New Paper: Towards a science of AI agent reliability
Quantifying the capability-reliability gap
Latest AI/ML News
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Last Week in AI #336 - Sonnet 4.6, Gemini 3.1 Pro, Anthropic vs Pentagon
Anthropic releases Sonnet 4.6, Google Rolls Out Latest AI Model Gemini 3.1 Pro, Pentagon threatens to cut off Anthropic in AI safeguards dispute
We are Changing our Developer Productivity Experiment Design
METR previously published a paper which found the use of AI tools caused a 20% slowdown in completing tasks among experienced open-source developers, using data from February to June 2025. To understand how AI is impacting developer productivity over time, we started a new experiment in August 2025 with a larger pool of developers using the latest AI tools. Unfortunately, given participant feedback and surveys, we believe that the data from our new experiment gives us an unreliable signal of the current productivity effect of AI tools. The primary reason is that we have observed a significant increase in developers choosing not to participate in the study because they do not wish to work without AI, which likely biases downwards our estimate of AI-assisted speedup. We additionally believe there have been selection effects due to a lower pay rate (we reduced the pay from $150/hr to $50/hr), and that our measurements of time-spent on each task are unreliable for the fraction of developers who use multiple AI agents concurrently. Based on conversations with study participants, we believe it is likely that developers are more sped up from AI tools now — in early 2026 — compared to our estimates from early 2025. However, because of the selection effects in our experiment, our data is only very weak evidence for the size of this increase. Our raw results show some evidence for speedup. Our early 2025 study found the use of AI causes tasks to take 19% longer, with a confidence inte…
Demo Day: How the Vector Institute helps Canadian startups turn innovative ideas into commercial reality
By Daniel Kitts “Where were those 10 years ago in AI?” said Stephen Southin to the crowd at Vector’s first Demo Day. The AI industry veteran was energized after hearing […] The post Demo Day: How the Vector Institute helps Canadian startups turn innovative ideas into commercial reality appeared first on Vector Institute for Artificial Intelligence .
Import AI 446: Nuclear LLMs; China's big AI benchmark; measurement and AI policy
Will AIs be jealous of one another?
Scaling Localization with AI at Lyft
Written by Stefan Zier For years, Lyft’s localization infrastructure relied exclusively on human translation. While this model usually ensured excellent quality, it was bound by multi-day turnarounds and costs that scaled linearly with every new language. For the few languages Lyft initially supported (Spanish, Portuguese, and French), these limits were acceptable. However, Lyft’s expansion goals quickly outpaced what traditional workflows could support. Lyft’s recent Québec launch required compliance with Bill 96 (legislation mandating French-first user experiences) which demanded faster turnaround than multi-day cycles allowed. Simultaneously, the Lyft Urban Solutions (“LUS”: Bikes & Scooters) division sought to expand into European markets, requiring six new languages. The business need had changed as we now needed to move faster without sacrificing quality. This post explores how we re-architected Lyft’s Translation Pipeline to leverage AI alongside linguist oversight and ultimately unlock new market launches. We will walk through context injection, decoupling content generation from evaluation, implementing guardrails, and treating prompts as version-controlled production code. The new pipeline reduces translation latency from days to minutes while maintaining the fidelity required for legal compliance and brand integrity. Note : We will walk through our batch translation pipeline — used for 99% of app and web content — which targets a 30-minute SLA for 95% of translati…
Teladoc Health hires Daniel Murgueitio from BCG to lead Peru business
Multinational telemedicine company Teladoc Health has appointed Daniel Murgueitio as its new Country Manager for Peru. Daniel Murgueitio joins the company from Boston Consulting Group (BCG), where he demonstrated a rapid professional ascent over the last several years.
