Opus 4.8 is live. Benchmarks especially significant jump in Agentic coding, but more important: „Fast mode is available for Opus 4.8. It's the same model at roughly 2.5x the speed, and we've made it three times cheaper than before.“
COMPUTING
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Data Infrastructure Optimization for AI Model Training
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We are starting to be quite bullish about getting in the data infrastructure business.
— Julien Chaumond (@julien_c) 28 mai 2026
I just cloned 68 TB (while I only have a 4TB local disk) to my @huggingface training bucket in 1 minute 55 seconds, thanks to Xet deduplication and all our infra optimizations.
You can host… pic.twitter.com/qfm9QvaIdjWe are starting to be quite bullish about getting in the data infrastructure business. I just cloned 68 TB (while I only have a 4TB local disk) to my @huggingface training bucket in 1 minute 55 seconds, thanks to Xet deduplication and all our infra optimizations. You can host
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Perplexity Computer now helps prepare federal tax returns
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https://t.co/b774dXzDJ7 https://t.co/NeC9DfB4rI
— Aravind Srinivas (@AravSrinivas) 28 mai 2026Perplexity Computer can now help prepare your federal tax return. Select “Navigate my taxes” on Computer to give it a shot.
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Why Inference Latency Matters Over Training Throughput
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Why does this matter for AI inference specifically? Training = throughput problem. Inference = latency problem. When a user talks to an AI assistant, tokens have to return fast. Latency, memory access, bandwidth, and interconnect all matter, not just raw compute. In large AI
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Wafer-Scale AI Compute: Dedicated Silicon Architecture
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“This technology, this work would be big enough to require dedicated silicon… you needed to start with a clean sheet of paper and you needed to do something fundamentally different.”@andrewdfeldman joined @PeterDiamandis on MOONSHOTS to discuss wafer-scale AI compute, fast… pic.twitter.com/RNv3i2VTZL
— Cerebras (@cerebras) 28 mai 2026“This technology, this work would be big enough to require dedicated silicon… you needed to start with a clean sheet of paper and you needed to do something fundamentally different.” @andrewdfeldman joined @PeterDiamandis on MOONSHOTS to discuss wafer-scale AI compute, fast
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Get 1 Billion Free LLM Tokens Monthly by Combining Free Tiers
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Want ONE BILLION free LLM tokens a month without juggling a dozen different APIs? Now you can *legally* unlock that massive inference capacity by combining the free tiers of Google, Groq, SambaNova, Mistral, and GitHub Models. The only problem is the headache of managing all
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Looping transformer layers boosts AI language model efficiency
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What if you could make AI language models smarter by reusing the same layers over and over? Researchers from KAIST, KRAFTON, and UC Berkeley present LoopMDM（Looped Diffusion Language Models）. They selectively loop early-middle transformer layers in masked diffusion models—no
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Dynamo Snapshot: Fast Inference Startup on Kubernetes
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Introducing Dynamo Snapshot, our approach for fast startup for inference workloads on Kubernetes, which reduces startup time from minutes to under 5 seconds. In production inference deployments demand fluctuates over time. Cold-starting inference workloads can take minutes,
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WordPress categories covering AI topics
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Web designers after reading this: https://t.co/yONuEtjT8L pic.twitter.com/p3y16ldruL
— Charly Wargnier (@DataChaz) 27 mai 2026Web designers after reading this:
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Codex for parallel browser-using subagents
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Codex for parallel browser-using subagents: https://t.co/Iqa3RgcBwD
— Greg Brockman (@gdb) 27 mai 2026Codex for parallel browser-using subagents: