Some of the best learning happens when practitioners volunteer to teach what they do every day. BeSA (Become a Solutions Architect) is exactly that. It's a free, fully online program where solutions architects (many who work at AWS) mentor aspiring cloud professionals through their own time and expertise. The next cohort kicks off August 22, with live Saturday sessions at 2 PM GMT on YouTube. If you're exploring a path into cloud architecture, or know someone who is, this program is worth your time. Here's where you can register and learn more ➡️ besaprogram.com/

If you are trying to figure out your career in this new AI-dominated world, this book is really good. Also great for those of you who want to transition into a gen-AI career. I haven't finished reading it, but here is what I can tell you: • The book maps the different careers emerging now around gen-AI and agentic AI. • Helps you understand what skills you should be developing. • It explains how you can transfer your existing skills into a new, AI-focused career. • Includes practical advice on portfolios, personal branding, networking, and career progression. By the way, the book focuses on building a long-term career around AI, not just learning the latest frameworks. Here is the link: a.co/d/072UhPfb

Which Kiro app should you pick? 👀 We’re building a family of front doors onto one increasingly capable engine, each shaped for a different person and a different moment, none of them trying to eat the others. Pick the door that fits the task: •IDE → code-first work •CLI → terminal + pipelines •Web → control plane for cloud sessions •Mobile → approve, nudge, check in •Crew → delegate the autonomous stuff Full breakdown 👉 spr.ly/6015B1u041

🚨 New research! Excited to share a new working paper that I've been working on as a visiting economics researcher at OpenAI. We analyze over 17 million messages sent by workers at over 1,500 organizations to document how organizations are using generative AI. Details below 👇

Why does CLAUDE.MD keep growing? If you maintain a CLAUDE.md or an AGENTS.md, this one is worth your time. (bookmark it) This work traces why these files grow without bound. Appending an instruction is free. Deleting one after its rationale is gone costs exponential verification, so nobody removes anything. Across 247,694 instruction lifetimes in 1,867 repositories, prompts more than tripled over their lifetime and gained 4.9 net instructions per commit. The older an instruction gets, the less likely anyone is to delete it. The proposed fix is comments. Writing down the reasoning behind an instruction removed 99.3% of excess instructions in verifiable settings and improved real agentic instruction-following by up to 23.1%. Paper: arxiv.org/abs/2608.11095 Track more trending AI papers in our academy: academy.dair.ai/

🚀 The future of AI is agentic — this is one message echoed across every stage at the Agentic AI Summit 2026 (Aug 1 & 2), the largest gathering dedicated to agentic AI: 🏛️ ~5,000 attendees in person at UC Berkeley 🌍 ~100,000 joined online from around the world 🎤 ~200 world-class speakers plus ~200 poster presentations, from frontier AI researchers and visionary founders to leaders and pioneers across academia and industry. 💡 Here are just a few glimpses into the ideas that shaped the conversations at the summit: 💬 "The text box is AI's radio-on-TV phase…. Every new medium starts by imitating the old one." — Peter Steinberger @steipete, Creator of OpenClaw, OpenAI 💬 "AI infrastructure isn't a chip problem. It isn't a model problem. It's a systems problem." — Peter DeSantis, SVP, Foundational AI Models, Custom Silicon, Quantum Computing, Amazon 💬 "There will be no AI job apocalypse... we just can't find enough skilled AI engineers." — Andrew Ng @AndrewYNg, Founder, DeepLearning .AI 💬 "Coding capabilities and cyber capabilities are two sides of the same coin—you cannot make models better at coding without also making them better at cyber." — Dawn Song, Professor, UC Berkeley; Co-Director, Berkeley RDI; VP of AI Research, Meta Superintelligence Labs 💬 "'Curfew' comes from the French word for extinguishing fire. Medieval cities tried to restrict fire, yet London still burned. AI resilience won't come from one breakthrough. There's no silver bullet for AI safety—only an ecosystem." — Wojciech Zaremba, Co-Founder, OpenAI 💬 "This is the biggest scientific bet our civilization has ever made—bigger than the Apollo program, the internet buildout, and the Manhattan Project combined." — Jasjeet Sekhon, Chief Strategy Officer, Google DeepMind 💬 "Our generation was too late to explore the Earth, too early to explore the stars—but right on time to build superintelligence." — Richard Socher, Founder/CEO, Recursive Superintelligence 💬 "I genuinely believe the next two years will be the time of architecture—the biggest gains will come from stepping away from transformers." — Jerry Tworek, CEO, Core Automation; Former VP of Research at OpenAI 💬 "Recursive Self-Improvement isn't one capability. It's four: Ideation, Implementation, Experimentation and Evaluation.." — Oriol Vinyals @OriolVinyalsML, Former VP of Research, Google DeepMind; Co-Founder, Discovery Loop 💬 "We are in a capability overhang—models are far more capable than they're able to side-effect into the world today." — Ryan Lopopolo, Principal Engineer, Agentic Google Cloud Platform; Previously Led Dark Factory at OpenAI 💬 "Stop thinking about evaluation as the last check before shipping—think of it as an engine that helps you ship a better agent every single day." — Michele Catasta, President and Head of AI, Replit 💬 "The bottleneck becomes your attention as an agent-using engineer. … We're moving from seeing the code to seeing the entire business." — Alex Graveley, Co-Founder of FlyingObject .ai; Co-creator, GitHub Copilot & Perplexity Computer 💬 "An agent isn't just an LLM — it's an LLM surrounded by what I call infrastructure... another word for that is computer science." — Jonathan Cohen, VP of Applied Research, Nvidia; Academy Scientific and Technical Award Winner 💬 "We don't arbitrate the truth. We give people the most powerful tools to make up their own minds." — Chris Bregler, Senior Director / Distinguished Scientist, Google DeepMind; Academy Scientific and Technical Award Winner 💬 “Thinking doesn’t have to be in text! ... We can even use multiple modalities simultaneously to “think” at the right level of abstraction for the problem at hand” — Sergey Levine, Co-Founder, Physical Intelligence; Professor, UC Berkeley 💬 “Video is the most general modality that we have that allows us to simulate real-world experience." — Anastasis Germanidis, Co-Founder/Co-CEO, Runway 💬 “The relationship between AI and enterprise data is not one-directional. Understanding both sides of that equation is the difference between AI that works and AI that disappoints.” — Dan Roth, Chief AI Scientist, Oracle; Professor, UPenn 💬 "Maybe 99.9% of training data in the next step will be synthetic." — Weizhu Chen, Technical Fellow & CVP, Microsoft AI 💬 "It's maybe the best time ever to start a company—but most 'obvious' AI products will be outcompeted by the frontier labs. The real opportunities lie in solving specific customer problems." — Alfred Lin, General Partner, Sequoia Capital ✨Over two days, we explored one central question: How do we build AI systems that are not only more capable, but also more trustworthy, more secure, and ultimately more beneficial for humanity? This wasn't the end of a conference - it was the beginning of the next chapter for agentic AI. Join us to shape and steward the future of AI for human flourishing! 🙏 A heartfelt thank you to our speakers, sponsors, volunteers, partners, and every attendee (in-person or online) who made this summit possible. 👇 What are your learnings, insights, favourite talk, quote, or moment from the summit? We'd love to hear it below!