Five lessons from having helped run an AI-Biology RCT
Evidence-based AI policy is important but hard. We need more in-depth studies – which often don’t fit into commercial release cycles. NOTE: This post reflects my personal meta takeaways about the role of Randomized Controlled Trials (RCTs) in AI safety testing. If you have not yet read the Active Site RCT study itself, consider doing so first: see the main results and forecasts . In early 2025, AI systems began outperforming biology experts on biology benchmarks – OpenAI’s o3 outperformed 94% of virology experts on troubleshooting questions in their own specialties. However, it remained unclear how much this translated to real-world novice “uplift” : Could a novice actually use AI to perform wet-lab tasks they could not otherwise perform? Over the summer, I tested this question directly with Active Site (formerly called Panoplia Laboratories). We recruited 153 novices and randomly divided them into an LLM group and an Internet-only group. Over 8 weeks, participants performed fundamental wet-lab tasks involved in molecular biology workflows like reconstructing a virus from a genetic sequence. We found that, while AI showed signs of helpfulness at individual steps, it did not produce a significant effect on end-to-end success across the three core tasks together – a result that surprised many experts . The result provided a mid-2025 snapshot of how well AIs assist novices at molecular biology. I think there are at least two reasons why this result is very informative: It surpr…
After Orthogonality: Virtue-Ethical Agency and AI Alignment
Preface This essay argues that rational people don’t have goals, and that rational AIs shouldn’t have goals. Human actions are rational not because we direct them at some final ‘goals,’ but because we align actions to practices [1] : networks of actions, action-dispositions, action-evaluation criteria,
Cognitive Synthesis and Neural Athletes
As AI accelerates innovation and adoption, leaders are facing rising cognitive load, shifting systems, and new emotional realities inside their organizations. In this episode, Deloitte’s Chief Innovation Officer Deborah Golden joins us to explore how AI is reshaping leadership, why vulnerability and empathy are critical in this moment, and how anti-fragility, not just resilience, will define the future of work. Featuring: Deborah Golden – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: Deloitte Sponsor: Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAI Upcoming Events: Register for upcoming webinars here !
Endeavor partners with McKinsey to reimagine its support for Brazil’s entrepreneurs
In a major overhaul of its organizational strategy, Endeavour worked with McKinsey & Company on its ambitious plan to transform Brazil into a top global hub for innovation by 2035. Endeavor, a non-profit with chapters around the world, identifies and supports high-impact entrepreneurs and founders that use technology to make a difference.
The authenticity gap: What Gen Z really think about brands
A global study from SKIM has found that young consumers from around the world value transparency above all else, while nearly a third reject brands for “trying too hard” with forced messaging. Mariana Abelha and Patricia Fujisawa, senior members in the firm’s LATAM business, explore why authenticity isn’t optional for Gen Z and what this means for brands.
A Guide to Which AI to Use in the Agentic Era
It's not just chatbots anymore
Weaviate Authentication & Authorization: A Complete Security Guide
Learn how to secure your Weaviate vector database with API keys, OIDC, and role-based access control (RBAC). Includes practical examples and setup steps.
Introducing Weaviate Agent Skills
Build production-ready agent workflows with a single prompt in Claude Code, Cursor, and GitHub Copilot.
How We Protect Confidential Information
METR works with AI developers, governments, and other research organizations who sometimes provide nonpublic model access and proprietary information. Over time, we’ve developed confidentiality and security measures to protect such access and information. This post describes our approach at a high level. Confidentiality measures Our confidentiality policy, setup, and norms primarily address the risk of leaks during conversation and in infrastructure, though they also reduce insider threat risk by limiting who knows what. Policy Our confidentiality policy assigns information—including (but not limited to) nonpublic access, lab relationships, policy work, and funding—to our six confidentiality levels, ranging from public to internally siloed, based on sensitivity. At the most restricted end, information about nonpublic models (including capabilities, evaluation timelines, and which developer we’re working with) is limited to researchers directly involved and discussed only by codename. Our own methodology, tasks, and infrastructure are available more broadly within METR, and much of this work is eventually published. Our policy also provides standard responses for sensitive questions, guidance on edge cases, quick rules of thumb with examples and FAQs, and possible slip-ups to watch out for. Table of Contents Commenting on AI developers Easy places to slip up Don’t comment on labs based on non-public info. Any comments […] should be rigorously substantiated by public informati…
