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The Decoder / 12:58 PM

Iris-mini and Iris-pro are the strongest open-weight search agents in their class

The AllSpark team has released Iris-mini and Iris-pro, two open-source search agents built on Qwen models that lead benchmarks among open-weight models in their size classes. According to the paper, the training data and models also improved performance on tasks they were never trained for, including general tool use and office work. The article Iris-mini and Iris-pro are the strongest open-weight search agents in their class appeared first on The Decoder .

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Simon Willison LLMs / 11:56 PM

Generating running routes with GPT-6 Astra and ChatGPT Work

Here's a neat thing I had ChatGPT Work with GPT-6 Astra (Max) do this morning: I live at <my address>. Figure out 5K and 10K running routes from me that loop from my house. Use OSM data. It worked for 27 minutes and produced exactly what I'd asked for, as both an embedded visualization and downloadable GPX file and GeoJSON files. Here's that 5K route: When I asked it how it had created the route, it replied: I used Nominatim to locate the address and Overpass to download local OpenStreetMap roads and trails , then calculated the loops locally. Frustratingly, the actual code it ran and exact details of what it did weren't visible to me in the ChatGPT UI. I see this lack of transparency is an anti-feature. By the time I thought to ask for a copy of the Python code it had used, ChatGPT was unable to provide it. This appears to be because the thread had been compacted. I think any LLM system that uses compaction needs to both preserve the pre-compacted text and make that text available via agent tool calls, to protect against this kind of problem. As for displaying the map to me, that used the visualize skill . It created a file called /workspace/el-granada-5k-share.html to embed directly into the ChatGPT UI. Here's a copy of that HTML , which starts like this: < div id =" eg-share-loop " > < div class =" viz-row " > < h3 > El Granada harbor loop </ h3 > < span class =" text-small " > 5.1 km </ span > </ div > < div id =" eg-share-stage " > </ div > < div class =" text-small text-muted " > Map data © < a href =" https://www.openstreetmap.org/copyright " target =" _blank " rel =" noopener " > OpenStreetMap contributors </ a > </ div > < style > # eg-share-loop { width : 100 % ; } # eg-share-loop # eg-share-stage { width : 100 % ; margin : 8 px 0 ; } # eg-share-loop . eg-share-map { display : block; width : 100 % ; touch-action : none; } # eg-share-loop . eg-share-map text { fill : var ( --foreground ); font-size : 12 px ; font-weight : 400 ; } # eg-share-loop . eg-share-label { paint-order : stroke; stroke : var ( --background ); stroke-width : 3 px ; stroke-linejoin : round; } </ style > < script type =" application/json " id =" eg-share-data " > { "route" : { "type" : "LineString" , "coordinates" : [ [ - 122.467425 , 37.4997753 ] . . . </ script > < script src =" https://cdn.jsdelivr.net/npm/[email protected]/dist/d3.min.js " > </ script > < script > (() => { const root=document.getElementById('eg-share-loop'); The <script type="application/json"> element contains the full geometry needed to render both the running route and the map itself, using D3, which is loaded from an allow-listed CDN location described in this section of the visualize skill : External resources The CSP allows only cdnjs.cloudflare.com , esm.sh , cdn.jsdelivr.net , unpkg.com , fonts.googleapis.com , fonts.gstatic.com , and fonts.bunny.net . Other origins are blocked and fail silently. Tags: geospatial , ai , d3 , openai , generative-ai , chatgpt , llms , skills , gpt-6-astra

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The Information AI / 3:01 PM

