Turing Post

The world's largest open model sold out three days after launch. WHAT?! Moonshot paused new Kimi K3 subscriptions after demand pushed its GPUs to the limit. The same weekend: Xi Jinping personally endorsed open-source AI at WAIC, Alibaba teased a 2.4T-parameter Qwen 3.8 with an open-weight promise, and Axios reported that Washington may ban Chinese models entirely.

In this episode, we explain why serving an agentic model is so expensive, how a GPU shortage became an IPO pitch, what the July 27 weights release changes, and what a ban of an open model can and cannot actually reach.
Attention Span is here to explain the technical and business choices shaping AI.

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*Links:* 
Kimi K3: The open-weights escalation https://www.interconnects.ai/p/kimi-k3-the-open-weights-escalation
Kimi.ai capacity announcement https://x.com/Kimi_Moonshot https://x.com/Kimi_Moonshot/status/2078855608565207130
Xi Jinping's Big AI Speech, Annotated https://mattsheehan.substack.com/p/xi-jinpings-big-ai-speech-annotated
Kimi K3: The open-weights escalation https://www.interconnects.ai/p/kimi-k3-the-open-weights-escalation
Reuters, Moonshot pauses subscriptions amid IPO push SCMP, Kimi K3 developer suspends new subscriptions The Decoder, membership split into two tiers Qwen 3.8 announcement https://x.com/Alibaba_Qwen/status/2078759124914098291 MarkTechPost, Qwen 3.8 preview without benchmarks or license

Reuters, Xi's WAIC keynote Quartz, WAICO launch and membership Axios (Maria Curi), the administration's signals on Chinese models Nathan Lambert, Interconnects: Notes from inside China's AI labs https://www.interconnects.ai Previous episode: Kimi K3 and Inkling

#AI #ArtificialIntelligence #OpenSourceAI #KimiK3 #Qwen #LLM #MachineLearning #TechNews #China #AIPolicy

Two major open-model releases arrived this week from very different directions. Both geographically and conceptually.

Kimi K3 is a 2.8-trillion-parameter Chinese model that jumped from #18 to #1 in the Frontend Code Arena, ahead of Claude Fable 5. WHAT?! 

Inkling is the first major model from Mira Murati’s Thinking Machines Lab, and the company states directly that it is not the strongest model available. WHAT?!

Both strategies make sense once we examine what the companies are building. 
In this episode, we explain K3’s 896-expert architecture, why Moonshot recommends at least 64 accelerators, how LoRA customization works, what community quantization changed for Inkling, and which organizations gain meaningful control from open weights.

Attention Span is here to explain the technical and business choices shaping AI.

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👉 Into videos? Check our IG https://www.instagram.com/turingpost_tv and TikTok https://www.tiktok.com/@turingpost_tv 
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Links:
About LoRA https://www.turingpost.com/p/lora 
Moonshot AI: The Chinese Unicorn Revolutionizing Long-Context AI https://www.turingpost.com/p/moonshotai 
Thinking Machines Lab, “Inkling: Our open-weights model” https://thinkingmachines.ai/news/introducing-inkling/ 
Thinking Machines Lab, Inkling Model Card https://thinkingmachines.ai/model-card/inkling/ 
Thinking Machines Lab, Tinker https://thinkingmachines.ai/tinker/ 
Moonshot AI, “Kimi K3: Open Frontier Intelligence” https://www.kimi.com/blog/kimi-k3 
Arena, Kimi K3 Frontend Code Arena result https://x.com/arena/status/2077824029126504525  
Semianalysis about Kimi K3 https://x.com/SemiAnalysis_/status/2077966560447074689 
Arena, Code Arena methodology https://arena.ai/blog/code-arena/ 
Artificial Analysis, Kimi K3 https://artificialanalysis.ai/models/kimi-k3 
Artificial Analysis, Inkling https://artificialanalysis.ai/articles/thinking-machines-has-released-inkling-the-new-leading-u-s-open-weights-model 
Unsloth, community Inkling quantizations https://unsloth.ai/docs/models/inkling

#AI #ArtificialIntelligence #OpenSourceAI #LLM #MachineLearning #GenerativeAI #KimiK3 #MiraMurati #AIModels #TechNews

We're no longer in the world of chatbots – we're in the world where AI systems take real-world action. So what does responsible AI even mean when agents write the code, review the code, and act across organizational boundaries?

