Fewer people having to run around with their MacBooks open as it makes running agents in the cloud easy.
COMPUTING
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Use reference photos, AI generates everything but facial expressions
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Take a bunch of pictures with different expressions and use those as reference. Have the AI model generate everything but the facial expressions.
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10 GitHub repos that defined 2026: openclaw, anthropics skills, ECC
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10 github repos that defined 2026 so far: 1. openclaw
your own personal ai assistant. any os, any platform. 378k stars and counting. 2. anthropics skills
the official agent skills library. the standard everyone else builds against. 3. everything claude code (ECC)
the agent -
Fully solving coding shows it wasn’t the hard part
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Once we *fully* solve coding, we’ll realize that coding wasn’t the hard part.
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Terminal command executing codex with GPT-5.5-cyber
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codex -C ~/projects/openclaw -m gpt-5.5-cyber time pic.twitter.com/6ANgzM1JKJ
— Peter Steinberger 🦞 (@steipete) 12 juin 2026codex -C ~/projects/openclaw -m gpt-5.5-cyber time
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Edge AI 2026: Technologies Enabling On-Device Generative AI
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Edge #AI in 2026: The Technologies Making On-Device #GenerativeAI Possible
— Ronald van Loon (@Ronald_vanLoon) 12 juin 2026
via @WevolverApp#ArtificialIntelligence #MachineLearning #ML #DL pic.twitter.com/kE5um3nNy4Edge #AI in 2026: The Technologies Making On-Device #GenerativeAI Possible
via @WevolverApp #ArtificialIntelligence #MachineLearning #ML #DL -
First infrastructure benchmark for agentic AI
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The first infrastructure benchmark for agentic AI has arrived. An AI agent chains tens to hundreds of AI model calls, using tools, gathering context, and iterating until the task is complete. Existing benchmarks
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Apple uses NVIDIA Blackwell B200s on Google Cloud for private inference
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I had already wondered how Apple manages to perform inference at Google while simultaneously protecting their privacy, essentially their unique selling point. The answer: the heaviest requests run on Blackwell B200s inside Google Cloud, with NVIDIA's Confidential Computing
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Asynchronous AI Cuts Energy by Orders of Magnitude while Learning Continuously
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Asynchronous #AI cuts computing energy by orders of magnitude while learning continuously
by Daegan Miller @TechXplore_com Learn more: https://
bit.ly/4fuihmz #MachineLearning #ArtificialIntelligence #DL #ML -
ASI emerges from scale, speed, coordination, and recursion
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The most important reframing: ASI may not require a single model to become "godlike". It could emerge from scale, speed, coordination, recursion, and institutionalized machine cognition. The transition AGI → ASI is therefore a