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THE BEST visual explainer of how information propagates through a transformer. If you want to have more than intuition about how the Transformer architecture is ruling the LLM world - open-source project explains everything about LLM Transformer Models! - A great resource for anyone looking to gain a deeper...

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Rohan Paul

150,798 subscribers

106,897 次观看 • 1 年前 •via X (Twitter)

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Wow. This is one of the best interactive sites I’ve seen for learning how LLMs work! 🔥 It starts w/ a clear intro and guides you through every core component: from Embedding, Layer Norm, and Self-Attention to MLPs, Transformer blocks, Softmax, and Output layers. link in 🧵↓
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Wow. This is one of the best interactive sites I’ve seen for learning how LLMs work! 🔥 It starts w/ a clear intro and guides you through every core component: from Embedding, Layer Norm, and Self-Attention to MLPs, Transformer blocks, Softmax, and Output layers. link in 🧵↓

Charly Wargnier

52,092 次观看 • 1 年前

Part 2/30 of the LLM Series: RoPE (Rotary Position Embedding) How does a transformer know the difference between - "the dog bit the man" and "the man bit the dog"? The words are almost identical, but the meaning changes completely. RoPE encodes position as rotation, allowing transformers to understand relative order through geometry. Read more:
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Part 2/30 of the LLM Series: RoPE (Rotary Position Embedding) How does a transformer know the difference between - "the dog bit the man" and "the man bit the dog"? The words are almost identical, but the meaning changes completely. RoPE encodes position as rotation, allowing transformers to understand relative order through geometry. Read more:

TensorTonic

11,766 次观看 • 1 个月前

I’m launching my first open-source project. If you are building an app like sage by buildspace It’s a generative UI framework for AI applications. This is the demo we created at the ai tinkerers hackathon this Saturday. I am looking for users & contributors. Drop a comment below with how you want to use it.
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I’m launching my first open-source project. If you are building an app like sage by buildspace It’s a generative UI framework for AI applications. This is the demo we created at the ai tinkerers hackathon this Saturday. I am looking for users & contributors. Drop a comment below with how you want to use it.

Michael Magán

14,631 次观看 • 2 年前

i just ran Google's brand new Unsloth Gemma4 12B dense GGUF on my RTX 4060 using llama.cpp + CUDA 13.2 21 tokens per second. on a budget consumer GPU. locally. no API. no cloud. no subscription. and the benchmarks are absolutely cooked # first let's talk architecture because this is genuinely different every multimodal model you've used has a frozen vision encoder + frozen audio encoder + LLM backbone glued together Gemma 4 12B is different it's a single decoder only transformer. that's it. vision? raw 48×48 pixel patches → one matmul → projected directly into the LLM audio? raw 16kHz signal sliced into 40ms frames → linear projection → same LLM input space no encoder tax. no latency penalty. no fragmented memory to put the encoder savings in perspective: old Gemma 4 26B approach: - 550M param vision encoder (frozen) - 300M param audio encoder (frozen) - LLM backbone Gemma 4 12B: - 35M param vision embedder (a single matmul) - no audio encoder at all - LLM backbone handles EVERYTHING 550M → 35M for vision alone. that's a 15x reduction this is why the gemma-4-12b-it-Q4_K_M.gguf is just 6.6 GBs!!! and it has 256K native context context # Benchmarks: AIME 2026 (math olympiad): 77.5% GPQA Diamond (expert science): 78.8% LiveCodeBench v6 (real code): 72% Codeforces ELO: 1659 MMLU Pro: 77.2% MATH-Vision: 79.7% BigBench Extra Hard: 53% inference → llama.cpp, LM Studio, vLLM, SGLang llamacpp flags: -m "gemma-4-12b-it-Q4_K_M.gguf" -ngl 99 -c 8000 -v --port 8080 Available on huggingface now! Link below
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i just ran Google's brand new Unsloth Gemma4 12B dense GGUF on my RTX 4060 using llama.cpp + CUDA 13.2 21 tokens per second. on a budget consumer GPU. locally. no API. no cloud. no subscription. and the benchmarks are absolutely cooked # first let's talk architecture because this is genuinely different every multimodal model you've used has a frozen vision encoder + frozen audio encoder + LLM backbone glued together Gemma 4 12B is different it's a single decoder only transformer. that's it. vision? raw 48×48 pixel patches → one matmul → projected directly into the LLM audio? raw 16kHz signal sliced into 40ms frames → linear projection → same LLM input space no encoder tax. no latency penalty. no fragmented memory to put the encoder savings in perspective: old Gemma 4 26B approach: - 550M param vision encoder (frozen) - 300M param audio encoder (frozen) - LLM backbone Gemma 4 12B: - 35M param vision embedder (a single matmul) - no audio encoder at all - LLM backbone handles EVERYTHING 550M → 35M for vision alone. that's a 15x reduction this is why the gemma-4-12b-it-Q4_K_M.gguf is just 6.6 GBs!!! and it has 256K native context context # Benchmarks: AIME 2026 (math olympiad): 77.5% GPQA Diamond (expert science): 78.8% LiveCodeBench v6 (real code): 72% Codeforces ELO: 1659 MMLU Pro: 77.2% MATH-Vision: 79.7% BigBench Extra Hard: 53% inference → llama.cpp, LM Studio, vLLM, SGLang llamacpp flags: -m "gemma-4-12b-it-Q4_K_M.gguf" -ngl 99 -c 8000 -v --port 8080 Available on huggingface now! Link below

