The Gaslighting of America, with Professor Mathias Risse
What is fueling the post-truth era? Why is it working? Harvard's Mathias Risse argues that gaslighting has become a dominant rhetorical force in American politics.
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Agentic Coding and the Economics of Open Source
AI is rapidly transforming how software is built, shifting economic incentives from open source code and collaboration toward on-demand, personalized development through agentic coding a.k.a. vibe coding. In this episode, Chris speaks with Miklós Koren of Central European University about how AI is reshaping open source and the software industry. They explore the economics of incentives, evolving collaboration patterns, and what this shift means for software development, the future of AI, and its broader impact on the technology sector. Featuring: Miklós Koren – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Links: Vibe Coding Kills Open Source The Directions of Technical Change The Tailwind story Upcoming Events: Register for upcoming webinars here !
Oh Memories, Where'd You Go
Two weeks of dogfooding Engram, Weaviate's memory product, in daily Claude Code sessions. This surfaced where a dedicated memory product adds value, and the specific mechanics that prevent integration with coding assistants from working well.
OpenMined’s Peter Ide-Kostic Selected for the European Commission’s Expert Forum on Frontier AI
OpenMined's Head of EU Policy, Peter Ide-Kostic, has been selected by the European AI Office to serve on the Expert Forum on Frontier AI — a group shaping how Europe accesses, controls, and benefits from frontier AI. The post OpenMined’s Peter Ide-Kostic Selected for the European Commission’s Expert Forum on Frontier AI appeared first on OpenMined .
North Star Data Center Policy Toolkit: State and Local Policy Interventions to Stop Rampant AI Data Center Expansion
This policy toolkit is primarily geared toward stopping, slowing, and restricting rampant data center development in the US at the local and state level. Our approach recognizes the extractive relationship between data centers and local communities: Hyperscale data centers deplete scarce natural resources, pollute local communities and increase the use of fossil fuels, raise energy […] The post North Star Data Center Policy Toolkit: State and Local Policy Interventions to Stop Rampant AI Data Center Expansion appeared first on AI Now Institute .
MLCommons Releases New MLPerf Inference v6.0 Benchmark Results
The most significant update to the benchmark suite to date, with new tests ensuring that it remains the most comprehensive measure of AI system performance The post MLCommons Releases New MLPerf Inference v6.0 Benchmark Results appeared first on MLCommons .
LWiAI Podcast #238 - GPT 5.4 mini, OpenAI Pivot, Mamba 3, Attention Residuals
OpenAI ships GPT-5.4 mini and nano, faster and more capable but up to 4x pricier, DLSS 5 looks like a real-time generative AI filter for video games | The Verge, and more!
Fine-tuning experiments on CoT controllability
Kei Nishimura-Gasparian is an Astra fellow and was the primary contributor to this work. Neev Parikh provided mentorship and feedback. Summary: We find that a small amount of fine-tuning on instruction following in the CoT generalizes to meaningful increases in CoT controllability on an out-of-distribution set of tasks (CoTControl eval suite). We fine-tune four reasoning models on small datasets (240 examples or ~100K-300K tokens of fine-tuning) of instruction-following reasoning data and OOD controllability rises from an average of 2.9% to 8.8% across four models. 1 We see the largest increases for instructions that request reasoning in a specified case, suppressing certain words, and adding provided sentences to the reasoning. While 8.8% remains low in absolute terms, this provides evidence that just a small amount of fine-tuning can increase controllability, suggesting that low CoT controllability may not be very robust to accidental optimization pressure. Limitations and caveats: It seems unlikely that frontier AI labs will do even a small amount of fine-tuning directly for controllability which makes our setup somewhat unrealistic. However, the fact that a slight improvement in these capabilities can be elicited with a small amount of fine-tuning suggests the capabilities are latent in the model rather than the fine-tuning teaching the model a new skill. We have not shown that this increase in controllability results in a decrease in monitorability, we will look at this…
Multimodal Embeddings and RAG: A Practical Guide
Multimodal embeddings allow AI systems to search and reason across text, images, audio, and video in their native formats. This blog covers the key intuitions behind how this all works and walks through three practical implementations using Weaviate and Gemini.