Meta’s Muse Agent Almost Cost Me $408

• The Big Read: Investor Anjney Midha cut Anthropic an early check. His hot streak has kept going • Plus, Recommendations—our weekly pop culture picks: “ Our Town ,” “ Profits, Prophets, Coaches, and Kings ” and “ The Gentlemen ” A hotel room’s quality can be measured in terms of abundance: extra towels, spare bathrobes—plenty of Nespresso pods. Multiples of everything are almost always better. But what’s less pleasant is to inadvertently end up with multiple hotel rooms . A couple days ago, though, I found myself with the unfortunate opportunity to occupy a pair of accommodations at a Santa Monica, Calif., Marriott thanks to an error by my lil’ digital buddy: Muse, Meta Platforms’ new personal agent. The cumbersome, vexing tool hopes to seize on Silicon Valley’s zeitgeisty enthusiasm for popularizing autonomous AI. After a few days with it, I can’t imagine it’s the technology that will take agents mainstream. That hotel snafu came when I gave Muse the hotel’s name and asked it to book two nights. I plugged in my credit card details—a circuitous process that involved both Chase and Stripe—and was met with the digital equivalent of a shrug: An error message informed me that Muse hadn’t completed the transaction, and while it really didn’t know why it couldn’t, it assured me that my credit card hadn’t been charged. A little human skepticism led me to double-check its promise—good thing I did. In fact, the charge had gone through. What happened next still intrigues me. I gave Muse a screenshot showing the charge on my credit card and told it that the charge had gone through. Lickety-split, it returned with a Marriott confirmation number. Not bad—it finished the task without being instructed to do so. But somehow the AI managed to make two bookings: When I showed up to the hotel, I found two rooms waiting for me. The front-desk clerk showed some mercy and canceled one. Had a little human kindness not prevailed, I would’ve been out an extra $408, plus taxes and fees. I’d been pretty gung-ho to try Muse. We’ve spent the past year in increasingly complex discussions about agents ever since OpenClaw came out in January , marking what has felt like the beginning of a distinct new chapter in the AI era. Most of the agents that initially captured attention were meant to simplify work tasks. More recently, a couple of startups have captured buzz with ones devoted to improving life outside the office: The most talked-about one is from Instinct, a startup founded last year. (Instinct is so popular that it now faces a profound compute shortage, and it has recently been seeking additional fundraising that would value it at around $10 billion. Just a month ago, it was valued at $2.5 billion.) But Instinct isn’t widely available, so Muse felt like my first real chance to see what a personal agent could do. The recent reporting from my colleague Jyoti Mann made me even more curious to play around with it: Clearly, Meta sees Muse as a major new product , one aimed at the same everyday people who log on to Instagram and Facebook by the billions. Still, if their experiences are anything like mine, they’ll find Muse something of a misery. The hotel problem wasn’t the only hiccup I ran into with Muse. When I initially set up a account while on my work laptop using my phone number, I later couldn’t access it on my Mac mini at home or my iPhone: Login codes sent via text led me nowhere—just to more error messages. (Frankly, it’s a marvel I found the Muse app at all earlier this week, buried as it was below several other apps also named Muse in Apple’s App Store; it has since risen to the top.) Eventually, I caved and created a second account—this one linked to my Facebook account, which itself is tied to my phone number. Why couldn’t Muse pick up on the overlapping connections? I wish it could have. Later, I did get Muse to complete a Resy reservation and schedule an Uber. I can’t truthfully tell you it was faster or easier than if I’d just gone directly to those apps. After a while, I hit an existential conundrum. I’d given it the tasks that occurred to me, which numbered a small handful. What else could I get Muse to do? I’ve found interacting with the AI something like trying to wrangle a lackluster employee. If I wanted to maximize its potential, I’d need to think deeply and creatively about what else it could possibly do, coax it and baby it, then bite my nails and hope it actually carried out what I wanted. As I relate these frustrations, I can already hear someone shouting “Skill issue!” at me. But really, I stand by the conviction that mass-market consumer technology shouldn’t require any technical savvy or a lot of effort. Certainly, the best versions of such products do not—even the early versions. If three cars had routinely shown up each time someone ordered their very first Uber ride back in the 2010s, I promise you we’d see more yellow taxis on the road today and fewer Ubers. I don’t see agentic AI as some passing fad, nor am I hoping for such an outcome. The technology’s promise—to automate away some of life’s tedium—is damn alluring. What I expect will happen is that agentic AI will get woven into many existing apps, just as chatbots already populate the internet. People will make regular use of agentic software without ever really knowing it; Anthropic’s Claude, OpenAI’s ChatGPT and Google’s Gemini all already have some agentic capabilities. Obviously, Apple and Google will want to use the technology to make iPhone and Android phones smarter and more useful, and the agents will sync up with what those devices already know and store about us. That would reduce