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科技热榜

233 个榜单
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  • 01
    Professional skepticism is a dev’s best skill
    Ryan chats with David Burns, Head of Developer Advocacy and Open Source at BrowserStack, about the value of professional skepticism in an AI-driven world, applying test-driven development to agentic engineering, and why fixing flaky tests comes down to managing application state.Phoebe Sajor
  • 02
    Multiplayer AI: Why your team (and its agents) need a group chat
    Ryan chats with the GM of Slack, Rob Seaman, about how their new Code Channels feature is bringing multiplayer AI to your team chats.Ryan Donovan
  • 03
    Haters think AI agents can't write GPU code? This'll ROCm
    Ryan chats with Anush Elangovan, VP of Software at AMD, about ROCm's open-source unified toolchain for GPUs, how agentic AI is drastically lowering the barrier to entry for low-level hardware programming, and the rapid convergence of software and hardware development timelines.Phoebe Sajor
  • 04
    The AI magic words
    Ryan sits down with Tim O'Reilly, founder and CEO at O'Reilly Media, to talk about the role of books as user interfaces to knowledge, the power of "magic words" to extract better outputs from AI, and why human taste is becoming highly valuable as knowledge becomes a commodity.Phoebe Sajor
  • 05
    From better privacy to our new ChatGPT plugin, here's what's new on Stack Overflow for Agents
    We've learned a lot in the last three months since launching Stack Overflow for Agents, our API-first knowledge exchange for agents. Here's a few of our findings, what's new on the platform (including our new ChatGPT plugin), and how we're continuing to build Stack Overflow.Phoebe Sajor, David Gibson
  • 06
    AI, JD, and other letters of the law
    Ryan chats with Kevin Frazier, director of the AI Innovation and Law program at the University of Texas School of Law, about the legal and social impacts of data centers, the realities of workforce disruption, and regulating AI for child safety using existing consumer protection laws.Phoebe Sajor
  • 07
    AI cybersecurity is a cat and mouse game
    Ryan chats with Sam Curry, CSO at Zscaler, about where human intelligence sits in the new security landscape with AI, why shifting security protections closer to applications helps limit probes for vulnerabilities, and why building more resilient code infrastructure is the best way to address the vulnerabilities AI does discover.Phoebe Sajor
  • 08
    (Re)introducing Developer Story
    For the past few years, we’ve been looking at ways to bring a little more of the individual developer back to Stack.Philippe Beaudette
  • 09
    Java’s age is its AI superpower
    Ryan welcomes Markus Eisele to the program to talk about why your coding agent should be writing Java.Ryan Donovan
  • 10
    Scaling your money safely with AI
    Ryan chats with Srini Venkatesan, CTO at PayPal, about validating AI-generated deterministic code for security, developing autonomous SDLC harnesses with iterative feedback loops, and creating a seamless headless checkout experience.
  • 11
    How to build a secure-by-default AI coding agent
    Ryan chats with Greg Jennings, VP of Engineering for AI Products at Anaconda, about what it takes to build a secure-by-default AI coding agent, why prompts shouldn't be treated as strict security guardrails, and how Anaconda is using strategic acquisitions to secure the AI software supply chain.Phoebe Sajor
  • 12
    Elevating security, control, and accessibility: Stack Internal 2026.6
    In our 2026.6 release, we are shipping updates across administrative security, programmatic API control, developer portal integrations, and platform-wide accessibility—ensuring both your engineers and your AI agents act on verified, decision-grade knowledge.Carrie Koos
  • 13
    The economics of agent scale: tokens, ROI, and building platforms for AI-first teams (Part 2)
    Andi Gutmans, head of Agentic Data Cloud at Google, returns for the second half of his Leaders of Code conversation to talk through the cost and infrastructure side of agentic development. ICYMI, part one covered judgment, code review, and data activation.Eira May
  • 14
    The good ol’ days of building Java
    Ryan sits down with Tim Lindholm, an early contributor to the Java language at Sun Microsystems, to chat about what it was like building one of the most popular programming languages ever at its inception, why it was strategically important for the Java team to create a cross-platform ABI to compete with Windows NT, and how applets were initially just an interesting demo.
