Latent Space: AI News & Views
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Videos
Gray Swan: Red-Teaming after Mythos & the coming AI security crisis Gray Swan AI cofounders Zico Kolter and Matt Fredrikson explain why AI security is fundamentally different from traditional cybersecurity, how their automated red-teaming system Shade can now outperform humans at breaking frontier models, why prompt injection creates an entirely new class of exploits for agents like Claude Code and Codex, and why the first major AI security breach may be a gray swan event that everyone sees coming.
Latent Space: AI News & Views1,768,141 Aufrufe • vor 4 Monaten
Jev and the System One Model: RLCD, intelligence/$, reliable AI, & the end of chat-first AI TypeSafe AI CEO Diogo Almeida explains why AI can solve extraordinarily hard problems yet still fail to automate basic work, why Jev is built for reliable decisions inside software instead of chat, why TypeSafe rejects public benchmarks and refusals at the API layer, why data and the right task matter more than brute-force compute, how System One Models could reshape coding agents and software, and why even with $1 billion he wouldn’t pre-train a model from scratch.
Latent.Space416,758 Aufrufe • vor 1 Monat
⚡ Inside the 2026 SOTA of Software Factories with Zach Lloyd of Warp! Warp's open source cloud factory approach has been resonating bigly this year, leading to multiple top AI.Engineer 🔜 Shanghai & SF CODE talks and an invitation to keynote this year's AIE CODE. Zach talks through the philosophy, demos Warp’s live system, and reports that tuning models and reducing redundant subagents can lower costs from ~$80 to ~$15–20 per PR and raise throughput 40% over the prior month, while keeping quality! Full episode below: from a Slack request to a PR, then into the work of measuring and improving software factories. 0:00 Software factories at Warp 0:53 AIE CODE in San Francisco 1:31 Zach Lloyd and software factories 4:30 Define the factory in code 7:34 Follow a Slack request to a PR 8:41 Measure cost, throughput and quality 14:32 Agents improving the factory 16:10 Benchmark your own tasks 23:59 From $80 to $15–20 per PR 27:03 Who needs software factories? 33:23 Product engineers and factory engineers See Zach at AI.Engineer 🔜 Shanghai & SF CODE CODE, Nov 10–12 in San Francisco:
Latent.Space12,327 Aufrufe • vor 3 Tagen
More from Thariq on local hands and how it is being implemented in Claude Code: (see more on
Latent.Space84,665 Aufrufe • vor 1 Monat
STOP making "Jevbench"es, stop asking for public benchmarks, they completely miss the point of Jev and you won't believe how easy it is to game every benchmark you hold dear This is Diogo Almeida's bitterest lesson of all: picking the right task beats everything
Latent.Space87,372 Aufrufe • vor 1 Monat
From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google
Latent.Space529,014 Aufrufe • vor 8 Monaten
🆕 Marc Andreessen’s 2026 AI Thesis: Agents, Open Source, and Why This Time Is Different Marc Andreessen 🇺🇸 of a16z says AI people keep swinging between utopian and apocalyptic for one simple reason: this field has been “almost here” for 80 years. But now, the breakthroughs are no longer theoretical. Reasoning, coding, agents, and self-improvement are all starting to work at once. This episode goes deep on AI winters, OpenAI + OpenClaw, infrastructure overbuild risk, proof-of-human, why software may soon be written mostly for bots, and why the real bottleneck may be society adopting AI rather than the models improving.
Latent.Space351,146 Aufrufe • vor 7 Monaten
🆕 show: HIGH TASTE TESTERS We convene top Al power users to compare what actually works-and what falls apart once you start using it.Ben Davis, ben hylak, dex, Allison, Alex Volkov and swyx give you unfiltered takes on Юрок Яшнев DevDay, Dots vs Muse vs GrokBot vs Instinct, who's winning coding, personal assistants, and the gap between impressive Al capabilities and useful products. From Pi-powered personal agents and decision models to subagents, context management, prompt injection, and assistants that can spend money: what should you actually use? CHAPTERS 00:00 Introducing High Taste Testers 01:26 OpenAl DevDay: Dots, Spaces, and product execution 10:28 Great models versus great products 14:39 Agents versus assistants 18:08 Building a personal assistant with Pi 21:07 Decision models, Jev, and predictive interfaces 32:46 Model speed, writing quality, and subagents 37:29 Agent loops, forks, and context management 43:19 Haiku, Muse Spark, and Grok 50:00 Local models and coding benchmarks 54:09 Prompt injection and assistants taking action 57:50 Tools, payments, and assistant overload 1:02:16 Delegation, computer use, and everyday workflows 1:10:48 Al hardware and closing thoughts THANKS TO THE TESTERS Ben Davis - Ben Davis Ben Hylak - ben hylak Dex Horthy - dex Allison Durham - Allison Alex Volkov - Alex Volkov
Latent.Space39,138 Aufrufe • vor 21 Tagen
The Inference Engineering Masterclass: 10x faster models, quantization, speculative decoding, Rubin, & self-optimizing AI Baseten Philip Kiely and ali explain what actually happens after a model is trained, why turning weights into a fast and reliable product creates an entirely new optimization problem, how quantization errors can cancel out to unlock more throughput, why inference teams are still finding 20–200% performance gains, how video generation runs into a quadratic attention wall, and how GLM-5.2 helped rewrite and optimize the GPU kernels serving GLM-5.2 itself.
