This is bigger than it looks. Agentic workflows + visual editing + live deploy
= one-person startups at scale.
SOFTWARE
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Agentic workflows, visual editing, live deploy enable one-person startups at scale
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Google to Integrate Gemini Deep Research with NotebookLM for Enhanced Sourcing
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DERNIÈRE MINUTE : Google s’apprête à ajouter Deep Research de Gemini à NotebookLM ! Ce serait énorme. Il pourrait ainsi être possible de tirer des sources à la fois du web et de Google Drive. Et ce n’est pas tout, ils prévoient d'autres surprises
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Mock Services for Faster API Development Session Online
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Drupal GovCon is over, but our sessions are online! Check out Ken Rickard's session on how to use mock services for faster API development: https://
hubs.ly/Q03DVth60 -

Developers Build AI-Powered Software Products in Hours
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On Saturday at the Buildathon hosted by AI Fund and http://
DeepLearning.AI, over 100 developers competed to build software products quickly using AI assisted coding. I was inspired to see developers build functional products in just 1-2 hours. The best practices for rapid -
Open Models Power 70% of AI Inference Workloads
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𝗧echnology ecosystem and install base Open models power over 70% of AI Inference workloads. NVIDIA supports 1,000+ OSS projects and 450+ open models like Llama, Gemma, and GPT-OSS, and collaborates on frameworks like @GoogleDeepMind JAX, @PyTorch
, @lmsysorg (SGLang), -
NVIDIA Blackwell GB200 NVL72 Delivers 50× AI Productivity Gains
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𝗔rchitecture and software The NVIDIA Blackwell platform + GB200 NVL72 rack system = up to 50× higher AI factory productivity. Throughput + energy and water efficiency gains + full-stack orchestration = scalable inference.
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NVIDIA Inference Platform: Balancing Accuracy, Latency, and Cost
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𝗠ulti-dimensional performance Optimal Inference is a trade-off: accuracy, latency, and cost. Some tasks need ultra-low latency (real-time translation), while others prioritize throughput (multi-million-token queries). The NVIDIA Inference Platform accelerates models
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Smart Inference Deployment: Scaling AI Across Enterprise Systems
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Deploying AI at scale requires you to 𝗧𝗵𝗶𝗻𝗸 𝗦𝗠𝗔𝗥𝗧 about inference: 𝗦cale and complexity 𝗠ulti-dimensional performance
𝗔rchitecture & software
𝗥OI driven by performance
𝗧echnology ecosystem & install base Let’s break it down -

Sesame TTS Outperforms Competitors in Quality and Speed
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I still can't explain why Sesame in TTS is so much better than all the competition: it sounds more natural, has less latency and is funnier.
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Discover Qoder AI IDE for Intelligent Code Development
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Try out @qoder_ai_ide here: https://
qoder.com