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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.

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Zhongguancun AI North Latitude Community AI Hackathon opens recruitment: three days of hands-on competition from October 23 to 25

Zhongguancun AI North Latitude Community will hold the "North Latitude Community AI Hackathon" from October 23 to 25, and recruitment is now open. Participants will work in teams to complete topic selection, development, and pitching within three days. The event will award one first prize, one second prize, one third prize, two excellence awards, and one "Best Popularity Award" chosen by audience vote, and will invite AI experts with backgrounds at major internet companies to serve as mentors.

e-Conomy SEA Report: Southeast Asia's Digital Economy to Reach $1 Trillion by 2035

The 11th edition of the e-Conomy SEA report, released by Google, Temasek and Bain & Company, forecasts that Southeast Asia's digital economy GMV will grow 17% year-on-year to $368 billion in 2026 and surpass $1 trillion by 2035. Generative AI is already used daily by 243 million people in Southeast Asia, growing four times faster than the mobile internet, and the report projects that regional AI usage will grow 60-fold over the next five years. 75% of users are willing to let AI agents compare prices and complete transactions on their behalf, but the region will need to expand data center capacity roughly threefold over the next five years to support computing demand.

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Miso Info Tech Participates in 13.8 Billion Won AI Mold Maintenance Project

Miso Information Technology announced on October 20 that it has been selected for the tandem press autonomous manufacturing R&D project led by South Korea's Ministry of Trade, Industry and Energy (MOTIR), with a total budget of 13.8 billion won, led by auto parts manufacturer Sewon Mulsan. Project goals include reducing single mold change time from about 40 minutes to under 5 minutes, achieving a 90% unmanned operation rate and 98% anomaly detection accuracy. Miso will develop a manufacturing AI data platform and predictive maintenance technology based on its IoT Prism platform, integrating equipment, sensor, production and quality data.

MiniMax Releases M3.1 Flash Model, ALE Score of 29.6% Ranks First Among Domestic Models

MiniMax officially released M3.1 Flash, formerly known as Space Bunny (Yutu), which previously topped the charts anonymously on OpenRouter. The model scores 29.6% on Agents' Last Exam, ranking first among domestic models, and scores 43 on the AA leaderboard; it achieves 73.7% on DeepSWE v1.1, 82.2 points on CyberGym, 76.2 points on BankerToolBench, tying for first with Claude Opus 5, and 61.7 points on OSWorld V2. The model adopts a sparse attention architecture, with output speeds of up to 200+ TPS and a median of 170+ TPS, input at 1 yuan per million tokens, output at 4 yuan, and cache reads at 0.1 yuan, with comprehensive usage costs reduced by 70% compared to the previous generation M3.

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.

MiniMax Releases M3.1 Flash, Scoring 29.6% on Agents' Last Exam to Rank First Among Chinese Models

MiniMax has officially released M3.1 Flash, the model previously known as Space Bunny (Yutu) that topped the charts anonymously on OpenRouter. It scores 29.6% on Agents' Last Exam, ranking first among Chinese models. The model achieves 73.7% on DeepSWE v1.1, 82.2 points on CyberGym, and 61.7 points on OSWorld V2, with output speeds of up to 200+ TPS and a P50 maintained at 170+ TPS. Pricing is 1 yuan per million input tokens, 4 yuan for output, and 0.1 yuan for cache reads.

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MIT Course MAS.S62: Two Lectures on the Evolution of Harnesses and Multi-Agent Systems

Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system and provides about 400 times the amount of feedback information of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on tasks with sequential dependencies, and that CORAL achieves new SOTA on 8 tasks, with multi-agent evolution improving over single-agent by 4%-20%.

Viggle Releases JST-2, a 3D-Native World Model

Viggle has released JST-2, building its entire model stack in 3D space and representing the world with Physics-Native Tokens that carry xyz coordinates, attribute vectors, and dynamics vectors, rather than implicitly inferring physical laws from video pixels. The architecture consists of three parts: the JST encoder, JST generator, and JST decoder. A single forward inference generates a complete 3D scene in about 1 second, at a cost below $0.01 (B200), and supports up to 64 input views as a spatial context window. Blind human preference tests published by Viggle show that character generation wins 58.4% against Meshy 7.1 and 73.2% against Tripo H3.1, while video-generated motion wins 89.1% against DeepMotion. JST-3 plans to extend the learned dynamics to object interaction.

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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.

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