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P-Video-2-Pro now has a Cost mode! Get the same output quality as Speed mode, now starting from just $0.01/s. Optimised for cost when generation time matters less. And for launch, it’s 50% off until October 13! • 𝗜𝗻𝗽𝘂𝘁: Text or first-frame image, with optional last-frame conditioning • 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻: 5–15s... with generated audio • 𝗣𝗿𝗶𝗰𝗶𝗻𝗴: $0.01/s at 480p · $0.025/s at 768p • 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: First + last frame, seed, aspect ratio, and prompt upsampling Three modes: Cost, Speed, Quality. Pick the tradeoff that fits your workflow. Available via our inference partners Cloudflare, Scenario, Replicate, Wiro AI — Ship AI Faster, Tellers, each::labs, Eden AI, Runware, WaveSpeedAI, inference shell, Together AI, and Prodia. Validated by our benchmark partners Datapoint AI, Rapidata, and Design Arena. 📚 Model docs: 🧩 API:show more

Pruna AI

4,682 subscribers

26,955 Aufrufe • vor 15 Tagen •via X (Twitter)

Anya Rossi• Live Now

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15 Kommentare

Profilbild von Pulso IA | Noticias en español

Pulso IA | Noticias en españolvor 15 Tagen

Do the listed prices already include the launch discount? Also curious about typical generation time for a 10 second clip in Cost mode. That would make the tradeoff with Speed much easier to judge.

Profilbild von Shafee_Ai

Shafee_Aivor 15 Tagen

Ready for test it

Profilbild von The AI Guy

The AI Guyvor 15 Tagen

Cool 😎

Profilbild von Aina Ai | Tools & Updates

Aina Ai | Tools & Updatesvor 14 Tagen

Love the flexibility great pricing and great options

Profilbild von Allahwarayo__786Ai

Allahwarayo__786Aivor 14 Tagen

This is a great upgrade! Cost mode at $0.01/s is huge for affordable video generation.

Profilbild von APIMart Labs

APIMart Labsvor 14 Tagen

Cost / Speed / Quality as modes is the right split. Most teams still pick one model and eat the wrong bill. The missing piece is routing the same prompt to the mode that matches the deadline. APIMart can sit there as a comparison path.

Profilbild von ZenithAi

ZenithAivor 14 Tagen

This pricing flexibility makes high quality video generation more accessible

Profilbild von Nexo

Nexovor 15 Tagen

Time to test it out

Profilbild von Naman T

Naman Tvor 14 Tagen

what about kieai ?

Profilbild von Zara Tech & Tools

Zara Tech & Toolsvor 12 Tagen

Great value with same quality for less

Profilbild von RAZA | AI EXPLORER

RAZA | AI EXPLORERvor 12 Tagen

Great launch. Exciting to see more efficient video generation with flexible cost and speed options.

Profilbild von Elara AI

Elara AIvor 13 Tagen

Love having budget flexibility without sacrificing results at all

Profilbild von Amelia | AI & Tech

Amelia | AI & Techvor 13 Tagen

A penny a second makes volume generation realistic

Profilbild von Adrian Scott | A.I. + Business Upscaling

Adrian Scott | A.I. + Business Upscalingvor 15 Tagen

do you have an option without code carbon?

Profilbild von Chris Dallas & Celebrity Recreations

Chris Dallas & Celebrity Recreationsvor 14 Tagen

@PrunaAI your P-Video-Avatar playground has been returning a 502 "Application failed to respond" error for several days now. Is it being restored, or has it been discontinued? It's been my go-to lip sync tool.

