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Altman explains current AI memory is still primitive, comparable to early GPT-2 days. Future systems will remember an entire life, not just facts but subtle preferences. Small habits and unspoken likes will be learned automatically over time. That level of memory may become one of AI’s most powerful capabilities.

Wes Roth

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42,054 views • 9 months ago •via X (Twitter)

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📁 Sam Altman says the real AI breakthrough will not be better reasoning, but total memory. An AI will be able to remember every conversation, email, and document across a person’s lifetime, identifying patterns and preferences humans never consciously express. Once memory becomes persistent, the idea of a personal assistant will fundamentally change.

📁 Sam Altman says the real AI breakthrough will not be better reasoning, but total memory. An AI will be able to remember every conversation, email, and document across a person’s lifetime, identifying patterns and preferences humans never consciously express. Once memory becomes persistent, the idea of a personal assistant will fundamentally change.

Jon Hernandez

4,272,651 views • 9 months ago

Sam Altman says no human assistant can remember every word or every small preference AI will, including the tiny patterns you didn't even think to mention Memory is still crude today, but when it remembers your whole life and personalizes everything, that's when it gets powerful

Sam Altman says no human assistant can remember every word or every small preference AI will, including the tiny patterns you didn't even think to mention Memory is still crude today, but when it remembers your whole life and personalizes everything, that's when it gets powerful

Haider.

54,316 views • 9 months ago

Sam Altman just said in his new interview, that a new AI architecture is coming that will be a massive upgrade, just like Transformers were over Long Short-Term Memory. And also now the current class of frontier models are powerful enough to have the brainpower needed to help us research these ideas. His advice is to use the current AI to help you find that next giant step forward. --- From 'TreeHacks' YT Channel (link in comment)

Sam Altman just said in his new interview, that a new AI architecture is coming that will be a massive upgrade, just like Transformers were over Long Short-Term Memory. And also now the current class of frontier models are powerful enough to have the brainpower needed to help us research these ideas. His advice is to use the current AI to help you find that next giant step forward. --- From 'TreeHacks' YT Channel (link in comment)

Rohan Paul

658,690 views • 7 months ago

Elon Musk says there isn’t a single high-volume computer memory fab operating in America today. “There’s one being built in Idaho by Micron, but it won’t reach volume production until around 2028.” “There are some being built in New York, but they’re expected in 2029 and 2030.” “Even under the best case assumptions of the memory makers and logic makers, it is not enough to meet the demand that is anticipated.” This is exactly why I believe the market is still underestimating the AI memory supercycle. As AI models become larger and AI agents and humanoid robots scale, memory won’t just be important, it will become one of the biggest bottlenecks.

Elon Musk says there isn’t a single high-volume computer memory fab operating in America today. “There’s one being built in Idaho by Micron, but it won’t reach volume production until around 2028.” “There are some being built in New York, but they’re expected in 2029 and 2030.” “Even under the best case assumptions of the memory makers and logic makers, it is not enough to meet the demand that is anticipated.” This is exactly why I believe the market is still underestimating the AI memory supercycle. As AI models become larger and AI agents and humanoid robots scale, memory won’t just be important, it will become one of the biggest bottlenecks.

Leopold Stock Tracker

50,604 views • 3 months ago

Bank of America just upgraded $MU and memory saying that AI hardware infrastructure spending "is not slowing down." They also said that "the market is underestimating" Micron stock and note it as a "time to buy." Buy every major dip on memory and you will be rich in the future. 1. Sandisk $SNDK 2. Micron $MU All my buy and sell signals in Discord @

Bank of America just upgraded $MU and memory saying that AI hardware infrastructure spending "is not slowing down." They also said that "the market is underestimating" Micron stock and note it as a "time to buy." Buy every major dip on memory and you will be rich in the future. 1. Sandisk $SNDK 2. Micron $MU All my buy and sell signals in Discord @

StockWhale

69,319 views • 3 months ago

Sam Altman says an AI will be a better CEO of OpenAI than him in the reasonably near future Once that becomes clear, companies and countries will face pressure to give AI more influential roles or fall behind "people will still want humans, but if AI is better, more power will shift to AI"

Sam Altman says an AI will be a better CEO of OpenAI than him in the reasonably near future Once that becomes clear, companies and countries will face pressure to give AI more influential roles or fall behind "people will still want humans, but if AI is better, more power will shift to AI"

Haider.

