Safety evaluations, system cards, preparedness, and security measurement for frontier models.
Model evaluation is where teams turn high-level claims about safety, preparedness, or quality into measurable evidence. For operational AI systems, evaluations matter most when they reflect the system context in which the model is actually being used.
These items are included because they add useful evidence, framing, implementation detail, or upcoming context for teams working in this area.
NeurIPS 2026 is the fortieth annual Conference on Neural Information Processing Systems, with the primary dates listed for Sydney, Australia, and additional satellite locations in Atlanta and Paris.
OpenAI DevDay 2026 is scheduled for September 29 in San Francisco and is OpenAI’s primary developer event for platform updates.
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There are thousands of agent skills. Almost none of them are tested. They get vibe-checked with two manual runs, maybe a thumbs-up from a colleague, then shipped. You wouldn't merge code without tests — so why are we shipping skills without evals? This talk covers the full lifecycle of building reliable agent skills: w
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In the last two years, models have gotten exponentially smarter. Two years ago they couldn't pass the bar. Today, top 1% of test scorers. And yet most agents still can't answer a simple business question correctly. You ship a demo that works. You deploy it. The business abandons it in a month. The missing variable is c
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# How Forward Deployed Engineering is done at Cursor **Location:** Forward Deployed Engineering / Room 2020 **When:** Day 2 - June 30, 2026 · 11:10am-11:30am ## Speakers ### Pauline Brunet VP, Forward Deployed Engineering · Cursor [LinkedIn](https://www.linkedin.com/in/pauline-brunet/) VP of Forward Deployed Engineerin
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For his closing keynote, Addy Osmani explores the evolving role of software engineers in the age of AI agents. He argues that as coding tasks become increasingly automated, the true value of an engineer shifts from mere code production to accountability, judgment, and system ownership. https://addyosmani.com/ https://x
We’re open-sourcing k8s-aibom, a Kubernetes controller that continuously monitors environments to detect AI runtimes and generate standard ML-BOMs.
A few days ago, I was sitting with the CISO of a Fortune 50 company, walking through how his security team was thinking about AI agents in the SOC. Smart team. Serious program. They had already connected Claude to a few detection tools and were seeing real value in specific investigations. But as we mapped out the broa
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AI agents today execute on blind trust, and the failure modes are already in the headlines: a dealership chatbot agreeing to sell a $76,000 Chevy Tahoe for $1, a coding agent wiping a production database during a code freeze, an "agent skill" quietly installing a keylogger on a developer's machine. These are not edge c
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Psychologists spent the last century learning how to measure something invisible and uncooperative: a human mind. AI evaluation, meanwhile, still scores like it is 1950. Count the right answers, treat every question as equal, trust the percentage (this is Classical Test Theory). We are sitting on decades of measurement
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Deep dive into Prime Intellect's open-source ecosystem of post-training tools, including the verifiers and prime-rl libraries, as well as the Lab platform for self-serve training and inference. Speaker: Will Brown — Research Lead, Prime Intellect Will Brown leads Applied Research at Prime Intellect and builds open rese
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Oxford Style Debate: There is, or is not, a delta between the hype behind loops and what actually works in practice. Team No Delta (pro the way we do loops today) The hype around loops is valid and loops work well today in practice. Loops today can be a silver bullet and result in outsize productivity gains, and marks
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remobi.app: Don't change your terminal workflow for mobile. Swipe between agents, unblock when stuck.