Turning Data into Velocity: Caper’s Edge and Cloud Data Flywheel with Capsight
Key Contributors: Youming Luo, Andrew Tanner, Matas Sriubiskis, Sylvia Lin, Sikun Zhu, Lei Li, Xiao Zhou Introduction Caper is Instacart’s AI-powered smart cart that provides customers with a fast, seamless, and intuitive shopping experience. We achieve this through computer vision and multi-sensor fusion to power accurate product recognition and effortless checkout. Delivering this experience requires Caper’s AI models to understand what truly happens in stores — the movement, intention, and decisions unfolding across every grocery aisle. Historically, our ability to learn from production environments was limited. Even though the carts were deployed in stores, we lacked a scalable way to collect real‑world data that would allow us to rapidly iterate and improve our models. This resulted in three core challenges: Scalable Onboard Observability : We had little visibility into what was happening on the cart, in the stores. When something went wrong, it was hard to understand or reproduce the scenario. At the same time, each cart generates gigabytes of multimodal data, from sources such as cameras, weight sensors, and localization sensors. We needed a centralized way to capture key moments so the team could clearly understand what the cart experiences, how users interact with it, and where to improve — all while maintaining a magical user experience and minimal impact on the network. Data Quality and Diversity: Our models were primarily trained on manually-collected data that d…
Building a Movie Recommendation Engine with Hugging Face and Voyage AI
This guest blog post is from Arek Borucki, Machine Learning Platform & Data Engineer for Hugging Face - a collaboration platform for the machine learning community. The Hugging Face Hub works as a central place where anyone can share, explore, discover, and experiment with open-source ML. HF empowers the next generation of machine learning engineers, scientists, and end users to learn, collaborate and share their work to build an open and ethical AI future together. With the fast-growing community, some of the most used open-source ML libraries and tools, and a talented science team exploring the edge of tech, Hugging Face is at the heart of the AI revolution. Traditional movie search relies on filtering by genre, actor, or title. But what if you could search by how you feel? Imagine typing: "something uplifting after a rough day at work" "a movie that will make me cry" "I need adrenaline, can't sleep anyway" "something to watch with grandma who hates violence" This is mood-based semantic search: matching your emotional state to movie plot descriptions using AI embeddings. In this tutorial, you will build a mood-based movie recommendation engine using three powerful technologies: voyage-4-nano (a state-of-the-art open-source embedding model), Hugging Face (for model and dataset hosting), and MongoDB Atlas Vector Search (for storing and querying embeddings at scale). Why mood-based search? Genre tags are coarse. A "drama" can be heartwarming or devastating. A "comedy" can be…
Congratulations to our Co-Director!
Bernhard Schölkopf elected one of 40 members of the new UN Independent International Scientific Panel on AI.
Analyzing coding agent transcripts to upper bound productivity gains from AI agents
Introduction Human uplift studies like the one we did in 2025 are becoming more expensive as working without AI becomes increasingly costly. In this post, I investigate whether coding agent transcripts could serve as a cheaper alternative for estimating uplift. I prototyped this using 5305 Claude Code transcripts generated in January 2026 by 7 METR technical staff 1 . I used an LLM judge to estimate how long each task would have taken an experienced software engineer without AI tools, then compared that to the time people actually spent on these tasks to calculate a time savings factor . Takeaways This method estimates a time savings factor of ~1.5x to ~13x on Claude Code-assisted tasks for 7 METR technical staff in January 2026 – though this result comes with substantial caveats. I believe the true productivity multiplier is substantially lower, and the time savings factor is a soft upper bound for the true uplift that the individuals experienced. Increased agent concurrency may contribute to a higher time savings factor on the Claude Code-assisted task distributions. Limitations The time savings factor on the coding agent-assisted task distributions does not equal the productivity multiplier. People likely do not create 10x as much value with AI, even if we observe a 10x time savings factor on tasks that people do with AI. I believe the time savings factor overestimates AI-enabled productivity gains for reasons including: Task Substitution. With AI assistance, people somet…
LWiAI Podcast #234 - Opus 4.6, GPT-5.3-Codex, Seedance 2.0, GLM-5
An action-packed episode!
Import AI 445: Timing superintelligence; AIs solve frontier math proofs; a new ML research benchmark
Will 2026 be looked back on as the pivotal year for making decisions about the singularity?