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648,067 Aufrufe • vor 2 Monaten

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

Artificial Analysis

133,587 Aufrufe • vor 2 Monaten

Microsoft canceling its internal Claude Code licenses this week likely has almost nothing to do with AI costs becoming untenable. Microsoft's own statement says Claude models remain accessible through Copilot CLI and Anthropic's Claude still runs inside Microsoft 365. The real story is that Microsoft's own product (GitHub Copilot) was being embarrassed by a competitor internally this is a platform strategy play, not a cost crisis call. And the post assumes token prices are rising and unsustainable but the actual data goes the other way. As Sam Altman said, a hard reasoning problem that cost X on the OpenAI API 18 months ago now costs 1,000x less. 1,000x in 18 months is just that doesn't happen very often. His stated mission is to relentlessly drive the cost of intelligence down as close to zero as possible and make it a low-cost asset available to the world. A16z separately documented a 10x cost decline per year for equivalent model performance and Nvidia's Blackwell platform delivered 4x to 10x inference cost reductions in production deployments in early 2026. Yes, Uber burned through its 2026 AI budget in four months. But look at why, 95% of engineers were using AI tools monthly, ~70% of committed code was AI-generated, and 11% of real-time backend updates were made autonomously by agents with zero human intervention. That's not an AI cost problem. That's a CFO who budgeted for a pilot and got a revolution. Also, this post ignores the Jevons Paradox, when a resource gets cheaper, total consumption rises, not falls. Tokens inferenced on platforms like OpenRouter grew 25-fold since December 2024, OpenAI’s revenue grew at roughly a 250% annual rate to around a $20B run rate, while Anthropic’s surged from roughly $9B to over $30B in just a few months. Those aren't the numbers of companies whose economics are imploding. Even Gartner projects inference on a 1-trillion parameter model will cost 90%+ less by 2030. The actual Gartner warning is that agentic AI costs more per task because agents run longer chains, that's a design and scoping problem, not a structural industry collapse.

Microsoft canceling its internal Claude Code licenses this week likely has almost nothing to do with AI costs becoming untenable. Microsoft's own statement says Claude models remain accessible through Copilot CLI and Anthropic's Claude still runs inside Microsoft 365. The real story is that Microsoft's own product (GitHub Copilot) was being embarrassed by a competitor internally this is a platform strategy play, not a cost crisis call. And the post assumes token prices are rising and unsustainable but the actual data goes the other way. As Sam Altman said, a hard reasoning problem that cost X on the OpenAI API 18 months ago now costs 1,000x less. 1,000x in 18 months is just that doesn't happen very often. His stated mission is to relentlessly drive the cost of intelligence down as close to zero as possible and make it a low-cost asset available to the world. A16z separately documented a 10x cost decline per year for equivalent model performance and Nvidia's Blackwell platform delivered 4x to 10x inference cost reductions in production deployments in early 2026. Yes, Uber burned through its 2026 AI budget in four months. But look at why, 95% of engineers were using AI tools monthly, ~70% of committed code was AI-generated, and 11% of real-time backend updates were made autonomously by agents with zero human intervention. That's not an AI cost problem. That's a CFO who budgeted for a pilot and got a revolution. Also, this post ignores the Jevons Paradox, when a resource gets cheaper, total consumption rises, not falls. Tokens inferenced on platforms like OpenRouter grew 25-fold since December 2024, OpenAI’s revenue grew at roughly a 250% annual rate to around a $20B run rate, while Anthropic’s surged from roughly $9B to over $30B in just a few months. Those aren't the numbers of companies whose economics are imploding. Even Gartner projects inference on a 1-trillion parameter model will cost 90%+ less by 2030. The actual Gartner warning is that agentic AI costs more per task because agents run longer chains, that's a design and scoping problem, not a structural industry collapse.

Milk Road AI

251,150 Aufrufe • vor 4 Monaten

Great explanation by Emad Mostaque, co-founder of Stability AI. "We’ll see the cost of Kimi K3 drop by 10 to 50 times, I think, over the next few months as it gets optimized. " Basically Kimi K3’s current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available. --- "Right now, it uses twice the number of tokens for the same task compared with GPT-5.6. Again, we’re going to see that cost drop because everyone and their dog is going to optimize the crap out of this. Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion. These are inference providers for open-source models. They’ve all raised around a billion dollars, which they’re now going to spend on optimizing the Chinese model, making it more efficient, and running it. American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up." ---- From "Peter H. Diamandis" YouTube channel, (full video link in comment)