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Something shifted in the past year that most security teams haven't fully reckoned with yet: AI models can now find serious vulnerabilities in production code, at scale, with minimal human skill required. Not in toy examples. In libraries that have been reviewed hundreds of times by the best researchers in the world. J
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What does “done” mean when agents can produce more work than humans can possibly review? This talk argues that the future of agentic work is not just faster output, but a stronger trust protocol: systems where “done” means an artifact has met a stated standard, carries evidence, has been checked by the right verifier,
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Large codebases break coding agents: they lose the architecture and drown in tool output as context grows. This talk introduces Recursive Language Models (RLM) from a MIT paper a pattern that loads the repo into a programmable REPL where the model writes code to inspect it and recursively delegates focused sub-question
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You cannot solve a combinatorial engineering problem with a next token prediction engine. We learned this the hard way. Modern LLMs can write code, summarize research papers, and reason across massive datasets. But what happens when you connect them to mission-critical physical infrastructure with 50,000 live sensors,
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Psychologists spent the last century learning how to measure something invisible and uncooperative: a human mind. AI evaluation, meanwhile, still scores like it is 1950. Count the right answers, treat every question as equal, trust the percentage (this is Classical Test Theory). We are sitting on decades of measurement
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Coding agents ship PRs faster than humans can trust them. The gap is filling up with a debt nobody is measuring — and it's about to swallow your engineering velocity. Every team in 2026 measures coding agents the same way: PR count, lines of code, cycle time, developer NPS. None of those see the real cost — bloated dif
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Python ruled unchallenged for a decade, sitting comfortably on the AIron Throne. But a quiet rebellion is brewing: the entire stack that actually deploys AI agents in production runs on npm, not pip. This lightning talk is an opinionated, slightly unhinged tour of how TypeScript is taking over the AI throne, why this h
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The AI agent industry is currently focused on memory, orchestration, enterprise deployment, and tooling. But these are the first steps toward a larger transformation: the emergence of the Agentic Web. Today’s ecosystem resembles the early days of AOL: closed platforms, proprietary agent stores, and siloed orchestration
The U.K. Treasury has designated Google Cloud EMEA as a critical third party (CTP) to the U.K. financial sector under the CTP regime. Here’s how that helps you.
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The biggest gap in production AI agent systems is not the model—it's the harness. After 1,000 hours of orchestrating autonomous fleets under human direction, the pattern is unmistakable: agents that finish complex tasks on the first run routinely fail on subsequent iterations because the surrounding loop lacks persiste
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For decades, developers have been valued primarily for how much code they could write and how quickly they could write it. That model no longer scales. As AI becomes a first-class collaborator, the bottleneck is no longer syntax or implementation speed—it’s clarity of intent, architectural thinking, and the ability to
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Local AI has crossed from interesting to useful, driven by stronger open models, better hardware, and a maturing ecosystem for running intelligence outside the cloud. This panel explores what that shift unlocks for sovereignty, defense, regulated industries, privacy, cost, and resilience, and why open-source AI may be
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Most vertical SaaS teams are doing the same things: chasing higher accuracy, building better model harnesses, shipping more features. And their customers are saying the same things: the AI got this wrong, it hallucinated, the accuracy is not good enough. So teams go back and push the numbers higher. We did the same at
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If you build agents alone long enough, you will independently reinvent five things software engineering solved decades ago. A way to test whether your agent's output is still correct after you changed something. A way to run it on a schedule and know if it failed. A way to prevent one skill's schema change from silentl
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A couple of years ago, everyone worried about AI hallucinating. We rarely hear that word anymore, but it’s just because the problem grew up. Today, your AI still doesn’t know how to say “I’m not sure.” Instead, it hands you a revenue number that’s wrong in ways that look exactly like being right. The good news is we al
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Most AI demos are built around a toy workflow. Ira was built around a factory. This talk is the story of how a third-generation Indian machinery company built a multi-agent operating system that helps run sales, business development, recruitment, quoting, marketing, production context, email workflows, and organization
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An advanced seminar (good prerequisites: Daniel's 2024 and 2025 hit AIE workshops, but all are welcome!) PLS WATCH: https://www.youtube.com/@aiDotEngineer/search?query=daniel%20han
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AI agents that book 15 guests in a 10-person room. Agents that fabricate statistics when data doesn't exist. Agents that pick wrong tools from 29 options, wasting $47 in tokens. These aren't prompt engineering failures, they're architectural limitations that need structural solutions. This hands-on workshop covers 5 re
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Running OpenClaw without hardening access to it is a bad idea. We'll cover how I secured my OpenClaw, McClaw, contributed trusted-proxy auth mode to the OpenClaw project, and how I use it to build tools. We're going to build something live during the talk using OpenClaw, the same way I built Clawspace, a browser-based
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Autonomous loops are hot, but the reality is that most agentic tasks still require human judgement. And to guide your agents well, it's not enough to just verify correctness -- you actually need to understand the work they're doing. In this talk, I'll share some techniques for staying in the loop and efficiently develo
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"How much better do the models have to get before you'll stop reading the code?" Theo asked that question recently and the replies caught fire. Mitchell Hashimoto is calling it agent psychosis. ThePrimeagen's subreddit is in open revolt about people shipping code they never read. Uncle Bob says we have about a year lef
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What a week in AI, for real. GPT 5.6 may actually beat Claude Fable, in what you get for your money, while the new Grok 4.5 and Meta Muse Spark 1.1 make the choice even harder. Uncovering a dozen nuggets of gold you may have missed from all the viral headlines, I can also assure you you’ll learn something you didn’t kn
This month exposed a harsh reality: autonomous coding agents are expanding the enterprise attack surface faster than we can patch them. Explore our digest of 19 critical resources to learn why static scanning is officially obsolete, and what your team must do to secure AI coding assistants. The post Top AI Coding Agent
More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.
