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Let's talk about MoE: 🔶 How many experts should you use? 🔶 How does dynamic routing actually behave in production? 🔶 How do you debug a model that won’t train? 🔶 What does 8x7B actually mean for memory and compute? 🔶 What hardware optimizations matter for sparse models? Mixture... show more

Cerebras

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345,732 просмотров • 1 год назад •via X (Twitter)

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A common misconception in AI right now is that everything we’re excited about is new. When I graduated from MIT in 2023, only 3% of students majored in AI (Course 6-4). I wasn’t one of them...oops. What I know about models, agents, frameworks, and hardware I learned after graduating. And if you’re newer to the field like me, it’s easy to assume today’s breakthroughs are fairly recent. After all, GPT-1 shipped in 2018... so it’s tempting for us to think the important ideas are less than a decade old. They aren’t. Mixture-of-Experts, the backbone of top models from companies like OpenAI and Google was invented in 1991 by Jacobs, Jordan, and Nowlan in “Adaptive Mixtures of Local Experts.” Over 30 years ago! In this conversation with Daria Soboleva, we discuss why MoEs are resurfacing now, the rapidly emerging compute bottlenecks, and how newer hardware architectures change the equation (Cerebras). A reminder that what feels “frontier” today is often decades in the making :)

A common misconception in AI right now is that everything we’re excited about is new. When I graduated from MIT in 2023, only 3% of students majored in AI (Course 6-4). I wasn’t one of them...oops. What I know about models, agents, frameworks, and hardware I learned after graduating. And if you’re newer to the field like me, it’s easy to assume today’s breakthroughs are fairly recent. After all, GPT-1 shipped in 2018... so it’s tempting for us to think the important ideas are less than a decade old. They aren’t. Mixture-of-Experts, the backbone of top models from companies like OpenAI and Google was invented in 1991 by Jacobs, Jordan, and Nowlan in “Adaptive Mixtures of Local Experts.” Over 30 years ago! In this conversation with Daria Soboleva, we discuss why MoEs are resurfacing now, the rapidly emerging compute bottlenecks, and how newer hardware architectures change the equation (Cerebras). A reminder that what feels “frontier” today is often decades in the making :)

Sarah Chieng

360,067 просмотров • 6 месяцев назад

New short course with Mistral AI ! Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token. In Getting Started with Mistral, you’ll learn from Mistral’s Sophia Yang, Ph.D. to: - Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website - Implement JSON mode to generate structured outputs to integrate directly into larger software systems. - Use function calling for Tool Use, such as calling custom Python code that queries tabular data - Ground your LLM's response with external knowledge sources using RAG - Build a Mistral-powered chat interface that can reference external documents This course will help deepen your prompt engineering skills. Please sign up here:

New short course with Mistral AI ! Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token. In Getting Started with Mistral, you’ll learn from Mistral’s Sophia Yang, Ph.D. to: - Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website - Implement JSON mode to generate structured outputs to integrate directly into larger software systems. - Use function calling for Tool Use, such as calling custom Python code that queries tabular data - Ground your LLM's response with external knowledge sources using RAG - Build a Mistral-powered chat interface that can reference external documents This course will help deepen your prompt engineering skills. Please sign up here:

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.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"

.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"

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Some of our top customers are still choosing Llama 3.1 8B. For a while, we jumped to whatever hottest, latest model was taking up our twitter feed. 🙈 But as we are quickly realizing, to create a SOTA product, you need a model that fits your exact use case. Here’s what our customers tell us: > a lot of the legwork is actually around prompting > there’s an art to selecting and combining multiple models > benchmarks only show part of the picture. you have to understand the unique quirks of each model. Especially as model releases become more and more frequent, we need a clear way to evaluate new models. We have to break free of the naive trend to migrate to the ‘latest and greatest’. And you can easily achieve this using tools like Cerebras and Braintrust to swap models safely (without breaking production).