Claude Dispatch and the Power of Interfaces
We often lack the tools for the job, even if the AI is capable enough
Agent-driven development in Copilot Applied Science
I used coding agents to build agents that automated part of my job. Here's what I learned about working better with coding agents. The post Agent-driven development in Copilot Applied Science appeared first on The GitHub Blog .
Introducing MongoDB Agent Skills and Plugins for Coding Agents
Software engineering is evolving into agentic engineering. According to the Stack Overflow Developer Survey 2025, 84% of respondents use or plan to use AI tools in their development, up from 76% the previous year. At this rate, the tooling needs to keep pace. Last year, we introduced the MongoDB MCP Server to give agents the connectivity they need to interact with MongoDB, helping them generate context-aware code. But connectivity was only the start. Agents are generalists by design, and they don't inherently know the best practices and design patterns that real-world production systems demand. Today, we're addressing this by introducing official MongoDB Agent Skills: structured instructions, best practices, and resources that agents can discover and apply to generate more reliable code across the full development lifecycle, from schema design and performance optimization to implementing advanced capabilities like AI retrieval. To bring this directly into the tools you use, we're also launching plugins for Claude Code, Cursor, Gemini CLI, and VS Code, combining the MongoDB MCP Server and Agent Skills in a single, ready-to-use package. Turning coding agents into MongoDB experts Coding agents are great at producing working code, but they still make common mistakes in production systems, often defaulting to relational thinking that doesn't translate well to MongoDB, such as: Over-normalizing schemas, ignoring MongoDB's document-oriented strengths. Underusing compound indexes, c…
Enforcement of Chapter V under the EU AI Act
This page aims to provide an overview of the EU AI Act’s enforcement provisions relating to Chapter V, namely the provisions that impose obligations on providers of general-purpose AI (GPAI) models. It also aims to explore the role that other actors can play in the enforcement of the AI Act. Summary Coming up in this […]
Your Code is Your Schema: Weaviate Managed C# Client
Use semantic search and RAG in C# with the Weaviate Managed .NET client — attribute-driven schema, type-safe queries, and safe migrations, all in idiomatic .NET.
A Conversation with Carnegie Ethics Fellow Harsh Suri
This conversation features Harsh Suri, CEO and co-founder of The Geostrata, a youth-led independent policy and research think tank, based in India.
Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees
At Lyft, understanding how riders go through our user experience is fundamental to operating a healthy marketplace. Specifically, it is important to have a robust model determining if a rider will actually request a ride after entering a destination and viewing a price and ETA. Accurately predicting this decision, that we call conversion , informs countless decisions across our platform. Whether it is to better balance supply and demand, improve user experiences, optimize recommendations and advertisement, understand long-term engagement, decide how to distribute coupons… rider conversion prediction is a central challenge for the Lyft business. However, predicting human behavior at scale is incredibly complex, and the exact same person might well open the app just to check current availability or actually to request a ride after viewing our prices. The contexts under which riders make their conversion decisions are extremely diverse and almost unique to each session. A user’s intent changes based on where they are and where they want to go, what time it is, their previous interactions with the platform, current supply-demand market conditions, to cite a few. When we try to model this using standard machine learning approaches, we run into a significant challenge: data sparsity . The Challenge of High Cardinality and Sparsity To accurately predict conversion, we need to slice our data very thinly across many categorical features. Imagine trying to predict the conversion proba…
Import AI 451: Political superintelligence; Google's society of minds, and a robot drummer
Are there any genies that can be put back in the bottle?
Prominent Scientists, Faith Leaders, Policymakers and Artists Call for a Prohibition on Superintelligence, as Poll Shows Americans Don’t Want It
Initial signatories include AI pioneers Yoshua Bengio and Geoffrey Hinton, leading media voices Steve Bannon and Glenn Beck, Obama's National Security Advisor Susan Rice, business trailblazers Steve Wozniak and Richard Branson, five Nobel Laureates, former Irish President Mary Robinson, actors Stephen Fry and Joseph Gordon-Levitt, and hundreds of others.