Sarah Bird, Chief Product Officer of Responsible AI at Microsoft, has been in this field since it was a niche. Now it's everywhere. In this episode of Inference, she explains why the entire software development lifecycle is changing – and why human oversight has to evolve with it.

*In this episode of Inference, we get into:*
- Why "responsible AI" is the wrong framing – and why "trustworthy AI" matters more 
- What changes when agents write the code AND review the code 
- Why open-sourcing responsible AI tools is non-negotiable – "we don't want to be competing on this" 
- The dual-use problem: AI that finds vulnerabilities helps defenders AND attackers 
- Why fixing it "in the model" sounds easy but doesn't work – and who's actually responsible at each layer 
- The shift from chat to agentic to physical AI – and why consequences scale dramatically 
- What kids should learn about AI: the "stoplight" framework from New South Wales schools 
- Why Sarah is surprisingly optimistic – and why low p(doom) is a rational position 
- The three problems her team is solving right now: emerging risks, agent governance, and the new software lifecycle

We also talk about regulation, why responsible AI requires linguists alongside engineers, the rise of "psychosocial risk," and why humans + AI is more exciting than AGI alone.

This is a conversation about what it actually takes to make AI systems we can trust – and why humans are not optional. *Watch it!*

*Guest:* 
Sarah Bird, Chief Product Officer of Responsible AI at Microsoft https://www.linkedin.com/in/slbird/ 
https://x.com/slbird   
https://www.microsoft.com/en-us/ai/responsible-ai 

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📰 Want the transcript and edited version? Subscribe to Turing Post: https://www.turingpost.com/subscribe

*Chapters:*
0:00 Is Responsible AI Possible?
1:21 The Pace of AI Innovation
3:10 Tools: Assert & Agent Control
5:21 Defining Trustworthy Contexts
6:38 Diverse Teams & Cross-Domain Work
8:16 The Need for Regulation
11:37 Defining AGI & Human-in-the-Loop
13:55 Limits of Agent Delegation
16:12 Generative vs. Physical AI
17:25 Addressing Accountability
20:36 User Responsibility & Best Practices
22:01 AI Literacy for Children
23:28 Democratizing Innovation
29:21 Three Strategic Focus Areas
31:37 Book Recommendation: The Culture Map

*Turing Post* is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live.

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Follow us - https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

#ResponsibleAI #Microsoft #AIAgents #AGI #TrustworthyAI #AIGovernance #AIRisk #SarahBird