What If Intelligence Didn't Evolve? It "Was There" From the Start! - Blaise Agüera y Arcas
Blaise Agüera y Arcas presenting at ALife 2025 — the most technically detailed public walkthrough of the ideas in his *What is Life?* and *What is Intelligence?* books that we've come across. He covers the BFF experiments (self-replicating programs emerging spontaneously from random noise), the mathematical framework connecting Lotka-Volterra population dynamics with Smoluchowski coagulation, eigenvalue analysis of cooperation matrices, and his central claim that symbiogenesis — not mutation — is the primary engine of evolutionary novelty. The experimental results are genuinely striking: complex self-replicating code arising from random byte strings with zero mutation, a sharp phase transition that looks like gelation, and a proof that blocking deep symbiogenetic ancestry trees prevents the transition entirely. A few things worth flagging for critical viewers: — The substrate is more carefully engineered than the framing sometimes suggests. The choice of language, tape length, interaction protocol, and step limits all shape what emerges. Their own SUBLEQ counterexample (where self-replicators *don't* arise despite being theoretically possible) highlights that these design choices matter substantially — and a general theory of which substrates support this transition is still missing. — The leap from "self-replicating programs on fixed-length tapes" to "life was computational and intelligent from the start" involves significant philosophical extrapolation beyond what the expe…
Last Week in AI #335 - Opus 4.6, Codex 5.3, Gemini 3 Deep Think, GLM 5, Seedance 2.0
A crazy packed edition of Last Week in AI! Plus some small updates.
AI incidents, audits, and the limits of benchmarks
AI is moving fast from research to real-world deployment, and when things go wrong, the consequences are no longer hypothetical. In this episode, Sean McGregor, co-founder of the AI Verification & Evaluation Research Institute and also the founder of the AI Incident Database, joins Chris and Dan to discuss AI safety, verification, evaluation, and auditing. They explore why benchmarks often fall short, what red-teaming at DEFCON reveals about machine learning risks, and how organizations can better assess and manage AI systems in practice. Featuring: Sean McGregor– LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: AI Verification & Evaluation Research Institute AI Incident Database 38th convening of IAAI BenchRisk State of Global AI Incident Reporting Upcoming Events: Register for upcoming webinars here !
AI Won’t Automatically Make Legal Services Cheaper
Applying the AI as Normal Technology framework to legal services
Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models
written by Ross Chu and Shima Nassiri The Causal Frontier: Measurement Beyond Randomization The gold standard for determining the causal impact of a policy or product change at a company like Lyft is the A/B test (randomized experiment). By randomly assigning users to a treatment or control group, A/B tests inherently eliminate bias, providing clean estimates of the Average Treatment Effect (ATE). However, many critical business questions and large-scale initiatives simply cannot be randomized . This forces scientists to move past traditional experimentation and leverage quasi-experimental methods. We rely on non-randomized measurement in several key scenarios across Lyft: Partnerships and Policies: Assessing the incremental impact of a partnership (e.g., linking two company accounts) is often a non-randomized assignment. Since these collaborations require coordinated operational work across both companies and are typically announced or promoted broadly, this makes controlled randomization impractical. Long-Term Effect (LTE): Measuring effects that unfold over a long period, like the LTE of high prices on future rides, is typically handled by observational studies. Post-Launch Evaluation: Continuous monitoring of a policy after it has been fully rolled out requires a method that doesn’t involve costly holdout groups or degradation tests. Biased Data: In cases where pre-existing experimental data is found to have an imbalance, a quasi-experimental approach can potentially lev…
microgpt
This is a brief guide to my new art project microgpt , a single file of 200 lines of pure Python with no dependencies that trains and inferences a GPT. This file contains the full algorithmic content of what is needed: dataset of documents, tokenizer, autograd engine, a GPT-2-like neural network architecture, the Adam optimizer, training loop, and inference loop. Everything else is just efficiency. I cannot simplify this any further. This script is the culmination of multiple projects (micrograd, makemore, nanogpt, etc.) and a decade-long obsession to simplify LLMs to their bare essentials, and I think it is beautiful 🥹. It even breaks perfectly across 3 columns: Where to find it: This GitHub gist has the full source code: microgpt.py It’s also available on this web page: https://karpathy.ai/microgpt.html Also available as a Google Colab notebook NEW : buy microgpt as a triptych on my art store at karpathy.art :) The following is my guide on stepping an interested reader through the code. Dataset The fuel of large language models is a stream of text data, optionally separated into a set of documents. In production-grade applications, each document would be an internet web page but for microgpt we use a simpler example of 32,000 names, one per line: # Let there be an input dataset `docs`: list[str] of documents (e.g. a dataset of names) if not os . path . exists ( 'input.txt' ): import urllib.request names_url = 'https://raw.githubusercontent.com/karpathy/makemore/refs/heads/…