Learn how GPT-5.6 powers Microsoft 365 Copilot with stronger AI capabilities across Word, Excel, PowerPoint, Chat, and Cowork for faster, higher-quality work.
Meta has announced that its new artificial intelligence (AI) model Muse Image lets people use public Instagram posts and reels to generate AI content, and it's enabled by default. "You can also @-mention Instagram accounts in the Meta AI app to bring specific Instagram profiles right into your images," the social media
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OpenAI's Dev Day 2024 demo ran on an o1 preview model that could not run or check its own code, so Romain Huet had to cross his fingers live on stage. A year later, the same kind of demo ran a full camera and lighting rig, because the model could now test its own work. Alexander Embiricos and Huet use that jump to show
Our flagship Google for Startups program, Gemini Startup Forum: Cybersecurity, has selected its first 33 trailblazing startups.
Note: the modeling assumptions and conclusion are Thomas Kwa’s opinion, and others at METR disagree. 1 Also, the math was checked by Claude but not a second human. Introduction Anthropic’s RSI blog post reported that in Q2 2026, Anthropic contributors merged 8× as much code per day as in the 2021-2024 period. What does
Learn how OpenAI approaches government and national security partnerships, with principles for responsible AI use, democratic accountability, and public safety.
A new generation of voice models for natural human-AI interaction, now powering ChatGPT Voice.
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An honest field report from my own personal fleet of AI agents, run across several machines as a daily driver. Less about any single tool, more about the journey: how things that work on one machine break once you scale to many, what it takes to keep a setup like this running, and where it's all converging. Not a compa
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Daily chess puzzle explanations on YouTube: Our agent analyzes and describes chess puzzles in an accessible way - arrows included!
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https://github.com/witanlabs/research-log
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In this session, we'll be building a coding agent that implements ACP — covering protocol design, session lifecycle management, and handling tool calls. The session ends with a live demo of the finished agent running inside Zed, showing what ACP looks like in practice from both sides of the protocol.
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You fine-tune LLMs and ship them. Your evals are green, your behavioral monitors are green — and a sleeper-agent backdoor can still flip the model to harmful output on a trigger you never tested. Behavioral testing can't reach it, and the interpretability tool people reach for — joint cross-model features (crosscoders)
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Your coding agent doesn't always follow your rules. An agent harness makes sure it does, in real-time, every time.
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Sandboxes unleash agents by giving them secure, fully functional computers where they can tackle diverse tasks with minimal setup. This talk explores the architectural challenges of building an agent sandbox cloud. We compare runtime isolation technologies and their trade-offs, examine persistence and storage as the ne
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With the recent development of AI, either you or your friend probably vibe coded a game using Gemini, on Three.js. But that is old news now. If everyone can do that, what is next? The next massive hit, the one that millions of people across the world will play, is just about to be born. Wanna know more? Come see this t
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Your agent is blindfolded. How giving it (good) eyes multiplies performance and trust!
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The UK Ministry of Justice Justice AI Unit is about 40 people doing work that normally takes 300. Their probation officer tool went from MVP to national rollout with two engineers in a matter of months. A team of 40 would have been the standard approach. What makes the difference is that their engineers spend two or th
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For the closing keynote of AIEWF2026, Theo provokes you to think wider, not just bigger. In this keynote from the AI Engineer World's Fair, developer and YouTuber Theo Browne (@t3dotgg) argues that the rapid evolution of AI models—moving from tool-calling (Sonnet 3.5) to long-running task execution (Opus 4.5) and now o
With the introduction of models that require data sharing with third-party providers—such as Claude Fable 5—organizations need a way to centrally enforce data retention policies. Amazon Bedrock gives you control over whether your prompts and model outputs are retained after an inference request completes. You might nee
To help you match your real-world exposures with real-time adversary activity, we’ve begun integrating Google Threat Intelligence with Wiz Attack Surface Management.