Alok

278,930 次观看 • 1 个月前

Can industrial AI do more than just observe? Nicolas Cerisier, Vice President of 3DEXPERIENCE Platform R&D at Dassault Systèmes explains how the shift from SaaS to "Agent-as-a-Service" is introducing "Industry World Models" that understand the scientific laws of physics and engineering to power autonomous virtual companions.
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Can industrial AI do more than just observe? Nicolas Cerisier, Vice President of 3DEXPERIENCE Platform R&D at Dassault Systèmes explains how the shift from SaaS to "Agent-as-a-Service" is introducing "Industry World Models" that understand the scientific laws of physics and engineering to power autonomous virtual companions.

NVIDIA

25,605 次观看 • 2 个月前

Republicans like Nancy Mace insist that Liam should be using the female restroom. Really? A few questions: - Why? - How does this make sense to anyone beyond a kindergarten-level understanding of gender? - Have you actually thought this through, or is this just about scoring cheap political points? The point of posts like this is simple: Complex, multi-faceted issues can’t be reduced to oversimplified, black-and-white answers. But hey, I guess nuance isn’t for everyone.
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Republicans like Nancy Mace insist that Liam should be using the female restroom. Really? A few questions: - Why? - How does this make sense to anyone beyond a kindergarten-level understanding of gender? - Have you actually thought this through, or is this just about scoring cheap political points? The point of posts like this is simple: Complex, multi-faceted issues can’t be reduced to oversimplified, black-and-white answers. But hey, I guess nuance isn’t for everyone.

Brian Krassenstein

621,322 次观看 • 1 年前

Tired of manual prompt tweaking? Watch the latest Llama tutorial on how to optimize your existing GPT or other LLM prompts for Llama with `llama-prompt-ops`, the open-source Python library! In this video, Partner Engineer Justin Lee demonstrates installation, project setup, migrating your first prompt, and analyzing performance gains. Watch now to discover: ✨ Why systematic prompt optimization is crucial for migrating from GPT to Llama. 💻 A live code walkthrough of `llama-prompt-ops` in action for a customer service classification task. Watch the full tutorial here:
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Tired of manual prompt tweaking? Watch the latest Llama tutorial on how to optimize your existing GPT or other LLM prompts for Llama with `llama-prompt-ops`, the open-source Python library! In this video, Partner Engineer Justin Lee demonstrates installation, project setup, migrating your first prompt, and analyzing performance gains. Watch now to discover: ✨ Why systematic prompt optimization is crucial for migrating from GPT to Llama. 💻 A live code walkthrough of `llama-prompt-ops` in action for a customer service classification task. Watch the full tutorial here:

