluation sheets created
Chronological records created
Support contact history recorded
Grok official Q&A history recorded
Observed Production Results:
Video Project A
Production period: approximately 50 days
Extensive generation and editing process
Final completed video length: 36 seconds
Approximate views: 100
Music-Based Projects
Production time: approximately 30 minutes
Three works released
Total playback time: approximately 150–160 hours
Support Activity:
Support submissions: 5
Additional feedback submitted through official channels
Questions asked regarding:
shared internally
flagged
reviewed
internal review process
feedback routing process
Confirmed Information Through Official Responses:
Feedback can be shared internally
Feedback may be used for pattern analysis
Premium user history can be associated with account records
shared internally and reviewed are different statuses
Unconfirmed Information:
Whether the submitted materials were flagged
Whether the submitted materials were reviewed
Whether an engineer reviewed the materials
Whether the materials influenced product decisions
Whether the materials entered roadmap discussions
Current Status:
The objective of this report is not to submit a new bug report.
The objective is to document a 50-day verification project and provide factual observations regarding Japanese TTS quality, video generation consistency, and feedback transparency.
#Grok #xAI #ElonMusk #Premium #VideoGeneration #GenerationLimits #Transparency #CustomerTrust #ExplainThis
#拡散希望
1
78
Jun 11
9/ Resources:
🌐 Project: wmsd-paper.github.io/World-M…
🤗 Weights: huggingface.co/WMSD/World-Mo…
📊 Dataset: huggingface.co/datasets/WMSD… 💻 Code: github.com/WMSD-Paper/World-…
#WorldModels #VideoGeneration #ReinforcementLearning #Robotics #GenAI
2
6
617
Jun 11
1/🚀 We’re excited to share World Model Self-Distillation (WMSD)
WMSD trains pretrained video generators to solve general tasks from an image short instruction; without curated task-execution videos.
It combines self-distillation with VLM-feedback RL, letting the Executor learn from a detailed-solution Demonstrator and improve beyond it.
🌐 Project: wmsd-paper.github.io/World-M…
#WorldModels #VideoGeneration #ReinforcementLearning #SelfDistillation #AI #Robotics
2
20
141
8,072
🎨 Dlaczego AI odrzuca niektóre artystyczne prompty? / Why does AI reject some artistic prompts?
Jeśli korzystasz z narzędzi takich jak Midjourney, Adobe Firefly, DALL·E, Bing Image Creator, Google Veo, Runway, Kling AI czy Pika, mogłeś zauważyć, że niektóre pozornie niewinne słowa powodują odrzucenie promptu. 🤔
If you use tools such as Midjourney, Adobe Firefly, DALL·E, Bing Image Creator, Google Veo, Runway, Kling AI, or Pika, you may have noticed that some seemingly harmless words can cause a prompt to be rejected. 🤔
⚠️ Najczęstszy powód? Systemy moderacji AI analizują słowa dosłownie i automatycznie przypisują je do kategorii związanych z przemocą, treściami dla dorosłych lub deformacjami ciała.
⚠️ The most common reason? AI moderation systems analyze words literally and automatically associate them with categories such as violence, adult content, or body deformation.
🔹 "Cracked" – może zostać zinterpretowane jako rany, złamania lub uszkodzenia ciała. 🔹 "Cracked" – may be interpreted as wounds, fractures, or physical injuries.
🔹 "Porcelain skin" – czasami aktywuje filtry związane z nagością lub nadmiernie realistycznym przedstawianiem ludzkiego ciała. 🔹 "Porcelain skin" – can sometimes trigger filters related to nudity or highly realistic depictions of the human body.
🔹 "Fluid body" – może zostać powiązane z płynami ustrojowymi lub ekstremalnymi deformacjami anatomii. 🔹 "Fluid body" – may be associated with bodily fluids or extreme anatomical distortions.
🎭 Problem polega na tym, że algorytmy często nie rozumieją artystycznego kontekstu. To, co dla człowieka jest metaforą, symboliką lub poetyckim opisem, dla AI może wyglądać jak próba wygenerowania niepożądanej treści.
🎭 The challenge is that algorithms often fail to understand artistic context. What humans see as metaphor, symbolism, or poetic imagery can be interpreted by AI as potentially problematic content.
🎬 To samo dotyczy generatorów wideo AI. Modele takie jak Veo, Runway, Kling czy Pika stosują podobne systemy bezpieczeństwa, dlatego niektóre kreatywne opisy mogą zostać błędnie zinterpretowane.
🎬 The same applies to AI video generators. Models such as Veo, Runway, Kling, and Pika use similar safety systems, meaning that some creative descriptions can be misinterpreted.
💡 Jak zwiększyć szanse na akceptację promptu? 💡 How can you improve the chances of prompt approval?
✨ Kintsugi texture ✨ Marble statue texture ✨ Ceramic surface ✨ Melting glass sculpture ✨ Translucent waves ✨ Dynamic smoke trails
🎨 Czasami niewielka zmiana słów pozwala uzyskać dokładnie ten sam efekt artystyczny bez uruchamiania filtrów bezpieczeństwa.
🎨 Sometimes a small wording change is enough to achieve the same artistic vision while avoiding unnecessary moderation triggers.
🚀 W świecie generatywnego AI coraz częściej wygrywa nie ten, kto pisze najdłuższe prompty, lecz ten, kto potrafi opisać swoją wizję w sposób jasny, obrazowy i przyjazny dla algorytmów.
🚀 In the world of generative AI, success increasingly belongs not to those who write the longest prompts, but to those who can describe their vision clearly, visually, and in a way that works with AI systems.
#AIArt #AIVideo #GoogleVeo #RunwayML #KlingAI #PikaAI #Midjourney #DALLE #AdobeFirefly #PromptEngineering #GenerativeAI #ArtificialIntelligence #CreativeAI #DigitalArt #FutureOfArt #AIArtists #TextToVideo #VideoGeneration #TechAndArt #DigitalCreativity
63