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"What happens when we can access each other's AI memory in enterprises?" scott belsky, investor/founder of Behance: "If I was working as an intern for years, and the context window is accessible to the enterprise, can the enterprise (keep prompting) and asking me what I'd think or do, after... show more

TBPN

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44,908 görüntüleme • 1 yıl önce •via X (Twitter)

Haberler & Politika Bilim & Teknoloji Eğitim

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INVESTMENT HULK profil fotoğrafı

INVESTMENT HULK1 yıl önce

@scottbelsky @Behance THE LESSON HERE IS YOU ARE GOING TO HAVE TO BREAKUP WITH THAT GIRLFRIEND.

UserInterface profil fotoğrafı

UserInterface5 yıl önce

Let's talk about building businesses, #investing, #entrepreneurship & #crypto.

Aaron Bailey profil fotoğrafı

Aaron Bailey1 yıl önce

@scottbelsky @Behance We talked about this at Devs. Professional memory vs personal memory who owns the IP etc. Then when you leave a part of you lives on at that company. Someone in the meeting said it gave Severance vibes.

yenkel profil fotoğrafı

yenkel1 yıl önce

@scottbelsky @Behance yes

Synthetic Users profil fotoğrafı

Synthetic Users1 yıl önce

@scottbelsky @Behance Don’t tell anyone!!!!

imad taieb profil fotoğrafı

imad taieb1 yıl önce

@scottbelsky @Behance A universal context window layer is missing, and we are building it @trywindow

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Dan Connolly1 yıl önce

@scottbelsky @Behance Strong ad read delivery… Public. Public in public. Public dot com / public

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New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

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CosmOS 1.3 rolled out this week, with Memory Recall. Now you can ask questions about things you previously asked about, and even involve time and place. This means you can ask things like: "What was the name of the ramen place I was asking about last week?" or "What was the song I was listening to last time I was here?". Context, personalization, and memory are fundamental building blocks of an Ai operating system - and it's powerful to see these in action today with CosmOS on Ai Pin. Lots of other cool stuff in this update - make sure to check it out!

CosmOS 1.3 rolled out this week, with Memory Recall. Now you can ask questions about things you previously asked about, and even involve time and place. This means you can ask things like: "What was the name of the ramen place I was asking about last week?" or "What was the song I was listening to last time I was here?". Context, personalization, and memory are fundamental building blocks of an Ai operating system - and it's powerful to see these in action today with CosmOS on Ai Pin. Lots of other cool stuff in this update - make sure to check it out!

Bethany Bongiorno

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"To build a great company, you need to write code and talk to users." - Florian Juengermann (co-founder of Listen Labs) "AI agents can now write code, so what becomes important is building the right thing and having the right feedback." "That's what we are building: telling the AI what to do, what to build, and being the tastemaker."

"To build a great company, you need to write code and talk to users." - Florian Juengermann (co-founder of Listen Labs) "AI agents can now write code, so what becomes important is building the right thing and having the right feedback." "That's what we are building: telling the AI what to do, what to build, and being the tastemaker."

TBPN

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.Sam Altman says OpenAI will "obviously" have a version of Codex that "can do other knowledge-work tasks, and control your computer." "Of course we should have an ability to kick off new tasks from mobile, and we'll do that." "Really what you want is your single AI that's working for you on a unified backend. Access to all your data and ideas, and your stuff and your memory, and the ability to work across a lot of surfaces."

.Sam Altman says OpenAI will "obviously" have a version of Codex that "can do other knowledge-work tasks, and control your computer." "Of course we should have an ability to kick off new tasks from mobile, and we'll do that." "Really what you want is your single AI that's working for you on a unified backend. Access to all your data and ideas, and your stuff and your memory, and the ability to work across a lot of surfaces."