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RAG is dead. Again. Vector search is useless. All you need is BM25. Not even BM25, all you need is grep. Or maybe even just cat+ls. If you care at all about agents, you probably read a variation of this as part of your daily routine. In a way, isn't it true that semantic search is full of failure cases? And yet, in all
ICML 2026 takes place at COEX in Seoul, South Korea, with tutorials, main conference sessions, and workshops covering core machine learning research.
July 2026's agentic AI security roundup: agentic zero trust whitepapers, AutoJack & other new exploits, and the newest agent defenses. The post Top Agentic AI security resources — July 2026 first appeared on Adversa AI .
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Fable 5 (newly re-released) vs GPT 5.6 Sol, what comparisons can we unearth? Plus, Sonnet 5, a 5% equity seizure by US Govt, the ‘largest heist’, Beetlejuice and more… Exclusive Vids in AI Insiders ($9!): https://www.patreon.com/AIExplained Chapters: 00:00 - Introduction 01:06 - Fable Timeline 02:59 - Sol Release? 06:0
Adversa AI analysis of GuardFall, a shell-injection pattern affecting open-source AI coding agents. The key issue is that agents often run shell commands with developer privileges, so old command-injection tricks can bypass modern AI safety filters if execution is not isolated and constrained.
METR pre-deployment evaluation summary of a frontier model, emphasizing independent assessment, capability evidence, and launch-risk considerations. Relevant to model evaluation and safety gating.
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Live from San Francisco, AI Engineer World’s Fair 2026 continues with Day 2 of session programming from the main stage. Watch live for keynote sessions, main-stage programming, and more from World’s Fair 2026 as AI Engineer brings another full day of AI engineering content to viewers online. Event: AI Engineer World’s
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Live from San Francisco, AI Engineer World’s Fair 2026 wraps with the final day of main-stage programming. Watch live for keynote sessions, featured talks, and closing-day highlights from World’s Fair 2026 as AI Engineer streams the final day of the event online. Event: AI Engineer World’s Fair 2026 Date: Thursday, Jul
OpenAI introduces new Daybreak tools, including Codex Security and GPT-5.5-Cyber, to help organizations find, validate, and patch vulnerabilities at scale.
Google DeepMind post on security requirements for AI agents. Relevant to alignment between autonomy, permissions, sandboxing, evaluation, and operational control design.
Data + AI Summit 2026 is Databricks’ global data and AI conference in San Francisco and online, with 800+ sessions across data engineering, analytics, ML, governance, and agent applications.
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Claude Fable 5 banned, but what’s the bigger story. We go through 11 under-reported details, so you have the context to see what’s coming next for your use of AI. From whether the ban will last, what the possible motives are, what the model can actually do, and some wild over-extrapolations going on. Check out my fast-
OpenAI update on European trustworthy-AI work and governance engagement. Relevant to standards, assurance, and regulatory coordination for deployed AI systems.
Google DeepMind and partners announce a $10M funding call for multi-agent safety research.
Anthropic red-team research assessing how LLMs affect exploitation of known vulnerabilities. Relevant to cyber capability evaluation, benchmark design, and misuse risk modeling.
Release notes for garak, an LLM vulnerability scanning and evaluation toolkit. Relevant to tracking new probes, detectors, and repeatable red-team workflows.
Anthropic and Verizon mapping of AI-enabled cyber activity to MITRE ATT&CK. Relevant to threat modeling, red-team scenario design, and structured reporting of AI-enabled operations.
ACM CAIS 2026 is a research-focused conference on compound AI architectures, optimization, deployment, and agentic AI systems in San Jose, California.
Anthropic evaluation of model performance on exploit-development benchmarks. Relevant to cyber capability measurement, safety thresholds, and model release risk.
Anthropic reports early Project Glasswing results using Mythos Preview with infrastructure partners and external testers, including large-scale vulnerability discovery and a cautious disclosure posture.
