Truth is Expensive: Why RAG is Doomed and What Actually Matters You're not building a truth machine. You're building the world's most efficient fiction amplifier. That's not a bug. It's the inevitable result of solving the wrong problem. Yuval Noah Harari's core insight—delivered in his latest book Nexus and his viral TED thesis—is brutally simple: Humans are a post-truth species. We didn't conquer the planet by being right. We conquered it by believing in shared fictions: gods, nations, money, brands. These stories don't need to be true. They need to be believed. [1] And now we're hardcoding that vulnerability into AI.

26.9.6 is out ! github.com/ddalcu/mlx-serve/… * M5 Ultra optimizations (some apply to all M class) * Laya typed decisions (local-JEV!) * Fixed handling of media and preserve_thinking * UI Updates * Renamed to MLX-Serve (no more MLX Core) * Bonsai-2 Optimizations Sound On 🤘

TensorFold Inference Engine is here 🚀 I spent six months making one weight read count for more than one token on Apple Silicon. Draft tokens run through parallel lanes; the model verifies them together and keeps only what passes. Qwen 3.8 27B MLX 4Bit - 120-124tks Nemotron Lightning MLX 4Bit - 188-206tks Qwen3.8 Flash Next MLX 4Bit - 88-92tks CUDA Implementation is in Alpha showing strong gains. The Repo is in the comments 👇🏼

Realtime sentiment analysis with Jev and ElevenLabs. Each phrase takes on the color of the emotion it carries while the caller is still talking. Six meters on the right track the mood of the call.

When several people talk at once, a transcript can get messy fast. Our new Nemotron 3 Diarization model tracks who spoke when, even when voices overlap. It handles up to eight speakers, has 100M parameters, and is now available on @huggingface 🤗

Run open models like Gemma 4 completely offline in the Antigravity SDK. Built on Google AI Edge’s LiteRT, you can now run Gemma 4 directly on your local GPU. Zero API costs, total data privacy, and no internet required.

Announcing tev1-4B-experimental, a Jev-like classifier finetuned on top of Qwen3.5 4B for only $17. I'm releasing everything: the weights, data recipe, & a full tutorial on how to train your own. You can try it today on Together serverless at $0.042/1M input & $0/M output.

