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‘This isn’t freedom’: anger, anxiety and tears as Iran’s internet flickers back

After 88 days of near-total blackout, first reactions to the return of partial connectivity were not celebratoryAfter 88 days of near-total internet blackout in Iran, long-delayed messages, images and poems flooded phones and social media feeds at about 5pm on Tuesday, when still-limited connectivity flickered back to life.The first reactions, however, were not celebratory. Many […]

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The strange surveilled life of Piper Rockelle: why did a former child influencer decide to go on OnlyFans?

She made millions as a tween and teenager by posting clips of herself and her friends on YouTube. Then the business collapsed amid acrimony. What does her success in the adult industry, at 18, say about surveillance, social media and sexualisation?‘Honestly, the answer is kind of gross,” says Piper Rockelle, in a recent TikTok video,

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AI ‘art’ is boring, soulless theft – and when I see it as an artist I see red | Jess Harwood

I draw the old way – with my hand. Doing it with AI would not make me more creative, it would drain the colour out of my existenceLast week I went to a gig by myself for the first time. I sat myself down in my single seat, possibly the youngest person in the room

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Sakana AI Proposes DiffusionBlocks: a Block-wise Training Framework That Converts Residual Networks into Independently Trainable Denoising Modules

Researchers from Sakana AI and the University of Tokyo propose DiffusionBlocks. It trains transformer-based networks one block at a time. Training memory is reduced by a factor of B, where B is the number of blocks. Performance is maintained across diverse architectures. The Memory Problem in Neural Network Training End-to-end backpropagation requires storing intermediate activations

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NVIDIA Releases Polar, a Token-Faithful Rollout Framework for GRPO Training Across Codex, Claude Code, and Qwen Code

Reinforcement learning for language agents is growing more complex. Agents now manage multi-turn tool use, long-running contexts, and multi-agent orchestration. The main engineering challenge is connecting existing agent software to training pipelines without breaking how those tools work. NVIDIA’s research team introduced Polar, a rollout framework that lets researchers run reinforcement learning over any agent

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Tony Blair is strong on diagnosis, deluded on prescription: Britain’s ills can’t be fixed by him | Larry Elliott

The former PM’s essay rightly calls for a coherent economic plan, but then sets too much store by AI – and a worldview stuck in the pastTony Blair is right. Labour has made some big and avoidable mistakes since it came to power nearly two years ago. Keir Starmer had a strategy for winning the

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Samsung memory chip staff in line for £310,000 bonuses after AI profit-sharing deal

Agreement shows the latest impact of AI boom as two more chipmakers join $1tn clubEmployees at Samsung Electronics’s memory chip division are to receive bonuses averaging about £310,000 each through a landmark profit-sharing agreement, as the AI boom drives up chipmakers’ profits.Fears of a strike at Samsung were averted on Wednesday after two unions for

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People in the US: what are your views on Pope Leo’s comments about AI?

We’d like to hear what Americans think about the pope’s latest remarks criticizing the rapid development of AIThe first American pope has made waves since taking the position. However, after his latest remarks – that AI could make civilization less human and a plea for governments globally to actively slow AI development – people in

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https://vllm.ai/blog/2026-05-26-eagle-3-1

Meet EAGLE 3.1: The Speculative Decoding Algorithm That Fixes Attention Drift in LLM Inference

Speculative decoding is a technique for speeding up large language model inference. A small, fast draft model proposes several tokens. The large target model verifies them in parallel. If accepted, inference is faster. If rejected, the system falls back gracefully. EAGLE Team, vLLM Team, and TorchSpec Team has launched the EAGLE series including EAGLE 1,

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MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters

Large language models become static after pretraining. Their knowledge does not update as the world changes. Retraining a full LLM is too expensive at modern scales. Fine-tuning risks degrading previously learned knowledge. Retrieval-augmented generation (RAG) struggles when answers require reasoning across many documents. A team of researchers from the National University of Singapore, MIT CSAIL,

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