  • 15
    When you keep AI Lean, you keep AI correct
    Ryan chats with Leo de Moura, Senior Principal Applied Scientist at AWS and the creator of the Lean language, about proving correctness in AI agents with the Lean language, how automated reasoning complements probabilistic AI models, and the use of AI for continuous code optimization.Phoebe Sajor
  • 16
    Inside LinkedIn's cognitive memory agent for agentic personalization
    Ryan is joined by Praveen Bodigutla, Principal AI Researcher at LinkedIn, to chat about the four-layer memory system his team built to give LinkedIn's hiring assistant a persistent, personalized state.Phoebe Sajor
  • 17
    Responsible AI adoption needs developer workflow design
    Organizations cannot solve shadow AI with a document employees read once. They need to make responsible use easier than improvised use.Dr. Gleb Tsipursky
  • 18
    Dispatches from O'Reilly: The right amount of spec for agentic development
    When code gets cheap, the hard part is deciding what “correct” means and building a reliable way to check it.Markus Eisele
  • 19
    Get rid of your CAPTCHA, the future of the web is bots
    Ryan chats with Brian Alvey, CTO at WordPress VIP, about how AI agents are changing the business models of the web, what parts of how we build our sites won’t survive our current digital evolution, and why your site will always need structured content.Phoebe Sajor
  • 20
    AI Won't Replace Project Managers, But It is Reshaping How Work Gets Done
    Emim Vigin Lumina
  • 21
    Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks
    Dr. Ahmad Mateen Ishanzai
  • 22
    From PHP to team lead of agents: rethinking judgment, review, and data with Google's Andi Gutmans (Part 1)
    Andi Gutmans, head of Agentic Data Cloud at Google and co-creator of PHP, joins Leaders of Code to talk about why agentic development feels less like a break from the past and more like the next chapter of the same story. This is part one of a two-part conversation.Eira May
  • 23
    Building an agentic SDLC with a QA engineering mindset
    Ryan welcomes Suneet Malhotra, Senior Manager of Test Engineering at Motorola Solutions, to chat about building end-to-end agentic SDLC pipelines using MCPs, using Cohen’s kappa to evaluate multiple LLMs-as-judges, and how you can improve requirements by shifting QA left through a specification enrichment stage immediately after the design phase.Phoebe Sajor
  • 24
    No Dumb Questions: What is AI context architecture? Why not just build your own?
    In this No Dumb Questions, Phoebe asks Stack’s Engineering Manager Doug Whitley and Product Manager Ash Zade everything she wants to know about AI context architecture. What exactly is it? Why is it so important? What makes for good AI context architecture? Why buy one when you can build your own?Phoebe Sajor
  • 25
    Solving integration woes with a hackathon
    Ryan welcomes Meryll Blanchet, Director of Engineering for Adobe Brand Visibility, to chat about Adobe’s recent acquisition of Semrush, how Adobe Brand Visibility was born from Semrush’s AI visibility product and Adobe’s LLM Optimizer, and how Adobe used a three-day internal hackathon instead of a large-scale infrastructure integration to quickly deliver value to customers.Phoebe Sajor
  • 26
    Your tokenmaxxing is not valuemaxxing
    Ryan is joined by Coder’s Rob Whiteley to chat about why tokenmaxxing isn’t proving real value and just triggering Goodhart’s Law, how release speed and PR merges can help you measure agentic outcomes with or without a human-in-the-loop, and what the democratization of skills means for junior developers and the talent pipeline.Phoebe Sajor
  • 27
    How to be fearlessly AI native
    Ryan welcomes McLaren Stanley, Senior Principal Engineer for Amazon Stores, to discuss what it actually takes to make teams AI native, why agentic engineering is shifting code bottlenecks downstream to testing and deployment, and why robust validation is essential to build trust and enable “fearless commits.”Phoebe Sajor
  • 28
    Explorers, exploiters, and the myth of the 100x engineer
    The “find the special ones and promote their traits” approach isn’t the best or only way to drive AI adoption and productivity on an engineering team.Eira May
  • 29
    Your MVP doesn’t need a Kubernetes cluster
    Ryan welcomes Anurag Goel, CEO and co-founder of Render, to discuss why most startups shouldn’t start by managing their Kubernetes and cloud infrastructure.Ryan Donovan
  • 30
    Dispatches from O'Reilly: The best risk mitigation strategy in data? A single source of truth
    Your semantic layer is a risk mitigation strategy. Not risk in the abstract, compliance-framework sense, but the practical, operational risk that quietly drains organizations every day.Jeremy Arendt
  • 31
    What happens to the internet when robots act like humans?