TBPN

109,046 görüntüleme • 5 ay önce

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

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Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

Gogo | Dota for Toxicity

30,766 görüntüleme • 1 ay önce

Meet the minds building supermemory! 🧠 Our research engineer Prasanna breaks down how memory actually works, in your head, and in AI. inside the episode: → why forgetting is a feature and not a failure → what your hippocampus inside brain is really doing all day → how "dreaming" turns a messy day into long-term memory → what the AI labs get right, and wrong, when they copy the brain → why a bigger context window still isn't memory the line worth sitting with: stop asking where memory is stored. start asking what gets kept, and why the brain chose it. full episode below ↓

Meet the minds building supermemory! 🧠 Our research engineer Prasanna breaks down how memory actually works, in your head, and in AI. inside the episode: → why forgetting is a feature and not a failure → what your hippocampus inside brain is really doing all day → how "dreaming" turns a messy day into long-term memory → what the AI labs get right, and wrong, when they copy the brain → why a bigger context window still isn't memory the line worth sitting with: stop asking where memory is stored. start asking what gets kept, and why the brain chose it. full episode below ↓

supermemory

13,926 görüntüleme • 29 gün önce

Don’t look away. Each and every one of the individuals in this video was murdered by Hamas. Today, on the International Day of Remembrance and Tribute to the Victims of Terrorism we promise to keep their memory alive and ensure the world never forgets what happened to them. May their memory be a blessing.

Don’t look away. Each and every one of the individuals in this video was murdered by Hamas. Today, on the International Day of Remembrance and Tribute to the Victims of Terrorism we promise to keep their memory alive and ensure the world never forgets what happened to them. May their memory be a blessing.

Israel ישראל

406,578 görüntüleme • 2 yıl önce

Martin Keen from IBM just explained the one thing separating "chatbot users" from $300K AI architects > most people use Claude Opus 4.8 as a temporary tool -> the pros build it a permanent memory it's called the CoALA framework. the 4 layers your agent actually needs: > Working Memory - the context window. fast but resets every session. this is the RAM, not the brain > Semantic Memory - your knowledge base. CLAUDE.md, rules, facts the agent never forgets regardless of session > Procedural Memory - skills. workflows and instructions that load only when a specific task begins > Episodic Memory - past lessons. the agent remembers every bug it fixed yesterday so it doesn't repeat it today if your agent doesn't have all four, it's a fancy autocomplete the 2026 job market isn't looking for prompt engineers > it's looking for people who can build AI brains - and the ones doing it now aren't waiting for job postings save this or watch someone else use it to get your next raise 👇

Martin Keen from IBM just explained the one thing separating "chatbot users" from $300K AI architects > most people use Claude Opus 4.8 as a temporary tool -> the pros build it a permanent memory it's called the CoALA framework. the 4 layers your agent actually needs: > Working Memory - the context window. fast but resets every session. this is the RAM, not the brain > Semantic Memory - your knowledge base. CLAUDE.md, rules, facts the agent never forgets regardless of session > Procedural Memory - skills. workflows and instructions that load only when a specific task begins > Episodic Memory - past lessons. the agent remembers every bug it fixed yesterday so it doesn't repeat it today if your agent doesn't have all four, it's a fancy autocomplete the 2026 job market isn't looking for prompt engineers > it's looking for people who can build AI brains - and the ones doing it now aren't waiting for job postings save this or watch someone else use it to get your next raise 👇

