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Wednesday

  1. Huxiu

    Developers use AI reverse engineering to run iOS 16 on Android phones such as Redmi

    Developer dipa-studio successfully booted and ran iOS 16.7.16 on a Redmi Note 10 Pro, able to enter the home screen, operate Settings and Control Center, and subsequently similar demonstrations appeared on devices such as the Redmi Note 12, Nothing Phone, and OnePlus 12. The porting process extensively used Claude and Codex to write driver adaptation code, and a reverse-engineering tool REA that automatically analyzes hardware interfaces also emerged. In the current version, cellular network, Bluetooth, camera, and speaker are all unavailable; GPU hardware acceleration has not been made to work, causing lag; booting takes 4 minutes; code signing verification and sandbox restrictions have also been relaxed; it is only suitable for early-adopter demonstrations.

    Developer dipa-studio successfully booted and ran iOS 16.7.16 on a Redmi Note 10 Pro, able to enter the home screen, operate Settings and Control Center, and subsequently similar demonstrations appeared on devices such as the Redmi Note 12, Nothing Phone, and OnePlus 12. The porting process extensively used Claude and Codex to write driver adaptation code, and a reverse-engineering tool REA that automatically analyzes hardware interfaces also emerged. In the current version, cellular network, Bl

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    #hot-signal#English text is being prepared.#Open-source ecosystem#Agents
  5. Latent SpaceSelected

    DeepSeek open-sources the DeepInfer inference framework, processing 155 trillion tokens per day in a single cluster

    DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also states that components such as KV cache management and load balancing are not yet fully open-sourced, while the vLLM team says that under a matching GB200 configuration, decoding throughput is within 6% of the results published by DeepInfer.

    DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also

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    #newsletter#English text is being prepared.#Reasoning#English text is being prepared.
  6. Latent Space

    DeepSeek open-sources the DeepInfer inference framework, processing 155 trillion tokens per day on a single cluster

    DeepSeek has open-sourced DeepInfer, the production inference framework supporting V4.1 Flash, and published production data including 155 trillion tokens processed per day on a single cluster, a 96% input cache hit rate, an average 400ms time to first token, and 230 tokens/s generation. The framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Ascend 950. DeepSeek says components such as KV cache management and load balancing are not fully open-sourced because they are coupled with internal infrastructure, while the vLLM team reports that under matched GB200 configurations, decoding throughput differs from DeepInfer's published results by less than 6%.

    DeepSeek has open-sourced DeepInfer, the production inference framework supporting V4.1 Flash, and published production data including 155 trillion tokens processed per day on a single cluster, a 96% input cache hit rate, an average 400ms time to first token, and 230 tokens/s generation. The framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Asce

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    #newsletter#English text is being prepared.#Reasoning#English text is being prepared.
  11. Simon Willison

    Simon Willison built Battle Drones, an audio-networked RTS game, with Claude Code

    Simon Willison released Battle Drones, a real-time strategy game developed entirely through vibe coding with Claude Code. It went through 12 major versions and 321 commits and runs in mobile and desktop browsers. The game includes 9 building types, 7 ground vehicles, 6 air vehicles and 3 infantry units, and supports fog of war, random map generation, AI versus AI, and an Agent battle mode in which two LLMs fight each other. He also implemented a multiplayer method in which two devices synchronize game state via microphone audio, and because the audio caused headaches, he added online multiplayer based on a WebSockets relay server.

    Simon Willison released Battle Drones, a real-time strategy game developed entirely through vibe coding with Claude Code. It went through 12 major versions and 321 commits and runs in mobile and desktop browsers. The game includes 9 building types, 7 ground vehicles, 6 air vehicles and 3 infantry units, and supports fog of war, random map generation, AI versus AI, and an Agent battle mode in which two LLMs fight each other. He also implemented a multiplayer method in which two devices synchroniz

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    #newsletter#Tutorials / practice#Agents#English text is being prepared.
  13. Fortune Tech

    How non-gamer Xbox CEO Asha Sharma plans to save Microsoft's gaming business

    Microsoft CEO Satya Nadella appointed Asha Sharma, who has zero gaming industry experience, to succeed Phil Spencer as Xbox CEO in February of this year. She has already laid off thousands of employees, consolidated studios and reorganized the technology stack, seeking to reverse the financial troubles and market share decline of this $21 billion business. Observers are also questioning whether Nadella is preparing Xbox for a sale or spin-off at a time when Microsoft is fully committing to the AI race.

    Microsoft CEO Satya Nadella appointed Asha Sharma, who has zero gaming industry experience, to succeed Phil Spencer as Xbox CEO in February of this year. She has already laid off thousands of employees, consolidated studios and reorganized the technology stack, seeking to reverse the financial troubles and market share decline of this $21 billion business. Observers are also questioning whether Nadella is preparing Xbox for a sale or spin-off at a time when Microsoft is fully committing to the A

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    #hot-signal#Industry news#Microsoft
  18. Claude Code：GitHub Releases

    Claude Code v2.1.305 Released

    Claude Code v2.1.305 fixes a regression introduced in 2.1.304: when providers or gateways such as Vertex and Bedrock reject tool search, all requests would fail; it now automatically retries and skips that feature. Added the sandbox.allowNestedUserNamespaces option, which allows Linux sandbox commands to create user namespaces, and improved the /usage warning into a one-line fix prompt that can be applied by pressing f.

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    #engineering#Product updates#English text is being prepared.#GitHub

Tuesday

  2. LMSYS BlogSelected

    SGLang-TorchTPU Open-Sourced: Bringing the SGLang Serving Stack to TPU

    The SGLang team, in collaboration with Google and Meta, has open-sourced SGLang-TorchTPU, a PyTorch-based native backend that brings the full SGLang serving stack to Google Cloud TPU and is already in large-scale production use at Meta. Initial support covers models including Muse Spark Open, GLM-5.3, Kimi K3, DeepSeek V4, Qwen3.8, and Gemma 4, with features such as speculative decoding, KV cache offloading, PD disaggregation, and FP8 KV cache. The team also used TorchTitan and Miles to complete GRPO training of Qwen3-4B on a single 4-chip TPU v7x, with the reward curve matching a 6× H200 baseline within random fluctuation.

    SGLang-TorchTPU

    The SGLang team, in collaboration with Google and Meta, has open-sourced SGLang-TorchTPU, a PyTorch-based native backend that brings the full SGLang serving stack to Google Cloud TPU and is already in large-scale production use at Meta. Initial support covers models including Muse Spark Open, GLM-5.3, Kimi K3, DeepSeek V4, Qwen3.8, and Gemma 4, with features such as speculative decoding, KV cache offloading, PD disaggregation, and FP8 KV cache. The team also used TorchTitan and Miles to complete

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    #company#English text is being prepared.#English text is being prepared.#Open-source ecosystem