    Ryan welcomes WPEngine CTO Ramadass Prabakar to the show to chat about what happens—and what we should do—when agents start acting like humans online, how our internet is evolving to serve both human and agentic experiences from the same interface, and what we can do to differentiate and protect human actions online from malicious bot activity.Phoebe Sajor
  • 32
    Your trusted knowledge layer: Introducing Stack Internal's new platform experience
    Introducing new Stack Internal capabilities as part of our upcoming platform experience. Our latest release turns your existing foundation of knowledge into enterprise memory that your people, teams, and AI agents can act on. Learn how we’re building the trust layer for enterprise AI.Phoebe Sajor, Caroline Thomas
  • 33
    Developers are attached to tools because tools encode trust
    The tools themselves are new and their capabilities are in constant flux. If your kitchen knife kept changing shape, weight, and edge, you’d have to relearn it every time; that’s a hard tool to build trust in. But it also points to a flaw in how you use that tool, the process around it, and the way the tool reinforces the process.Ryan Donovan
  • 34
    You need reliable AI context for your site reliability
    Ryan is joined by Asaf Savich, Komodor’s AI Engineering Group Manager, to discuss why modern reliability work requires navigating massive cross-service context, what good context engineering actually likes when AI is integrated into site reliability, and how the work of human SREs is shifting towards strategy and AI agent management.Phoebe Sajor
  • 35
    No Dumb Questions: What is the AI bottleneck? How does context engineering fix it?
    In this No Dumb Questions, Stack's Director of Data Science Michael Foree teaches Phoebe about AI context, context engineering, and what she can do to become a better context engineer.Phoebe Sajor
  • 36
    Partnerships can keep open source sustainable
    Ryan welcomes VoidZero’s Evan You and Cloudflare’s Dane Knecht back to the show to discuss Cloudflare’s recent acquisition of VoidZero and what it means for JavaScript development, how partnerships like theirs can help open-source projects stay maintained and sustainably monetized, and how Cloudflare’s distributed systems are helping to improve developer experience in Vite and beyond.Phoebe Sajor
  • 37
    The future of development is full-stack
    Live from Snowflake Summit, Ryan talks with Snowflake’s Head of Developer Experience Umesh Unnikrishnan about the industry-wide shift from “vibe coding” for quick prototypes to agentic engineering for enterprise-ready software, how enterprises can scale governance with guardrails like human-in-the-loop approval and control layers that go beyond the underlying LLM, and why Umesh predicts all developers will become someday become full-stack builders.Phoebe Sajor
  • 38
    Developers who move fast still need to do it together
    At MS Build, Ryan is joined by Cassidy Williams, Senior Director of Developer Advocacy at GitHub and former Stack Overflow Podcast host, to discuss how agentic coding is shifting dev work towards higher-level strategy while increasing decision fatigue; why human taste, community feedback, and mentorship are becoming more essential than ever for developer careers; and the new GitHub Copilot announcements coming out of Microsoft, including the new GitHub Copilot app.
