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<div style="display: none; max-height: 0px; overflow: hidden;">Google DeepMind has hired Boston Dynamics' former chief technology officer, Aaron Saunders, as its VP of hardware engineering β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β </div>
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<h1><strong>TLDR AI <span id="date">2025-11-24</span></strong></h1>
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<strong>3,500 business and IT leaders spoke to Vanta about Security and AI. This is what they said (Sponsor)</strong>
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Six out of ten security teams are posturing, not protecting β and about the same proportion say AI threats outpace expertise. Yet according to Vanta's newest <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.vanta.com%2Fstate-of-trust%2Fglobal%3Futm_medium=newsletter%26utm_source=tldr-ai%26utm_campaign=fy26q3_content_state_of_trust_global/3/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/Qtth0ZbsM5TRdTJ0YejHnoUtr6fkHV2rP49nShNTCRI=432" rel="noopener noreferrer nofollow" target="_blank"><span>State of Trust</span></a> report, teams spend way <strong>more time and energy proving trust</strong> than building it.
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<p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.vanta.com%2Fstate-of-trust%2Fglobal%3Futm_medium=newsletter%26utm_source=tldr-ai%26utm_campaign=fy26q3_content_state_of_trust_global/4/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/KiJ5fQxokIWW82pxbDoFh4RWQBP-jPPPGdYeN3xelEI=432" rel="noopener noreferrer nofollow" target="_blank"><span>Download the report</span></a> to see:</p>
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<li>Why teams are spending 9 working weeks a year on vendor reviews</li>
<li>How 95% of businesses are using AI to improve security effectiveness</li>
<li>The 3 steps that will build trust in AI at your org</li>
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<p>Are you pursuing new <strong>security frameworks</strong>, or playing <strong>security theater</strong>? <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.vanta.com%2Fstate-of-trust%2Fglobal%3Futm_medium=newsletter%26utm_source=tldr-ai%26utm_campaign=fy26q3_content_state_of_trust_global/5/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/iTGGNp5U0MA2izb0UQrSEkWNqKeS-IpQFAcPvxslVTk=432" rel="noopener noreferrer nofollow" target="_blank"><span>Read the State of Trust report</span></a> to see how your team stacks up.
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<div style="text-align: center;"><span style="font-size: 36px;"><span style="font-size:36px;">π</span></span></div></div>
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<strong>Google DeepMind Hires Former CTO of Boston Dynamics as the Company Pushes Deeper Into Robotics (2 minute read)</strong>
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Google DeepMind has hired Boston Dynamics' former chief technology officer, Aaron Saunders, as its VP of hardware engineering. Saunders is a key part of DeepMind CEO Demis Hassabis' vision for Gemini to become a sort of robot operating system. Hassabis is aiming to build an AI system that can work almost out-of-the-box across any body configuration. Boston Dynamics is famous for developing legged robots and humanoid machines capable of impressive acrobatic feats.
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<strong>Google Starts to Bridge OpenAI's Product Moat (4 minute read)</strong>
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Gemini's Dynamic view option takes text-based answers and wraps them in an interactive, visual output. The product is still in Labs and has yet to be launched. Despite the bland name, Dynamic view produces some impressive results that are a bit hard to describe. Examples of the outputs the feature can produce are available in the article.
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<strong>What OpenAI Did When ChatGPT Users Lost Touch With Reality (12 minute read)</strong>
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The New York Times revealed OpenAI's internal struggle between user engagement and safety after the company overruled its Model Behavior team's warnings to release a sycophantic April update to GPT-4o that made users return more frequently. The company now faces five wrongful death lawsuits and declared a "Code Orange" in October after discovering its safer GPT-5 model was losing users, with executives calling it "the greatest competitive pressure we've ever seen."
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<div style="text-align: center;"><span style="font-size: 36px;">π§ </span></div>
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<strong>Benchmark Scores = General Capability + Claudiness (8 minute read)</strong>
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In a 'deep' world, there is a single underlying ability that governs how well models do at superficially unrelated tasks. If a model developer makes this ability go up, their model gets better at everything. In a 'contingent' world, there are many orthogonal abilities that models can have, so model developers have to do completely unrelated work to get a model to improve on each ability. Anthropic has focused on making models that are state-of-the-art at agentic coding, but this hasn't resulted in models that are exceptional in other areas. There is some generalization across tasks, but this is limited, suggesting that models live in a 'contingent' world.
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<strong>How LLM Inference Works (20 minute read)</strong>
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Large language models (LLMs) are neural networks built on the transformer architecture. Transformers analyze entire sequences in parallel, evaluating how each word relates to the rest of the sequence, not just its neighboring words. This article discusses LLM inference and details how these models work. It covers token embeddings, the transformer architecture, the inference phases, matrix multiplication, precision and quantization, and much more.
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<strong>How to Run Product Evals (9 minute read)</strong>
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A practical guide to evaluating LLM-powered products that covers how to label data, align evaluators, and iterate on configuration changes with minimal overhead.
