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Hardware. Data access. Applications. Shelby’s three-layer stack makes streaming fast, scalable, and creator-owned. Andrew Hariri explains the stack and demos how creators can use it.

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Shelby

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25,220 görüntüleme • 11 ay önce •via X (Twitter)

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AI is not one model. It is a five layer industrial stack: energy, chips and systems, infrastructure software, models, and applications. When America leads across that stack, we lead the next wave of growth.
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NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.
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NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

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Properly storing cement keeps it strong, saves you money, and makes every project smoother. - Stack neatly for easy access - Use your oldest bags first - Keep the area dry and elevated It’s that simple.
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With the stack fixed, can an AI agent choose data and experiment like a researcher? At Evolvent AI, we built RSIBench-Data to test whether agents can diagnose failures, design data, train checkpoints, and use feedback. 🧵 📄 💻

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YC W24's nuvi.dev is a testing and simulation stack to harden LLM-based applications. AI teams leverage Relari to stress test mission-critical GenAI use cases with modular evaluation and synthetic data. Congrats on the launch, Yi Zhang and Pasquale!

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Our co-founder Pratyush Kumar highlighted Sarvam's "contrarian bet" of being a full-stack AI platform. That means we build every layer: the models, how they run (inference), how agents work with each other (orchestration), and the AI applications people use. This lets us build AI that actually works for India's unique context and diverse language needs.

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Starting at RMB 89,900 (about $13.2K), Astribot T1 just landed as a much lower-cost entry point into embodied AI hardware 🤖 This is not trying to be a polished home butler. It is a cable-driven robot platform for developers: configurable AI compute, open interfaces, expandable peripherals, and dexterous manipulation. The real signal is price compression. S1 already pushed Astribot’s cable-driven stack into high-speed manipulation demos and early real-world deployments; T1 turns that stack into something labs, startups, and industry teams can test faster. For data collection, manipulation research, multi-scenario demos, and early service or industrial prototypes, this is the lane T1 is aiming at.

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Santiago Carmuega (Santiago Carmuega) joins us for a technical deep dive into Dolos, a lightweight Cardano data node alternative built completely in Rust. 🚀 This Developers Office Hour breaks down TxPipe's design philosophy and architecture for optimizing ledger data access: • The Data Node Role: Bypassing heavy consensus validation to optimize for fast, resource-efficient ledger queries. • Rust Infrastructure: Leveraging a modern performance-first tech stack built on top of Cardano open protocols. • Lightweight Use Cases: Overcoming standard node hardware limitations for client-side applications and indexers. Check out the documentation and open-source repositories mentioned in the session: 🔹 Dolos Docs: 🔹 GitHub Repository: 🔹 Subscribe to the calendar:

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Rauora is Bugged with new update , MAX she can use is 2 Panels and if you dont use them quick enough max you can use is 1 panel , it seems the problem is it cant stack the panels
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Rauora is Bugged with new update , MAX she can use is 2 Panels and if you dont use them quick enough max you can use is 1 panel , it seems the problem is it cant stack the panels

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ROCm 7 is built for developers: open, accessible, and fast. Mark Papermaster and Anush Elangovan break down how the AMD open software stack for AI makes it easier for developers in the latest episode of Advanced Insights. Watch this clip 👇 and see the full episode:
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ROCm 7 is built for developers: open, accessible, and fast. Mark Papermaster and Anush Elangovan break down how the AMD open software stack for AI makes it easier for developers in the latest episode of Advanced Insights. Watch this clip 👇 and see the full episode:

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Excited to announce my new short course: Building Agentic Applications with Replit Agent and n8n. With AI this capable, I believe anyone can become a builder. The stack I use here will teach you how to rapidly build agentic apps with no-code tools.

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The SOTA in humanoid locomotion is already insanely good. The control stack is almost fully leveraging what the hardware can do. For real-world use, the active safety is still remain unsolved: Common-sense intelligence to move safely around people, and graceful handling of possible hardware failures in the wild.

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Noble Machines' Moby robot hauling a 50 lb crate. The whole-body control (WBC) is the physical foundation of the learning stack. It absorbs payload variation, contact response, and center-of-mass shifts in real time at the control layer and enabled cleaner data-focused learning
1:37

Noble Machines' Moby robot hauling a 50 lb crate. The whole-body control (WBC) is the physical foundation of the learning stack. It absorbs payload variation, contact response, and center-of-mass shifts in real time at the control layer and enabled cleaner data-focused learning

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