Whoa, Meta released a new open-weight LLM yesterday, something that hasn't happened since the good old Llama days. Their Meta Muse Glimmer model is a 30B multimodal reasoning model with a Gemma-like architecture design. (“Glimmer” is probably a wordplay on “Spark,” the more likely capable model from which Glimmer was distilled. Muse Spark is only available through Meta’s Model API, though.) Architecture-wise, here are some of the main points: 1. "Only" a 131k context window, compared to Qwen3.6 and Gemma 4, which support 2x that natively; it's reasonable, but maybe on the shorter end in the age of agent harnesses 2. It's a dense model, not a mixture-of-experts. (So, it's fairer to compare it to Qwen3.6 27B than Qwen3.6 30B-A3B.) 3. Hybrid attention with grouped-query attention (GQA) and sliding window attention (SWA); the SWA:GQA pattern is a 3:1 local:global ratio. Other models like Gemma 4, which uses similar components, have a 5:1 ratio for comparison. 4. It adopts gated attention for both GQA and SWA; gated attention has become quite common in recent months. It basically applies a sigmoid gate to the attention output to decide how much of the attention information enters the residual connection. The interesting point is that it uses relatively standard GQA and SWA rather than hybrid attention mechanisms such as Nemotron or Qwen3.6. 5. A very extreme GQA ratio: 32 query heads and only 2 KV heads; for comparison, Gemma 4 31B uses 32 Q / 16 KV in the local heads and 32 Q / 4 KV in the global heads. This means that Meta Glimmer has a very small KV cache. Overall, the probably most similar architecture is Gemma 3 27B (including the Gemma-style pre/post RMSNorm placement) and Gemma 4 31B, but with some tweaks like SwiGLU instead of GeGLU activations, gated attention, and the more extreme GQA:SWA pattern mentioned before. What stands out is its extreme KV-cache efficiency. I.e., the KV CACHE / TOKEN ratios (in BF16) are: - Muse Glimmer: 52 KiB (lower is better) - Qwen3.6 27B: 64 KiB - Gemma 4 31B: 840 KiB Modeling-performance-wise, their own benchmarks show that it's mostly ahead of Qwen3.6. According to the independent composite benchmarks on the Artificial Analysis Intelligence Index, it's slightly behind Qwen3.6 (see figure below). So, a few days of using it will tell where it really ranks. Overall, it looks like a solid model, particularly for agentic workflows. What stands out most is its very low memory footprint and also pretty fast prefill and decode speed. It’s also just great to see Meta releasing open weights again :).

Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows. Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on consumer hardware like a Mac or PCs with performant GPUs. In keeping with our long tradition of sharing fundamental AI research, we’re releasing model weights under a permissive Apache 2.0 license. 🧵👇

Announcing Imagine Image 2.0, our next generation image model with precision editing, crisp text rendering, improved factuality, and real world usefulness. Image 2.0 helps you make images for real work. x.ai/news/grok-imagine-image…

Indeed all tools should be about augmenting human agency, including AI! I had a fun chat with @hubermanlab .

I am finishing a book with @patchenbarss that gives a non-technical explanation of how AI works and explains why it is dangerous and what we should do about it. @VikingBooks @VikingBooksUK penguinrandomhouse.com/books…

Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is: It has been an absolute pleasure to work with you and to help build some of the most widely used and impactful products of all time. As a kid, I dreamed of helping build software that would be used by many people, and Google now has thirteen products used by more than a billion people (amazing!). Our work has had a tremendous impact in the world, and I have been lucky enough to collaborate and form friendships with many colleagues that I deeply admire, respect, and enjoy. It still brings me joy every time I see people out in the world using our products to find information, handle email, translate documents, watch videos, learn new things, navigate and understand the physical world, browse the web, use their phone, run large-scale computations on our infrastructure, ride in an autonomous vehicle, or perform complex tasks with the help of our AI systems. I hope you all share this sense of joy, because it is a shared accomplishment! Thank you to all of my colleagues at Google over many years! Now I'm excited to go start @DiscoLoopAI with my longtime friends and colleagues @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix. (Updated post: slightly redacted to not have some personal info)