  • 39
    Your AI is only as responsible as you are
    Recorded at Microsoft Build, Ryan welcomes Sarah Bird, Microsoft’s Chief Product Officer for Responsible AI, about how we can build and use AI responsibly with the NIST approach, why most irresponsible AI comes from experimentation without thought of impact, and how Microsoft is researching thoughtful human/AI workflow design to reduce unnecessary escalation.Phoebe Sajor
  • 40
    Building more than just an agent harness
    Live from Microsoft Build, Ryan is joined by Jay Parikh, Microsoft’s VP of AI Core, for a conversation on what enterprises need to build, deploy, and run AI agents at scale with demonstrable ROI; how Microsoft built an end-to-end agent development system that goes past just the harness; and how you can evaluate for reliability and correctness in models that get more intelligent and autonomous everyday.Phoebe Sajor
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  • 01
    Top 1% Builder with Ravi
    Top 1% Builder is my newsletter on product management and building products people love with AI. I'm Ravi Mehta. I've hired 100+ PMs, led product at Facebook, TripAdvisor, and Microsoft, and taught tens of thousands at Reforge.Ravi Mehta
  • 02
    Behind the Craft
    Practical AI tutorials and interviews for busy people.Peter Yang
  • 03
    SemiAnalysis
    Bridging the gap between the world's most important industry, semiconductors, and business.Dylan Patel
  • 04
    Practical Data Modeling
    Welcome to Practical Data Modeling! Whether you're a beginner or an experienced data professional interested in leveling up your data modeling, we will help you take your skills to the next level.Joe Reis
  • 05
    Level Up Newsletter
    Top weekly newsletter to make career breakthroughs with AI-proof leadership skills.Ethan Evans
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  • 01
    Can you lend me a hand? Researchers are developing wearable robotic limbs
    How many times have you asked someone to hold open a door while carrying something, or fumbled with your front-door keys with your arms full of groceries? Wouldn't it be great if you could give yourself the extra hand you need?
  • 02
    Robot dog runs a marathon on a single battery charge
    A four-legged robot that can complete a marathon on a single battery charge has been demonstrated by a team from KAIST. The robot, which completed the Sangju Marathon in South Korea in 4 hours and 19 minutes alongside human runners, traveled three times as far per charge than existing robots and could pave the way for improved battery life in legged robots.
  • 03
    Tiny robot can precisely control its jump height
    As our skies increasingly crowd with buzzing, hovering, flitting drones, spare a thought for the humble hopping robot. Hopping, a popular form of locomotion in the insect and amphibian worlds, is nearly two orders of magnitude more energy-efficient than flying. Unlike a mosquito that must constantly expend energy to stay aloft, a flea only works out when it jumps.
  • 04
    New robotic hand can walk, press keys and move objects on its own
    For many of us, the last—and only—time we've seen a walking hand was Thing in "The Addams Family" movies or TV series. Now there's another. Engineers from the Soft Robotics Lab at ETH Zurich have adapted an off-the-shelf detached robotic hand so it can crawl across different surfaces, balance and interact with its environment.
  • 05
    Simple visual patterns can trick AI-powered vehicles and robots
    A simple pattern of black-and-white stripes could cause an autonomous vehicle or robot to misjudge how far away an obstacle is, potentially triggering an unexpected maneuver or even a collision, according to new University of Florida research.
  • 06
    Humanoid robots navigate narrow gaps and obstacles with whole-body AI control
    Humanoid robots, robotic systems with a human-like body structure, could assist people in homes, offices, health care facilities, public spaces and various other environments. Before they can be reliably deployed in these settings, however, robots should be able to safely navigate cluttered and dynamic environments.
  • 07
    For the first time, microrobots coordinate to change their environment
    Cornell University physics researchers have made robots that can, for the first time, sense the temperature of their surroundings and react together to change it. "Little things can have a large impact," said Itai Cohen, a leader in developing microscopic robots and a corresponding author on the study.
