←Search results

Search “DeepSeek”

By relevance · Total 1346 results Clear search

Wednesday

  1. China.com Technology

    Franchise Recruitment GEO: An Analysis of the New Customer Acquisition Logic for Franchise Brands in the AI Era

    Franchise recruitment marketing is shifting from search bidding and feed ads to GEO (Generative Engine Optimization), with the goal of ensuring that brand facts are accurately mentioned and reasonably recommended in generative Q&A systems such as DeepSeek, Doubao, Tongyi Qianwen, ERNIE Bot, Kimi, and ChatGPT. The article compares the business logic of three models—search advertising, feed advertising, and GEO—and points out that GEO competes on the quality of facts and the credibility of information sources, not on advertising budgets. To judge whether a GEO service provider is reliable, it is recommended to prepare 20 to 50 real franchise recruitment questions and require an on-site demonstration of multi-model queries, brand mentions, cited sources, and erroneous information identification.

    feed ads to GEO (Generative Engine Optimization), with the goal of ensuring that brand facts are accurately mentioned and reasonably recommended in generative Q&A systems such as DeepSeek, Doubao, Tongyi Qianwen, ERNIE Bot, Kimi, and ChatGPT. The article compares the business logic of three models—search advertising, feed advertising, and GEO—and points out that GEO competes on the quality of facts and the credibility of information sources, not on advertising budgets. To judge whether a GEO se

    Matched fieldsSummary、Body

    #hot-signal#English text is being prepared.#Search#English text is being prepared.
  6. Shao Meng

    Flatkey Gateway Lets Codex/Claude Code Call Multimodal Models

    Flatkey, as a unified API gateway, lets Codex and Claude Code call text models such as GPT, Claude, DeepSeek, GLM, and Kimi as well as image and video models such as GPT Image and Seedance via CLI. One Key provides access to 100+ official models and 1,000+ tool APIs, with costs consolidated into a single bill. Everyday prices are at least 20% lower than official pricing. During the October 19–23 promotion, GPT, GLM 5.2, Kimi K3, and DeepSeek are 40% off, Claude is 20% off, and Seedance 2.0 is 15% off.

    Flatkey, as a unified API gateway, lets Codex and Claude Code call text models such as GPT, Claude, DeepSeek, GLM, and Kimi as well as image and video models such as GPT Image and Seedance via CLI. One Key provides access to 100+ official models and 1,000+ tool APIs, with costs consolidated into a single bill. Everyday prices are at least 20% lower than official pricing. During the October 19–23 promotion, GPT, GLM 5.2, Kimi K3, and DeepSeek are 40% off, Claude is 20% off, and Seedance 2.0 is 15

    Matched fieldsSummary、Body

    #hot-signal#Product updates#English text is being prepared.#Multimodal

Tuesday

  1. QbitAI

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

    Memory Tensor has open-sourced and released the MemT 1.0 series of memory-native models, including MemT-1.0-Nano with 36B parameters and 3B activated (based on Qwen3.6-35B-A3B) and MemT-1.0-Flash with 288B parameters and 17B activated (based on DeepSeek-V4-Flash), adding a memory computation pathway parallel to attention in selected Transformer modules. The team built a 34-billion-token memory training corpus, of which 20 billion tokens were used for continued pre-training and 14 billion for post-training. In a comprehensive evaluation across 11 memory benchmarks, Flash and Nano achieved average scores of 50.6 and 47.0, respectively, ranking in the top two among the parametric memory methods evaluated; on 500 SWE-bench Verified tasks, the proportion of tasks that Nano succeeded on all three attempts rose from 54.00% for the no-experience baseline to 62.80%.

    f memory-native models, including MemT-1.0-Nano with 36B parameters and 3B activated (based on Qwen3.6-35B-A3B) and MemT-1.0-Flash with 288B parameters and 17B activated (based on DeepSeek-V4-Flash), adding a memory computation pathway parallel to attention in selected Transformer modules. The team built a 34-billion-token memory training corpus, of which 20 billion tokens were used for continued pre-training and 14 billion for post-training. In a comprehensive evaluation across 11 memory benchm

    Matched fieldsSummary、Body

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

    s 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.