quite a few hurdles to agentic AI. But will new stand-alone apps like Muse take off? I have my doubts—not unless they get much, much simpler and more reliable. Or maybe I’ll just need to learn to see the silver lining in their mistakes. You know what? If I ever again find myself billed twice and double-booked by accident, I know just what to do: Throw a rager in one room, sleep until noon in the other. Maybe Muse can handle ordering the booze. A good party can never have too much. —Abram Brown ( [email protected] ) Weekend’s Latest Stories The Big Read Early Anthropic Investor Seeks VC Glory With Cash and Compute Anjney Midha, 34, wants to get chips in the hands of fledgling startups and academics while he bets on a moment in tech that he describes as the “revenge of the scientists.” Listening: “ Our Town ” The good folks of Gainesboro, Tenn. (population: around 900), know that plenty of outsiders look down their noses at their part of the world—figuring them to be a buncha “backwards, hateful racists,” as one longtime Gainesboro resident puts it. That impression of the place does certainly seem to be why Gainesboro found itself under siege a few years ago when a group of wealthy Christian nationalists started to buy up property in secret, hoping to transform the town into a hotbed for other Christian nationalists: They figured they’d encounter little pushback, especially considering how far their money could go in such an impoverished parish. (I could describe them as white supremacists, but the Christian nationalists do take great umbrage with that label.) “Our Town,” a fast-paced and confidently told podcast from Bloomberg and iHeartRadio, looks at how those Christian nationalists staged their attempted takeover of Gainesboro and how they accumulated their wealth and followers, the latter largely through podcasts. (Sigh—the internet.) It also documents the swift, spirited resistance Gainesboro mounted against them, which cheers the soul and asks us to revisit at least a few of the assumptions about America—and our fellow Americans—that may have become entrenched in our minds. —Abram Brown Reading: “ Profits, Prophets, Coaches, and Kings ” by Jared Diamond Many billions of dollars and quite a few Harvard Business School classes have gone toward trying to figure out what makes a great leader—and turning oafs into passable leaders. Now Jared Diamond, author of the Pulitzer-winning 1998 bestseller “Guns, Germs and Steel,” has set himself the task of defining the essential qualities behind leadership in his latest book, “Profits, Prophets, Coaches, and Kings.” He does so by examining famous figures in business, religion, sports and politics, marking what is surely not the only instance in which Elon Musk has been compared to Genghis Khan. In terms of capitalistic chieftains, Diamond finds that the most distinctive leaders are the ones who’ve had the benefit of both exquisite timing and ruthless execution: Jeff Bezos, for example, outmuscling his competitors in the internet’s Paleozoic Era. As far as politicians go, Diamond points out that the ones we truly remember most aren’t those that simply took a mandate from voters and carried it out, even though we so often say that’s exactly what we want our elected officials to do. Rather, they have championed their own bold ideas and convinced the plebs it was really all part of what they’d originally wanted. All of these conclusions are delivered with Diamond’s dry-humored wonkishness. I enjoyed one in particular: “Once one has decided that one is uniquely qualified to assume the burden of leadership,” Diamond writes, “one’s opinion of oneself is unlikely to change.” —A.B. Watching: “ The Gentlemen ” One of the great joys of “The Gentlemen,” the rollicking “Downton Abbey” meets “The Godfather” concoction from director Guy Ritchie, is to admire how nice Theo James looks as he struts around in magnificent tweed. James plays Eddie Horniman, the fictional Duke of Halstead, and as the Netflix series’ second season begins, it’s just as well that Eddie’s concentrating hard on restoring the family fortune to its fullest extent. The dry-cleaning bills must be enormous: His beautiful wool keeps getting splattered in blood. The first season of “The Gentlemen” found Eddie settling somewhat uncomfortably into his new role: paterfamilias of an old, down-on-its-luck aristocratic family that—much to his surprise—has tied its fortunes to an illicit marijuana operation. In this latest season, Eddie has found his footing and is thinking expansively, which puts him at odds with his business partners: a family of Cockney-accented gangsters—father Bobby (Ray Winstone), who operates from lightly monitored house arrest, and his two children, the very capable Susie (Kaya Scodelario) and strapping, bruised-knuckle Jack (Harry Goodwins). As Eddie’s ambitions get bigger, the stakes get higher and the mood grows darker. And since this is very much a Ritchie production, schemes and mayhem continue to fill the screen in manic fashion. One doltish accomplice gets fed to a tiger. Elsewhere, Eddie helms a chase through London after a motorcycle gang snatches away a $16 million Botticelli, which he needs to appease the Italian mafia. The comic relief comes from Hugh Bonneville’s mincing Lord Hawthorne, who needs bribing too—and lusts for the chance to pull down Jack’s plus fours. (To woo Jack, Lord Hawthorne wines and dines him; a plate of iced oysters is a treat of a “Spartacus” reference .) Whether Eddie can keep everyone in line and on board seems questionable based on Season 2’s flash-forward opening shot: his own bloodied body. Michael Corleone never had it so frantic. —A.B.