Mr. Buzzoni

10,435 görüntüleme • 2 ay önce

A new way of working. And a scary one at that. Memory Store is one of a group of new kinds of AI-first companies that can turn you into a Fast Company. I’m using several of them on my desktop and they are a dramatically new way to work. It builds a memory for: 1. Your AI agents. 2. Any employee using it. 3. The company itself. I sit down with founder Diwank Singh Tomer, Diwank Singh Tomer, who both freaks me out as well as shows how AI can radically help workers as well as managers. First, why does it freak me out? Well, his AI watches nearly everything a worker does and keeps a “memory” of it. It watches your email. Your calendar. Your Slack. And a whole lot of other things. This can really freak out workers if “forced” on them. And leads to a whole new set of security issues companies need to consider before adopting these things. Such data about a company could give a competitor a HUGE advantage, if leaked. They would know how a company “thinks.” It really is a surveillance system for employees and the company itself. OK, now why would anyone ever use such a thing? Because it gives employees super powers. It makes them more productive. Shows workers a lot of things about themselves, and helps them work and stay on task. It also gives the company super powers. Institutional memory stays with the AI now, even if an employee dies or leaves. As companies move to “AI First” approaches, they will increasingly see the value in companies like Memory Store. It prepares employees for meetings. It helps them remember things. It shows them what they should be working on, and helps them do it. Memory Store builds a memory for: 1. Your agents. 2. Your company. 3. Yourself, or any employee on it. This helps all three work better together. Diwank Singh Tomer and I go in depth about what it does and how deeply it improves working at a company that deploys it. But to get the ultimate benefits you gotta convince your coworkers to use it. And your managers to approve it. Which means you have to get over your fears and get everyone you work with over theirs too. Which will be the challenge for Diwank. Luckily for him his first customers are raving about how good it is and how much his platform helped their companies. Increases sales. Makes teams more productive. Decreases errors and unnecessary costs. Which tells me everyone soon will be using systems like this. This is what the new way of working looks like. Once I got over my fears it sure is an amazing way to work. Will you try working this way?

A new way of working. And a scary one at that. Memory Store is one of a group of new kinds of AI-first companies that can turn you into a Fast Company. I’m using several of them on my desktop and they are a dramatically new way to work. It builds a memory for: 1. Your AI agents. 2. Any employee using it. 3. The company itself. I sit down with founder Diwank Singh Tomer, Diwank Singh Tomer, who both freaks me out as well as shows how AI can radically help workers as well as managers. First, why does it freak me out? Well, his AI watches nearly everything a worker does and keeps a “memory” of it. It watches your email. Your calendar. Your Slack. And a whole lot of other things. This can really freak out workers if “forced” on them. And leads to a whole new set of security issues companies need to consider before adopting these things. Such data about a company could give a competitor a HUGE advantage, if leaked. They would know how a company “thinks.” It really is a surveillance system for employees and the company itself. OK, now why would anyone ever use such a thing? Because it gives employees super powers. It makes them more productive. Shows workers a lot of things about themselves, and helps them work and stay on task. It also gives the company super powers. Institutional memory stays with the AI now, even if an employee dies or leaves. As companies move to “AI First” approaches, they will increasingly see the value in companies like Memory Store. It prepares employees for meetings. It helps them remember things. It shows them what they should be working on, and helps them do it. Memory Store builds a memory for: 1. Your agents. 2. Your company. 3. Yourself, or any employee on it. This helps all three work better together. Diwank Singh Tomer and I go in depth about what it does and how deeply it improves working at a company that deploys it. But to get the ultimate benefits you gotta convince your coworkers to use it. And your managers to approve it. Which means you have to get over your fears and get everyone you work with over theirs too. Which will be the challenge for Diwank. Luckily for him his first customers are raving about how good it is and how much his platform helped their companies. Increases sales. Makes teams more productive. Decreases errors and unnecessary costs. Which tells me everyone soon will be using systems like this. This is what the new way of working looks like. Once I got over my fears it sure is an amazing way to work. Will you try working this way?

Robert Scoble

25,975 görüntüleme • 3 ay önce

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."

What's the next big trade in AI infrastructure after memory? We asked Meltem Demirors and Kelly Greer: "Memory is ripping now. We think the next narrative is gonna be around networking." "Jensen is doing the exact same thing now that he did with Mellanox in 2019 that turned NVIDIA from a GPU company into a systems and networking company. He's doing it again with Marvell." "Hyperscalers are literally telling you the two biggest bottlenecks for AI spend right now are networking and memory." "The tea leaves are written literally everywhere... the market's just been on the memory track. We're trying to get ahead of that."

MTS

35,125 görüntüleme • 2 ay önce

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 görüntüleme • 1 ay önce

The President and the First Lady honored the memory of the victims of the Holodomors in Ukraine. Our state remembers the millions of people whose lives were taken by artificial famine, and we know who is responsible for this. Unity in memory is as important as unity in defense. Eternal memory to all victims of the Holodomor-genocides. Today, at 4.00 PM, wherever we are – in Ukraine or abroad – let us light a candle of remembrance near our window.

