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Most AI builders underestimate how expensive transcription becomes once their product starts scaling. For a lot of teams, speech-to-text quietly turns into one of the biggest infrastructure bills. Just tested Velma Transcribe from Modulate and the pricing is surprisingly low compared to typical STT APIs. If you're building: •... show more

Kaitee

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50,886 次观看 • 6 个月前 •via X (Twitter)

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Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison @matiii ElevenLabs

Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison @matiii ElevenLabs

Stripe

18,535 次观看 • 5 个月前

WHY ARE AI DEVELOPERS STILL PAYING FOR 5+ DIFFERENT APIs? GPT -> Claude -> Gemini -> DeepSeek -> Grok. Different providers. Different keys. Different integrations. And a growing API bill. DIT ai changes that. → One API gives you access to 50+ leading AI models with a single key. → Lower costs requests are routed across 160+ providers to find competitive real time pricing, with typical savings of 30–70% depending on the model and route. → Keep your stack — DIT ai is OpenAI compatible. Change your base URL + API key instead of rebuilding your entire integration. → Automatic failover — if an upstream provider fails before the response begins, DIT ai automatically reroutes to another qualified provider. For AI agents, SaaS products, startups, and anyone pushing serious API volume, this could make managing AI infrastructure a lot simpler. Same models. One key. Lower bill. LINK BELOW TO TRY IT OUT

WHY ARE AI DEVELOPERS STILL PAYING FOR 5+ DIFFERENT APIs? GPT -> Claude -> Gemini -> DeepSeek -> Grok. Different providers. Different keys. Different integrations. And a growing API bill. DIT ai changes that. → One API gives you access to 50+ leading AI models with a single key. → Lower costs requests are routed across 160+ providers to find competitive real time pricing, with typical savings of 30–70% depending on the model and route. → Keep your stack — DIT ai is OpenAI compatible. Change your base URL + API key instead of rebuilding your entire integration. → Automatic failover — if an upstream provider fails before the response begins, DIT ai automatically reroutes to another qualified provider. For AI agents, SaaS products, startups, and anyone pushing serious API volume, this could make managing AI infrastructure a lot simpler. Same models. One key. Lower bill. LINK BELOW TO TRY IT OUT

MAX

45,561 次观看 • 12 天前

most AI APIs either cost too much or lock the best models behind expensive plans. then i came across Agnes AI. it’s one of the few platforms giving developers access to text, image, and video AI models through a single API. their ecosystem gives you direct access to top-tier models like - Agnes-2.0-Flash for text - Agnes-Image-2.0 for image - Agnes-Video-V2.0 for video Instead of managing different providers for different tasks, everything works from one place. what stood out to me: > one API for text, image, and video generation > OpenAI-compatible API, so integration is straightforward > built for speed and scalability > free access for developers to start building > useful for everything from chatbots to image generation and content automation if you’re building AI products, it’s worth taking a look while it’s still flying under the radar. sometimes the biggest advantage is finding great tools before everyone else does. check out Agnes AI here, get free access for developers : and if you’re building with it, join the developer community here: Agnes AI #AgnesAI #AgnesAPI #BuildWithAgnes #MultimodalAI #AIforDevs #DeveloperAPI #AIParity

most AI APIs either cost too much or lock the best models behind expensive plans. then i came across Agnes AI. it’s one of the few platforms giving developers access to text, image, and video AI models through a single API. their ecosystem gives you direct access to top-tier models like - Agnes-2.0-Flash for text - Agnes-Image-2.0 for image - Agnes-Video-V2.0 for video Instead of managing different providers for different tasks, everything works from one place. what stood out to me: > one API for text, image, and video generation > OpenAI-compatible API, so integration is straightforward > built for speed and scalability > free access for developers to start building > useful for everything from chatbots to image generation and content automation if you’re building AI products, it’s worth taking a look while it’s still flying under the radar. sometimes the biggest advantage is finding great tools before everyone else does. check out Agnes AI here, get free access for developers : and if you’re building with it, join the developer community here: Agnes AI #AgnesAI #AgnesAPI #BuildWithAgnes #MultimodalAI #AIforDevs #DeveloperAPI #AIParity

MIKE

14,591 次观看 • 2 个月前

I built a service desk agent using ElevenLabs’ new Conversational AI Agents feature. Watch the video to see how responsive it is! Previously, I used ElevenLabs for cloning my voice and used my generated voice for narrations in some of my youtube videos. This feature takes elevenlabs' voice AI to a whole new level! It simplifies systems that used to require separate TTS (text-to-speech) and STT (speech-to-text) processes for both sides of the conversation. Now, it’s much simpler! Create your own agent here What will you create with this?

