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Been experimenting with Apple's new Core AI framework over the past few days Built a mobile app that runs local AI models directly on device, exploring Core AI model deployment (.aimodel), model specialization, caching, and inference across Apple Silicon Also spent some time integrating with Apple's Foundation Models framework... show more

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13,221 Aufrufe • vor 2 Monaten •via X (Twitter)

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What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

D4rsh🦅

13,266 Aufrufe • vor 1 Jahr

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

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At Google I/O, I sat down with Omar Sanseviero and 👩‍💻 Paige Bailey from Google DeepMind to talk about Gemma, open models, AI Studio, on-device AI, sovereign AI and the future of AI development. A great conversation on how building with AI is becoming more open, local and accessible.

At Google I/O, I sat down with Omar Sanseviero and 👩‍💻 Paige Bailey from Google DeepMind to talk about Gemma, open models, AI Studio, on-device AI, sovereign AI and the future of AI development. A great conversation on how building with AI is becoming more open, local and accessible.

Chubby♨️

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The dominant story in AI has been the growing cloud: bigger clusters, larger models, more gigawatts. We believe the future is in the opposite direction: on-device inference, smaller models, watts instead of gigawatts. Today we're releasing OpenJarvis v1.0: a personal AI assistant that lives, learns, and works on your device.

The dominant story in AI has been the growing cloud: bigger clusters, larger models, more gigawatts. We believe the future is in the opposite direction: on-device inference, smaller models, watts instead of gigawatts. Today we're releasing OpenJarvis v1.0: a personal AI assistant that lives, learns, and works on your device.

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Here's a quick demo of an agentic loop running offline entirely on-device on Apple Watch Series 9. --- We have been spearheading small models and one of the biggest unlock it provides for consumer is having intelligence right at their fingertips, no cloud-round trip = faster, cheaper, private, more control. This also unlocks a whole suite of new use-cases with product specific models - or models built specifically for and shipped alongside an app. We now have full expertise on the entire on-device Apple Intelligence stack. We have pioneered training and running models with Apple native APIs on iPhones, Macs and Watches. Already working with Apple DR team and some of the biggest wearable brands exploring running AI on different form factors. If you're a team exploring this domain, we are here to help. Reach out -> follow Conscious Engines • Studio for consumer AI

Here's a quick demo of an agentic loop running offline entirely on-device on Apple Watch Series 9. --- We have been spearheading small models and one of the biggest unlock it provides for consumer is having intelligence right at their fingertips, no cloud-round trip = faster, cheaper, private, more control. This also unlocks a whole suite of new use-cases with product specific models - or models built specifically for and shipped alongside an app. We now have full expertise on the entire on-device Apple Intelligence stack. We have pioneered training and running models with Apple native APIs on iPhones, Macs and Watches. Already working with Apple DR team and some of the biggest wearable brands exploring running AI on different form factors. If you're a team exploring this domain, we are here to help. Reach out -> follow Conscious Engines • Studio for consumer AI

Kautuk | Conscious Engines

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✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, and across systems. We’re unlocking new possibilities for developers: • AMD Ryzen AI Halo, a local-first developer system, preorder starting in June, develop AI without limits on your desk • Gorgon Halo with up to 192GB unified memory, supporting 300B+ parameter models locally, run massive models locally We’re excited to partner with clem 🤗🤗, Co-founder and CEO of Hugging Face, to advance open-source AI for Ryzen AI. Our focus is seamless AI, from model to deployment. Cloud, edge, device. One continuum. Multi-agent systems, local inference at scale, open models as infrastructure. This is the next computing era 🚀

✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, and across systems. We’re unlocking new possibilities for developers: • AMD Ryzen AI Halo, a local-first developer system, preorder starting in June, develop AI without limits on your desk • Gorgon Halo with up to 192GB unified memory, supporting 300B+ parameter models locally, run massive models locally We’re excited to partner with clem 🤗🤗, Co-founder and CEO of Hugging Face, to advance open-source AI for Ryzen AI. Our focus is seamless AI, from model to deployment. Cloud, edge, device. One continuum. Multi-agent systems, local inference at scale, open models as infrastructure. This is the next computing era 🚀

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⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

OptimAI Network

43,645 Aufrufe • vor 11 Monaten

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

151,698 Aufrufe • vor 8 Monaten

AI agents are evolving and becoming more proactive. From cloud-based to on-device, these systems can run continuously, handle tasks, and keep sensitive data local. New Liquid Foundation Models bring agents directly to your laptop and phone, even while you are offline. Watch the latest episode of Advanced Insights to learn more about our partnership with Liquid AI:

AI agents are evolving and becoming more proactive. From cloud-based to on-device, these systems can run continuously, handle tasks, and keep sensitive data local. New Liquid Foundation Models bring agents directly to your laptop and phone, even while you are offline. Watch the latest episode of Advanced Insights to learn more about our partnership with Liquid AI:

AMD

30,054 Aufrufe • vor 4 Monaten

1/ Excited to launch an experiment today - introducing Window, a way to use your own AI models on the web - including local ones! It's a bet on a new kind of AI app emerging, one that shifts model authentication and management to the user.