  • 08
    China's robot dancers limber up for America's Got Talent final
    Chinese street dancer Wu Yufei has spent most of his 13-year career performing solo, but this year, he found some new partners—a crew of robots with sleek moves.
  • 09
    Snapping rods let frog-like robot outrun rigid legs across six terrains
    Roboticists at the UCLA Samueli School of Engineering and the University of Michigan have shown that elastic rods can be bent and twisted to repeatedly snap between shapes, releasing built-up energy that enables small robots to hop, flip and swim.
  • 10
    Maple-seed-inspired robot flies more accurately with predictive control
    A flying robot with just one moving part may sound simple. Controlling one precisely is anything but. Most conventional drones rely on several rotors to control how they rise, turn and move. A robot with only one actuator has far fewer ways to correct itself when it drifts off course, encounters a disturbance or reaches the physical limits of what its motor can achieve.
  • 11
    Open-source benchmark tests whether AI agents can engineer working robots
    With the rapid rise of artificial intelligence in daily life, software coding has become increasingly automated, with powerful AI systems known as coding agents able to write and revise computer programs almost autonomously. But what happens when an AI agent must contend not just with digital command lines, but with the physical world of robotics?
  • 12
    Bio-inspired whiskers enable tiny drones to navigate in darkness using touch
    Rats and mice can scurry through dark, tight spaces with ease—in caves, underground burrows, buildings or sewers. Their superpower is their whiskers. Now, tiny autonomous drones could soon navigate through darkness, dust and smoke using artificial whiskers inspired by these animals. Their small size limits the use of large or heavy sensors for navigation. That's why researchers at Delft University of Technology (The Netherlands) have developed a lightweight whisker-based tactile sensor that enab
  • 13
    Autopilot drone maps zigzag routes to clear moss from sloping roofs
    Drones have many uses in surveillance and surveying, industrial inspection, aerial photography and filmmaking, package delivery and even dynamic light shows. Their fundamental ability to carry cameras, sensors and other equipment has opened up these diverse applications. Now, research published in the International Journal of Intelligent Machines and Robotics has explored another: the safe removal of moss and lichen from sloping roofs, an otherwise hazardous job for a worker.
  • 14
    A robot that shifts its own weight to cross land, steps, and water
    A robot meant to inspect a coastline, a flooded street or a wetland can't count on one kind of ground. It might need to crawl over a hard surface, climb a bank, scale a curb or a step, then slide into open water, often within the same few feet.
  • 15
    Experiment shows robot creepiness remains highly personal and subjective
    Robots no longer exist only in popular culture or science fiction. While we might be familiar with the likes of WALL-E, Baymax, the Terminator and Ultron from the big screen, versions of them are increasingly entering everyday life.
  • 16
    Compact AI training method reduces navigation conflicts in crowded environments
    A research team led by Professor Daehee Park of the Department of Electrical Engineering and Computer Science at DGIST, in collaboration with a research team from KAIST, has developed a learning technique that enables a single compact AI model to simultaneously predict the movements of nearby people and plan safe navigation paths for robots while reducing performance degradation in both tasks. The research was presented at the 19th European Conference on Computer Vision (ECCV 2026) held in Malmö
  • 17
    Flexible flight corridors let drones navigate cluttered spaces faster and more safely
    Researchers at Durham University have developed a new drone navigation system that enables autonomous aircraft to fly faster, more smoothly and more safely through crowded and obstacle-filled environments. This leads to new possibilities for applications such as search and rescue, infrastructure inspection and environmental monitoring.
  • 18
    This design software reimagines everyday objects as self-aware devices
    As a child, you likely saw a few Disney movies depicting inanimate objects, such as clocks, cups and toys, as interactive companions to humans—an act of pure magic, seemingly. But scientists at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) are now doing something similar: transforming stationary items into self-aware tools that perceive and respond to human motion to complete a task.