I built a service desk agent using ElevenLabs’ new Conversational AI Agents feature. Watch the video to see how responsive it is! Previously, I used ElevenLabs for cloning my voice and used my generated voice for narrations in some of my youtube videos. This feature takes elevenlabs' voice AI to a whole new level! It simplifies systems that used to require separate TTS (text-to-speech) and STT (speech-to-text) processes for both sides of the conversation. Now, it’s much simpler! Create your own agent here What will you create with this?

Melvin Vivas

27,722 次观看 • 1 年前

AI transcribing 1 hour of audio in less than 30 seconds! Think about that! That's lightning-fast, especially when it can do that with very few mistakes. The team Deepgram released their new Nova 2 model. It's another leap forward: • 36% more accurate than OpenAI Whisper • 5-40x faster than every other alternative • 30% fewer errors than the competitors in real-time transcriptions I like to travel and can't wait for headphones with integrated live transcription and translation. We are getting close! By the way, this is the cheapest transcription service out in the market now (if you discount Whisper, which is open-source.) It costs $0.0043 to transcribe a minute of audio. When you sign up to access their API, you get $200 of credit for free, which will let you transcribe around 45,000 minutes of audio! Here is the link: I wanted to record a quick video showing their live transcription in Spanish and English. You can see it attached here. This post was sponsored by Deepgram.

AI transcribing 1 hour of audio in less than 30 seconds! Think about that! That's lightning-fast, especially when it can do that with very few mistakes. The team Deepgram released their new Nova 2 model. It's another leap forward: • 36% more accurate than OpenAI Whisper • 5-40x faster than every other alternative • 30% fewer errors than the competitors in real-time transcriptions I like to travel and can't wait for headphones with integrated live transcription and translation. We are getting close! By the way, this is the cheapest transcription service out in the market now (if you discount Whisper, which is open-source.) It costs $0.0043 to transcribe a minute of audio. When you sign up to access their API, you get $200 of credit for free, which will let you transcribe around 45,000 minutes of audio! Here is the link: I wanted to record a quick video showing their live transcription in Spanish and English. You can see it attached here. This post was sponsored by Deepgram.

Santiago

354,297 次观看 • 3 年前

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.

Gipp 🦅

119,018 次观看 • 2 个月前

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,868 次观看 • 1 年前

We want to make AI accessible for everyone, so we're reducing our API prices by ~50%. Consumer AI growth is still blocked by model costs. 97% of consumer AI users will never pay. But every session still consumes tokens. This is the math we've found every consumer AI developer is stuck with. The model pricing they're forced to use, was built for $300/mo enterprise seats, not $10/month consumer subscriptions. So we're taking a step to solve this with reduced pricing across TTS, STT and LLMs. Teams like Wishroll, Biblechat, Talkpal, and Luvu are already seeing 40-95% reductions in their AI costs. Full breakdown on our blog: Pricing details in bio link.

We want to make AI accessible for everyone, so we're reducing our API prices by ~50%. Consumer AI growth is still blocked by model costs. 97% of consumer AI users will never pay. But every session still consumes tokens. This is the math we've found every consumer AI developer is stuck with. The model pricing they're forced to use, was built for $300/mo enterprise seats, not $10/month consumer subscriptions. So we're taking a step to solve this with reduced pricing across TTS, STT and LLMs. Teams like Wishroll, Biblechat, Talkpal, and Luvu are already seeing 40-95% reductions in their AI costs. Full breakdown on our blog: Pricing details in bio link.

Inworld AI

623,332 次观看 • 3 个月前

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses three NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,801 次观看 • 8 个月前

🚨 Founders: free, private, 100% local AI is finally here I downloaded Qwen 3.6 and ran it on my MacBook inside Strawberry browser. I couldn't believe it. We now have 100% free and private AI that is on par with the best models from OpenAI, Anthropic and Google. (see video!) The CTO of Hugging Face called this "the second revolution of AI." He's right. If you are a building an AI product, you HAVE to start thinking about how you can leverage open source models in your product. It's only a matter of time until local models are mainstream and can run on most people's devices. LLMs are commoditizing. The human brain only requires a AAA battery's worth of energy to run. If biology figured out efficient intelligence, software can too. Maybe we won't need artificial super intelligence and thousands of data centres to do most economically valuable work. Maybe there's a future where AI agents just run on your laptop, fine-tuned to you, fully owned by you, costing only what your energy bill costs. In this future, it's all about building the most delightful, intuitive experience. We've been building for exactly this moment. Stay tuned.