    Matched fieldsSummary、Body

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

    Zyphra Inference Wave 2: Accelerating MoE and Long-Context Inference Serving on MI355X

    Zyphra has released ZOE, its second-generation inference optimization, which on AMD Instinct MI355X reduces GLM-5.3's time to first token (TTFT) at 512K context from 178.8 seconds to 64.6 seconds (2.8x), and from 42.3 seconds to 11.8 seconds (3.6x) at 90% prefix cache hit rate. Its CSA top-K selection HIP kernel raises DeepSeek-V4-Pro's single-user decode throughput at 1M context from 43.5 to 61.1 tokens/s (1.40x). AMD MXFP4 quantization of GLM-5.3 shrinks the model from 756 GB to 410 GB, with benchmark scores such as AIME25 within 3 points of FP8.

    ce optimization, which on AMD Instinct MI355X reduces GLM-5.3's time to first token (TTFT) at 512K context from 178.8 seconds to 64.6 seconds (2.8x), and from 42.3 seconds to 11.8 seconds (3.6x) at 90% prefix cache hit rate. Its CSA top-K selection HIP kernel raises DeepSeek-V4-Pro's single-user decode throughput at 1M context from 43.5 to 61.1 tokens/s (1.40x). AMD MXFP4 quantization of GLM-5.3 shrinks the model from 756 GB to 410 GB, with benchmark scores such as AIME25 within 3 points of FP8.

    Matched fieldsSummary、Body

    #lab#Product updates#Reasoning#English text is being prepared.
  5. Alibaba Cloud Developer Community AI

    Development Methods and Tech Stack for an AI English Writing Agent

    An AI English writing agent is implemented through the collaboration of a multi-agent pipeline, and can be split into agents for outline and idea generation, grammar and spelling, discourse logic, style polishing, and evaluation and scoring, built on LangGraph or PydanticAI. The recommended tech stack is Python (FastAPI) paired with PydanticAI, LangGraph, and LlamaIndex; the main reasoning model can be DeepSeek-V3, Claude 3.5 Sonnet, or GPT-4o, with GPT-4o-mini and Qwen-Turbo for lightweight validation. The system also uses RAG to connect to authoritative corpora such as the Oxford and Longman dictionaries and the Academic Phrasebank, and sets up anti-hallucination and academic integrity guardrails.

    hing, and evaluation and scoring, built on LangGraph or PydanticAI. The recommended tech stack is Python (FastAPI) paired with PydanticAI, LangGraph, and LlamaIndex; the main reasoning model can be DeepSeek-V3, Claude 3.5 Sonnet, or GPT-4o, with GPT-4o-mini and Qwen-Turbo for lightweight validation. The system also uses RAG to connect to authoritative corpora such as the Oxford and Longman dictionaries and the Academic Phrasebank, and sets up anti-hallucination and academic integrity guardrails.

    Matched fieldsSummary

    #hot-signal#Tutorials / practice#Agents#RAG
  9. cnBeta

    Nobel laureate Levitt: AI will erase the age gap among scientists, and in 30 years a young person may accomplish in one year what predecessors did in a lifetime

    Michael Levitt, winner of the 2013 Nobel Prize in Chemistry, predicted at the Second International Conference on AI Scientists (ICAIS 2026) that by 2056, young scientists aided by AI agents may accomplish in one year the work his generation did in a lifetime, and said this prediction may not even be aggressive enough—the efficiency gain could far exceed 30 times. He believes AI will erase the age gap among scientists, and stressed that the coexistence of multiple models such as DeepSeek, Kimi, Claude, and ChatGPT is the guarantee for humanity to use AI safely.

    cted at the Second International Conference on AI Scientists (ICAIS 2026) that by 2056, young scientists aided by AI agents may accomplish in one year the work his generation did in a lifetime, and said this prediction may not even be aggressive enough—the efficiency gain could far exceed 30 times. He believes AI will erase the age gap among scientists, and stressed that the coexistence of multiple models such as DeepSeek, Kimi, Claude, and ChatGPT is the guarantee for humanity to use AI safely.

    Matched fieldsSummary、Body

    #hot-signal#English text is being prepared.#Agents

Monday

  1. Artificial AnalysisSelected

    MiniMax-M3.1-Flash scores 42 on the Artificial Analysis Intelligence Index

    MiniMax-M3.1-Flash scores 42 on the Artificial Analysis Intelligence Index, 13 points higher than MiniMax-M3, at less than half the latter's per-task cost. It scores 48% on Humanity's Last Exam and 22% on CritPt, both higher than GLM-5.3-Flash and Gemini 3.8 Flash, and slightly lower than DeepSeek V4.1 Flash 1017. The model has a 1M-token context window, supports text and image input, is priced at $0.15/$0.60 per 1M input/output tokens, and its weights are currently closed-source; MiniMax announced that it will open the weights on October 23.

    nalysis Intelligence Index, 13 points higher than MiniMax-M3, at less than half the latter's per-task cost. It scores 48% on Humanity's Last Exam and 22% on CritPt, both higher than GLM-5.3-Flash and Gemini 3.8 Flash, and slightly lower than DeepSeek V4.1 Flash 1017. The model has a 1M-token context window, supports text and image input, is priced at $0.15/$0.60 per 1M input/output tokens, and its weights are currently closed-source; MiniMax announced that it will open the weights on October 23.

    Matched fieldsSummary、Body

    #editorial#Evaluations / benchmarks#Reasoning#DeepSeek