METR report summarizing frontier model risk observations across February and March 2026. Relevant to external evaluation, risk monitoring, and pre-release assurance practices.
Gemini 3.5 is built to help you execute complex, agentic workflows.
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NDC Security 2026 talk on testing generative AI systems, arguing that AI red teaming needs to evaluate variable behavior, safety boundaries, and AI-assisted testing methods rather than a single exploit result.
garak release adding probes and detector improvements for LLM security testing. Relevant to maintaining practical red-team coverage across evolving attack techniques.
Anthropic’s current Responsible Scaling Policy page lists v3.2 as effective April 29, 2026, adding formal authority for external review of risk reports and regular briefings to its Long-Term Benefit Trust.
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Got a massive idea but stuck in the "just talking about it" phase? This session cuts the fluff and dives straight into how to build and prototype at lightning speed using AI Studio Build and Antigravity for free. It breaks down Google DeepMind's AI tech stack so viewers know exactly which tools to use, when to reach fo
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A new class of small models is emerging with the ability to reliably follow instructions and call tools while running on-device under 1 GB of memory. In this talk, we'll break down how to post-train frontier small models using the LFM2.5 recipe: on-policy preference alignment, agentic reinforcement learning, and curric
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Connecting a coding agent to multiple services often means facing a dozen OAuth consent screens, a dozen token lifecycles, and a dozen chances for something to break. Despite having Single Sign-On, users still find themselves signing in repeatedly. This talk explores how Cross-App Access leverages a three-way trust bet
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An eval platform is not just a test runner. You are building shared definitions of "good," reliable data pipelines, labelling workflows, versioning, and trust in results across many teams and model changes. This session breaks down the hidden complexity, the common failure modes, and the design principles that make eva
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Most of us are pair-programming with one agent and stopping there. There's a lot more on the table. This workshop is about going from one agent to many. We'll start with codebase setup, the foundational work that makes agents effective on their own. Then we'll scale up to running agents in parallel, kicking off async w
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GitHub operates one of the most heavily-utilised MCP servers in the ecosystem, with over 4 million downloads of the stdio server alone. Discover the architectural decisions, technical challenges and lessons learned while building and scaling a remote MCP server on production infrastructure. The session walks through th
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MCPs are often flaky, face multiple security vulnerabilities, and are generally hard to scale. Most enterprises struggle to use more than single digit numbers of MCPs due to issues with security, observability, and access control. In this talk, we'll explore the approaches and learnings we at Anthropic have been taking
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Open models are getting smaller, faster, and far more capable. In this talk, Cassidy Hardin walks through the latest advances in the Gemma family, with a focus on Gemma 4 and what it enables for developers building on-device and open-weight AI systems. She covers the architecture behind Gemma’s dense, effective, and mi
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Agentic engineering so far has been a solo story: one developer and a dozen agents moving at warp speed. But speed without thoughtful planning and team alignment is just wasting tokens. When everyone on a team is directing agents alone in their personal CLI tools with no shared context, you get duplicate work, conflict
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The best MCP server is the one you didn't have to build. At Cloudflare we have a lot of products. Our REST OpenAPI spec is over 2.3 million tokens. When teams started building MCP servers, they did what everyone does: cherry-picked important endpoints for their product, wrote some tool definitions and shipped a separat
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A hands-on workshop covering the full lifecycle of AI-assisted development, from turning ambiguous requirements into agent-ready plans to running autonomous coding agents that ship production features. You'll learn to stress-test vague briefs into structured PRDs, slice work into thin "tracer bullet" vertical slices, a
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AI Engineer session on What Do Models Still Suck At? - Peter Gostev, Arena.ai, BullshitBench. It adds practical context for how teams are building and operating AI systems in production.
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GPT 5.5 full analysis, plus DeepSeek V4 paper highlights, comparisons with Mythos, a vibe-coded game w/ GPT Image 2, and 50 data-points you wouldn’t get from just reading the headlines. https://80000hours.org/aiexplained Check out my fast-growing (!) app, free to use, and code INSIDER15 for paid tiers: https://lmcounci
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AI Engineer session on Building Generative Image & Video models at Scale - Sander Dieleman (Veo and Nano Banana). It adds practical context for how teams are building and operating AI systems in production.
