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Tuesday

  1. Claude Code：GitHub Releases

    Claude Code v2.1.304 released

    Claude Code v2.1.304 adds a guided `/auto-mode-setup` setup that can read the project and recent sessions to let auto mode understand the environment, and now supports Bedrock, Vertex, Foundry, the Claude apps gateway, and sessions with telemetry disabled. This version also adds `/claude-api managed-agents-secure`, which checks Managed Agents configuration against Anthropic's public security guidelines without making any changes. In addition, it fixes several issues, including Linux sandbox command startup, the `rm -r` mounted-drive safety prompt, and plugins continuing to run beyond `--max-turns`.

    Claude Code v2.1.304 adds a guided /auto-mode-setup setup that can read the project and recent sessions to let auto mode understand the environment, and now supports Bedrock, Vertex, Foundry, the Claude apps gateway, and sessions with telemetry disabled. This version also adds /claude-api managed-agents-secure , which checks Managed Agents configuration against Anthropic's public security guidelines without making any changes. In addition, it fixes several issues, including Linux sandbox command

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    #engineering#Product updates#English text is being prepared.#Anthropic
  3. Huxiu

    Tencent's Lvpaopao Is WeChat "Going Against Itself"

    Tencent launched the short-drama product Lvpaopao, operated by the WeChat short-drama content center, to take on ByteDance's Hongguo short dramas. But Hongguo is an "anti-WeChat" product that runs counter to WeChat in every way, so Lvpaopao amounts to WeChat "going against itself." Hongguo's app DAU has reached 168 million, and its average daily usage time of 125 minutes exceeds Douyin's 120 minutes, while WeChat's is only 85 minutes. Nomura Securities data shows Hongguo's usage-time share rose from 2.2% to 5.9%, while WeChat's fell from 19.3% to 18.3%. The Lvpaopao team has only about 10 people and did not receive a core WeChat entry point; it started three years later than Hongguo, and short-drama supply is oversupplied, with the top 5% of projects taking 70% of profits.

    Tencent launched the short-drama product Lvpaopao, operated by the WeChat short-drama content center, to take on ByteDance's Hongguo short dramas. But Hongguo is an "anti-WeChat" product that runs counter to WeChat in every way, so Lvpaopao amounts to WeChat "going against itself." Hongguo's app DAU has reached 168 million, and its average daily usage time of 125 minutes exceeds Douyin's 120 minutes, while WeChat's is only 85 minutes. Nomura Securities data shows Hongguo's usage-time share rose

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    #hot-signal#Industry news#Agents
  4. AWS Machine Learning

    Amazon SageMaker JumpStart 2026 Progress Review

    From January to September 2026, Amazon SageMaker JumpStart added more than 100 models from 30 providers, of which Alibaba (Qwen) contributed 29, NVIDIA 17, Mistral 7, Google 6 Gemma 4 variants, IBM 4, and Cohere 4. The new models cover multimodal, speech, image, and video generation, and introduce quantized and compact models such as GLM-5.2 FP8 and JetBrains Mellum2-12B to reduce inference costs.

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    #lab#Product updates#English text is being prepared.#English text is being prepared.
  6. 36Kr · AI

    Young people today are paying to "hand-craft" AI personas

    AI role-play prompts have spawned a niche, clearly priced business on social platforms. Custom AI personas generally cost 5–8 yuan, single-character prompts usually stay within 10 yuan per 1,000 characters, delivery takes 2–3 days, and rush orders or buyouts cost double. Users try models such as DeepSeek, Doubao, Gemini, and ChatGPT, and some keep paying because they find DeepSeek "the most human-feeling." The business is still at a low price point, popular settings are easily copied, and disputes remain over ownership of settings and the boundaries of originality.

    AI role-play prompts have spawned a niche, clearly priced business on social platforms. Custom AI personas generally cost 5–8 yuan, single-character prompts usually stay within 10 yuan per 1,000 characters, delivery takes 2–3 days, and rush orders or buyouts cost double. Users try models such as DeepSeek, Doubao, Gemini, and ChatGPT, and some keep paying because they find DeepSeek "the most human-feeling." The business is still at a low price point, popular settings are easily copied, and disput

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    #hot-signal#English text is being prepared.#Agents
  14. 36Kr · AI

    Memory Tensor Open-Sources MemT 1.0 Series of Memory-Native Models

    Memory Tensor has open-sourced the MemT 1.0 series of memory-native models, comprising two variants: Nano with 36B parameters and 3B activated, and Flash with 288B parameters and 17B activated, built on the Qwen3.6-35B-A3B and DeepSeek-V4-Flash bases, respectively. The models add a memory branch parallel to attention within selected Transformer modules, encoding experience into an updatable matrix state that participates in subsequent generation. Across 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus a no-experience baseline of 66.07%; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth that of the full-context KV Cache.

    Memory Tensor has open-sourced the MemT 1.0 series of memory-native models, comprising two variants: Nano with 36B parameters and 3B activated, and Flash with 288B parameters and 17B activated, built on the Qwen3.6-35B-A3B and DeepSeek-V4-Flash bases, respectively. The models add a memory branch parallel to attention within selected Transformer modules, encoding experience into an updatable matrix state that participates in subsequent generation. Across 500 SWE-bench Verified tasks, Nano achieve

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    #hot-signal#Model releases#Open-source ecosystem#English text is being prepared.

Monday

  1. n8n Blog

    How to run n8n AI workflows on NVIDIA DGX Spark without calling cloud APIs

    n8n Blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to perform transcription, OCR, document Q&A, and image generation without calling cloud APIs. The entire stack exposes an OpenAI-compatible endpoint through LiteLLM; the main model is the NVFP4 version of Qwen3.6-35B-A3B, with a single-user generation speed of 120 tokens/s and an aggregate of 359 tokens/s across eight users. The author's testing found that image generation reduces chat speed to about one-third, and recommends that small teams queue image tasks or schedule them outside working hours.

    n8n Blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to perform transcription, OCR, document Q&A, and image generation without calling cloud APIs. The entire stack exposes an OpenAI-compatible endpoint through LiteLLM; the main model is the NVFP4 version of Qwen3.6-35B-A3B, with a single-user generation speed of 120 tokens/s and an aggregate of 359 tokens/s across eight users. The author's testing found that image gene

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    #engineering#Tutorials / practice#English text is being prepared.#Open-source ecosystem