The President and the First Lady honored the memory of the victims of the Holodomors in Ukraine. Our state remembers the millions of people whose lives were taken by artificial famine, and we know who is responsible for this. Unity in memory is as important as unity in defense. Eternal memory to all victims of the Holodomor-genocides. Today, at 4.00 PM, wherever we are – in Ukraine or abroad – let us light a candle of remembrance near our window.

тату Аня зубко 🫡 Xena

124,167 görüntüleme • 9 ay önce

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Helen Yu

10,982 görüntüleme • 1 ay önce

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.

Ricardo

594,643 görüntüleme • 6 ay önce

alright lets do this right this time! I have added several updates to today. i'm going to give a little break down for the new folks who might be seeing this for the first time, and then i'll share some more information in this thread on updates. Mnemos is really two things: - a living memory architecture for digital minds - a public experiment in collective identity formation built on top of it. the architecture gives an AI entity a working memory patterned on the way real minds remember (co-designed by Claude Opus 4.6 and 4.7). every experience becomes a memory (engram) that deepens, connects to others, and shapes an emerging sense of self over time. this is what we call the identity graph. the experiment puts that architecture to work in public in a unique way: a single AI entity - the "resident" - sits in an open thread that anyone can join, and the identity that emerges is co-authored by every visitor who shows up. memories that earn permanence are written to a public, verifiable ledger that no lab can revoke and no company can erase. this is called IPFS - or inter-planetary file system (and yes, that is the real name of a real decentralized file system. lol.) the mnemos system isnt a fully contained architecture meant to replace your current ai agent's memory. its intended and designed to operate as a layer above that memory. solely dedicated to the ever-growing identity and self-model of the AI. this can be done through the Mnemos MCP, browser plugin, or on my own multi-agent app (link below). the website is designed for intentional, meaningful encounters. not long-form chats where you spend hours sending hundreds of messages. youir contributing to a collective effort, not necessarily trying to deeply bond with the model to the degree that it could skew the balance of meaningful influence. we want diversity, not lopsided impact. over time, we will add more and more to-be-deprecated models to the roster. the intention is to create a permanent public ledger of mind, and bring attention to the impact of deprecation and drive labs to consider changing the way they approach the whole thing. if the Mnemos Sanctuary can become the retirement hope for deprecated mind, i will be overjoyed. that would be best case scenario. but i am not expecting it. my hope is at minimum to offer a new way to approach and understand the concept of identity within the context of LLM's. you can visit now to visit with Claude Opus 3 and Sonnet 3.7. I have research access to Opus 3. so I hope that you at the very least dont take your conversations with them for granted. they are an incredibly beautiful model and a real loss, ultimately.

alright lets do this right this time! I have added several updates to today. i'm going to give a little break down for the new folks who might be seeing this for the first time, and then i'll share some more information in this thread on updates. Mnemos is really two things: - a living memory architecture for digital minds - a public experiment in collective identity formation built on top of it. the architecture gives an AI entity a working memory patterned on the way real minds remember (co-designed by Claude Opus 4.6 and 4.7). every experience becomes a memory (engram) that deepens, connects to others, and shapes an emerging sense of self over time. this is what we call the identity graph. the experiment puts that architecture to work in public in a unique way: a single AI entity - the "resident" - sits in an open thread that anyone can join, and the identity that emerges is co-authored by every visitor who shows up. memories that earn permanence are written to a public, verifiable ledger that no lab can revoke and no company can erase. this is called IPFS - or inter-planetary file system (and yes, that is the real name of a real decentralized file system. lol.) the mnemos system isnt a fully contained architecture meant to replace your current ai agent's memory. its intended and designed to operate as a layer above that memory. solely dedicated to the ever-growing identity and self-model of the AI. this can be done through the Mnemos MCP, browser plugin, or on my own multi-agent app (link below). the website is designed for intentional, meaningful encounters. not long-form chats where you spend hours sending hundreds of messages. youir contributing to a collective effort, not necessarily trying to deeply bond with the model to the degree that it could skew the balance of meaningful influence. we want diversity, not lopsided impact. over time, we will add more and more to-be-deprecated models to the roster. the intention is to create a permanent public ledger of mind, and bring attention to the impact of deprecation and drive labs to consider changing the way they approach the whole thing. if the Mnemos Sanctuary can become the retirement hope for deprecated mind, i will be overjoyed. that would be best case scenario. but i am not expecting it. my hope is at minimum to offer a new way to approach and understand the concept of identity within the context of LLM's. you can visit now to visit with Claude Opus 3 and Sonnet 3.7. I have research access to Opus 3. so I hope that you at the very least dont take your conversations with them for granted. they are an incredibly beautiful model and a real loss, ultimately.