Some of our top customers are still choosing Llama 3.1 8B. For a while, we jumped to whatever hottest, latest model was taking up our twitter feed. 🙈 But as we are quickly realizing, to create a SOTA product, you need a model that fits your exact use case. Here’s what our customers tell us: > a lot of the legwork is actually around prompting > there’s an art to selecting and combining multiple models > benchmarks only show part of the picture. you have to understand the unique quirks of each model. Especially as model releases become more and more frequent, we need a clear way to evaluate new models. We have to break free of the naive trend to migrate to the ‘latest and greatest’. And you can easily achieve this using tools like Cerebras and Braintrust to swap models safely (without breaking production).

Cerebras

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Beam Gamers

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The more AI can do, the more we need to ask what it should and shouldn’t do. OpenAI researcher Jason Wolfe joins host Andrew Mayne to explore the Model Spec, the public framework that defines how models are intended to behave. They break down how it works in practice, from the chain of command that resolves conflicting instructions to the way it evolves over time through real-world use, feedback, and new model capabilities.

The more AI can do, the more we need to ask what it should and shouldn’t do. OpenAI researcher Jason Wolfe joins host Andrew Mayne to explore the Model Spec, the public framework that defines how models are intended to behave. They break down how it works in practice, from the chain of command that resolves conflicting instructions to the way it evolves over time through real-world use, feedback, and new model capabilities.

OpenAI

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AI is changing everything, but how does it actually work? We break down machine learning, training, inference, and what happens behind the scenes when you use AI in the latest episode of #TheBringUp, featuring a hands-on model demo. 🎥 Watch now:

AI is changing everything, but how does it actually work? We break down machine learning, training, inference, and what happens behind the scenes when you use AI in the latest episode of #TheBringUp, featuring a hands-on model demo. 🎥 Watch now:

AMD

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Jensen Huang says, In the future, we will be able to talk to a protein We'll ask it: "what are you? are you soluble? how do you react to heat or chemicals?" This is how AI can talk to an image today, and how we've moved from only processing data to actually understanding it

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Haider.

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EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think

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Yesterday, I joined TBPN to talk about what it really means to give AI a memory. And, why that’s becoming the next big moat. We discussed: - Why today’s AI apps are stateless - How memory is quickly becoming the real moat for AI systems - How Mem0 is decoupling memory from models - Why users should be able to own and carry their AI memories across apps - And, how 200 cold emails changed my life🙂 Appreciate John Coogan, Jordi Hays & the TBPN team for having me and for such a thoughtful discussion around the future of AI infrastructure.

Yesterday, I joined TBPN to talk about what it really means to give AI a memory. And, why that’s becoming the next big moat. We discussed: - Why today’s AI apps are stateless - How memory is quickly becoming the real moat for AI systems - How Mem0 is decoupling memory from models - Why users should be able to own and carry their AI memories across apps - And, how 200 cold emails changed my life🙂 Appreciate John Coogan, Jordi Hays & the TBPN team for having me and for such a thoughtful discussion around the future of AI infrastructure.

Taranjeet

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Hurley

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The biggest model is no longer the advantage. The right model is. Generic AI can do a little of everything, but it rarely optimizes for what matters in production. Oumi is changing that by making custom models fast and accessible. It feels like we are entering the era of building your own AI. Manos Koukoumidis Oumi

The biggest model is no longer the advantage. The right model is. Generic AI can do a little of everything, but it rarely optimizes for what matters in production. Oumi is changing that by making custom models fast and accessible. It feels like we are entering the era of building your own AI. Manos Koukoumidis Oumi

Leonardo

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They Want To Know How Many Bedrooms Are In Your Home Why? The Reason Should Worry You! The 2026 Census long form doesn't just ask how many people live in your home. It asks how many rooms are in your dwelling. Then it asks how many of those rooms are bedrooms. Why does the government need to know that? Look at what is already happening in the UK. Some local councils are implementing what they call under-occupancy premiums. If you have rooms in your home that you are not using they are taxing you for them. This is not a conspiracy theory. It is happening right now. And the Canadian government just made you tell them exactly how many rooms you have. Connect the dots. watch my full breakdown 👇