1/ Excited to launch an experiment today - introducing Window, a way to use your own AI models on the web - including local ones! It's a bet on a new kind of AI app emerging, one that shifts model authentication and management to the user.

Alex Atallah

390,051 Aufrufe • vor 3 Jahren

What happens in a world post-foundation model? "Foundation models are not going away, they will remain a core part of future systems. The question is not whether they get replaced, but what gets built on top of them. Future AI systems will likely combine foundation models with additional components to create more complete systems." Demis Hassabis Love to hear your thoughts Yann LeCun François Chollet Jürgen Schmidhuber Aran Komatsuzaki

What happens in a world post-foundation model? "Foundation models are not going away, they will remain a core part of future systems. The question is not whether they get replaced, but what gets built on top of them. Future AI systems will likely combine foundation models with additional components to create more complete systems." Demis Hassabis Love to hear your thoughts Yann LeCun François Chollet Jürgen Schmidhuber Aran Komatsuzaki

Harry Stebbings

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For the first time, the latest LLMs run on the Apple Neural Engine — and NexaSDK is the only framework that makes it possible, powered by the NexaML engine. Last year, our two co-founders were invited by Apple DMLI team (Data & Machine Learning Innovation) to share their research about on-device multimodal model for local AI agents. One of the big questions in the room was: “Can the newest LLMs actually run on ANE?” At the time, nobody had a clear path. Today, that path exists. NexaSDK now runs Granite-4.0 (IBM), Qwen3 (Qwen), Gemma3 (Google), and Parakeet-v3 (NVIDIA) fully on Apple’s NPU — unlocking low-power, always-on, fast inference across Mac and iPhone. A new wave of NPU-first local AI apps is coming to Apple devices. Start with one line of code on Mac. iOS SDK coming soon.

NEXA AI

30,213 Aufrufe • vor 9 Monaten

🌟 Another Epic Sneak Peek: OptimAI Core Node’s Edge AI Computing Power! We’re excited to continue unveiling the upcoming OptimAI Core Node features! This time, we’re introducing Edge AI Computing, a game-changing feature that will make your devices even more powerful in contributing to the OptimAI Network. 🔥 What is Edge AI Computing? With the OptimAI Core Node, your device’s idle computing resources (CPU/GPU) and storage will be put to work, powering critical AI computing tasks, including: 🔸Edge Inference: Running real-time AI predictions directly on your device without sending data to centralized servers. 🔸Hot LLM Models: Accelerating large language models for high-speed NLP tasks. 🔸Generative AI Models and More: Contributing to the training and inference of cutting-edge generative AI, like text-to-image models, deep learning, and more. More Contributions, Bigger Rewards Await! This Edge AI Computing feature allows us to leverage the full potential of decentralized computing power, reducing latency, increasing efficiency, and making AI more accessible. The more you contribute, the bigger the rewards!🔥 Stay in the Loop—Start Today with OptimAI Lite Node! While the OptimAI Core Node is still in the works, you don’t have to wait to join the action. The more you contribute, the greater the rewards! Let’s build a stronger, smarter, and more decentralized AI network together. Stay tuned for more exciting updates! 🔸Extension Node: 🔸Telegram Node: 🔸Register at: #DePIN

🌟 Another Epic Sneak Peek: OptimAI Core Node’s Edge AI Computing Power! We’re excited to continue unveiling the upcoming OptimAI Core Node features! This time, we’re introducing Edge AI Computing, a game-changing feature that will make your devices even more powerful in contributing to the OptimAI Network. 🔥 What is Edge AI Computing? With the OptimAI Core Node, your device’s idle computing resources (CPU/GPU) and storage will be put to work, powering critical AI computing tasks, including: 🔸Edge Inference: Running real-time AI predictions directly on your device without sending data to centralized servers. 🔸Hot LLM Models: Accelerating large language models for high-speed NLP tasks. 🔸Generative AI Models and More: Contributing to the training and inference of cutting-edge generative AI, like text-to-image models, deep learning, and more. More Contributions, Bigger Rewards Await! This Edge AI Computing feature allows us to leverage the full potential of decentralized computing power, reducing latency, increasing efficiency, and making AI more accessible. The more you contribute, the bigger the rewards!🔥 Stay in the Loop—Start Today with OptimAI Lite Node! While the OptimAI Core Node is still in the works, you don’t have to wait to join the action. The more you contribute, the greater the rewards! Let’s build a stronger, smarter, and more decentralized AI network together. Stay tuned for more exciting updates! 🔸Extension Node: 🔸Telegram Node: 🔸Register at: #DePIN

OptimAI Network

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Compute has always been core to the Verne Robotics story. Getting there meant negotiating long, expensive contracts with big neoclouds, and projecting compute needs years into the future. Verne wasn't ready for that. Then came the Y Combinator cluster, built on Together AI. Verne trained a model 3x larger, on almost 100x more data. More data in, bigger foundation models out.