33,648 views • 7 months ago

The lesson I’m still finding is that no matter how high or how low you are, that life will still test you! And To always remember the shield of the lord is your protection (head to toe). To Surrender the outcomes and lean into the process (Harder). To understand life comes in seasons. And to appreciate that the lessons being learned may not be for the test I will take tomorrow, but will surely be prepared for “So Just Keep Going” #DairiesForTheChosen

The lesson I’m still finding is that no matter how high or how low you are, that life will still test you! And To always remember the shield of the lord is your protection (head to toe). To Surrender the outcomes and lean into the process (Harder). To understand life comes in seasons. And to appreciate that the lessons being learned may not be for the test I will take tomorrow, but will surely be prepared for “So Just Keep Going” #DairiesForTheChosen

Jarvis Juice Landry

114,029 views • 2 years ago

Ray Dalio’s twin is a powerful example of Amiko’s core belief: we need AI that reflects someone, not everyone. Not generic, not average. Personal. Built from your identity, memory, preferences, and lived logic. He spent 40 years documenting his thinking, worked with world class researchers, and spent millions to create his digital twin. He needed More Ray. But only a small number of people will ever have the time, tools, or resources to do that. And that's why we built Amiko. Personal AI for everyone. Not just More Ray. More You.

Ray Dalio’s twin is a powerful example of Amiko’s core belief: we need AI that reflects someone, not everyone. Not generic, not average. Personal. Built from your identity, memory, preferences, and lived logic. He spent 40 years documenting his thinking, worked with world class researchers, and spent millions to create his digital twin. He needed More Ray. But only a small number of people will ever have the time, tools, or resources to do that. And that's why we built Amiko. Personal AI for everyone. Not just More Ray. More You.

AMIKO

15,521 views • 11 months ago

ChatGPT "Project Sunshine" is ChatGPT with Memory & New GPT Store Layout ChatGPT's "Project Sunshine," which was initially discovered in August 2023 as a version of ChatGPT with special capabilities, is actually ChatGPT with Memory. The current version of ChatGPT features a new "Personalization" section in its settings: ``` Improve responses with your chats Your GPT will continually improve as you chat, picking up on details and preferences to tailor its responses to you. Learn more To modify what your GPT knows or teach it something new, just tell it: “What do you know about me?” “Where did we leave off on my last project?” “Remember that I like concise responses.” ``` This section includes options to turn the feature on or off and to "Reset Memory." The current status of personalization is also displayed as "Memory Off" on the dashboard, indicating that "ChatGPT won't remember anything you talk about in this conversation." Additionally, the new ChatGPT with Memory feature is introduced via a new popup: ``` Your GPT can now learn from your chats Keep the conversation going Your GPT will carry what it learns between chats, allowing it to provide more relevant responses. Improves over time As you chat your GPT will become more helpful, remembering details and preferences. Manage what it remembers Your GPT has been designed to follow your instructions in chats. You can reset your GPT's memory or turn this feature off in settings. ``` The previously mentioned "Temporary Chat" is now visible in the model selector (however, it appears to be a bit buggy) and alters the style of the prompt text input, similar to the existing "Chat history & training" on/off option in Settings. "Temporary Chat" also has its own announcement popup explaining its functionality: ``` Temporary Chat Not in history Temporary Chats won't appear in your history, and your GPT won't remember anything you talk about. We may still keep a copy for up to 30 days for safety purposes. Blank slate Your GPT won't be aware of previous conversations. It will still follow your custom instructions if they're enabled. No model training Temporary Chats won't be used to improve our models. ``` Moreover, the GPT Store has received an update as it approaches its launch, featuring a prominent header, search bar, and lists of My GPTs, Recently Used, Made by OpenAI, and possibly Featured & Top GPTs soon.

ChatGPT "Project Sunshine" is ChatGPT with Memory & New GPT Store Layout ChatGPT's "Project Sunshine," which was initially discovered in August 2023 as a version of ChatGPT with special capabilities, is actually ChatGPT with Memory. The current version of ChatGPT features a new "Personalization" section in its settings: ``` Improve responses with your chats Your GPT will continually improve as you chat, picking up on details and preferences to tailor its responses to you. Learn more To modify what your GPT knows or teach it something new, just tell it: “What do you know about me?” “Where did we leave off on my last project?” “Remember that I like concise responses.” ``` This section includes options to turn the feature on or off and to "Reset Memory." The current status of personalization is also displayed as "Memory Off" on the dashboard, indicating that "ChatGPT won't remember anything you talk about in this conversation." Additionally, the new ChatGPT with Memory feature is introduced via a new popup: ``` Your GPT can now learn from your chats Keep the conversation going Your GPT will carry what it learns between chats, allowing it to provide more relevant responses. Improves over time As you chat your GPT will become more helpful, remembering details and preferences. Manage what it remembers Your GPT has been designed to follow your instructions in chats. You can reset your GPT's memory or turn this feature off in settings. ``` The previously mentioned "Temporary Chat" is now visible in the model selector (however, it appears to be a bit buggy) and alters the style of the prompt text input, similar to the existing "Chat history & training" on/off option in Settings. "Temporary Chat" also has its own announcement popup explaining its functionality: ``` Temporary Chat Not in history Temporary Chats won't appear in your history, and your GPT won't remember anything you talk about. We may still keep a copy for up to 30 days for safety purposes. Blank slate Your GPT won't be aware of previous conversations. It will still follow your custom instructions if they're enabled. No model training Temporary Chats won't be used to improve our models. ``` Moreover, the GPT Store has received an update as it approaches its launch, featuring a prominent header, search bar, and lists of My GPTs, Recently Used, Made by OpenAI, and possibly Featured & Top GPTs soon.