Meta for Developers

38,047 次观看 • 1 年前

What if you kept asking an LLM to "make it better"? In some recent work at FAIR, we investigate how we can efficiently use RL to fine-tune LLMs to iteratively self-improve on their previous solutions at inference-time. Training for iterated self-improvement can be costly. The naive approach to training for K self-improvement steps leads to K times the number of rollout steps per episode. We introduce Exploratory Iteration (ExIt), an RL-based automatic curriculum method that bootstraps diverse training distributions of self-improvement tasks by upcycling the LLM's own responses at previous turns as the starting points for both self-improvement and *self-divergence.* In order to decide what task to train on next, the curriculum prioritizes sampling of partial turn histories that led to higher return variance in its GRPO group (a learnability score that comes for free). This automatic curriculum over the bootstrapped task space teaches the model how to perform iterated self-improvement while only ever training the model on single-step self-improvement tasks. We look at ExIt's impact in both single-turn (contest math problems) and multi-turn (BFCLv3 multi-turn tasks), as well as MLE-bench, where the LLM is run in a search scaffold to produce solutions to real Kaggle competitions. Across these eval settings, we find ExIt produces models with greater capacity for inference-time self-improvement compared to GRPO. Notably, ExIt models can self-improve on test tasks for many more steps than the typical solution depth encountered during training, including a 22% improvement in MLE-bench performance compared to GRPO.
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What if you kept asking an LLM to "make it better"? In some recent work at FAIR, we investigate how we can efficiently use RL to fine-tune LLMs to iteratively self-improve on their previous solutions at inference-time. Training for iterated self-improvement can be costly. The naive approach to training for K self-improvement steps leads to K times the number of rollout steps per episode. We introduce Exploratory Iteration (ExIt), an RL-based automatic curriculum method that bootstraps diverse training distributions of self-improvement tasks by upcycling the LLM's own responses at previous turns as the starting points for both self-improvement and *self-divergence.* In order to decide what task to train on next, the curriculum prioritizes sampling of partial turn histories that led to higher return variance in its GRPO group (a learnability score that comes for free). This automatic curriculum over the bootstrapped task space teaches the model how to perform iterated self-improvement while only ever training the model on single-step self-improvement tasks. We look at ExIt's impact in both single-turn (contest math problems) and multi-turn (BFCLv3 multi-turn tasks), as well as MLE-bench, where the LLM is run in a search scaffold to produce solutions to real Kaggle competitions. Across these eval settings, we find ExIt produces models with greater capacity for inference-time self-improvement compared to GRPO. Notably, ExIt models can self-improve on test tasks for many more steps than the typical solution depth encountered during training, including a 22% improvement in MLE-bench performance compared to GRPO.

Minqi Jiang

41,066 次观看 • 10 个月前

Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”
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Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”

Bilawal Sidhu

92,792 次观看 • 2 年前

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.
0:18

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,190 次观看 • 2 个月前

Again, in any other presidency this by itself would be impeachable. Trump tells a story of how he forced the Navy to “put a little more spirit in the hull” of a battleship. “I want that ship to look gorgeous… Forget about stealth…“ Endangering our military personnel for “great looking equipment” is a bizarre admission of deliberate negligence at best, if not borderline treason. “The new battleship that we have, which I've seen and helped design, I put a little more spirit in the hull, a little more, give me a little bit more hull. I want that ship to look gorgeous, you know. Forget about stealth, they say, "Oh, it's stealth." They design ugly equipment for stealth, well, we designed a great looking equipment, also stealth.”
0:18

Again, in any other presidency this by itself would be impeachable. Trump tells a story of how he forced the Navy to “put a little more spirit in the hull” of a battleship. “I want that ship to look gorgeous… Forget about stealth…“ Endangering our military personnel for “great looking equipment” is a bizarre admission of deliberate negligence at best, if not borderline treason. “The new battleship that we have, which I've seen and helped design, I put a little more spirit in the hull, a little more, give me a little bit more hull. I want that ship to look gorgeous, you know. Forget about stealth, they say, "Oh, it's stealth." They design ugly equipment for stealth, well, we designed a great looking equipment, also stealth.”

Jim Stewartson, Decelerationist 🇨🇦🇺🇦🇺🇸

154,578 次观看 • 5 个月前

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.
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🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 次观看 • 10 个月前

Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at hello@qualiastudios.dev More soon
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Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at [email protected] More soon