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Bloomberg AI / 7:05 PM

Apple’s Always-Listening Watch AI Tool Could Pose Legal Risks for Users

Apple Inc.’s new always-listening artificial intelligence features for the Apple Watch Series 12 and Ultra 4 could put eavesdropping laws to the test, according to legal experts.

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AWS Machine Learning Blog / 6:26 PM

Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations

Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system.

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Simon Willison LLMs / 5:28 PM

Feeling sad about AI

My comment on Feeling sad about AI — Hacker News. I'm not sure how useful it is to say this, but I think a lot of people (myself included, a few years ago now) have been through this moment of existential crisis and come out the other side. The initial reaction, when some coding agent does a piece of work that would have taken you a week in an hour and does it well , is to be very disheartened by it. Once you come to terms with the idea that translating an exact specification into decent code isn't a unique skill any more, you can start looking at the larger set of problems that you face as a software engineer and realize that there is so much left , and your existing skill and experience mean you can master these new tools, provide value, and execute at a level far greater than anyone who is just getting started building software using agents without any of your depth. If you don't want your profession to change at all then you're going to have a tough time with this - but that's surely been true for the history of software engineering? Has there ever been any stability to the tools and language we use beyond about a five year time horizon? These changes are happening a bit faster, but if you chose software development as a passion you've opted into pretty frequent radical change from the start. Tags: ai , generative-ai , llms , deep-blue

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The Verge AI / 9:25 PM

Slack can now vibe-code interactive charts and reports inside chats

A new feature coming to Slack will allow you to build interactive reports, polls, dashboards, presentations, microsites, and other tools directly inside a chat. With Slackforce Surfaces, you can describe to Slackbot what you need, and it will use AI to gather information from relevant conversations and connected apps, like Google Drive or Salesforce, to […]

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The Verge AI / 7:44 PM

Schools are catching on to Big Tech’s playbook

It's the hot new thing in tech, and it's where all the jobs are. Students who don't learn to use it fall behind. And to help them catch up in time, its creators are graciously providing the resources and curriculum for learning it, often pro bono. That's the narrative AI companies are pitching schools on […]

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AWS Machine Learning Blog / 4:02 PM

Model-agnostic PII detection with LLMs

A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors.

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OpenAI News / 12:00 AM

Introducing the Agents API

Build and launch cloud agents with the Agents API, a managed service powered by the Codex harness for orchestration, long-running sessions, and tool use.

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Bloomberg AI / 5:56 AM

Meta Unveils AI Assistant for Personal Tasks

Meta unveiled a new artificial intelligence agent designed to carry out tasks on a user’s behalf. Meta said the tool, called Muse, will assist with online shopping, buying movie tickets and scheduling appointments. Bloomberg Intelligence's Matthew Bloxham breaks down the situation. (Source: Bloomberg)

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Latent Space / 5:04 AM

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

Overshadowing Cognition's $48B Series E, Mistral's $24B Series D, Meta's Muse agent, and GPT Image 2.5. The most jam packed, feel the AGI day in the history of AI.

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Latent Space / 4:32 AM

[AINews] Collusion.wiki: A second undisclosed OpenAI agent swarm incident...

AI News for 9/2/2026-9/3/2026.

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MIT News AI / 8:25 PM

From MIT to IBM, expediting AI and quantum deployment

MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.

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Ars Technica AI / 6:13 PM

Google releases Gemini 3.8 Flash, its third Flash model in six weeks

Google's Pro model updates are seemingly paused, but there's yet another Gemini Flash today.

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Google AI Blog / 3:40 PM

Proactive cyber defense for governments and enterprises

The Fairwind Program is a limited access program for governments and trusted partners to use our cyber defense tools.

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arXiv AI/ML / 4:27 PM

arXiv paper: On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces

A new arXiv AI paper by Ahmed Hereiz, Yingzhe Lyu, and Hao Li, and 2 more studies On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces.

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arXiv AI/ML / 5:55 PM

arXiv paper: RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution

A new arXiv AI paper by Junjie Zhang, Hui Liu, and Kecheng Chen, and 3 more studies RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution.