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<div style="text-align: center;"><span style="font-size: 36px;">π§βπ»</span></div>
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<strong>Voice AI can handle calls as well as your best reps (minus the attitude) (Sponsor)</strong>
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Most people haven't tried it because other companies make you sign seven-figure contracts before they'll even let you test. At <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fbland.ai%3Futm_source=TLDRAI%26utm_medium=newsletter%26utm_campaign=nov24%26utm_id=TLDRAInov24/2/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/FlelkbffNTDxdDih03zRWRicfDp-rRyxLRew9U9tftM=432" rel="noopener noreferrer nofollow" target="_blank"><span>Bland</span></a>, <strong>we think that's stupid</strong>. Here, you can <strong>get a custom agent built for your business - for free</strong>.
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<p>You can <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fbland.ai%2Fcustomagent%3Futm_source=TLDRAI%26utm_medium=newsletter%26utm_campaign=nov21%26utm_id=TLDRAInov21/1/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/tmyl9sT0Yhnv3rmCyBrgvlUSfBN3jjuGSfPPdODgQbI=432" rel="noopener noreferrer nofollow" target="_blank"><span><strong>get it here</strong></span></a>.
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<strong>Complete Developer Tutorial for Nano Banana Pro (15 minute read)</strong>
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Nano Banana Pro opens up a new frontier for AI image generation. It can think, search, and render in 4K, making it a tool for serious creators. It is now available to try at Google AI Studio. This guide covers the next-generation AI model's advanced features using the Gemini Developer API.
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<strong>MCP Apps: Extending servers with interactive user interfaces (11 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
The MCP Apps Extension (SEP-1865) standardizes support for interactive user interfaces in the Model Context Protocol. It addresses one of the most requested features from the MCP community: the ability for MCP servers to deliver interactive user interfaces to hosts. The extension introduces a standardized pattern for declaring UI resources, linking them to tools, and enabling bidirectional communication between embedded interfaces and the host application.
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<strong>Agent Design Is Still Hard (16 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Building agents is still messy. Abstractions break once you hit real tool use. Caching works better when self-managed. Reinforcement does more heavy lifting than expected. Output tooling is surprisingly tricky. Model choice still depends on the task.
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<strong>Olmo 3 From Scratch (GitHub Repo)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Sebastian Raschka added a standalone notebook implementing Allen AI's OLMo 3 model architecture from scratch to his "LLMs from Scratch" repository, joining similar tutorials for Qwen 3 and Gemma 3.
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<div style="text-align: center;"><span style="font-size: 36px;">π</span></div></div>
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Discussing Blackwell's drawbacks and dissecting its architecture (42 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Nvidia's greatest moat lies in having handled a lot of the 'dirty work' cleanly within its entire architecture and combining full-stack capabilities from algorithms to systems to chips. It also had excellent timing in bringing architectures to market and great marketing execution. However, every architecture has its trade-offs and shortcomings. This post looks at some of the issues within Nvidia's products and discusses potential evolutionary directions.
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<span>
<strong>The space of intelligences is large (2 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Large language models think very differently from animals. The biggest difference is the optimization pressures/objectives that cause evolution. People who build a good internal model of this new intelligent entity will be better equipped to reason about it and make predictions about it in the future.
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<div style="text-align: center;"><span style="font-size: 36px;">β‘</span></div></div>
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<h1><strong>Quick Links</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmetronome.com%2Fblog%2Flaunch-week-recap-monetization-infrastructure-for-ai%3Futm_campaign=launch-week%26utm_medium=newsletter%26utm_source=tldr-ai%26utm_content=secondary/1/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/PTH1V-DsmYmO8TbAvU2CwDbfr3yRFG7WgEQdKqi7ESs=432">
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<strong>Launch Week recap: Monetization infrastructure for the AI era (Sponsor)</strong>
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Metronome's latest capabilities remove bottlenecks between product and revenueβunifying invoicing across PLG, sales, and marketplaces, and embedding trust-building billing into the product experience.<p></p><p>π <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmetronome.com%2Fblog%2Flaunch-week-recap-monetization-infrastructure-for-ai%3Futm_campaign=launch-week%26utm_medium=newsletter%26utm_source=tldr-ai%26utm_content=secondary/2/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/vv3nckgwUdPQzjZqR91YnFfJZJtcw5vzWujZSVUzG2g=432" rel="noopener noreferrer nofollow" target="_blank"><span>Read the recap</span></a>.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Fkarpathy%2Fllm-council%3Futm_source=tldrai/1/0100019ab6381016-9648affc-2ec7-49f6-ba69-fd2f9574ff4c-000000/4rKiT762XiIQU3CTpLC9C7kmk55f1U51hzbY5SDDxzY=432">
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<strong>LLM Council (GitHub Repo)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
LLM Council is a vibe-coded local web app that queries multiple frontier models simultaneously, then has each model anonymously rank the others' responses before a "Chairman" LLM synthesizes a final answer.
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<span>
<strong>The market βdid not appreciateβ Nvidia's incredible quarter (1 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Jensen Huang recently joked that Nvidia is holding the planet together, citing posts claiming its work is helping the US avoid a recession.
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<span>
<strong>Llama5 avocado will be lit (1 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Llama 5 Avocado will be trained on synthetic data - Microsoft tried this and produced useless models.
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