#491 – OpenClaw: The Viral AI Agent that Broke the Internet – Peter Steinberger
Peter Steinberger is the creator of OpenClaw, an open-source AI agent framework that’s the fastest-growing project in GitHub history. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep491-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/peter-steinberger-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Peter’s X: https://x.com/steipete Peter’s GitHub: https://github.com/steipete Peter’s Website: https://steipete.com Peter’s LinkedIn: https://www.linkedin.com/in/steipete OpenClaw Website: https://openclaw.ai OpenClaw GitHub: https://github.com/openclaw/openclaw OpenClaw Discord: https://discord.gg/openclaw
Transcript for OpenClaw: The Viral AI Agent that Broke the Internet – Peter Steinberger | Lex Fridman Podcast #491
This is a transcript of Lex Fridman Podcast #491 with Peter Steinberger. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Go back to this episode’s main page Watch the full YouTube version of the podcast Table of Contents Here are the loose “chapters” in the conversation. Click link to jump approximately to that part in the transcript: 0:00 – Episode highlight 1:30 – Introduction 5:36 – OpenClaw origin story 8:55 – Mind-blowing
Future of Life Institute Launches Multimillion Dollar Nationwide AI Regulation Campaign
The Protect What’s Human campaign will push for commonsense AI safety rules at federal and state level
From print to digital: Making weekly flyers shoppable at Instacart through computer vision and LLMs
From Print to Digital: Making Weekly Flyers Shoppable at Instacart Through Computer Vision and LLMs Key contributors: Prithvi Srinivasan, Shishir Kumar Prasad, Kristen Morgan, Bryan Pham, Rick Shukla, Preeti Chadha, Vipul Bahubali, Ahmad Sajedi, and Ali Maleky Introduction Grocery flyers have long been a cornerstone of retail promotions, from paper inserts in the newspaper to email blasts featuring weekly deals. As more grocery shopping shifts online, however, these static promotions haven’t kept pace with customer expectations for convenience and interactivity. At Instacart, we recognized the opportunity to transform static promotional content into interactive, shoppable experiences. In 2024, we launched grocery flyers on our platform[1] , enabling retailers to upload their weekly and monthly promotions. This enabled our customers to browse through weekly deals for their favourite retailers, providing easy ways to save. Fig 1: Sample grocery flyer Customers expect digital flyers to look and feel like the physical versions they’re used to, with the added ability to tap on items and shop directly. To deliver that experience early on, we relied on a manual digitization process. This involved drawing bounding boxes around every deal and accurately matching those deals to products to our catalog — a painstaking task that required 3–4 hours per flyer. As the feature gained traction with retailers, this manual approach quickly became unsustainable. With dozens of retailers uploadi…
Import AI 444: LLM societies; Huawei makes kernels with AI; ChipBench
How can you quantify creativity?
Learning path and canonical resources for Mechanistic Interpretability
Recently I read Julian Mendel's article, " Mechanistic Interpretability: Peeking Inside an LLM " on Towards Data Science( https://towardsdatascience.com/mechanistic-interpretability-peeking-inside-an-llm/ ), and I became fascinated by the idea of moving beyond treating Large Language Models as "black boxes." Summary of the article's premise: The post proposes that we can understand and even manipulate an LLM’s behavior by examining its internal architecture—specifically the residual stream, attention heads, and MLP layers. It treats the model as a circuit that can be reverse-engineered to see how information is processed and stored. Key examples mentioned: World Models: How models represent internal states of games like Chess or Othello. Induction Heads: Specific attention heads that allow for in-context learning. Superposition: The phenomenon where neurons are "polysemantic," representing multiple concepts at once. Steering Vectors: Using activation addition to modify a model's behavior (e.g., making it more honest or preventing refusals) without retraining weights. Conclusion of the article: The author concludes that while manual mechanistic analysis provides deep insights into safety, reliability, and human-like cognition, the field is rapidly moving toward automated interpretability to handle the sheer scale of modern models. The Question : As someone looking to transition from a general understanding of Transformers to actually performing research or experiments in Mech…
LWiAI Podcast #233 - Moltbot, Genie 3, Qwen3-Max-Thinking
Google adds Gemini AI-powered ‘auto browse’ to Chrome, Users flock to open source Moltbot for always-on AI, Qwen3-Max-Thinking debuts, and more!
