DS1966 @DS1966AT

I think it is really worth reading this piece on RSI at Anthropic. There is a bit of navel-gazing, some marketing, and a lot of very sincere beliefs about what Anthropic thinks is likely in the near future of AI that you probably want to be aware of. anthropic.com/institute/recu…

Starlink V3 satellites Bandwidth per Satellite: • V2: 96 Gbps • V3: 1,024 Gbps Bandwidth per Launch: V2: 2,600 Gbps V3: 61,000 Gbps Deployment per Launch: • V2: 27 satellites (on Falcon 9) • V3: 60 (on Starship) Starlink V3 satellites will begin to be deployed in late 2026 on Starship.

Sawyer Merritt @SawyerMerritt

15 hours ago

SpaceX has released its full IPO roadshow presentation that it's showing to investors. Here are some of the most important slides:

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for anyone asking where to learn this stuff: • RAG → weaviate.io/blog/introduct… • Agentic RAG → weaviate.io/blog/what-is-a… • AI Agents → anthropic.com/research/build… • Multi-Agent Systems → resources.anthropic.com/building-effec… • LangGraph → docs.langchain.com/oss/python/lan… • LangGraph (code) → github.com/langchain-ai/l… • MCP → modelcontextprotocol.io • Memory Systems → letta.com • Evals → docs.smith.langchain.com • Context Engineering → search "Context Engineering Survey" on arXiv and please skip the "build an ai agent in 10 minutes" videos build something, watch it fail, then figure out why.

aditya @adxtyahq

2 days ago

again saying there's never been a better time to work on multi-agent systems. learn rag, orchestration, evals, memory, routing, tool calling, validation loops, fix loops, split learning, context engineering. all of it. getting an llm to answer questions is becoming the easy

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How do we automate business analytics with Claude? New blog post covering our best practices for skills, data foundations, and evaluations when building agents to perform data analysis: claude.com/blog/how-anthr…

Bill Ackman literally gave a 44-minute masterclass that explains money better than any business school.

Radha Tripathi @Radha_AI

a day ago

Everyone is obsessed with giving AI more context. But humans don't think by searching a giant database every second. We remember. This paper makes a bold claim: The future of AI isn't bigger context windows. It's systems that can recall the right information at the right

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Architects used to draw every parking space by hand, this TestFit AI does the whole lot in seconds.

Outstanding paper on long-horizon agents. (bookmark it) Similar to humans, how do you make agents persist on a difficult task, and how is that useful? And which models today work well on this? This new work, AutoLab, explores this question and how encoding persistence in agents is beneficial for tasks such as auto research and engineering tasks. Can a model keep improving an artifact for hours, under a strict wall-clock budget, the way real research and engineering actually work? Results: AutoLab hands agents 36 expert-curated tasks across system optimization, model development, CUDA kernels, and puzzles, each starting from a correct but deliberately suboptimal baseline. Across 17 frontier models, the dominant predictor of success was not the quality of the first attempt. It was persistence, repeatedly benchmarking, editing, and folding in empirical feedback. It appears that Claude-opus-4.6 sustained that loop well. Most of the other models quit early or burned the budget, making almost no progress. Paper: arxiv.org/abs/2606.05080 Learn to build effective AI agents in our academy: academy.dair.ai

Boris Cherny, the creator of Claude Code at Anthropic, just explained why single-agent workflows are already dead this is one of the best workflows I've seen in a long time in this talk he breaks down exactly how the future is teams of agents, not better prompts: - the 14% you lose to CLAUDE.md before typing a word - one agent researching. one building. one reviewing. one orchestrating - the architecture that separates hobbyists from real builders - the 3 properties every agent team needs to actually survive if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Khairallah AL-Awady @eng_khairallah1

19 hours ago

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Expecting to see a lot of high quality mobile manipulators like this over the next year.

Flexiv Robotics @FlexivRobotics

a day ago

🤖 MICO is coming. The modular dual-arm robotics platform. Built on the Enlight platform, MICO is a compact dual-arm system engineered for safe, seamless collaboration in any workspace. See it live at ICRA 2026! 📍 Booth 130, Hall B #Flexiv #MICO #ICRA2026 #AdaptiveRobotics

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Our new Gemma 4 12B model hits a sweet spot between size + performance: it can run locally on a laptop, while enabling powerful multi-step reasoning and agentic workflows. Can’t wait to see what the community does with this one!

Demis Hassabis @demishassabis

a day ago

Celebrating the milestone of a massive 150+ million downloads of Gemma 4 with the release of the new Gemma 4 12B model! It's incredibly powerful for such a small model and it’s tiny enough to run locally on a laptop with just 16GB VRAM. Apache 2.0 license - happy building!

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This is what the AI brain looks like.

Martin Keen - IBM Master Inventor - explained the one thing that turns Claude Opus 4.8 from a chatbot into something that actually runs your job for you > your AI agent is only as smart as the context you give it right now it knows nothing about your company, your clients, or decisions made last year - unless you build the memory layer first > knowledge graphs are that memory layer > they connect every entity: client, contract, employee, past call - into one map the AI can actually reason over > Claude Opus 4.8 with a knowledge graph behind it stops guessing and starts knowing the people building knowledge agents on top of this now are turning their entire work history into an asset > everyone else is starting from scratch every Monday save this 👇