The World is Changing: AI For Creativity By Jeffrey Katzenberg A few months ago, I sat in my office in Silicon Valley and watched as a tech founder showed me something extraordinary. On the screen was a fully realized, beautifully lit, well-composed animated scene. It was stunning and it made me feel exactly what I felt in 1986 watching Luxo Jr. That was the first time I watched a computer-animated 3D character take a breath and seem, against all reason, to have life. It left me in awe. Later that day, I received a text from an artist I've known for thirty years, 350 miles to the south, in the city where I spent most of my career. After seeing a similar video, she texted: "Is this the end of us?" My answer was, "Certainly not.” I have spent the better part of the last decade in Silicon Valley, but the heart of my career has been in Hollywood. Being deeply connected to both worlds means I have deep loyalties to each and a responsibility to speak honestly to both. In 2023, I said that these new AI tools would cut the time and cost of producing world-class animation by as much as ninety percent within three years. Some colleagues were alarmed, many were furious. There is growing fear and resistance surrounding AI within the creative community. I deeply understand it, because I've spent countless hours walking through animation studios watching gifted artists bent over their desks, rebuilding a single second of film for the tenth time because the ninth version wasn't quite right. I've sat in screening rooms where four years of people's labor played out in minutes, and I knew the name of every person that had spent countless hours bringing those images to life. The creative process is a calling, there's really no other way to describe it. From the outside some see resistance. From the inside, it is love. People do not fight this hard for things they don't care about. The pushback coming out of Hollywood represents the collective effort of people who are deeply passionate about their craft. Is History Repeating Itself? The history here is more complicated than either side may realize. In 1906, the most famous composer in America, John Philip Sousa, published an essay titled “The Menace of Mechanical Music." He warned that the phonograph would become "a substitute for human skill, intelligence and soul." Sousa's fight was not really about the machine, it was about money. The machines were playing his compositions, and the men who built them weren't paying him a cent. His campaign helped create the Copyright Act of 1909. He did not stop the technology. He changed the terms under which it could use his work. A hundred years ago, sound came to the movies. We remember it now as a miracle, and it was. What we forget is who paid for it. Before sound, tens of thousands of musicians made their living in the orchestra pits of movie houses, scoring every film live, every night, in towns all over the world. When the soundtrack arrived, the work of one composer and one orchestra was recorded for a film that went into thousands of theaters. The union fought back with everything it had, taking out newspaper ads across the country warning against the menace of "canned music," one of them showing a mechanical man tearing the strings out of a harp while an angel wept. They were not fools, and they were not Luddites. They were right. Those pit jobs did not come back. And yet (this is the part we have to be brave enough to admit), sound gave us the movie musical, the modern score, sfx, sound design, audio engineering, and an art form vastly larger than the one it disrupted. And it helped keep Hollywood in the forefront of world entertainment for the rest of the century and into the next. The loss was real. And yet the art form expanded. This is a story that has been told over and over again. To resist technology is to risk irrelevance. Just look at Kodak or Blockbuster. To embrace technology is to open doors of new possibility. Just consider Apple and Netflix. What I Learned From Walt Disney In the mid-1980s, I was tapped to lead Disney's animation division at a moment when the studio was at an inflection point. Animation wasn't just another business unit. It was the soul of the company, a medium revered because of Walt's genius and his passion. But the production system was cumbersome and unforgiving. A single movie was 125,000 individual hand-drawn and painted cels, photographed one frame at a time. Every revision carried a cost measured in months. These degrees of difficulty shaped the kinds of stories we could tell. We found our way forward in an unexpected place: Walt himself. The Disney archives held astonishing recordings of Walt explaining his creative process. His own writings. His notes and storyboards. Work product captured at every stage of his process. This was truly a gift. Listening, reading, sitting with the work itself, we heard him talk about character, about emotion, about how an audience feels when a character truly comes alive. He talked about making bold choices and refining a scene until it genuinely moved people. We didn't hear a word about pencils or paintbrushes. In fact, Walt was famous for being a technologist, forever hunting for state-of-the-art tools, often inventing them himself to achieve the images he saw in his head. But he never defined animation by the tools. He defined it by whether the audience believed the character. His principles were timeless. The tools were not. That realization changed everything. We co-developed the Computer Animation Production System (CAPS) with a young Northern California company called Pixar, replacing hand-painted cels with CGI. In The Little Mermaid, the final scene shimmered with a dimensionality and light that the old process simply couldn't achieve. In Beauty and the Beast, the ballroom sequence moved with a cinematic sweep that placed the audience inside the emotion of the moment. In Aladdin, the Cave of Wonders felt vast and alive, and the Magic Carpet became an intricate, compelling character all its own. In The Lion King, the stampede carried a scale and intensity that raised the emotional stakes beyond anything we'd done before. Technology didn't diminish the craft, it expanded the canvas. It gave artists more room to create. A decade later, the canvas expanded again. When Disney released Pixar's Toy Story, it wasn't simply a technical milestone. It was proof that a fully computer-animated film could carry real emotional weight, that it could make audiences laugh, cry, and believe. At DreamWorks, we made the difficult decision to sunset hand-drawn animation and become a fully computer-animated studio. It was the right thing to do, but it was not without pain. It cost talented people their place in an industry where they had worked their whole lives. Some made the leap to the new tools and did the finest work of their careers. Some never did. Tools are never the point. The instruments change with every generation. What endures is taste and imagination. The magical ability to make an audience feel. One of the greatest storytellers of our generation, George Lucas, succinctly captured the eternal essence of this issue: “It’s not the how, it’s the why.” A Distinction With a Difference I asked one of the leading AI models a question that has been challenging me for months. What is the difference between reasoning and creating? Its answer changed how I think about almost everything happening in this industry. It said . . . Reasoning and creating are two distinct cognitive modes, though they also work together. Reasoning is fundamentally evaluative and analytical. It operates on what already exists: facts, premises, evidence. It moves toward a conclusion that was in a sense already implied by the input. Reasoning is constrained by logic and truth. Its goal is to arrive somewhere correct, not to invent somewhere new. Creating is fundamentally generative. It produces something that didn't exist before. And crucially, there's no single right answer waiting to be found. A blank page has infinite valid responses. Creation involves choices that can't be fully justified by logic alone. Taste, intuition and vision fill the gap where deduction runs out. Reasoning is what Silicon Valley has been perfecting. Creating is what Hollywood has been practicing for more than a century. AI today operates almost entirely on the reasoning side of the line. It can deduce, evaluate, optimize, and pattern-match brilliantly. And while it can create, there is a real distinction to being creative. What it doesn’t yet have is those things that make us human: empathy, devotion, serendipity, the kind of creativity that comes from a person trying to say something only they could say. When the bot generates a piece of art, it is not trying to communicate anything. It is statistics, not soul; it is emulating things that have been done. By contrast, human creativity isn’t about repeating patterns of zeros and ones; it is about doing something new. One day, AI may close this gap. Three years ago, the leaders building AI would have called what they are achieving today, improbable, if not impossible. Impossible is no longer improbable. Today, the line between reasoning and creating is real. Even the leading technologists acknowledge we are not there yet. There is no scientific path to crossing this divide that anyone in the field can articulate today. Understanding that gap is where we will find common ground. A Path Forward In 2016, I closed one chapter in Hollywood with the sale of DreamWorks and opened another in Northern California, co-founding WndrCo. We’ve backed more than 50 founders building the next generation of technology and watched how breakthroughs in Silicon Valley emerge, first as experiments, then as platforms, and finally as infrastructure that reshapes entire industries. It's worth remembering that the last great revolution in animation also came from the north. Pixar was a Northern California company, forged not in the conventions of the Hollywood studio system, but in the technological breakthroughs of Silicon Valley. I've spent years on both sides of this bridge. For sure, I don’t have all the answers (take Quibi, for one!). But, from my past and present vantage points of my long career, here is what I see . . . Brilliant people in Northern California building this technology have made something extraordinary. They have earned the right for the rest of us to be, if not believers, at least optimistic that what comes next will be remarkable. But they have not made an artist. The tools are powerful, but they are not what makes a story matter. That knowledge lives 350 miles to the south, inside people whose life's work has informed the very models you are building. The right path forward includes them by design, with credit, with consent, and with compensation. Build this with the storytellers. Not on top of them. Taste is not something that can be synthesized, it is uniquely human. At the same time, Hollywood needs to accept that AI is not going away. The energy they are spending trying to make it disappear is energy they are not spending deciding the terms on which it will exist. And the terms are everything. The north needs something from it that they cannot build and cannot buy: creativity. The kind that takes a blank page and conjures a single right answer where there was none and has held audiences for a century. Without it, the most powerful reasoning engine ever invented will still be missing the only thing that makes a story worth telling. The artists who learn to wield these new instruments will do things the engineers never dreamed of. They always have. Edison invented the motion picture but made terrible movies. It took Chaplin, Lloyd, Keaton and so many others to make movies emotional. Now, the canvas is about to expand yet again. We should decide now that we intend to paint on it. There are so many valuable lessons in history. This has happened many times before, and it was never settled by the technology. It was settled by the terms. Sousa did not stop the phonograph; he helped write the law that made sure composers got paid. And two years ago, when the writers and the actors walked out, they were fighting for the very things Sousa was fighting for in 1906. Consent, compensation, the basic recognition that human creative work has a price that must be paid. The terms of that fight are still being negotiated, but the principle is older than any of us. The tools-versus-no-tools argument is a trap. First, we must all agree that there should be terms. Then we can have the crucial debate about what fairness requires. What I Learned From Steve Jobs Years ago, Steve Jobs said, "It's in Apple's DNA that technology alone is not enough. It's technology married with the liberal arts, married with the humanities, that yields us the result that makes our hearts sing." He was describing a device. But he could just as easily have been describing this tale of two cities. What I See Coming Soon As the barriers and the costs come down, more films will get made, not fewer. Studios will get to take more risks. There will be more seats at the table, and very soon entirely new forms of storytelling. In the 1980s, animation was dismissed as a niche corner of the business. Today it is one of the most beloved and profitable forms of storytelling in the world. In live action, filmmakers like Steven Spielberg, James Cameron and Peter Jackson embraced new visual tools not as shortcuts, but as instruments, and expanded cinema in the process. Every time storytelling has met a genuine technological shift, from synchronized sound to color to computer animation, it has redefined the boundaries of the medium and grown larger in the process. Assuredly, I don’t have all the answers, but I am confident that the creative opportunities will expand yet again. How we come through this is a choice. The north has the new tools. The south has the creative soul. The best future will draw on the best of both worlds.

Hi @inferact, nice work. A few days ago, I wrote a TPU inference engine with Jax for GLM-5.3-Flash, the first engine for this model to my knowledge. I was limited to Kaggle's free TPU, though. github.com/ARahim3/kaggle-tp…

Jev + Treg = hyper personalised CRM

We just shipped Quiver 2.0 in @paper This is the best SVG generation possible, it's an incredible leap forward • Much faster AND better quality • Cleaner geometry • New Telos model for detailed work Next level vector generation -- available today in Paper

Own your intelligence stack, folks. You can't scale a company to the frontier by renting intelligence. Custom models, harnesses, and evals are becoming huge assets. And if you're building something new, you might want to consider working on any one of these, or a combination of them.

Jev isn't multimodal... but do you know what is? CLIP. A zero-shot multimodal classifier released nearly *6* years ago. Search 25,000 images in <50ms (no image labeling required). It can even run locally in your browser on WebGPU, powered by Transformers.js! Try it yourself 👇

Babe, wake up. A new open-source harness that easily beats Codex by a mile just dropped. We entered singularity and didn’t even notice that.

Rust FTW Elon was probably right here, no?

Your document agent is only as good as the parse behind it! I've built a Grounded Document Agent that answers questions across long PDFs, attaches citations to every response, and lets you inspect the exact source behind those citations. A lot of document-agent failures look like retrieval problems, but the damage often happens much earlier. If a PDF table is flattened incorrectly, a chart loses structure, or the reading order breaks, better embeddings cannot recover what was already lost. For this project, I used LlamaParse to convert PDFs into layout-aware Markdown while preserving page metadata. From there, LlamaIndex handles indexing and retrieval, while Ollama runs the embeddings and the local "qwen3:4b-instruct" model. For every question, the agent retrieves the most relevant sections, generates an answer with numbered citations, and lets you inspect the exact source text and page behind each citation. That matters because a citation does not automatically make an answer correct. If the underlying evidence was parsed incorrectly, the model can still cite the right page and give you the wrong answer. The useful part is being able to trace the answer through parsing, retrieval, and generation instead of treating a confident response as the final truth. PDF → LlamaParse → Local retrieval → Local LLM → Grounded answer with citations Here’s the complete project: github.com/Sumanth077/Hands-… I’ve also written a detailed article explaining how to build the complete grounded document agent step by step.

Finally, a company brain that actually works! Codos starts with the people doing the work. It interviews employees to find which tasks can be automated, then deploys the automations and keeps learning from the teams using them. I like how openly they explain the system. Under the hood, it builds a context graph and a layered memory of company facts, operational history, and working priorities. One midsize fintech freed up 21% of its team's capacity in six months.

Someone built voice-controlled computer use for Mac with Jev and it executes commands faster than the words can be spoken, opening apps before the sentence is even finished.

this Stanford paper is f*cking insane they just compressed the entire hedge fund playbook into a 17-page pdf. Stanford put out the complete Hidden Markov Model framework that quants at firms like Jane Street and Two Sigma are known to run, and released it for free. the crazy part is this isn't some watered-down summary, it's the actual mechanics behind models these desks keep locked up internally. most people assume this stuff never leaves institutional walls. this one hands you the framework directly, no gatekeeping. bookmark it and read before someone takes it down.

Did you know you can now put the text of almost all of arXiv on one cheap SSD. Someone just packaged an enormous arXiv snapshot on Hugging Face 📚 3,148,796 papers 📄 PDFs for 99.47% 💽 Full archive: 16.08 TB Sounds ridiculous for Local AI… Until you see that … 🔥 paper_text = 70GB That contains the resolved TeX text for … 🧠 2,856,227 papers 📊 90.7% of all arXiv And you can download just that dataset by itself. No 16TB NAS is necessary. So theoretically you could build a completely local research system using ThumbLLM.exe from a thumb drive!!! 💾 70GB arXiv text corpus 🔎 local search / embeddings 🧠 local LLM 🚫 no API required Ask your local model to search nearly 3 million scientific papers sitting on your own SSD. 👀 Caveats ⚠️ It’s a snapshot, not live arXiv ⚠️ latest submission is Aug. 27 ⚠️ the text is resolved TeX, so it still contains LaTeX syntax/macros and needs some cleaning for ideal RAG But 2.8M papers in ~70GB is a Localmaxxer dataset. Link in ALT