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Punch with moe infront his biological mother🥹🐵 Punch seems to have been adopted by Moe, who is so affectionate toward him 🥹. She even carries him on her back and walks with him. Suddenly, he stretches his body right in front of an adult female monkey… 💔and wait… that’s actually Kibe, Punch’s mother 😮‍💨😮‍💨 the one you mentioned yesterday. It really shows how destiny works… mother and child, separated for a while, crossing paths again in such an emotional way 🥺💔💔💔 #Punch #kibe #moe

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Animal Love

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The Vijñāna-Bhairava Tantra: 112 Gateways to Radical Presence.🕉️ Forget dense philosophy. The Vijñāna-Bhairava Tantra (VBT) is a masterclass in direct experience—a 163-stanza "field manual" for waking up. Presented as an intimate dialogue between the divine consciousnesses of Śiva and Śakti, it isn't meant to be read; it is meant to be practiced. 🔶The Essence of the Text:🔶 🔺A Radical Toolkit: Contained within these verses are 112 meditation techniques (dhāraṇās) designed to collapse the distance between you and supreme awareness. 🔺The Approach: It is deliberately terse and poetic. Each stanza acts as a "seed"—a concise, explosive prompt meant to trigger an immediate shift in perception. 🔺The Scope: The practices are brilliantly diverse, spanning breath-work, sensory immersion, the power of sound, lucid dreaming, and the contemplative plunge into the "void" (śūnya). 🔶Origins & Authority:🔶 🔺Lineage: Emerging from the 7th–10th century Kashmir Shaivism (Kaula/Trika tradition), this text is less a single book and more an ancient transmission of lived experience. 🔺Intellectual Pedigree: Despite its practical focus, it carries immense weight. Masters like Abhinavagupta held it in the same high regard as the sacred Upaniṣads, recognizing that true liberation happens in the body and mind, not just on the page. 🔶Why It Matters Now?🔶 In a world of information overload, the VBT is a surgical tool. It bypasses the intellect to offer immediate, experiential doorways into nondual awareness. Whether you are a scholar of traditions or a modern seeker looking for a meditation practice that actually works, the VBT provides a diverse, customizable toolkit to inhabit your own consciousness fully. "The VBT does not ask for belief; it demands experimentation. It is arguably the most practical, comprehensive, and adventurous meditation manual in the history of human consciousness." 📽️historified_in

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The Melting Pot

12,438 просмотров • 1 месяц назад

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)

Tyler

20,012 просмотров • 11 месяцев назад

What does autonomous AI actually mean for engineering? In the Season 3 premiere of Advanced Insights, Mark Papermaster sat down with Alex Starr to unpack a shift that’s redefining how systems are designed, built and evolved. Watch the full episode on YouTube:

What does autonomous AI actually mean for engineering? In the Season 3 premiere of Advanced Insights, Mark Papermaster sat down with Alex Starr to unpack a shift that’s redefining how systems are designed, built and evolved. Watch the full episode on YouTube:

AMD

15,643 просмотров • 4 месяцев назад

What is the New World screwworm? What does it mean for Texas? And what can you do about it? Experts across The Texas A&M University System have been preparing for this moment, but they cannot do it alone. Texans are urged to monitor their animals and report any unusual activity to Texas Animal Health Commission or Texas Parks & Wildlife. Learn how to report suspected cases and more here:

What is the New World screwworm? What does it mean for Texas? And what can you do about it? Experts across The Texas A&M University System have been preparing for this moment, but they cannot do it alone. Texans are urged to monitor their animals and report any unusual activity to Texas Animal Health Commission or Texas Parks & Wildlife. Learn how to report suspected cases and more here:

Texas A&M System

313,039 просмотров • 1 месяц назад