Hey, it’s Andreas.
Welcome back to Human in the Loop - your field guide to the latest in AI agents, emerging workflows, and how to stay ahead of what’s here today and what’s coming next.

This week:
→ Apple + Google team up to supercharge Siri with Gemini
→ IBM launches Granite-4.0 Nano, a Small Language Model for agentic workflows
→ Historical moment: the founders of modern AI - Hinton, LeCun, Bengio, Fei-Fei Li, Jensen Huang, and Bill Dally - shared one stage to discuss the future of AI

Plus: a deep dive into Moonshot AI’s Kimi K2 Thinking release - another DeepSeek moment that reveals how China’s “Six Tigers” are building frontier-level models at a fraction of the cost.

Let’s dive in.

Weekly Field Notes

🧰 Industry Updates
New drops: Tools, frameworks & infra for AI agents

🌀 Apple + Google team up to supercharge Siri
→ Apple officially partners with Google to supercharge Siri with Gemini.

🌀 Google goes orbital
→ Project Suncatcher explores space-based AI infra using solar-powered satellites with TPUs and optical links. Two prototypes launch in 2027. How about AI-powered space data centers?

🌀 IBM launches Granite-4.0 Nano
→ Compact 350M and 1B models built for agentic workflows and edge deployment. With 70% less memory use and 2× faster inference.

🌀 IBM releases ALTK (Agent Lifecycle Toolkit)
→ IBM open-sourced ALTK, a toolkit that adds reliability layers to AI agents with reasoning checks, tool validation, and policy guardrails.

🌀 OpenAI scales coding capabilities
→ Codex Mini model launches with 4× higher rate limits and faster latency. Designed for embedded coding assistants and dev agents.

🌀 Anthropic advances MCP research
→ Optimizes LLM tool-calling efficiency for multi-agent orchestration - fewer calls, better control, faster reasoning.

🌀 Perplexity introduces parallel multi-agent workflows
→ Comet Browser brings parallel multi-agent workflows to your tabs. Context, research, and writing run simultaneously - no extensions needed.

🌀 Windsurf releases Codemaps
→ Collaborative agentic coding meets visual navigation. Ideal for large-scale engineering teams managing shared codebases.

🌀 Snapchat integrates Perplexity
→ Real-time, AI-powered answers in chat. Another signal that conversational retrieval is becoming a platform default.

🌀 Amazon vs. Perplexity clash over AI shopping agents in landmark lawsuit
→ Amazon files lawsuit over AI shopping agents scraping proprietary data. First major legal test for “agentic commerce” boundaries.

🎓 Learning & Upskilling
Sharpen your edge - top free courses this week

📘 Stanford University AI & ML Cheatsheets
→ Stanford released official cheatsheets from its core AI and ML courses (CS221, CS229, CS230, CME102, CME106).

📘 DeepLearning.AI: Jupyter AI Course
→ A new course by Andrew Ng and Brian Granger on AI coding in notebooks. Learn to generate, debug, and explain code directly in Jupyter, build a book research assistant, and create stock analysis workflows.

📘 r/AI_Agents November Hackathon
→ The community’s fourth online hackathon runs Nov 22–29. Builders get one week to create AI agents, with the top team eligible for a $20K Beta Fund investment.

📘 Rubrik Agent Operations Platform Workshop
→ A technical deep dive on securing the autonomous enterprise with Rubrik Agent Operations. Live on Nov 12, 7–8:30 PM CET.

🌱 Mind Fuel
Strategic reads, enterprise POVs and research

🔹 The Founders of Modern AI on One Stage
→ Hinton, LeCun, Bengio, Fei-Fei Li, Jensen Huang, and Bill Dally shared the stage to discuss the future of AI after winning the 2025 Queen Elizabeth Prize for Engineering.

🔹 Microsoft on Humanist Superintelligence
→ Mustafa Suleyman calls for domain-specific, controllable AI-focused on learning, healthcare, and clean energy-over the race to AGI.

🔹 McKinsey on AI in 2025
→ Surveying 1,993 professionals across 105 countries, McKinsey found that most firms still pilot AI rather than scale it. 62% experiment with agents, but only 39% see EBIT impact.

🔹 OpenAI on the road to superintelligence
→ OpenAI expects that AI will make minor discoveries by 2026 and major ones by 2028, as the cost of intelligence decreases significantly each year.

🔹 Google Cloud CTO on their agentic AI plans
→ At TechCrunch Disrupt 2025, Will Grannis outlined how Google Cloud is retooling for AI-first enterprises.

🔹 Galileo on Multi-Agent Systems
→ Galileo released a 165-page guide on building and scaling multi-agent systems. It covers architecture patterns, coordination trade-offs, and reliability risks.

🔹 Kosmos: The AI Scientist
→ A fully autonomous research agent that analyzes data, reads papers, and writes reports with traceable citations. In tests, a 12-hour run matched six months of manual work, achieving 79% factual accuracy and even reproducing unpublished discoveries (demo can be found here).