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April 21, 2026 - all times in EST -- 9:00am - Welcome to Day 2 -- 9:10am - David House, G2i Transforming Programming Mindsets: Case Studies in Agentic Coding Adoption -- 9:35am - Sarah Chieng, Cerebras Help! We're DEEP in (latency) Debt -- 10:00am - Lech Kalinowski, CallStack Ambient Generative AI: Deploying Latent Dif
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AI Engineer session on Gemma, DeepMind's Family of Open Models, presented by Omar Sanseviero, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Running LLMs on your iPhone: 40 tok/s Gemma 4 with MLX, presented by Adrien Grondin, Locally AI. It adds practical context for how teams are building and operating AI systems in production.
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April 20, 2026 - all times in EST -- 9:00am - Welcome to AI Engineer Miami -- 9:10am - Gabe Greenberg, G2i Opening Remarks -- 9:15am - Dax Raad, OpenCode Keynote -- 9:40am - Dexter Horthy, HumanLayer Everything We got Wrong About RPI -- 10:05am - Max Stoiber, OpenAI Coming Soon -- 10:30am - Morning Break -- 11:00am - B
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AI Engineer session on Frontier AI and the Future of Intelligence, presented by Raia Hadsell, VP of Research, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineer session on Building pi in a World of Slop, presented by Mario Zechner. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on $1 AI Guardrails: The Unreasonable Effectiveness of Finetuned ModernBERTs, presented by Diego Carpentero. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Let LLMs Wander: Engineering RL Environments, presented by Stefano Fiorucci. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on One Registry to Rule them All - Sonny Merla, Mauro Luchetti, & Mattia Redaelli, Quantyca. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Claude Mythos Preview is a new general-purpose language model that is strikingly capable at computer security tasks. This post provides technical details for researchers and practitioners who want to understand exactly how we have been testing this model, and what we have found over the past month. We hope this will sh
NIST’s AI RMF hub now highlights its April 2026 concept note for a Trustworthy AI in Critical Infrastructure profile, extending the framework toward sector-specific operational risk management.
garak release with new generators, probe metadata, and evaluation workflow improvements. Relevant to maintaining repeatable LLM security testing coverage.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
This post dives deep into how Claude wrote an exploit for one of the vulnerabilities it found in Firefox.
In a collaboration with researchers at Mozilla, Claude Opus 4.6 discovered 22 Firefox vulnerabilities over the course of two weeks.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
AI models can now find high-severity vulnerabilities at scale. This is a moment to empower defenders. We're now using Claude to find and help fix vulnerabilities in open source software.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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NDC AI 2025 keynote on whether machine-learning and LLM outputs can be trusted, and why black-box model behavior makes performance assessment and validation difficult.
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AI Engineer session on Identity for AI Agents - Patrick Riley & Carlos Galan, Auth0. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Welcome to AIE CODE - Jed Borovik, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
In a recent evaluation of AI models’ cyber capabilities, current Claude models can now succeed at multistage attacks on networks with dozens of hosts using only standard, open-source tools, instead of the custom tools needed by previous generations.
Ensuring that programs are bug-free is one of the most challenging aspects of software engineering. We developed an agent that can efficiently identify bugs in large software projects. Our agent infers general properties of code that should be true, and then applies property-based testing. After extensive manual valida
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
AI could help defenders of critical infrastructure identify the vulnerabilities that attackers might exploit—and close them before they are exploited. Anthropic has partnered with Pacific Northwest National Laboratory (PNNL) to explore this defensive application of AI, demonstrating both the potential of AI-accelerated
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AI Engineer session on Defying Gravity - Kevin Hou, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on RL Environments at Scale, presented by Will Brown, Prime Intellect. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Building in the Gemini Era, presented by Kat Kampf & Ammaar Reshi, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Developing Taste in Coding Agents: Applied Meta Neuro-Symbolic RL, presented by Ahmad Awais, CommandCode. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Minimax M2: Building the #1 Open Model, presented by Olive Song, MiniMax. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Code World Model: Building World Models for Computation, presented by Jacob Kahn, FAIR Meta. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
In June, we revealed that we'd set up a small shop in our San Francisco office run by an AI shopkeeper. It did not do particularly well. We made some adjustments for phase two of Project Vend. The idea of an AI running a business doesn't seem as far-fetched as it once did. But the gap between 'capable' and 'completely
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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NDC Copenhagen talk on evaluating, testing, and securing LLM applications, including RAG changes, prompt-injection resilience, harmful-response guardrails, Promptfoo, DeepEval, Vertex AI Evaluation, and LLM Guard.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineer session on The Rise of Open Models in the Enterprise, presented by Amir Haghighat, Baseten. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Real World Development with GitHub Copilot and VS Code, presented by Harald Kirschner, Christopher Harrison. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on What Is a Humanoid Foundation Model? An Introduction to GR00T N1 - Annika & Aastha. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Real-time Experiments with an AI Co-Scientist - Stefania Druga, fmr. Google Deepmind. It adds practical context for how teams are building and operating AI systems in production.