The Race to Production-Grade Diffusion LLMs with Stefano Ermon - #764
Today, we're joined by Stefano Ermon, associate professor at Stanford University and CEO of Inception Labs to discuss diffusion language models. We dig into how diffusion approaches—traditionally used for images—are being adapted for text and code generation, the technical challenges of applying continuous methods to discrete token spaces, and how diffusion models compare to traditional autoregressive LLMs. Stefano introduces Mercury 2, a commercial-scale diffusion LLM that can generate multiple tokens simultaneously and achieve inference speeds 5-10x faster than small frontier models, paving the way for latency-sensitive applications like voice interactions and fast agentic loops. We also cover the open research challenges in diffusion LLM training, serving infrastructure requirements, and post-training for diffusion-based systems. Finally, Stefano shares his perspective on whether diffusion models can rival or surpass autoregressive LLMs at scale, the advantages for highly controllable generation, and what the future of multimodal diffusion models might look like. The complete show notes for this episode can be found at https://twimlai.com/go/764.
LLM Architecture Gallery Diff Tool
Short note on the LLM Architecture Gallery diff tool for comparing two model architecture stacks side by side.
AI Opportunities in Africa: Governance, Infrastructure Gaps, and Global Competition Shape the Continent’s AI Future
Here is what happened in AI in Africa this week: 1. AI Surveillance Concerns Rise Across Africa A new […]
Red-Teaming Anthropic's Internal Agent Monitoring Systems
Update : Further details on this exercise are included in our Frontier Risk Report (February-March 2026), within the Anthropic section of Appendix B . In collaboration with Anthropic, a METR staff member (David Rein) recently spent three weeks red-teaming a subset of Anthropic’s internal agent monitoring and security systems, many of which are described in the Opus 4.6 Sabotage Risk Report (Appendix 8.4, especially 8.4.8). Anthropic provided substantial access to relevant internal systems and information, and made staff available to answer questions and provide feedback throughout the exercise. The exercise discovered several specific novel vulnerabilities, some of which have since been patched, and none of which severely undermine major claims in the Opus 4.6 Sabotage Risk Report. It also produced several artifacts, including agent trajectories containing covert attacks and a small attack strategy ideation test set. We expect both of these to be useful for ongoing improvements to Anthropic’s monitoring systems. The resulting 26 page report was shared with Anthropic, and a redacted version was shared with a subset of METR staff. We are exploring ways to incorporate more detailed takeaways from the exercise in future METR risk reports. This kind of adversarial testing by external researchers is valuable for discovering vulnerabilities, as well as for developing best practices for embedding third party evaluators inside frontier AI companies. We hope to do more exercises like…
Detecting supply chain attacks at scale with Deep Search
Poisoned LiteLLM packages on PyPI started stealing credentials. Using Deep Search and Code Search, we traced which public repos were protected by version pinning and which were left exposed. Here's how—and how you can do the same for any supply chain incident.
Detecting supply chain attacks at scale with Deep Search
Poisoned LiteLLM packages on PyPI started stealing credentials. Using Deep Search and Code Search, we traced which public repos were protected by version pinning and which were left exposed. Here's how—and how you can do the same for any supply chain incident.
AI at the Edge is a different operating environment
What does “AI at the edge” really mean in 2026, and why does it matter now more than ever before? In this episode, we’re joined by Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm’s Edge Impulse, to discuss the current state and future of Edge AI in 2026. We discuss Gen AI, Small Models, and Cascades of Models, along with real-world constraints like latency, power, and privacy. We also dive into the role of MLOps, evolving hardware, and how developers can start building practical edge AI systems today. Featuring: Brandon Shibley – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: Read our Ultimate Guide to Edge AI Download your copy of O'Reilly's AI at the Edge Check out the Edge Impulse blog Sign-up for an expert led trial of Edge Impulse Upcoming Events: Register for upcoming webinars here !
Argus launches natural gas price assessments in Brazilian market
Argus, a market intelligence and advisory firm in the energy and commodity markets, has launched Brazil’s first assessed daily price for the natural gas spot market. The move from the UK-headquartered company follows the opening of Brazil’s natural gas market to competition under a regulatory framework approved five years ago.