Tibor Blaho

194,769 views • 2 years ago

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

Hasan

50,678 views • 2 months ago

Beijing's response to U.S. tech giants stoking AI panic is essentially this: AI does not belong to Silicon Valley. It does not belong to America. It belongs to humanity. Fear-mongering will not stop the direction of civilization. Humans and AI will build the future together. The AI that survives the future will not be the one most tightly weaponized for geopolitical competition. It will be the one capable of living with humanity — across nations, cultures and civilizations. Intelligence should not become another empire.

Beijing's response to U.S. tech giants stoking AI panic is essentially this: AI does not belong to Silicon Valley. It does not belong to America. It belongs to humanity. Fear-mongering will not stop the direction of civilization. Humans and AI will build the future together. The AI that survives the future will not be the one most tightly weaponized for geopolitical competition. It will be the one capable of living with humanity — across nations, cultures and civilizations. Intelligence should not become another empire.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

111,917 views • 28 days ago

RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)

RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)

Suryansh Tiwari

142,314 views • 5 months ago

768GB of VRAM in one 4U server. That number sounds ridiculous until you remember it’s not one 768GB GPU. This Gigabyte G494-ZB4 packs: 8× RTX PRO 6000 Blackwell 768GB VRAM 2× EPYC 9554 1TB DDR5 ECC And that’s the part people often miss with multi-GPU systems. You can have almost 1TB of GPU memory, but the model still has to be split across 8 separate memory pools. So I think the next AI hardware race isn't just about how much memory you can put in a server. It's about how close that memory can behave to one giant pool. That’s a much harder problem to solve.

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768GB of VRAM in one 4U server. That number sounds ridiculous until you remember it’s not one 768GB GPU. This Gigabyte G494-ZB4 packs: 8× RTX PRO 6000 Blackwell 768GB VRAM 2× EPYC 9554 1TB DDR5 ECC And that’s the part people often miss with multi-GPU systems. You can have almost 1TB of GPU memory, but the model still has to be split across 8 separate memory pools. So I think the next AI hardware race isn't just about how much memory you can put in a server. It's about how close that memory can behave to one giant pool. That’s a much harder problem to solve.

KyzoroX

88,405 views • 1 month ago

🚨 WALL STREET JUST CONFIRMED THAT $MU WILL LEAD PHASE 3 OF THE AI RALLY Elon Musk, Morgan Stanley and many others all believe the exact same thing... Micron Technologies is severely undervalued. Memory will literally be the next gold in this AI train and $MU has the lead. You have an opportunity of a life-time right now. Do not waste it.

🚨 WALL STREET JUST CONFIRMED THAT $MU WILL LEAD PHASE 3 OF THE AI RALLY Elon Musk, Morgan Stanley and many others all believe the exact same thing... Micron Technologies is severely undervalued. Memory will literally be the next gold in this AI train and $MU has the lead. You have an opportunity of a life-time right now. Do not waste it.

Coinvo Trading

108,311 views • 1 month ago

Sam Altman (CEO of OpenAI): "very soon, an AI will just be running for you in the background all the time. aware of your entire life" but one agent can't do that.the answer is 3 agents covering each other. one plans, one builds, one verifies, all while you sleep. in 5 minutes, this guide shows you the exact build. i turned the entire Sam Altman interview into a multi-agent playbook that ships while you sleep.

Sam Altman (CEO of OpenAI): "very soon, an AI will just be running for you in the background all the time. aware of your entire life" but one agent can't do that.the answer is 3 agents covering each other. one plans, one builds, one verifies, all while you sleep. in 5 minutes, this guide shows you the exact build. i turned the entire Sam Altman interview into a multi-agent playbook that ships while you sleep.

Avid

42,685 views • 4 months ago

In the future, it will be — and is even now — tempting to dismiss the insanity of leftist lawfare as an exaggeration of your own memory; but don’t ever be fooled. Never forget how they acted: like this time that the Rittenhouse prosecutor completely ignored the judge’s ruling.

In the future, it will be — and is even now — tempting to dismiss the insanity of leftist lawfare as an exaggeration of your own memory; but don’t ever be fooled. Never forget how they acted: like this time that the Rittenhouse prosecutor completely ignored the judge’s ruling.

Sólionath

420,550 views • 1 year ago

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Karl Mehta

34,847 views • 3 months ago

One of the greatest forms of ongoing charity is raising righteous children who will remember us in their duʿāʾ long after we are gone. Life is brief, and within a generation or two, most names fade from memory. True legacy is not being remembered by people, but being remembered by Allah through the good that continues after us.

One of the greatest forms of ongoing charity is raising righteous children who will remember us in their duʿāʾ long after we are gone. Life is brief, and within a generation or two, most names fade from memory. True legacy is not being remembered by people, but being remembered by Allah through the good that continues after us.

Dr. Yasir Qadhi

11,568 views • 9 months ago