♾️ Thought Loop
What I've been thinking, building, circling this week

Last week, Moonshot AI, backed by Alibaba and led by ex-DeepMind researchers, released Kimi K2 Thinking – a new open-weight reasoning model.

Early benchmarks suggest it matches or exceeds GPT-5 and Claude 4.5 Sonnet on reasoning and coding tasks, while costing an order of magnitude less to train. Reportedly under $5M, though that figure hasn’t been independently verified.

What stands out isn’t just benchmark results, but architecture:

• Can handle up to 300 connected steps in a single task – meaning it can reason through complex problems instead of stopping after one prompt (the examples here look very impressive).

• Uses a lightweight computing method (INT4) that makes it much faster and cheaper to run without noticeably reducing quality.

• Has a very large memory window (256K tokens), so it can keep track of long documents or multi-step conversations without losing context.

• Comes with an open license, so almost anyone can use or adapt it freely, even for commercial work.

It’s a statement: deep reasoning no longer requires a billion-dollar budget or the latest and most advanced GPUs for training.

The bigger shift: China on the rise

Six months ago, no one outside China could name a single AI lab. Now, you can’t talk about AI without mentioning them.

Jensen Huang said it best: China is “nanoseconds behind.” He’s right.

Kimi K2 Thinking from Moonshot AI proves it. An open-weight model that matches GPT-5 and Claude 4.5 - trained for under $5M.

That’s not a gap. That’s a signal.

And it’s not just Moonshot AI. In China, a new movement is emerging - recently dubbed as the “Six Tigers”. The term refers to six leading labs driving the country’s next AI wave. They’re not an official alliance, but they share striking similarities and a common mission: pushing the boundaries of AI innovation.

Definitely worth keeping a close eye here:

→ Zhipu AI – Founded in 2019 out of Tsinghua University, Zhipu is one of China’s earliest generative-AI pioneers. The startup recently released GLM-4-Voice for real-time, human-like speech in Chinese and English, and raised $140M from Alibaba, Tencent, and state investors.

→ Moonshot AI – Founded in 2023 by Yang Zhilin, a Tsinghua and Carnegie Mellon alumnus, Moonshot’s Kimi AI chatbot is one of China’s top five, with nearly 13M monthly users. Valued at $3.3B, Moonshot is backed by Alibaba and Tencent.

→ MiniMax – Established in 2021 by Yan Junjie, MiniMax built the viral chatbot Talkie (known as Glow in China). Focused on social-AI and gaming, it also developed Hailuo AI, a text-to-video generator. A $600M round led by Alibaba last year pushed its valuation to $2.5B.

→ Baichuan Intelligence – Founded in 2023 by former employees from Microsoft, Huawei, Baidu, and Tencent. Baichuan released two open-source models: Baichuan-7B and Baichuan-13B, which are widely used across different sectors in domains from law to medicine in China. Its July funding round raised nearly $688M, valuing the firm above $2.7B.

→ StepFun – Created in 2023 by Jiang Daxin, former Microsoft VP, StepFun has released 11 foundation models across text, image, and audio. Its Step-2 model, with one trillion parameters, ranks among DeepSeek and OpenAI peers on LiveBench. The company secured hundreds of millions in Series B funding led by Fortera Capital.

→ 01.AI – Founded by Kai-Fu Lee in 2023, 01.AI builds open-weight models Yi-Lightning and Yi-Large, both among the world’s top-ranked in reasoning and comprehension. Trained efficiently on just 2,000 NVIDIA H100s, Yi-Lightning performs on par with xAI’s Grok 2 - proving China’s edge in cost-optimized model training.

Each of the “six tigers” emerged from a fusion of top-tier academic research, former U.S. and Chinese big-tech talent, state support, and backing from national champions like Alibaba, Huawei, and Tencent.

Together, they form the core of China’s foundation model ecosystem - building everything from open-weight LLMs and multimodal models to agentic reasoning systems and edge-optimized architectures.

They’re the counterpart to the U.S. “frontier model” ecosystem - OpenAI, Anthropic, Google DeepMind, Meta - but their playbook is different:

• Faster iteration. New releases every few months.

• More open. Several publish weights or APIs for public use.

• Lower cost. Frontier-class models on a fraction of Western budgets.

In short: the Six Tigers are China’s next-generation AI powerhouses - researchers who once trained in U.S. labs, now building a parallel innovation engine back home.

P.S.: If you want to test Kimi K2, it is live at kimi.com (Chat Mode) and also available via API and on Hugging Face.

🔧 Tool Spotlight
A tool I'm testing and watching closely this week

DeepAgents CLI – finally fixes the most annoying flaw in AI development: agents that forget everything once you close the terminal. Now your AI assistant can actually remember conversations – context, code, and all.

Why it stands out:
→ Persistent memory across sessions
→ Full code access – read, write, edit with approval
→ Safe command execution in your terminal
→ Live web + API search via Tavily
→ Multiple agents, each with its own memory

How it works:
Getting started is incredibly simple. Just install and run:

pip install deepagents-cli && deepagents

It acts directly on your local codebase, recalling context, rules, and project history every time you start it up.
→ Read the full announcement here.