🚨 Founders: free, private, 100% local AI is finally here I downloaded Qwen 3.6 and ran it on my MacBook inside Strawberry browser. I couldn't believe it. We now have 100% free and private AI that is on par with the best models from OpenAI, Anthropic and Google. (see video!) The CTO of Hugging Face called this "the second revolution of AI." He's right. If you are a building an AI product, you HAVE to start thinking about how you can leverage open source models in your product. It's only a matter of time until local models are mainstream and can run on most people's devices. LLMs are commoditizing. The human brain only requires a AAA battery's worth of energy to run. If biology figured out efficient intelligence, software can too. Maybe we won't need artificial super intelligence and thousands of data centres to do most economically valuable work. Maybe there's a future where AI agents just run on your laptop, fine-tuned to you, fully owned by you, costing only what your energy bill costs. In this future, it's all about building the most delightful, intuitive experience. We've been building for exactly this moment. Stay tuned.

Charles Maddock

12,731 次观看 • 4 个月前

How did a tiny, scrappy team build one of the most powerful AI voice models? In a deep dive with Sesame CTO Ankit Kumar and a16z's Anjney Midha, we explore how Sesame is pushing the boundaries of AI conversation, why it open-sourced its speech generation model, and the power of small teams to outdo much larger AI labs on product focus. A part of their secret: a relentless focus on real-time, natural conversations over raw intelligence, and a deep commitment to voice, personality, and user experience. By opening up its speech generation model, Sesame is paving the way for even more breakthroughs in AI-native conversation 👇

How did a tiny, scrappy team build one of the most powerful AI voice models? In a deep dive with Sesame CTO Ankit Kumar and a16z's Anjney Midha, we explore how Sesame is pushing the boundaries of AI conversation, why it open-sourced its speech generation model, and the power of small teams to outdo much larger AI labs on product focus. A part of their secret: a relentless focus on real-time, natural conversations over raw intelligence, and a deep commitment to voice, personality, and user experience. By opening up its speech generation model, Sesame is paving the way for even more breakthroughs in AI-native conversation 👇

a16z

29,930 次观看 • 1 年前

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

Shay Boloor

122,716 次观看 • 4 个月前

Most founders accidentally train their customers never to pay them. Madhavan Ramanujam (the "pricing guru" of Silicon Valley - having worked with LinkedIn, Uber, and 30+ unicorns) calls it the 20/80 Pricing Trap • 20% of your features drive 80% of the willingness to pay • Founders give that 20% away for free to gain distribution • You are left trying to monetize the remaining 80% of features - the ones users don't actually value. The result? You build a charity, not a business. In this week's episode of The Library of Minds, we discuss the science of monetization and deconstruct how to architect ‘Profitable Growth’ - the core framework from his new book, Scaling Innovation. 03:33 - Netflix vs Blockbuster: The Pricing Decision That Changed Tech 08:06 - Why Most Startups Get Pricing Wrong 11:39 - Freemium vs Paid: When Free Destroys Value 15:38 - Pricing Models Matter More Than Price 16:28 - The AI Pricing Framework: Autonomy vs Attribution 21:49 - The Biggest Pricing Mistake Ever 25:05 - Why Steve Jobs Was a Pricing Genius 26:44 - Behavioral Pricing: How Founders 10× Deals Without Changing Product 31:02 - Data vs Conviction: How Great Founders Make Pricing Decisions

Most founders accidentally train their customers never to pay them. Madhavan Ramanujam (the "pricing guru" of Silicon Valley - having worked with LinkedIn, Uber, and 30+ unicorns) calls it the 20/80 Pricing Trap • 20% of your features drive 80% of the willingness to pay • Founders give that 20% away for free to gain distribution • You are left trying to monetize the remaining 80% of features - the ones users don't actually value. The result? You build a charity, not a business. In this week's episode of The Library of Minds, we discuss the science of monetization and deconstruct how to architect ‘Profitable Growth’ - the core framework from his new book, Scaling Innovation. 03:33 - Netflix vs Blockbuster: The Pricing Decision That Changed Tech 08:06 - Why Most Startups Get Pricing Wrong 11:39 - Freemium vs Paid: When Free Destroys Value 15:38 - Pricing Models Matter More Than Price 16:28 - The AI Pricing Framework: Autonomy vs Attribution 21:49 - The Biggest Pricing Mistake Ever 25:05 - Why Steve Jobs Was a Pricing Genius 26:44 - Behavioral Pricing: How Founders 10× Deals Without Changing Product 31:02 - Data vs Conviction: How Great Founders Make Pricing Decisions