GitHub's CPO Mario Rodriguez on how AI agents transformed the platform in December 2025 — record commits, new Copilot direction, and what "agent-native" coding actually means for developers.
What happened to GitHub then? Record acceleration across commits, PRs, Actions, and security scans – and a fundamental rethink of what GitHub even is.
*In this episode of Inference, we get into:*
The December 2025 capability jump
GitHub's massive scale challenge
"Low floor, high ceiling" – why lowering the barrier to creation may be the biggest GDP unlock in history
The Mozart problem: how many geniuses never had access to a piano – and why AI changes that
From UI → UX → AX:
The new Compiler app and "canvases" – bidirectional surfaces where humans and agents co-create in real time
Why Mario is anti-parallelization hype: "You could parallelize yourself to no value at all"
Macro vs. micro delegation
Why CoPilot will always be co-pilot, not pilot – and where the human stays in the loop
We also talk about the redefinition of "developer," why creation (not efficiency) drives human progress, and how GitHub plans to serve both the first-time builder and the Picasso-level craftsman on the same continuum.
This is a conversation about the future of software, the role of the human, and what it means when everyone becomes a builder. Watch it!
*Guest:*
Mario Rodriguez, Chief Product Officer at GitHub:
https://www.linkedin.com/in/mariorodriguez3/
https://x.com/mariorod1
https://github.com/mariorod
*Chapters:*
0:00 Intro — GitHub's Agentic Future
0:35 What Changed When AI Agents Started Working
4:41 The Engineering Challenges of Explosive AI-Driven Growth
6:59 GitHub's New Mission: Lower the Floor, Raise the Ceiling
9:58 Why AI Will Create More Builders, Not Fewer Developers
11:44 From UI to AX: Designing an Agent-Native GitHub
14:54 Is Everyone a Developer Now?
17:54 Advice for Young Developers in the AI Era
19:28 The Art of Micro-Delegation with AI Agents
21:14 Copilot Pricing, Token Costs, and Smarter AI Usage
24:40 AGI, Human Progress, and the Future of Creation
27:22 Why Humans Will Stay in the Loop Forever
29:24 The Books and Ideas That Shaped Mario Rodriguez
*Follow on*: https://www.turingpost.com/
📖 Related reading on turingpost.com:
→ AI Agents Vocabulary: https://www.turingpost.com/p/agentsvocabulary
→ The AI Software Stack Explained: https://www.turingpost.com/p/aisoftwarestack
→ Inside Cognition — Devin and the Coding Agent Era: https://www.turingpost.com/p/cognition
*Did you like the episode? You know the drill:*
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#GitHub #AI #AIAgents #Developers #Copilot #VibeCoding #AgenticAI #futureofcoding #githubcopilot #AIagents2026 #GitHubCPO #agenticcoding #futureofdevelopers #GitHubAI #codingagents #GitHub2026 #turingpost

What does it really take to put AI-powered building into the hands of millions and even billions – and what happens when everyone becomes a builder? How to do impossible things?
Ammaar Reshi leads Product and Design at Google AI Studio (DeepMind). His path is unusual: from writing iPhone app reviews as a teenager, to Palantir, Brex, and ElevenLabs, to now designing how millions of people build apps with AI in Google. His philosophy is simple – nothing is impossible, it's just time and iteration, and you learn by making and showing your work openly. Ammaar has a great energy and insights into how to stay relevant with AI.
*In this episode of Inference, we get into:*
How "tap, tap, tap" prompting helps anyone write rich specs without knowing what an NPM package is
Why the chat interface is evolving into something more like Slack – with daily standups with your agents
The case for abstraction: hiding Firebase, OAuth, and the messy parts from people who just want to build
Why speed (not intelligence) is now the real bottleneck for AI building
The gap between a stunning demo and a product that serves billions
How Ammaar is retraining Google itself – PMs across the company learning to vibe code and come up with amazing ideas
Why Ammaar open-sources almost every demo he builds – and how that inspires the next wave of builders
Ammaar's definition of AGI and lessons from Steve Jobs and Aurelius
A conversation about building, designing, and the new relationship between humans and intelligent tools. Watch it!
Did you like the episode? You know the drill: 📌 Subscribe for more conversations with the builders shaping real-world AI 💬 Leave a comment if this resonated 👍 Like it if you liked it 🫶 Thank you for watching and sharing!
Guest: Ammaar Reshi, Lead Product + Design at Google AI Studio (DeepMind) https://www.linkedin.com/in/ammaarsreshi https://x.com/ammaar https://ammaar.me
📰 Want the transcript and edited version? Subscribe to Turing Post: https://www.turingpost.com/subscribe
Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live.
Sign up: https://www.turingpost.com
Follow us - https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase
#AI #GoogleAIStudio #DeepMind #VibeCoding #AIAgents #AGI #Gemini