ContentPosts from @hardlaughx..
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@faun shared a link, 8 months ago
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Amazon CEO warns staff: Eat or be eaten by AI

Amazon'sCEO sounds the alarm: AI is gearing up to decimate office jobs. He urges employees to sharpen their skills or risk getting the axe, all while Amazon unleashes a cavalcade of over1,000generative AI projects... read more  

Amazon CEO warns staff: Eat or be eaten by AI
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ChatGPT polluted the world forever, like the first atom bomb

AI model collapsecould hit hard with synthetic data in play. Picturepre-2022 dataas the “low-background steel” savior for pristine datasets. The industry squabbles over thetrue fallout, while researchers clamor for policies that keep data unsullied. The worry? AI behemoths might lock everyone else o.. read more  

ChatGPT polluted the world forever, like the first atom bomb
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How to Build an Asynchronous AI Agent Network Using Gemini for Research, Analysis, and Validation Tasks

The Gemini Agent Network Protocol introduces powerful AI collaboration with four distinct roles. Leveraging Google’s Gemini models, agents communicate dynamically for improved problem-solving... read more  

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Lenovo introduces new AI-optimized data center systems

Lenovo'sThinkSystem SR680a V4doesn't just perform—it explodes with AI power, thanks to Nvidia'sB200GPUs. We're talking4nmchips with a mind-boggling208 billion transistors. Boost? Try11x... read more  

Lenovo introduces new AI-optimized data center systems
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The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

FrontierLarge Reasoning Models (LRMs)crash into an accuracy wall when tackling overly intricate puzzles, even when their token budget seems bottomless.LRMsexhibit this weird scaling pattern: they fizzle out as puzzles get tougher, while, curiously, simpler models often nail the easy stuff with flair.. read more  

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
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AI at Amazon: a case study of brittleness

Amazon Alexa floundered amid brittle systems: a decentralized mess where teams rowed in opposing directions, clashing product and science cultures in tow... read more  

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Deploying Llama4 and DeepSeek on AI Hypercomputer

Meta's Llama4models, Scout and Maverick, strut around with17B active parametersunder a Mixture of Experts architecture. But deploying onGoogle Cloud's Trillium TPUsor A3 GPUs? That's become a breeze with new, fine-tuned recipes. Utilizing tools likeJetStreamandPathways? It means zipping through infe.. read more  

Deploying Llama4 and DeepSeek on AI Hypercomputer
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Announcing up to 45% price reduction for Amazon EC2 NVIDIA GPU-accelerated instances

AWS chops up to45%from Amazon EC2 NVIDIA GPU prices. Now your AI training costs less even as GPUs play hard to get... read more  

Announcing up to 45% price reduction for Amazon EC2 NVIDIA GPU-accelerated instances
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Scaling Test Time Compute to Multi-Agent Civilizations

Turns out, Reasoning AIs use a single test compute unit to pack the punch of something 1,000 to 10,000 times its size—an acrobatics act impossible before the might of GPT-4.Noam Brown spilled the beans on Ilya's hush-hush 2021 GPT-Zero experiment, which flipped his views on how soon we'd see reasoni.. read more  

Scaling Test Time Compute to Multi-Agent Civilizations
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Training a Rust 1.5B Coder LM with Reinforcement Learning (GRPO)

DeepSeek-R1flips the script on training LLMs. Armed withGRPO, it challenges the industry heavies like OpenAI's o1 by playing smart with custom data and cleverly designed rewards. Imagine this: a humble 1.5B model, running on merely asingle H100, clocks in at an 80% build pass rate. It’s nibbling at .. read more  

Training a Rust 1.5B Coder LM with Reinforcement Learning (GRPO)