Dara

215,786 次观看 • 9 个月前

This is the fastest text-to-speech and speech-to-text API out there. It's under 250 ms to first-byte latency. That's what we need to deploy conversational AI applications everywhere! (I can't wait for the death of automated voice machines.) Look at the attached video. For the first time, I could have a conversation with an AI agent in real time without awkward pauses. This technology is Aura from Deepgram. It's the fastest text-to-speech option I've seen. Here is a GitHub repository with the demo in the video: You can use it to build a conversational AI application using natural voice. You'll need a Deepgram API Key and an OpenAI API Key. Thanks to Deepgram for giving me access to Aura and collaborating with me on this post. If you want to learn more about Aura, here is the announcement post:

This is the fastest text-to-speech and speech-to-text API out there. It's under 250 ms to first-byte latency. That's what we need to deploy conversational AI applications everywhere! (I can't wait for the death of automated voice machines.) Look at the attached video. For the first time, I could have a conversation with an AI agent in real time without awkward pauses. This technology is Aura from Deepgram. It's the fastest text-to-speech option I've seen. Here is a GitHub repository with the demo in the video: You can use it to build a conversational AI application using natural voice. You'll need a Deepgram API Key and an OpenAI API Key. Thanks to Deepgram for giving me access to Aura and collaborating with me on this post. If you want to learn more about Aura, here is the announcement post:

Santiago

185,640 次观看 • 2 年前

🚀 Just launched: The GitHub Models repo integration is in public preview. ✨ Now you can: ➡️ Version, review, and collaborate on .prompt.yml files just like code. ➡️ Compare outputs side-by-side from 40+ leading LLMs. ➡️ Easily set up with a single API key for FREE. ➡️ Benefit from enterprise-ready governance and security on GitHub and Azure. Ready to start building efficiently with AI models? Get started. 👇

🚀 Just launched: The GitHub Models repo integration is in public preview. ✨ Now you can: ➡️ Version, review, and collaborate on .prompt.yml files just like code. ➡️ Compare outputs side-by-side from 40+ leading LLMs. ➡️ Easily set up with a single API key for FREE. ➡️ Benefit from enterprise-ready governance and security on GitHub and Azure. Ready to start building efficiently with AI models? Get started. 👇

GitHub

37,951 次观看 • 1 年前

🔊🔛🔥 ... Groq Inc launched voice generation today. GroqCloud now has realtime transcription, LLMs, *and* text-to-speech. You can build super-responsive, ultra low-latency voice agents end-to-end entirely on Groq! At Daily, we're big fans of Groq's fast, low-latency inference. Pipecat AI supports all the Groq models, including the new voice model. Lately, we've been obsessively playing a voice chat game that Mark Backman wrote. (My high score is 6, so far.) Here's Mark, with Groq's `Celeste-PlayAI` voice.

🔊🔛🔥 ... Groq Inc launched voice generation today. GroqCloud now has realtime transcription, LLMs, and text-to-speech. You can build super-responsive, ultra low-latency voice agents end-to-end entirely on Groq! At Daily, we're big fans of Groq's fast, low-latency inference. Pipecat AI supports all the Groq models, including the new voice model. Lately, we've been obsessively playing a voice chat game that Mark Backman wrote. (My high score is 6, so far.) Here's Mark, with Groq's `Celeste-PlayAI` voice.

kwindla

31,843 次观看 • 1 年前

“Surge Pricing” in American grocery stores has arrived Kroger partnered with Microsoft to introduce AI-powered technologies for personalized pricing “Kroger just rolled out surge pricing using facial recognition technology: - Determine the maximum price of goods customers are willing to pay - Kroger has the ability to change the prices of their products depending on the facial image of the person buying them - Depending on the image of the person buying the product, they can access our information and dictate that price - Can also use our image to determine how often we buy certain products and increase the price of that product - Dynamic pricing in grocery stores could resemble the airline industry's ticket pricing strategies “This could lead to discriminatory practices using AI as a scapegoat to charge people of different races, different socioeconomic status, different family sizes, and different dietary restrictions different prices for the same products that they are buying” (Kroger, the largest grocery chain in the U.S., denied claims that ESLs would be used for surge pricing, insisting its pricing strategy aims to lower costs for consumers. "To be clear, Kroger does not and has never engaged in 'surge pricing.' Any test of electronic shelf tags is designed to lower prices for more customers where it matters most”) … Yeah right