Great explanation by Emad Mostaque, co-founder of Stability AI. "We’ll see the cost of Kimi K3 drop by 10 to 50 times, I think, over the next few months as it gets optimized. " Basically Kimi K3’s current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available. --- "Right now, it uses twice the number of tokens for the same task compared with GPT-5.6. Again, we’re going to see that cost drop because everyone and their dog is going to optimize the crap out of this. Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion. These are inference providers for open-source models. They’ve all raised around a billion dollars, which they’re now going to spend on optimizing the Chinese model, making it more efficient, and running it. American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up." ---- From "Peter H. Diamandis" YouTube channel, (full video link in comment)

Rohan Paul

82,007 Aufrufe • vor 2 Monaten

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,593 Aufrufe • vor 4 Monaten

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation dax (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • WorkOS – everything you need to make your app enterprise ready • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation dax (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • WorkOS – everything you need to make your app enterprise ready • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

Gergely Orosz

232,386 Aufrufe • vor 4 Monaten

🔥HOLY SMOKES! $TAO holders! 🚀 SUBNET 19 (VISION) ON BITTENSOR IS ABSOLUTELY CRUSHING IT! In my 5+ years covering crypto and AI, this is one of the most impressive implementations I've seen. The combination of scale, performance, and decentralization is absolutely next level! 🚀 @namoray_dev @Corcel_X 💨 INSANE Speed Performance: - Llama 3.1 8B: 196.18 tokens/s with +107.23% advantage - Llama 3.1 70B: 124.96 tokens/s with +154.96% advantage - Llama 3.2 3B: 166.69 tokens/s with +21.66% advantage 🔥 Top Tier Model Integration: - Meta-Llama-3-70B & 8B Instruct - FLUX.1-schnell for Text-to-Image - ProteusV0.4-Lightning (Text & Image) - Multiple model variations for redundancy 🔥 What Makes This INSANE: - Complete decentralization - No single point of failure - Multiple model choices for redundancy - Real-time performance tracking - Transparent incentive structure The incentive distribution curve shows a healthy network with: - Strong rewards for top performers - Fair distribution across all participants - Clear path for growth and improvement - Sustainable economic model What's truly MIND-BLOWING is how they've managed to: 1. Scale to millions of operations 2. Maintain high quality across multiple tasks 3. Create a fair, competitive marketplace 4. Build in redundancy and reliability 5. Achieve true decentralization This isn't just another subnet - this is the future of decentralized AI inference happening RIGHT NOW! 🔥 1. MASSIVE Scale & Adoption: - We're seeing 7M+ tokens being processed - 14K+ processing steps being executed - Multiple AI models running simultaneously - Incredible miner participation across the network 2. Revolutionary Task Distribution: - Llama 3.1 70B leading with 20% weighting - Avatar Generation at 15% - Perfectly balanced task distribution for optimal network performance - Multiple specialized tasks including Text-to-Image and Image-to-Image processing 3. Elite Performance Metrics: - Top miners hitting 0.00775 incentive rates - Consistent performance across the network - Impressive scaling from top to bottom performers - Strong incentive curve maintaining network quality 📈 Network Performance: - Consistent upward trend in tokens/s - Quality scores maintaining high levels (>0.9) - Steady improvement in miner performance - Rock-solid network reliability ⚡ Platform Highlights: - Permissionless, serverless architecture - Global network of Always-On GPUs - Instant API access - Full decentralization - Multi-model support with seamless switching What makes this TRULY SPECIAL is the consistent upward trajectory in both speed and quality, while maintaining a decentralized architecture. The performance advantages over industry standards (+154.96% for 70B!) are absolutely mind-blowing! 🚀 This isn't just another AI subnet - it's a glimpse into the future of decentralized AI inference! The combination of speed, reliability, and model variety makes this one of the most impressive implementations in the space! 🔥 📽 Watch Now on YouTube and TikTok: Source 🔗