Compute has always been core to the Verne Robotics story. Getting there meant negotiating long, expensive contracts with big neoclouds, and projecting compute needs years into the future. Verne wasn't ready for that. Then came the Y Combinator cluster, built on Together AI. Verne trained a model 3x larger, on almost 100x more data. More data in, bigger foundation models out.

Together AI

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Discover how to add AI-driven features to your site AND keep user data local using Chrome built-in AI APIs. We partnered with Nykaa Fashion to show how an AI model running inside the browser processes images to suggest personalized outfits. Watch the demo showing a dynamic experience with on-device AI →

Discover how to add AI-driven features to your site AND keep user data local using Chrome built-in AI APIs. We partnered with Nykaa Fashion to show how an AI model running inside the browser processes images to suggest personalized outfits. Watch the demo showing a dynamic experience with on-device AI →

Chrome for Developers

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🔱 Introducing Manta AI 🔱 In collaboration with Celestia, ORA, and Hyperbolic, we are excited to launch a comprehensive AI toolkit on #MantaPacific. Experience seamless training, deployment, and inference with our Universal Circuits, now enhanced with powerful AGI models like Stable Diffusion & Llama. 🌊 Dive into the future of AI on blockchain:

🔱 Introducing Manta AI 🔱 In collaboration with Celestia, ORA, and Hyperbolic, we are excited to launch a comprehensive AI toolkit on #MantaPacific. Experience seamless training, deployment, and inference with our Universal Circuits, now enhanced with powerful AGI models like Stable Diffusion & Llama. 🌊 Dive into the future of AI on blockchain:

Manta Network (🔱,🔱)

174,097 Aufrufe • vor 2 Jahren

i will die on this hill: Apple is a frontier AI company. they will inject AI into every place you scroll, type, read and listen imagine if you could “google” a past memory or ask siri to prove you’re right in a group chat ai models that run on-device with access to your entire life. the craziest part is the models don’t even need to be big! a 20B frontier model can run smoothly on apple’s chip architecture completely privately there are 3.5 billion apple devices, the gravitational pull this company has on societal culture is unmatched

i will die on this hill: Apple is a frontier AI company. they will inject AI into every place you scroll, type, read and listen imagine if you could “google” a past memory or ask siri to prove you’re right in a group chat ai models that run on-device with access to your entire life. the craziest part is the models don’t even need to be big! a 20B frontier model can run smoothly on apple’s chip architecture completely privately there are 3.5 billion apple devices, the gravitational pull this company has on societal culture is unmatched

Ejaaz

136,637 Aufrufe • vor 2 Monaten

"LM Arena is a cancer on AI. Labs have entire teams dedicated to hacking it." Edwin Chen (echen), CEO of Surge AI, on why the industry's favorite benchmark is broken and how Surge hit $1.2 billion in revenue without ever raising. Aravind Srinivas (Aravind Srinivas), CEO of Perplexity, on Apple's AI advantage, Claude Code economics, the endgame of coding, and Perplexity Computer. They join @jason on This Week in AI Episode 10: 00:00 Intro to Aravind Srinivas and Edwin Chen 05:25 Edwin on Surge: School for AGI 10:47 What Apple's next CEO should do 21:20 "The iPhone is not getting disrupted by AI" 23:55 Bootstrapping Surge past $1B without raising 30:58 Claude Code as a loss leader 33:30 Are we in the endgame for coding? 41:34 30% headcount growth, 5x revenue 50:29 "People don't buy models, they buy products" 58:00 "LM Arena is a cancer on AI" 1:05:41 Model Council and orchestrating frontier models Full episode on YT, Spotify, and Apple Podcasts below: Perplexity Surge AI

"LM Arena is a cancer on AI. Labs have entire teams dedicated to hacking it." Edwin Chen (echen), CEO of Surge AI, on why the industry's favorite benchmark is broken and how Surge hit $1.2 billion in revenue without ever raising. Aravind Srinivas (Aravind Srinivas), CEO of Perplexity, on Apple's AI advantage, Claude Code economics, the endgame of coding, and Perplexity Computer. They join @jason on This Week in AI Episode 10: 00:00 Intro to Aravind Srinivas and Edwin Chen 05:25 Edwin on Surge: School for AGI 10:47 What Apple's next CEO should do 21:20 "The iPhone is not getting disrupted by AI" 23:55 Bootstrapping Surge past $1B without raising 30:58 Claude Code as a loss leader 33:30 Are we in the endgame for coding? 41:34 30% headcount growth, 5x revenue 50:29 "People don't buy models, they buy products" 58:00 "LM Arena is a cancer on AI" 1:05:41 Model Council and orchestrating frontier models Full episode on YT, Spotify, and Apple Podcasts below: Perplexity Surge AI

This Week in AI

40,470 Aufrufe • vor 4 Monaten