“Surge Pricing” in American grocery stores has arrived Kroger partnered with Microsoft to introduce AI-powered technologies for personalized pricing “Kroger just rolled out surge pricing using facial recognition technology: - Determine the maximum price of goods customers are willing to pay - Kroger has the ability to change the prices of their products depending on the facial image of the person buying them - Depending on the image of the person buying the product, they can access our information and dictate that price - Can also use our image to determine how often we buy certain products and increase the price of that product - Dynamic pricing in grocery stores could resemble the airline industry's ticket pricing strategies “This could lead to discriminatory practices using AI as a scapegoat to charge people of different races, different socioeconomic status, different family sizes, and different dietary restrictions different prices for the same products that they are buying” (Kroger, the largest grocery chain in the U.S., denied claims that ESLs would be used for surge pricing, insisting its pricing strategy aims to lower costs for consumers. "To be clear, Kroger does not and has never engaged in 'surge pricing.' Any test of electronic shelf tags is designed to lower prices for more customers where it matters most”) … Yeah right

Wall Street Apes

688,890 次观看 • 1 年前

you can start using opus 4.8 and other leading AI models COMPLETELY FREE. no subscription. no API credits. 100% legit. amazon is giving eligible new AWS accounts up to $200 in credits. which can be used to access models through AWS services. with it, you can explore models such as: > anthropic's claude family, including opus 4.8 and earlier generations > google's gemma series > minimax 2.5, 2.2, and 2 > deepseek v3.2 and r1 and many more. if you've been looking for a way to experiment with frontier AI models without spending your own money, this is one of the easiest places to start. the setup only takes a few minutes, and the credits can go a surprisingly long way if you're learning, building side projects, or testing ideas.

you can start using opus 4.8 and other leading AI models COMPLETELY FREE. no subscription. no API credits. 100% legit. amazon is giving eligible new AWS accounts up to $200 in credits. which can be used to access models through AWS services. with it, you can explore models such as: > anthropic's claude family, including opus 4.8 and earlier generations > google's gemma series > minimax 2.5, 2.2, and 2 > deepseek v3.2 and r1 and many more. if you've been looking for a way to experiment with frontier AI models without spending your own money, this is one of the easiest places to start. the setup only takes a few minutes, and the credits can go a surprisingly long way if you're learning, building side projects, or testing ideas.

m0h

11,242 次观看 • 2 个月前

We spent months building this so you don't have to figure it out alone. A free 7-day course that takes you from zero to building real things with AI, no coding background, no expensive tools, no fluff, just results. From 0 -> to Claude code expert, completely free: > In 7 days, you’ll learn vibe coding, set up Claude Code, build your first AI app, create a prompt-built website, automate a task with n8n, and deploy it live to a real URL. No theory, no filler, just a real project you can show people by the end of the week. For those who want to go further, we spent even longer building another one, designed for people who are genuinely interested in AI deep learning and launching products. From Expert to Your First $10k Product {You can find it on the link above as well} The complete masterclass, full-stack AI apps, Supabase, n8n automations, AI agents, Telegram bots, SaaS products built from scratch, Stripe, subscriptions, client work, freelancing, pricing and scaling. This is the course that turns the skill into actual income. Me and Noisy put everything we know into both of these, you can start free, build something real, then decide how far you want to take it.

We spent months building this so you don't have to figure it out alone. A free 7-day course that takes you from zero to building real things with AI, no coding background, no expensive tools, no fluff, just results. From 0 -> to Claude code expert, completely free: > In 7 days, you’ll learn vibe coding, set up Claude Code, build your first AI app, create a prompt-built website, automate a task with n8n, and deploy it live to a real URL. No theory, no filler, just a real project you can show people by the end of the week. For those who want to go further, we spent even longer building another one, designed for people who are genuinely interested in AI deep learning and launching products. From Expert to Your First $10k Product {You can find it on the link above as well} The complete masterclass, full-stack AI apps, Supabase, n8n automations, AI agents, Telegram bots, SaaS products built from scratch, Stripe, subscriptions, client work, freelancing, pricing and scaling. This is the course that turns the skill into actual income. Me and Noisy put everything we know into both of these, you can start free, build something real, then decide how far you want to take it.

Defileo🔮

695,073 次观看 • 4 个月前

🚨 Something interesting is happening with Freepik AI pricing and most people seem to have missed it. Freepik has quietly reduced the number of credits required to generate AI images and videos across major models. This means you can now create significantly more content on the same subscription. If you use AI tools on a daily basis, this change genuinely matters. Follow the complete 🧵 for image and video generation tutorial. 🧵 (1/4)

🚨 Something interesting is happening with Freepik AI pricing and most people seem to have missed it. Freepik has quietly reduced the number of credits required to generate AI images and videos across major models. This means you can now create significantly more content on the same subscription. If you use AI tools on a daily basis, this change genuinely matters. Follow the complete 🧵 for image and video generation tutorial. 🧵 (1/4)

Abhishek Yadav

30,808 次观看 • 7 个月前