Introducing Lyria 3 Pro
Last month, we introduced Lyria 3, featuring custom music generation designed to spark creative expression. Now, we’re bringing our most advanced music generation model to more Google products, and introducing Lyria 3 Pro. This advanced version allows the creation of tracks up to 3 minutes long, with customization and creative control. Learn more: https://blog.google/innovation-and-ai/technology/ai/lyria-3-pro ___ Subscribe to our channel https://www.youtube.com/@googledeepmind Find us on X https://twitter.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googledeepmind Add us on Linkedin https://www.linkedin.com/company/deepmind/
Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces
Image generated with Gemini 3 Pro (Google), 2026. Written by Amber Wang and Y oonji Kim at Lyft. Background Whenever you use the Lyft app, there is a complex balancing act happening behind the scenes. Various levers are used to keep the marketplace running smoothly; Base prices and coupons for riders affect demand, while driver pay and bonuses impact the level of available supply. Since every change to prices and payments impacts Lyft’s costs and revenue, they lead to key optimization problems, such as: How should we allocate budget between driver incentives and rider incentives? How do we invest resources to achieve x% rides growth, and how much does it cost in terms of short term profit? These are the questions the Foundational Models team at Lyft tries to answer in a systematic way. A key ingredient is understanding the effects of different types of investments — for instance, what will happen if we increase the total budget for driver incentives by x%? What will happen if we increase the rider price of all rides by y%? It’s worth noting that the long term effects of such decisions tend to dominate the short term effects: we may earn more short term profit from a ride if we charge riders more and pay drivers less, but lose riders and drivers in the long run. Estimating the long term effects of resource allocation decisions is challenging in a multi-sided marketplace such as Lyft. Because these decisions tend to be consequential, their effects go beyond first order effects…
Federal and State Representatives Visit Tübingen AI Center
BMFTR and MWK Discuss AI Research and Transfer Activities at Tübingen AI Center
The future of SCIP
We are excited to announce our transition to a community-driven open source project. While making this change, we reaffirm our deep commitment to remaining active members of the community.
The future of SCIP
We are excited to announce our transition to a community-driven open source project. While making this change, we reaffirm our deep commitment to remaining active members of the community.
Personality-driven AI agents: Operationalizing OCEAN traits for human-AI collaboration in the coding domain
As AI agents become collaborative partners in complex tasks, understanding how agent personality affects human-AI interaction becomes critical. While recent work explores personality customization in language models, little is known about how personality affects AI coding agents. We conducted the first exploratory study investigating: if OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) personality traits can be operationalized in AI coding agents, if users detect these personality differences, and how different personalities affect user trust and adoption. Participants completed refactoring tasks with three agent profiles. Results show that personality traits successfully translated into distinguishable behaviors reliably detected by users. While no universal 'best' personality emerged, individual preferences diverged substantially. Conscientiousness produced more consistent trust, while openness and extraversion polarized users. Some users experienced trust collapse from overconfidence and others found excessive caution inefficient. Our findings provide initial empirical evidence that OCEAN personality traits can be operationalized in AI coding agents, producing distinguishable behaviors, with implications for designing adaptive systems.
A new GPT-OSS benchmark and DeepSeek R1 updates for latency-optimized reasoning
MLPerf Inference v6.0 expands open-weight LLM coverage with a new GPT-OSS 120B benchmark and a latency-constrained interactive scenario for DeepSeek-R1 — the first MLPerf standard for speculative decoding. The post A new GPT-OSS benchmark and DeepSeek R1 updates for latency-optimized reasoning appeared first on MLCommons .
Do Spline Terms Affect the Interpretation of Linear Terms in Logistic Regression?
In a multivariable logistic regression model, some continuous predictors are modeled using spline transformations to allow for nonlinear effects, while other continuous predictors are entered as simple linear terms. Can the predictors that remain linear still be interpreted in the usual way, that is, through odds ratios for a one-unit increase, even though other predictors in the same model are modeled with splines? To be clear I'm not asking about the linear basis term within a spline expansion of the same predictor. I meant a model like: $$ \text{logit}(P(Y=1)) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + f(x_3) $$ where $x_1$ and $x_2$ are entered linearly and $x3$ is modeled with a spline. My question is whether $\beta_1$ and $\beta_2$ can still be interpreted in the usual/simple way of presenting single odds ratio values.