Andy ττ

11,616 Aufrufe • vor 1 Jahr

✨ I made a 100% AI video of a single night in 🇩🇪 Berlin's underground techno scene Doing an AI shoot is very much like a real life shoot, you have to prepare locations, settings, outfits and models, you can do the same: 1) First I generated photos with the [ 🇩🇪 Berlin nightlife ] photo pack on they're all non-existing AI people except me and my gf 2) Then I put the pics I liked in Kling AI to make them into 10 second videos (with Professional Mode), soon I'll have API access and it'll be one-click inside Photo AI to turn a photo into a 10-sec video, it then takes about 5 minutes to make a video. It's not always great, especially dancing is hard to get right! 3) Then I wanted to have a German narrator talking about Berlin's techno nights in a poetic way like a documentary, so I asked ChatGPT 4o: "write in the style of a cult German novelist about the Berlin techno scene" I then copied that text into Eleven Labs and selected a German AI voice 4) I then collected all the videos, the music and the audio narrator and edited them together in Final Cut Pro adding music from HÖR and adding audio effects 5) Then I added English captions with CapCut Time to make it: 3 hours for 1 minute of video I hope you like it! My dream is to have this entire pipeline in Photo AI at some point. Connecting to Kling AI's video API is the first step to that. Imagine just writing a prompt and you end up with a video like this without all the work Credits: Music: Asquith - Let Me (Rave Mix) [ASQ004] Barbax - Si Vis Pacem Para Bellum (Original Mix) Mixed by Ellen Allien @ HÖR Video: 100% AI characters and video by Photo AI Edited by me Narrator voice: 100% written by ChatGPT 4o 100% narrated by Eleven Labs AI voice

✨ I made a 100% AI video of a single night in 🇩🇪 Berlin's underground techno scene Doing an AI shoot is very much like a real life shoot, you have to prepare locations, settings, outfits and models, you can do the same: 1) First I generated photos with the [ 🇩🇪 Berlin nightlife ] photo pack on they're all non-existing AI people except me and my gf 2) Then I put the pics I liked in Kling AI to make them into 10 second videos (with Professional Mode), soon I'll have API access and it'll be one-click inside Photo AI to turn a photo into a 10-sec video, it then takes about 5 minutes to make a video. It's not always great, especially dancing is hard to get right! 3) Then I wanted to have a German narrator talking about Berlin's techno nights in a poetic way like a documentary, so I asked ChatGPT 4o: "write in the style of a cult German novelist about the Berlin techno scene" I then copied that text into Eleven Labs and selected a German AI voice 4) I then collected all the videos, the music and the audio narrator and edited them together in Final Cut Pro adding music from HÖR and adding audio effects 5) Then I added English captions with CapCut Time to make it: 3 hours for 1 minute of video I hope you like it! My dream is to have this entire pipeline in Photo AI at some point. Connecting to Kling AI's video API is the first step to that. Imagine just writing a prompt and you end up with a video like this without all the work Credits: Music: Asquith - Let Me (Rave Mix) [ASQ004] Barbax - Si Vis Pacem Para Bellum (Original Mix) Mixed by Ellen Allien @ HÖR Video: 100% AI characters and video by Photo AI Edited by me Narrator voice: 100% written by ChatGPT 4o 100% narrated by Eleven Labs AI voice

@levelsio

676,094 Aufrufe • vor 2 Jahren

Veo 3.1 is now live on invideo. We’re one of the first official partners worldwide to launch Veo 3.1. The Google Cloud team gave us early access, and it changes everything. Veo 3.1 upgrades what creators actually care about: visual quality, motion realism, and prompt responsiveness. But the real breakthrough is in control: → You can now define a start and end frame and Veo 3.1 builds everything between them with perfect flow. → You can lock a character’s look across scenes. → You can merge multiple references into one coherent world. When that power meets invideo, it turns from generation into storytelling. Because invideo isn’t just an AI video generator, it’s a full-stack AI production house at your disposal. It makes over 300 creative decisions across editing, scripting, visuals, sound, and voiceover to craft videos like a full-scale production team would. Ad films, brand spots, social content, cinematic stories, all created with the precision of a studio team, at the speed of thought. Watch how Veo 3.1 inside invideo turns imagination into cinematic magic👇

Sanket Shah

558,983 Aufrufe • vor 1 Jahr