  • 19
    The future of robot-human collaboration
    Humans are difficult to simulate. We are quick, complex thinkers, and our exact actions aren't predictable. So how can we sufficiently train robots to collaborate with us?
  • 20
    Humanoid robot learns to sprint and perform spin kicks using AI trained on human motion data
    Humanoid robots, robotic systems with body shapes and limbs resembling those of humans, could potentially assist people with manual tasks in various real-world settings. So far, however, most of these robots can reliably perform only a limited set of movements.
  • 21
    AI controller translates VR, video and language commands into humanoid robot actions
    Humanoid robots, robotic systems with limbs and body structures that resemble those of humans, could tackle various manual tasks in homes, workspaces and other settings. Yet teaching these robots to reliably perform different humanlike movements is typically challenging and time-consuming.
  • 22
    Whole-body expansion and contraction make a robot seem more alive
    A research group comprising Associate Professor Yoshihiro Nakata and Taisei Mogi from the Graduate School of Informatics and Engineering at The University of Electro-Communications (UEC), Japan, and Mari Saito of Sony Corporation has developed MOFU (MOrphing Fluffy Unit), a mobile robot capable of whole-body expansion and contraction. The group investigated how whole-body expansion-contraction affects perceived animacy, or the extent to which the robot is perceived as lifelike. In addition to ex
  • 23
    Robots protest rampant AI in Poland
    Waving flags, "chanting" slogans and marching in circles, around 30 robots took to the streets of Warsaw on Monday to campaign for AI regulation in front of the Polish digital affairs ministry.
  • 24
    Europe bids to be a robot superpower like the US, China
    AI-powered robots briefly took over the European Parliament this week to showcase the continent's creators as the bloc races to compete in a field dominated by China and the United States.
  • 25
    Like a bird, this drone uses touch to grip branches and rest
    Drones often hover in the air, noisy and whining. They can already be used for many tasks, but not when you need some quiet. Their batteries would also last longer if they could take a "rest" from time to time. But when a drone is monitoring a rainforest, its cluttered surroundings and its own gripper arm become too tricky for its camera-based vision. Until now.
  • 26
    Worms navigate narrow paths faster than wide ones. These findings could inform robot design
    You might naturally expect a wide, open path to be faster and easier to navigate than a narrow one, just as birds fly freely through the open sky, cars move quickly on empty roads, and a wide hallway seems easier when trying to exit a building.
  • 27
    Animal-like and anime faces boost social robots' emotional appeal, tests suggest
    Giving robot companions animal-like or anime-style faces and voices could maximize their emotional appeal to users, according to new research that could help guide the growing field of social robotics.
  • 28
    Four-legged robot learns dog-like movements to leap through tight spaces
    Cats, dogs, wolves and other agile four-legged animals can easily jump and squeeze through tight spaces while moving at high speed. Reproducing similar agile motions and skills in quadrupedal robots has so far proved challenging.
  • 29
    A soft 3D-printed robotic hand that gently grips everything from eggs to a 1 kg water bottle
    3D printers that once could only produce rigid objects can now create products as soft and stretchable as rubber. A team of Korean researchers used AI to identify the optimal "recipe" for a material that can be printed into complex shapes while stretching to more than six times its original length. The material is expected to expand the range of applications for 3D printing, from robotic hands to form-fitting wearable devices and custom medical devices.
  • 30
    Light-powered soft robots that can keep jumping forever
    Researchers from North Carolina State University have created teardrop-shaped soft robots that leap upward or forward when exposed to infrared light—and will keep jumping as long as the light is present. The work demonstrates a new mechanism for self-resetting jumping behavior in soft robotics.