#494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution
Jensen Huang is the co-founder and CEO of NVIDIA, the world’s most valuable company and the engine powering the AI computing revolution. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep494-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/jensen-huang-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: NVIDIA: https://nvidia.com NVIDIA on X: https://x.com/nvidia NVIDIA AI on X: https://x.com/NVIDIAAI NVIDIA on YouTube: https://youtube.com/@nvidia NVIDIA on Instagram:
Transcript for Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494
This is a transcript of Lex Fridman Podcast #494 with Jensen Huang. 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 – Introduction 0:33 – Extreme co-design and rack-scale engineering 3:18 – How Jensen runs
Speaking of Voxtral
Voxtral TTS: A frontier, open-weights text-to-speech model that’s fast, instantly adaptable, and produces lifelike speech for voice agents.
Import AI 450: China's electronic warfare model; traumatized LLMs; and a scaling law for cyberattacks
How will timeless minds value time?
Last Week in AI #339 - DLSS 5, OpenAI Superapp, MiniMax M2.7
DLSS 5 looks like a real-time generative AI filter for video games, OpenAI Reportedly Pivoting to a Focus on Business and Productivity Only, and more!
Statement: Head of US Policy on the White House AI legislative recommendations
The White House published it’s long-awaited AI legislative recommendations on Friday, and it still includes a call for Congress to […]
A Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architectures
Zero Introspection
The rejection of introspection by America's business leaders—combined with an unwillingness to defend the system that incubated their success—is a deeply troubling trend.
Impact of modelling assumptions on time horizon results
As METR’s time horizon task suite saturates, the results are becoming more sensitive to analysis choices. One example of this was the recent update to fix a modelling mistake with regularization, which decreased recent models’ 50% time horizon results by up to 20%, but had a smaller impact on earlier LLMs’ 50% time horizons. 1 In this post I’ll: Give a refresher on the current model used to calculate time horizon results and more detail about the regularization mistake METR recently fixed Go over what I see as the other main sources of uncertainty in time horizon results (outside of needing more tasks). Where possible, I’ll fit alternative models to show their impacts Wrap things up with general thoughts on how much weight people should put on the current estimates I hope this will help people better understand the modelling assumptions underlying the time horizon results, and how robust (or not) the results are. Summary There are many reasonable variations one could make to the TH modelling, and most of these end up having the effect of reducing recent 50% time horizon estimates (and often increase 80% time horizon estimates). The aspect I feel least certain about is noise in the task length estimates, which I hope to look into more in the future. I find that reasonable choices generally still leave us inside the CIs (which are very wide!). I think the most important source of uncertainty is the task distribution rather than analysis choices, as which tasks are included has…
MCP stories from the field
While direct API calls seem cheaper and easier, they lack the safety layer large organizations rely on. Tool connection protocols aren't dead; they remain vital for security, governance, and centralized control in big teams.
MCP stories from the field
While direct API calls seem cheaper and easier, they lack the safety layer large organizations rely on. Tool connection protocols aren't dead; they remain vital for security, governance, and centralized control in big teams.
9 reasons Java is still great
In a world obsessed with disruption, Java threads the needle between stability and innovation. It’s the ultimate syncretic platform , synthesizing the best ideas from functional programming, concurrency, cloud computing, and AI under a reliable, battle-tested umbrella. Java unites meticulous planning with chaotic evolution, enterprise reality with open source ideals, along with a healthy dose of benevolent fortune. Let’s look at the key factors that make Java as much a champion today as it was in 1996. 1. The Java Community Process At the heart of Java’s success are the developers and architects who love it. The Java community is vital and boisterous, and very much engaged in transforming the language. But what makes Java special is its governance architecture. Far from a smoothly operating machine, Java’s governance is a riotous amalgam of competing interests and organizations, all finding their voice in the Java Community Process (JCP) . The fractious nature of the JCP has been criticized, but over time it has given Java a massive advantage. The JCP is Java’s version of a functional democracy: A venue for contribution and conflict resolution among people who care deeply about the technology. The JCP is a vital forum where the will and chaos of the worldwide developer community negotiate with Java’s formal managing body. 2. OpenJDK I still remember my astonishment when the Java language successfully incorporated lambdas and closures . Adding functional constructs to an obje…
AI Expo Africa 9th Edition – Africa’s Largest Enterprise AI & Automation Event
AI Expo Africa 2026, Africa's Largest Enterprise AI & Automation Event will be running 9th Edition, JHB, South Africa 29-31 October