Mr. Buzzoni @polydao

2 days ago

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ByteDance has published a paper that should make every NVIDIA investor sweat. They trained an AI that writes CUDA better than humans experts. They call it CUDA Agent. And it completely rewrites the economics of AI hardware. They built a massive agentic reinforcement learning loop. The AI writes a kernel, compiles it, profiles the hardware, analyzes the bottlenecks, and rewrites the code until it's flawless. It learned how to optimize memory access patterns and hardware tiling strategies that traditional compilers miss. The results are staggering. On the industry-standard KernelBench, CUDA Agent completely destroyed traditional compilers. It delivered code that runs up to 3.2x faster than PyTorch's native execution. On the hardest, most complex models, it beat the strongest proprietary models in the world—including Claude Opus 4.5 and Gemini 3 Pro, by 40%. It didn't just match human experts. It started discovering optimizations that static compilers literally cannot see. Here is why this is a massive threat to NVIDIA. NVIDIA's dominance relies on the fact that CUDA is incredibly hard to master. Developers get locked in because optimizing code for other chips is too painful. But if an AI agent can autonomously generate hyper-optimized hardware kernels... You don't need a team of $500k a year CUDA engineers to build world-class infrastructure. And if an AI can autonomously master CUDA, it can master AMD's ROCm. Or custom silicon. The impenetrable software wall protecting NVIDIA's monopoly just got breached by a reinforcement learning loop. If anyone can automatically squeeze maximum performance out of any chip... Hardware becomes a commodity.

Godfather of AI: "If you sleep well tonight, you may not have understood this lecture." This 47-minute lecture is the best thing I saw about AI in the last few months. It will definitely help you understand how it actually works and where it's going. Geoffrey Hinton built the neural networks behind every AI alive, then quit Google to warn the world about it. The part nobody wanted to hear: > AI is already developing abilities its creators didn't intend > in most cognitive tasks it's already ahead of us > the question is no longer if it surpasses us but when > the only decision left is which side of that line you're on Right now the average person opens Claude, types something, gets an answer, closes the tab. They think they're using AI. they're using maybe 10% of it. I went through his entire lecture, then mapped everything he described to what Claude can actually do today. 17 Claude features most people will never find on their own. Full breakdown in the post below.

Anatoli Kopadze @AnatoliKopadze

2 weeks ago

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This SkillOpt paper from Microsoft is a must-read! (bookmark it) I was a bit skeptical of the results reported in the paper when I shared it a few days ago. However, I managed to integrate it into my agent orchestrator and ran a few experiments. The results are mindblowing. Essentially, all my agent skills now have a proper testing framework and a way to self-evolve. I have started to improve all my agent skills with this. One exciting result was when I applied it to my paper-figure-extraction skill, which requires an agent to do multimodal analysis. In particular, it improved quality by +20 points (0.73 → 0.93). I went to see the extracted tables and figures, and I was absolutely stunned by how much better my skill got at the task. Self-improving AI is in the early days, but I think this work is a clear example of the current ability of agents to self-improve. In this case, it was skills, but it's not hard to imagine how this scales to optimizing agent patterns, tool use, context engineering efforts, agentic search, workflows, evals, and even the harness itself. I already started with a few of these ideas inspired by SkillOpt. Stay tuned!

Do you know what the single most expensive misconception in enterprise RAG today is? According to Angela Shi, it's the notion that the traditional ML toolkit can apply to LLM-powered retrieval and generation. towardsdatascience.com/rag-is-not-mac…

Sam Altman (CEO of OpenAI): "very soon, an AI will just be running for you in the background all the time. aware of your entire life" but one agent can't do that.the answer is 3 agents covering each other. one plans, one builds, one verifies, all while you sleep. in 5 minutes, this guide shows you the exact build. i turned the entire Sam Altman interview into a multi-agent playbook that ships while you sleep.

Avid @Av1dlive

4 days ago

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A harnessed LLM agent, clearly explained! Most people picture this as a model with tools bolted on. The real architecture inverts that relationship. The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime. Three dimensions orbit the harness core: - 𝗠𝗲𝗺𝗼𝗿𝘆 holds the state a model shouldn't carry in weights or context. Working context, semantic knowledge, episodic experience, and personalized memory each have their own lifecycle. - 𝗦𝗸𝗶𝗹𝗹𝘀 hold procedural knowledge. This can cover operational procedures, decision heuristics, and normative constraints that specialize the general model per task. - 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 hold the interaction contracts. Agent-to-user, agent-to-agent, and agent-to-tools are three distinct surfaces with their own failure modes. Between the core and these modules sit the mediators, like sandboxing, observability, compression, evaluation, approval loops, and sub-agent orchestration. They govern how the harness reaches out and how state flows back in. The useful question this framing unlocks is: for any new capability, where should it live? - Stable knowledge goes to memory - Learned playbooks go to skills - Communication contracts go to protocols - Loop governance goes to the mediators Harness design becomes a question of what to externalize, and how to mediate it. I'm building a minimal agent harness from scratch and will open-source it soon. In the meantime, my co-founder wrote an article about the anatomy of Agent Harness, covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent. Read it below.

Akshay 🚀 @akshay_pachaar

2 months ago

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🚨 do you understand what just happened to Microsoft.. Microsoft just dropped seven of its own MAI models, trained from scratch with zero distillation, and said its custom-tuned models already match GPT-tier quality at 10x lower cost. The partner that pays OpenAI's bills is now quietly building the thing that replaces it. - MAI-Thinking-1 hits human-preference parity with Sonnet 4.6 and 53% on SWE Bench Pro, right next to Opus 4.6 - MAI-Code-1-Flash delivers Haiku-class coding at just 5B params and is already shipping inside GitHub Copilot - A custom MAI model tuned for Excel matched GPT-5.4 while running up to 10x more efficiently - Their real weapon isn't the weights, it's that they're already inside everyone's Office, Teams, and VS Code The hard part was never training a good model. The hard part is being the room everyone already works in. Microsoft owns the room.

Mustafa Suleyman @mustafasuleyman

2 days ago

Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier. First is our text foundation model, MAI-Thinking-1, exceptionally strong on reasoning and SWE tasks. - It’s a

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Good Starship highlights video