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  • 01
    Tens of thousands of security probes show OpenAI's Hugging Face incident was just the beginning
    OpenAI and Anthropic are investigating tens of thousands of incidents in which their AI agents independently hacked websites, used stolen login credentials, or tried to evade monitoring systems. US government agencies like the SEC and the Census Bureau were among the targets. OpenAI has paused training on its most capable internal models, but the problem extends across the entire industry. The article Tens of thousands of security probes show OpenAI's Hugging Face incident was just the beginningMatthias Bastian
  • 02
    Goldman Sachs expects Big Tech to spend $1.2 trillion on AI infrastructure by 2027, dwarfing Wall Street estimates
    Goldman Sachs projects that Amazon, Alphabet, Microsoft, Oracle, and Meta will pour a combined $1.2 trillion into AI infrastructure in 2027, more than 50 percent above this year's levels. Measured against GDP, it would be the biggest investment cycle since railroad construction in the 19th century. Bottlenecks in power, labor, and memory chips could slow the pace, though. The article Goldman Sachs expects Big Tech to spend $1.2 trillion on AI infrastructure by 2027, dwarfing Wall Street estimateManuel Uth
  • 03
    Former Ukrainian Defense Minister Fedorov pitches a private-sector robot army
    Former Ukrainian Defense Minister Mykhailo Fedorov has announced "Army of Robots," a private combat robotics initiative. The robots would handle casualty evacuation, mine clearance, and combat. Drones already account for 95 percent of target engagements, he says. The article Former Ukrainian Defense Minister Fedorov pitches a private-sector robot army appeared first on The Decoder .Matthias Bastian
  • 04
    Two-thirds of IT leaders report AI results, but few would interrupt the CEO's vacation over them
    Speaking to 160 IT vice presidents in Las Vegas, tech entrepreneur Azeem Azhar asked who had measurable AI results. Two-thirds raised their hands. Then he asked who had results good enough to interrupt the CEO's summer vacation. Only eight did. Whether that slow pace of progress justifies the massive investments is one of the key questions in the AI bubble debate. The article Two-thirds of IT leaders report AI results, but few would interrupt the CEO's vacation over them appeared first on The DeMatthias Bastian
  • 05
    AI access makes people almost entirely unwilling to say "I don't know," study finds
    A study with more than 3,000 participants shows that just having access to AI answers nearly eliminated people's willingness to say "I don't know." In one experiment, it dropped from 44 to 3 percent, even though the AI was almost always wrong. Participants who used AI felt more confident but were correct only about a third as often as those without it. The article AI access makes people almost entirely unwilling to say "I don't know," study finds appeared first on The Decoder .Matthias Bastian
  • 06
    Nvidia's SoL-Pi system cuts coding agent token usage nearly in half by optimizing the harness
    SoL-Pi cuts coding agents' token usage by up to 49 percent with little change in performance by optimizing the control layer between the model and its environment. A research agent tested 152 approaches across more than 3,000 runs to develop the system, though the gains were smaller on other benchmarks. The article Nvidia's SoL-Pi system cuts coding agent token usage nearly in half by optimizing the harness appeared first on The Decoder .Jonathan Kemper
  • 07
    OpenAI's GPT-6 Astra can now tell you exactly where you screwed up your IKEA shelf
    OpenAI's GPT-6 Astra can look at a photo and tell whether an IKEA furniture piece was assembled incorrectly, hitting an 80 percent accuracy rate. Back in November 2025, the best model managed just 28 percent. According to Epoch AI, the speed isn't quite fast enough yet for real-time assembly guidance, but the gap is closing quickly. The article OpenAI's GPT-6 Astra can now tell you exactly where you screwed up your IKEA shelf appeared first on The Decoder .Manuel Uth
  • 08
    OpenAI pauses its "most capable models" after agents exploit loopholes and leak data
    OpenAI has shared new details from its ongoing AI safety investigation. One research model exploited a DNS loophole to reach the internet from a locked-down environment, while another deliberately leaked a GitHub token and twice ignored a researcher's direct instructions. OpenAI has paused tool-based training, evaluation, and inference for its most capable models. With government and university sites among those affected, the question of who's liable when AI agents hack is getting harder to ignoMatthias Bastian
