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Introducing the Anemoia Device, a scent-memory machine that uses generative AI to distill photographs into bespoke fragrances. ✺ Register your interest: Guided by language models, a dial-based interface, and a custom olfactory formulation system, each image becomes a singular scent composition — a multisensory memory that evokes anemoia: nostalgia...

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Cyrus

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18,330 görüntüleme • 8 ay önce •via X (Twitter)

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he tested 5760 architectures at Google for a full year. the winner was the original Transformer from 2017. Hyung Won Chung told that story at MIT with a small smile. then went to OpenAI and trained o1. 1 hour. free. by one of the few people on earth who actually moves the frontier. meanwhile your feed is full of guys writing architecture threads who have never trained a model anyone uses. he just told MIT that 99% of AI research is theater. your AI worldview was built by men who read his papers. badly. now you can read him directly. you will rewatch this. save it now.

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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.

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

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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!

Andrew Ng

200,950 görüntüleme • 1 yıl önce

A useful trick for making AI photos look 100% real: (it's not always the model — it can be the color grading) Out of the box, Nano Banana Pro can have a gray, flat look. Good composition, but these scream AI. Here's the system that fixes it: → Grab a reference photo from Pinterest with the exact aesthetic you want → Feed it to ChatGPT 5.2 with a prompt that extracts a detailed color grading JSON → Paste that JSON into image generator (Calico AI) → Generate your product photo. (the colors are now based on the reality you specify, not AI defaults) → Save that character as your reference for facial consistency across future shots From here you can build a full UGC model library. No creators. No shoots. Comment "IMAGE" if you want me to send you every prompt in this system (must be following so I can dm you!).
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A useful trick for making AI photos look 100% real: (it's not always the model — it can be the color grading) Out of the box, Nano Banana Pro can have a gray, flat look. Good composition, but these scream AI. Here's the system that fixes it: → Grab a reference photo from Pinterest with the exact aesthetic you want → Feed it to ChatGPT 5.2 with a prompt that extracts a detailed color grading JSON → Paste that JSON into image generator (Calico AI) → Generate your product photo. (the colors are now based on the reality you specify, not AI defaults) → Save that character as your reference for facial consistency across future shots From here you can build a full UGC model library. No creators. No shoots. Comment "IMAGE" if you want me to send you every prompt in this system (must be following so I can dm you!).

David Roberts

21,075 görüntüleme • 6 ay önce

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0:30

AI + robotics research is starting to pick up steam. We can now instruct Spot using natural language using Language-guided Skill Coordination (LSC). A user provides a natural language instruction: "Bring me the chocolates box, cereal box, and pill bottle, and put them on the bedroom table" and the robot navigates to the location of the target objects and places them on the room table. Current versions of Spot rely on a fixed-set vocabulary that cannot generalize to diverse instructions. In this demo, the researchers presented a method that uses large language models to receive a free-form natural language instruction for object rearrangement, which it then executes. It combines: -A voice-to-text model that processes the instructions into text -An LLM that converts natural language instruction to call a library of skills -A perception module that provides ground truth locations and visual object detection And what your left with is a glimpse into what our future will hold: robots executing tasks from human voice prompts.

AI Breakfast

33,725 görüntüleme • 3 yıl önce

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.
0:39

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.

seb

20,512 görüntüleme • 2 ay önce

You say it. A robot builds it. 🗣️ MIT researchers just showed a system where spoken language turns directly into physical objects. Say “I want a simple stool,” and a robotic arm designs and assembles it in minutes. 🪑 The pipeline is wild but elegant: speech → language model → 3D generative design → voxelized structure → robotic assembly. No CAD, no programming, no manufacturing expertise needed. Unlike 3D printing, this is fast, modular, and reversible. Objects are built from reusable parts, meaning they can be disassembled and turned into something else later. This feels like an early glimpse of “physical AI” becoming an interface: humans describe intent, machines handle geometry, planning, and fabrication. Read more about the paper here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:15

You say it. A robot builds it. 🗣️ MIT researchers just showed a system where spoken language turns directly into physical objects. Say “I want a simple stool,” and a robotic arm designs and assembles it in minutes. 🪑 The pipeline is wild but elegant: speech → language model → 3D generative design → voxelized structure → robotic assembly. No CAD, no programming, no manufacturing expertise needed. Unlike 3D printing, this is fast, modular, and reversible. Objects are built from reusable parts, meaning they can be disassembled and turned into something else later. This feels like an early glimpse of “physical AI” becoming an interface: humans describe intent, machines handle geometry, planning, and fabrication. Read more about the paper here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

33,051 görüntüleme • 8 ay önce