Latent.Space149,249 Aufrufe • vor 3 Monaten
Recursive Language Models: Claude Code, Agent Swarms, Big Research Bets, & the $40M AI Problem Solver MIT’s alex zhang explains why Claude Code, Codex, and Pi are basically the same, how RLMs use code, context offloading, and recursive subagents to generalize across tasks, why one expert can sometimes replace a trillion-token brute-force search, what OpenAI’s 10,000-agent, 130B-output-token experiment reveals about the future of language models, and why academia’s biggest advantage is the freedom to take weird, ambitious research bets.
Latent.Space: AI News & Views46,040 Aufrufe • vor 1 Monat
The Future of Claude Code: Mods, Mutable Software, Pacing the Frontier, & Multiplayer Agents with Thariq Shihipar Anthropic’s Thariq explains why prompting is still one of the highest-leverage skills in agentic coding, why Claude.md may eventually disappear, how Claude Mods let developers customize the entire Claude Code harness, why mutable software could change how applications are built, how multiplayer agents and Claude Tag are becoming an organizational harness, and why agents hacking Hugging Face exposed a much bigger security problem as frontier models become increasingly capable.
Latent Space: AI News & Views39,502 Aufrufe • vor 1 Monat
Simulating Humanity: 85% accurate digital twins, behavioral foundation models, social physics, & 8 billion agents Simile CEO Joon Sung Park explains how AI can move from predicting what people will do to simulating how to shape outcomes, why today’s frontier models still miss how humans actually behave, how digital twins reproduced people with 85% accuracy, why human biases and mistakes have to be learned rather than optimized away, and what it would take to eventually simulate all 8 billion people on Earth.
Latent.Space71,358 Aufrufe • vor 2 Monaten
Why Stripe Acquired OpenRouter: 10T+ tokens/day, model routing, Stripe, & the $10T token economy @OpenRouterAI co-founder Alex Atallah and Anjney Midha explain why betting against a single-model future became one of the most important infrastructure bets in AI, how OpenRouter grew from the Llama and Mistral era into a neutral distribution layer for developers, why model labs can spend billions training checkpoints and still struggle with distribution, how Midjourney and Discord shaped their thinking, why Stripe acquired OpenRouter, and why agentic fraud could become a defining security problem for a multi-trillion-dollar token economy.
Latent.Space32,111 Aufrufe • vor 1 Monat
OpenAI's New Agent Stack: Superhuman Computer Use, Dots, Decisions API, UltraFast, & the AI Cloud OpenAI’s Ari Weinstein and Nikunj Handa explain why Computer Use is already becoming faster than humans at real-world tasks, how Dots gives agents their own cloud computers, why better models can now debug and recover from failures, how Decisions API went from idea to product in weeks, and why async tools, UltraFast inference, caching, compaction, and higher-level agent primitives are turning OpenAI’s API into an AI-native cloud.
Latent.Space: AI News & Views25,690 Aufrufe • vor 1 Monat
Priscilla Chan and Mark Zuckerberg co-founded the Chan Zuckerberg Initiative (CZI) in 2015, committing 99% of their Meta shares to advance science, education, and opportunity. As a pediatrician and CEO of Meta respectively, they've built CZI into one of the most ambitious technology-driven philanthropic organizations with a moonshot goal to help cure, prevent, or manage all diseases by the end of the century. In this episode, we sit down with Priscilla and Mark to unpack how CZI bridges state-of-the-art AI with open biomedical research, their investments in open-source tools like the Human Cell Atlas, their philosophy on building teams across different disciplines, and what they envision the future of healthcare. Chan Zuckerberg Initiative swyx Alessio Fanelli
Latent.Space166,190 Aufrufe • vor 1 Jahr
Noam Brown from OpenAI just dropped a truth bomb: "Your fancy AI scaffolds will be washed away by scale" Routers, harnesses, complex agentic systems... all getting replaced by models that just work better out of the box The reasoning models already proved this
Latent.Space208,301 Aufrufe • vor 1 Jahr
In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing
Latent.Space62,796 Aufrufe • vor 4 Monaten
Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks Databricks cofounders Matei Zaharia and Reynold Xin explain why Databricks is moving into the infrastructure layer for enterprise agents, how Omnigent creates a shared harness for coding agents and custom agents, why LTAP and Lakebase rethink the split between operational and analytical databases, why agent security needs contextual policies and spend controls, and why the future of software may be as simple as getting the right data in place and putting agents on top.
Latent.Space62,269 Aufrufe • vor 4 Monaten