Qualia

87,429 次观看 • 1 个月前

🚨 SCIENTISTS SAY “MAGIC” MAY BE WHAT GIVES SPACE-TIME ITS GRAVITY. For years, physicists have understood how entanglement can build the structure of space-time in holographic models. But something was missing: why does space-time curve in response to matter the essence of gravity? A team including Charles Cao and John Preskill now proposes the missing ingredient is a quantum property called “magic” a measure of how complex and non-classical a quantum state is (the kind that makes quantum computers hard to simulate classically). In their theoretical framework, adding this magic turns rigid space into something that can bend. Matter can now tell space how to curve. Why this matters: • It offers a new way to think about how gravity emerges from quantum information • It connects ideas from quantum computing (error correction, magic states) directly to fundamental physics • It suggests space-time itself may be one of the most quantum objects in existence The deeper implication: Gravity may not be a fundamental force at all. It may be what happens when quantum information becomes sufficiently complex and “magical.” This is still early theoretical work in specific holographic models. But it hints that the pliability of the universe might have quantum roots we are only beginning to understand. What do you think is gravity ultimately just extremely complicated quantum information, or do you think we’re still missing something much deeper? Follow for more frontier quantum gravity and quantum information research.
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🚨 SCIENTISTS SAY “MAGIC” MAY BE WHAT GIVES SPACE-TIME ITS GRAVITY. For years, physicists have understood how entanglement can build the structure of space-time in holographic models. But something was missing: why does space-time curve in response to matter the essence of gravity? A team including Charles Cao and John Preskill now proposes the missing ingredient is a quantum property called “magic” a measure of how complex and non-classical a quantum state is (the kind that makes quantum computers hard to simulate classically). In their theoretical framework, adding this magic turns rigid space into something that can bend. Matter can now tell space how to curve. Why this matters: • It offers a new way to think about how gravity emerges from quantum information • It connects ideas from quantum computing (error correction, magic states) directly to fundamental physics • It suggests space-time itself may be one of the most quantum objects in existence The deeper implication: Gravity may not be a fundamental force at all. It may be what happens when quantum information becomes sufficiently complex and “magical.” This is still early theoretical work in specific holographic models. But it hints that the pliability of the universe might have quantum roots we are only beginning to understand. What do you think is gravity ultimately just extremely complicated quantum information, or do you think we’re still missing something much deeper? Follow for more frontier quantum gravity and quantum information research.

TheNewPhysics

15,329 次观看 • 1 个月前

Taste is invisible until you try to write it down. This is probably my biggest lesson with AI building as of late. At Sundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM. Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?" It's really humbling work! You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again. I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it! This gap *is* taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't. Being good at something is not the same as being able to articulate why you're good at it. I now see two bottlenecks to making something better than today's generic AI: 1. Can you *see* what better looks like in the first place? 2. Even if you can see, can you *articulate* what that is in a way that the LLM can understand and systemize? #2 is now a new craft, the art of distilling the art. The people who can do it well are the ones building standout products.
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Taste is invisible until you try to write it down. This is probably my biggest lesson with AI building as of late. At Sundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM. Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?" It's really humbling work! You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again. I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it! This gap *is* taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't. Being good at something is not the same as being able to articulate why you're good at it. I now see two bottlenecks to making something better than today's generic AI: 1. Can you *see* what better looks like in the first place? 2. Even if you can see, can you *articulate* what that is in a way that the LLM can understand and systemize? #2 is now a new craft, the art of distilling the art. The people who can do it well are the ones building standout products.

Julie Zhuo

17,493 次观看 • 2 个月前

Today, Nesa is excited to announce cross-chain support with Sei. As part of this new development, Nesa will be featuring a dedicated ramp for Sei’s rich ecosystem of applications to seamlessly integrate with AI. Now any Sei dapp can run AI inference on hundreds of models supported by Nesa, including the largest LLMs and most popular models across Vision, Language, and Generative AI. For developers, we’ll have information soon on how to access AI inference on Nesa via smart contracts from the Sei blockchain. Stay tuned for more details on this ground breaking development for decentralized AI.
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Today, Nesa is excited to announce cross-chain support with Sei. As part of this new development, Nesa will be featuring a dedicated ramp for Sei’s rich ecosystem of applications to seamlessly integrate with AI. Now any Sei dapp can run AI inference on hundreds of models supported by Nesa, including the largest LLMs and most popular models across Vision, Language, and Generative AI. For developers, we’ll have information soon on how to access AI inference on Nesa via smart contracts from the Sei blockchain. Stay tuned for more details on this ground breaking development for decentralized AI.

Nesa

79,657 次观看 • 2 年前

I’ve seen all I need to see, John Kiriakou. You are giddy like a school girl over the Iranian regime promoting you. You said, “I am a star in Iran”. Look at how excited you are that the leader of a terrorist regime wanted to meet with you. Of course I oppose you. You’re not a hero for leaking classified information about enhanced interrogation of Islamic terrorists. Why would you want anyone to have sympathy for Islamic terrorists? They all deserve to be tortured. We need more water boarding. Not less.
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I’ve seen all I need to see, John Kiriakou. You are giddy like a school girl over the Iranian regime promoting you. You said, “I am a star in Iran”. Look at how excited you are that the leader of a terrorist regime wanted to meet with you. Of course I oppose you. You’re not a hero for leaking classified information about enhanced interrogation of Islamic terrorists. Why would you want anyone to have sympathy for Islamic terrorists? They all deserve to be tortured. We need more water boarding. Not less.

Laura Loomer

153,908 次观看 • 3 个月前

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.
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The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

Santiago

30,178 次观看 • 20 天前