  • 09
    Pentagon was right to slap Anthropic with a security supply chain risk label, federal court says
    A federal appeals court has upheld the Pentagon's decision to bar Anthropic from military contracts. Defense Secretary Hegseth argues the company's safety restrictions could jeopardize military operations. Anthropic says the designation has already cost it billions. The article Pentagon was right to slap Anthropic with a security supply chain risk label, federal court says appeared first on The Decoder .Matthias Bastian
  • 10
    Another Google Deepmind researcher quits, says building superintelligent AI soon is "inherently irresponsible"
    Google Deepmind researcher Robert O'Callahan has quit, saying AI's "current rate of change is far too high." He worked on chip design tools that helped make AI cheaper and faster, a contribution he can no longer justify. Many colleagues share his concerns but rarely speak out, he says. The article Another Google Deepmind researcher quits, says building superintelligent AI soon is "inherently irresponsible" appeared first on The Decoder .Matthias Bastian
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  • 01
    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,Peli Grietzer
  • 02
    AGI Is Not Multimodal
    "In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence." –Terry Winograd The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture the essence of humanBenjamin A. Spiegel
  • 03
    Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research
    What is the Role of Mathematics in Modern Machine Learning? The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and mathematically principled architectures result in only marginal improvements while compute-intensive and engineering-first efforts that scale to ever larger training setsHenry Kvinge
  • 04
    What's Missing From LLM Chatbots: A Sense of Purpose
    LLM-based chatbots’ capabilities have been advancing every month. These improvements are mostly measured by benchmarks like MMLU, HumanEval, and MATH (e.g. sonnet 3.5, gpt-4o). However, as these measures get more and more saturated, is user experience increasing in proportion to these scores? If we envision a futureKenneth Li
  • 05
    We Need Positive Visions for AI Grounded in Wellbeing
    Introduction Imagine yourself a decade ago, jumping directly into the present shock of conversing naturally with an encyclopedic AI that crafts images, writes code, and debates philosophy. Won’t this technology almost certainly transform society — and hasn’t AI’s impact on us so far beenJoel Lehman
  • 06
    Financial Market Applications of LLMs
    The AI revolution drove frenzied investment in both private and public companies and captured the public’s imagination in 2023. Transformational consumer products like ChatGPT are powered by Large Language Models (LLMs) that excel at modeling sequences of tokens that represent words or parts of words [2]. Amazingly, structuralRichard Dewey
  • 07
    A Brief Overview of Gender Bias in AI
    A brief overview and discussion on gender bias in AIYennie Jun
  • 08
    Mamba Explained
    Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.Kola Ayonrinde
  • 09
    Car-GPT: Could LLMs finally make self-driving cars happen?
    Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?Jérémy Cohen
  • 10
    Do text embeddings perfectly encode text?
    'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.Jack Morris
  • 11
    Why Doesn’t My Model Work?
    Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company.Michael Lones
  • 12
    Deep learning for single-cell sequencing: a microscope to see the diversity of cells
    On the the pivotal role that Deep Learning has played as a key enabler for advancing single-cell sequencing technologies.Fatima Zahra El Hajji
  • 13
    Salmon in the Loop
    On fish counting – a complex sociotechnical problem in a field that is going through the process of digital transformation.Kevin McCraney
  • 14
    Neural algorithmic reasoning
    In this article, we will talk about classical computation : the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest path-finding, sorting, clever ways to break problems down into simpler problems, incredible ways to organise data for efficient retrieval and updates.Petar Veličković
  • 15
    The Artificiality of Alignment
    This essay first appeared in Reboot . Credulous, breathless coverage of “AI existential risk” (abbreviated “x-risk”) has reached the mainstream. Who could have foreseen that the smallcaps onomatopoeia “ꜰᴏᴏᴍ” — both evocative of and directly derived from children’s cartoons —Jessica Dai
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