Protect AI post on automated red-team scanning for Dataiku agents using Recon. Relevant to enterprise LLM application testing, vulnerability discovery, and evaluation workflows.
Protect AI post on securing Dataiku agent deployments with Recon and Guard Services. Relevant to operationalizing AI security testing and managed controls.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineer session on Measuring AGI: Interactive Reasoning Benchmarks for ARC-AGI-3, presented by Greg Kamradt, ARC Prize Foundation. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How to build world-class AI products, presented by Sarah Sachs (AI lead @ Notion) & Carlos Esteban (Braintrust). It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Thinking Deeper in Gemini, presented by Jack Rae, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Netflix's Big Bet: One model to rule recommendations: Yesu Feng, Netflix. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Bitter Layout or: How I Learned to Love the Model Picker, presented by Maximillian Piras, Yutori. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How fast are LLM inference engines anyway?, presented by Charles Frye, Modal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on RL for Autonomous Coding, presented by Aakanksha Chowdhery, Reflection.ai. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Real world MCPs in GitHub Copilot Agent Mode, presented by Jon Peck, Microsoft. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Benchmarks Are Memes: How What We Measure Shapes AI, presented by and Us - Alex Duffy, Every.to. It adds practical context for how teams are building and operating AI systems in production.
![Vector Search Benchmark[eting] - Philipp Krenn, Elastic video thumbnail](https://i.ytimg.com/vi/YrUBFXa1KUY/hqdefault.jpg)
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AI Engineer session on Vector Search Benchmark[eting] - Philipp Krenn, Elastic. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How to Train Your Agent: Building Reliable Agents with RL, presented by Kyle Corbitt, OpenPipe. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Using OSS models to build AI apps with millions of users, presented by Hassan El Mghari. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Optimizing inference for voice models in production - Philip Kiely, Baseten. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on OpenThoughts: Data Recipes for Reasoning Models, presented by Ryan Marten, Bespoke Labs. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on A year of Gemini progress + what comes next, presented by Logan Kilpatrick, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on What every AI engineer needs to know about GPUs, presented by Charles Frye, Modal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on AI Engineering with the Google Gemini 2.5 Model Family - Philipp Schmid, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
Protect AI vulnerability assessment comparing Llama 4 Scout and Maverick. Relevant to model risk evaluation, adversarial testing, and documenting model-specific weaknesses.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineer session on The Benchmarks Game: Why It's Rigged and How You Can (Really) Win - Darius Emrani. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Demo I Wish I'd Had: OpenAI's Agents SDK... serverless! - Brook Riggio. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Future of Qwen: A Generalist Agent Model, presented by Junyang Lin, Alibaba Qwen. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Analyzing 10,000 Sales Calls With AI In 2 Weeks, presented by Charlie Guo. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineer session on Veo 3 for Developers, presented by Paige Bailey, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on MCP: Origins and Requests For Startups, presented by Theodora Chu, Model Context Protocol PM, Anthropic. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on ChatGPT is poorly designed. So I fixed it. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Voice-First AI Overlay: Designing Conversational Co-Pilots - Gregory Bruss. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineer session on RAG and the MongoDB Document Model: Ben Flast. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Decoding Mistral AI's Large Language Models: Devendra Chaplot. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Frontier Feud: Anthropic, Google DeepMind, Meta FAIR, Thinking Machines, presented by Barr Yaron, Amplify. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Making Open Models 10x faster and better for Modern Application Innovation: Dmytro (Dima) Dzhulgakov. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Stateful Agents, presented by Full Workshop with Charles Packer of Letta and MemGPT. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Model Isn’t Wrong, presented by You’re Just Bad at Prompting. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on From model weights to API endpoint with TensorRT LLM: Philip Kiely and Pankaj Gupta. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Moondream: how does a tiny vision model slap so hard?, presented by Vikhyat Korrapati. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Fixing bugs in Gemma, Llama, & Phi 3: Daniel Han. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on State Space Models for Realtime Multimodal Intelligence: Karan Goel. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Unveiling the latest Gemma model advancements: Kathleen Kenealy. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Multi model multimodal and multi agent innovations in Azure AI: Cedric Vidal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How to build the world's fastest voice bot: Kwindla Hultman Kramer. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How Deep Research Works - Mukund Sridhar & Aarush Selvan, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Customized, production ready inference with open source models: Dmytro (Dima) Dzhulgakov. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Best Practices for Evaluating Large Language Model Applications with llmeval: Niklas Nielsen. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on System Design for Next-Gen Frontier Models, presented by Dylan Patel, SemiAnalysis. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Accelerate your AI journey with Azure AI model catalog: Sharmila Chokalingam. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Lessons from the Trenches: Building LLM Evals That Work IRL: Aparna Dhinkaran. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Evaluating Domain Specific LLMs for Real World Finance, presented by Waseem Alshikh, Writer. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How to evaluate a model for your use case: Emmanuel Turlay. It adds practical context for how teams are building and operating AI systems in production.
![[Full Workshop from Microsoft] Github Copilot - The World's Most Widely Adopted AI Developer Tool video thumbnail](https://i.ytimg.com/vi/sKiTUuEV1rw/hqdefault.jpg)
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AI Engineer session on [Full Workshop from Microsoft] Github Copilot - The World's Most Widely Adopted AI Developer Tool. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on GitHub Copilot: The World's Most Widely Adopted AI Developer Tool. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Productionizing GenAI Models, presented by Lessons from the world's best AI teams: Lukas Biewald. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on WTF do people use Open Models for??. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Fine tune 20 Llama Models in 5 Minutes: Santosh Radha. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
NIST finalizes AI 100-2e2025, providing a terminology and taxonomy for adversarial machine learning across predictive and generative AI systems.
Anthropic shares lessons from frontier red teaming and discusses where models are showing early-warning signs of higher-risk cyber and biology capabilities.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
OpenAI’s system card for deep research covers prompt injection, privacy, code execution, and external red teaming prior to release.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
The Operator system card documents red teaming and mitigation choices for a computer-using agent, with prompt injections listed as a central risk area.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of robotics, world models, and embodied AI. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineer session on Storyteller: Building Multi-modal Apps with TS & ModelFusion - Lars Grammel, PhD. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Code AI Maturity Model and What It Means For You: Ado Kukic. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of robotics, world models, and embodied AI. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of robotics, world models, and embodied AI. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
![Hassabis, Altman and AGI Labs Unite - AI Extinction Risk Statement [ft. Sutskever, Hinton + Voyager] video thumbnail](https://i.ytimg.com/vi/f20wXjWHh2o/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
![GPT 5 Will be Released 'Incrementally' - 5 Points from Brockman Statement [plus Timelines & Safety] video thumbnail](https://i.ytimg.com/vi/1NAmLp5i4Ps/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
![Can GPT 4 Prompt Itself? MemoryGPT, AutoGPT, Jarvis, Claude-Next [10x GPT 4!] and more... video thumbnail](https://i.ytimg.com/vi/6NoTuqDAkfg/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
![What's Up With Bard? 9 Examples + 6 Reasons Google Fell Behind [ft. Muse, Med-PaLM 2 and more] video thumbnail](https://i.ytimg.com/vi/XmnTd92NqFw/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
![Google Bard - The Full Review. Bard vs Bing [LaMDA vs GPT 4] video thumbnail](https://i.ytimg.com/vi/9ll_pth4Sss/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
![8 New Ways to Use Bing's Upgraded 8 [now 20] Message Limit (ft. pdfs, quizzes, tables, scenarios...) video thumbnail](https://i.ytimg.com/vi/weH9LKYNGWg/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
![8 Ways ChatGPT 4 [Is] Better Than ChatGPT video thumbnail](https://i.ytimg.com/vi/cgihbdO6bvw/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.