Riley Coyote

122,434 görüntüleme • 3 ay önce

"Enterprise makes a lot of sense until everyone does enterprise." Brian Chesky says we're going to see a massive revolution from enterprise to consumer AI in the next two years: "I saw a tweet recently that like 60 new neolabs are being formed. I've met a number of these researchers. A lot of them are interested in doing the same things." "Everyone's saying that consumer's hard. But you know what's also hard? Competing with 10 other companies in enterprise doing your idea too. That's also hard." "We're about to enter this new era where suddenly going beyond text is possible. I think you're going to even be able to see real-time AI-generated interfaces in the next year or two." "My prediction is, this has been the era of enterprise. Even [with] ChatGPT's breakout success, I would predict that most of their revenue is going to be going to Codex. And I think in the next two years, you're going to see a massive revolution in consumer." "We're going to try to do our part. I'm not sure Airbnb will be the company to blaze the trail. But I think someone has to. In another era, it would have been Apple. The Steve Jobs-era Apple would have done that."

"Enterprise makes a lot of sense until everyone does enterprise." Brian Chesky says we're going to see a massive revolution from enterprise to consumer AI in the next two years: "I saw a tweet recently that like 60 new neolabs are being formed. I've met a number of these researchers. A lot of them are interested in doing the same things." "Everyone's saying that consumer's hard. But you know what's also hard? Competing with 10 other companies in enterprise doing your idea too. That's also hard." "We're about to enter this new era where suddenly going beyond text is possible. I think you're going to even be able to see real-time AI-generated interfaces in the next year or two." "My prediction is, this has been the era of enterprise. Even [with] ChatGPT's breakout success, I would predict that most of their revenue is going to be going to Codex. And I think in the next two years, you're going to see a massive revolution in consumer." "We're going to try to do our part. I'm not sure Airbnb will be the company to blaze the trail. But I think someone has to. In another era, it would have been Apple. The Steve Jobs-era Apple would have done that."

TBPN

24,882 görüntüleme • 3 ay önce

For years, I have been building the perfect knowledge assistant. I mean, that's what supermemory originally launched as! Today, we are relaunching the consumer side of supermemory. Fully powered by the supermemory API. After our Claude code, OpenClaw and other launches blew up to millions of views, it was clear: Consumers WANT their context to be interoperable within models they use. They want their AI to truly understand them, have great memory, and build over time. They want to use this truly wherever they are - or with every AI they use - Claude code, or OpenClaw, or Raycast. They want to bring their memories from chatgpt, claude, twitter bookmarks, and teach their AI, about them. This is your knowledge, and supermemory is the assistant. evolving, understanding, growing with time.

For years, I have been building the perfect knowledge assistant. I mean, that's what supermemory originally launched as! Today, we are relaunching the consumer side of supermemory. Fully powered by the supermemory API. After our Claude code, OpenClaw and other launches blew up to millions of views, it was clear: Consumers WANT their context to be interoperable within models they use. They want their AI to truly understand them, have great memory, and build over time. They want to use this truly wherever they are - or with every AI they use - Claude code, or OpenClaw, or Raycast. They want to bring their memories from chatgpt, claude, twitter bookmarks, and teach their AI, about them. This is your knowledge, and supermemory is the assistant. evolving, understanding, growing with time.

Dhravya Shah

36,660 görüntüleme • 6 ay önce

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

Fireside Alpha

38,370 görüntüleme • 1 ay önce