Building DataBot: Our always-on data assistant
The hidden cost of being a data-driven company is context-switching for analysts due to "quick questions." DataBot allows the data team to focus on auditing analysis instead of performing it.
Transcript for GSP teaches Lex Fridman how to street fight
This is a transcript of “GSP teaches Lex Fridman how to street fight”. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Watch the full YouTube version of the video Georges St-Pierre (00:00:00) In a street fight, I would rather- …fight Francis Ngannou than fight Bas Rutten. In a street fight. Lex Fridman (00:00:06) Let me tell you first that I’ve been around. I’ve been a bouncer for many, many years. Bang! Bang!
OpenMined at the India AI Impact Summit 2026
OpenMined is participating in the India AI Impact Summit 2026 in New Delhi, in partnership with the Human Genome Project II (HGP2). Together, we’re demonstrating how privacy-preserving infrastructure can enable countries to participate in global AI-driven genomics research on their own terms, without centralising sensitive health data. We’re hosting three events across the Summit week. […] The post OpenMined at the India AI Impact Summit 2026 appeared first on OpenMined .
Transcript for 1984 by George Orwell | Lex Fridman
This is a transcript of “1984 by George Orwell | Lex Fridman”. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Watch the full YouTube version of the video Table of Contents Here are the loose “chapters” in the video. Click link to jump approximately to that part in the transcript: 0:00 – Intro 1:02 – World of 1984 4:19 – Love 12:42 – Hate 17:21 – Power 25:56 – Orwell 28:49 –
Last Week in AI #334 - Kimi K2.5 & Code, Genie 3, OpenClaw & Moltbook
China’s Moonshot releases a new open source model Kimi K2.5 and a coding agent, Google Brings Genie 3’s Interactive World-Building Prototype to AI Ultra Subscribers, and more!
The Limit in the Loop
Memory isn't just a feature for AI applications—it's infrastructure. As agents scale, the limited loop of stateless interactions breaks down, and continuity becomes a systems problem that requires active maintenance.
How our support engineers use Deep Search to investigate customer issues faster
Deep Search empowers our Support Engineers to bypass initial escalations, enabling them to dive directly into investigating the root cause of issues.
Edge AI Made Easy: MongoDB and ObjectBox Data Synchronization
AI is currently undergoing a shift, from massive centralized models to distributed, real-world deployments. While the cloud remains the foundation for large-scale AI training and analytics, AI’s next evolution lies at the edge—where data is created, where decisions require instant action, and where connectivity cannot be guaranteed. At MongoDB, we are committed to helping organizations build intelligent applications that span cloud and edge environments seamlessly. That’s why we are excited to highlight our work with ObjectBox, a lightweight, high-performance on-device database and sync solution purpose-built for edge AI and offline-first applications. Together, MongoDB and ObjectBox are making it easier for developers to build hybrid architectures that deliver fast, private, and resilient AI experiences across devices and environments. Figure 1. Example cloud-edge AI setup. Example cloud-edge AI setup. ObjectBox: A purpose-built database for the edge Founded by Markus Junginger and Dr. Vivien Dollinger, ObjectBox was designed specifically to support edge computing and offline-first use cases. At its core, ObjectBox’s design prioritizes efficiency (including speed, privacy, battery use, and memory consumption) and ease of development. This strong foundation makes ObjectBox particularly well-suited for next-generation applications that need to run reliably in edge environments—whether on a factory floor, in a retail store, or through a remote healthcare device. ObjectBox empo…
Inside an AI-Run Company
AI agents are moving from demos to real workplaces, but what actually happens when they run a company? In this episode, journalist Evan Ratliff, host of Shell Game , joins Chris to discuss his immersive journalism experiment building a real startup staffed almost entirely by AI agents. They explore how AI agents behave as coworkers, how humans react when interacting with them, and where ethical and workplace boundaries begin to break down. Featuring: Evan Ratliff – LinkedIn , X Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Links: Shell Game Upcoming Events: Register for upcoming webinars here !
Import AI 443: Into the mist: Moltbook, agent ecologies, and the internet in transition
Plus, a story about agents corrupting other agents
State of AI 2026 with Sebastian Raschka, Nathan Lambert, and Lex Fridman
I recently sat down with Lex Fridman and Nathan Lambert for a comprehensive 4.5 h interview to discuss the current state of progress of AI, and what the...
#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI
Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch). Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep490-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/ai-sota-2026-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring