It's different, but I'm also a huge fan and have been using it. These products are the future.
GENERATIVE AI
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X combats AI-generated content while promoting Grok
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X plans to combat AI-generated content while promoting Grok https://
buff.ly/1Ho4WfE
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LeWorldModel: JEPA Training Breakthrough by LeCun
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Yann LeCun and his team can't stop cooking "LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels" One of the biggest bottlenecks of JEPA is they are hard to train, and this new research changes that. They propose LeWorldModel, which shows that a
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GPT 5.4 Outperforms Competitors in Excel and Research Tasks
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GPT 5.4 is cheaper and more performant for xls and deep research than other SOTA models We have now managed to improve performance by 33% on other top Gemini or Claude models OpenAI needs to double down on this win and launch GPT 6.0 ASAP
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Uni-1 model integrates reasoning and image generation in a continuous process
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La IA ya no necesita dos pasos para crear.
— SONIA (@S0N_IA_) 23 mars 2026
Uni-1 unifica el pensamiento y la generación de píxeles en un único proceso continuo. Como imaginar y dibujar al mismo tiempo.
Esto es lo que viene después de los LLMs. https://t.co/ljsRMHMoCWLa IA ya no necesita dos pasos para crear. Uni-1 unifica el pensamiento y la generación de píxeles en un único proceso continuo. Como imaginar y dibujar al mismo tiempo. Esto es lo que viene después de los LLMs.
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GradMem: Gradient Descent Memory for Context Compression
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"GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent" Instead of giving LLMs huge KV caches or one-shot compressed summaries, GradMem shows that you can let a frozen model take some test-time gradient steps to write a long context into a small memory.
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Littlebird Desktop App Uses Screen Reading AI for Productivity
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This is a the future "Littlebird is a desktop app that remembers everything you’ve been working on – meetings, messages, docs, browsing – and helps you stay focused, prioritize, recall, and move projects forward. It uses screen reading to understand all the text on screen, for
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GPT-5.4 Pro Solves FrontierMath Open Problem
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That is really really impressive: GPT-5.4 pro has solved one of the open problems in FrontierMath. Kevin Barreto and Liam Price, using GPT-5.4 Pro, produced a construction that Will Brian confirmed, with a write-up planned for publication We are accelerating
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GPT-5.4 Pro Outperforms Other Models for Complex Tasks
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GPT-5.4 Pro continues to be the only model of its class. For anything really hard & complex, I throw it into the maw with every bit of context I can think of. More often than not, something very useful comes out. I can't get the same results from Codex or Code or anything else.
→ View original post on X — @mattshumer_, 2026-03-23 17:43 UTC
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Jensen Huang on AI Expanding Human Potential and Computing’s Future
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"Every carpenter, every accountant, every student, will use AI to build.”
— NVIDIA (@nvidia) 23 mars 2026
Jensen Huang on how AI expands human potential, reshapes work, and defines the next era of computing.
New episode with @lexfridman out now. https://t.co/K6Kn3G9I14"Every carpenter, every accountant, every student, will use AI to build.” Jensen Huang on how AI expands human potential, reshapes work, and defines the next era of computing. New episode with @lexfridman out now. Lex Fridman (@lexfridman) Here's my conversation with Jensen Huang, CEO of NVIDIA, the most valuable & one of the most influential companies in the history of human civilization. It is the engine powering the AI revolution. This was a fascinating & inspiring conversation, in parts super-technical on engineering of every part of the AI stack, memory, power, supply chain (TSMC, ASML, etc), in parts about leadership & psychology, and in parts personal & philosophical about life, consciousness, mortality, and human nature. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 – Introduction 0:33 – Extreme co-design and rack-scale engineering 3:18 – How Jensen runs NVIDIA 22:40 – AI scaling laws 37:40 – Biggest blockers to AI scaling laws 39:23 – Supply chain 41:18 – Memory 47:24 – Power 52:43 – Elon and Colossus 56:11 – Jensen's approach to engineering and leadership 1:01:37 – China 1:09:50 – TSMC and Taiwan 1:15:04 – NVIDIA's moat 1:20:41 – AI data centers in space 1:24:30 – Will NVIDIA be worth $10 trillion? 1:34:39 – Leadership under pressure 1:48:25 – games 1:55:16 – AGI timeline 1:57:29 – Future of programming 2:11:01 – Consciousness 2:17:22 – Mortality — https://nitter.net/lexfridman/status/2036123301140111406#m