The main risk in AI is concentration of power, capabilities and economic gains. Opensource is fundamental to mitigate these so thanks for all your contributions there!
AI
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AgentDoG 1.5: AI Agent Safety and Alignment Framework
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AgentDoG 1.5 A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
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Hugging Face Private Models Infrastructure Growth
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Most people know Hugging Face from its public models and datasets but few realize that 50% of the models and datasets stored on HF are private. This number has been increasing with buckets (our S3 alternative for AI) and enable companies to build AI more efficiently and
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Open-Source Voice Agents Replace Proprietary Paid Platforms
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You can now run unlimited voice agents for $0.
— AlphaSignal AI (@AlphaSignalAI) 29 mai 2026
Building a voice agent today usually means renting one.
You pay per minute. You hand over call data.
You hope the closed platform you depend on does not change terms.
A team of YC alumni just shipped the open source way out.… pic.twitter.com/AiNBJWULbgYou can now run unlimited voice agents for $0. Building a voice agent today usually means renting one. You pay per minute. You hand over call data. You hope the closed platform you depend on does not change terms. A team of YC alumni just shipped the open source way out.
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LangSmith Gateway enforces spend limits and redacts PII before model requests
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LangSmith LLM Gateway lets you enforce spend limits and redacts PII before requests reach the model. Not after the fact.
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Agentic layer enables autonomous fact-based optimization
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The agentic layer closes the loop entirely, pushing fact-based optimizations without waiting for human intervention.
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Four Layers of Industrial AI: From Prediction to Automation
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Industrial AI operates across four layers: predictive forecasts what's coming, prescriptive recommends actions, generative exposes insights in natural language, agentic pushes optimizations directly to control systems. #industrialai #manufacturing
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AI Performance as System-Level Enterprise Challenge
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The enterprise takeaway is simple: AI performance is now a system-level challenge. The winners will optimize chips, memory, interconnects, software, and architecture together. Less latency means faster intelligence.
Less movement means lower cost.
Less waste means AI that can -
LogicFolding: 3D Chip Architecture for AI Inference Optimization
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One concept that makes this practical is LogicFolding. Traditional chips spread logic across a flat surface. LogicFolding brings related logic closer together by moving toward more 3D structures. Less distance means less delay. And in AI workloads, small delays compound fast.