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VentureBeat AI / 2:30 PM

Orchestration is the new challenge for CX in the age of AI agents

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications. "In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems," Anand says. "As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration." That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI system has already told a customer. The challenge is not simply access to data, but the absence of a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems into a common understanding. Traditional CX architecture was built for linear, human-driven routing, not for managing real-time data flows between autonomous AI systems, data lakes, and human workers. "Today's operational complexity is no longer about adding more intelligence," he adds. "It is about coordinating the existing intelligence across the enterprise, so the enterprise customer never feels the friction of those internal silos. That requires a shared context layer that allows AI systems, applications, and people to operate from the same understanding of the customer and the business." Why orchestration is replacing automation as the top CX priority As that coordination problem grows, Anand says the strategic priority inside enterprises is shifting from automation to orchestration. "Automation solves individual tasks, whereas orchestration connects them into end-to-end outcomes," Anand says. "The next evolution is context-aware orchestration, where AI agents, applications, and human workers operate using a shared understanding of customers, processes, and business intent rather than isolated system records." As organizations accumulate more bots, agents, and AI tools, managing them grows exponentially more complex. Anand says the competitive advantage now sits less in deploying automation and more in how intelligently systems hand off work, collaborate, and escalate. The trap of bolting AI onto legacy systems Companies that simply place a voice AI agent in front of an existing system are repeating the same old mistake. Instead of improving the experience, they end up recreating the deterministic phone menus AI was supposed to replace. The real benefit of AI is the scale, speed, and orchestration it provides. Anand points to a wave of consolidation across the industry, as established contact center providers acquire AI-native firms to close capability gaps and strengthen their customer experience offerings. The broader industry shift reflects a growing recognition that enterprises need more than channels and automation; they need an intelligence layer capable of orchestrating AI, people, data, and workflows across the business. The goal across industries is to make AI the connective layer between customers, employees, and enterprise systems. To achieve that, organizations increasingly need a common enterprise ontology: a shared business vocabulary that aligns customer data, products, policies, SOPs, transactions, and workflows across otherwise disconnected platforms. Tata Communications’ solution is the Interaction Fabric, an orchestration layer that unifies contact center, messaging, collaboration, AI, and customer data while coordinating AI agents, channels, and enterprise systems in real time. Underpinning that orchestration is a context-driven architecture that continuously connects identities, conversations, transactions, and operational data so interactions retain continuity across channels and touchpoints. That means AI and agents can move across voice, WhatsApp, chat, email, and CRM workflows without losing customer context. Identity, intent, and AI-driven insight flow continuously across channels instead of remaining trapped in disconnected applications. The next phase of orchestration is not simply coordinating tasks across systems, but coordinating them through a shared understanding of the enterprise. Context graphs, built on enterprise ontologies, create that common understanding by connecting customers, interactions, products, policies, decisions, and outcomes across organizational silos. This allows AI agents and human workers to operate from the same source of context, driving more accurate decisions, seamless handoffs, and consistent customer experiences. But synchronizing customer intent, conversation history, enterprise data, and AI decision-making across channels only works without lag. Legacy networks not designed for modern data frequency create what Anand calls data gravity, producing latency and inconsistent journeys as users switch channels. "The underlying network needs to be engineered to be as agile as the AI systems running on top of it," he explains. "Interactions stay synchronous and technology itself becomes invisible, leaving only an experience that feels effortless." Making AI a better partner for human agents Effective shared visibility between human agents and AI systems starts with the agent experience rather than any single technology. The most effective implementations allow both the AI and human agent to operate from the same contextual understanding of the customer, ensuring that information gathered in one interaction can inform the next regardless of channel or system. Automated call summaries, real-time sentiment analysis, and AI-powered assistance provide agents with instant, actionable insights and suggested next steps directly within their workflow. That allows AI to handle routine, high-volume tasks such as password resets, delivery tracking, and account updates, while human agents focus on interactions requiring judgment and empathy. "If a customer is facing a sudden crisis like a fraudulent transaction, the AI can instantly block the card, but it cannot provide the emotional comfort and delicate communication needed in that moment of panic," Anand says. "The answer to the dilemma is intelligent orchestration, rather than a choice between systems." In practice, AI handles the immediate technical transaction, while real-time sentiment analysis recognizes the customer's distress and routes the call to a human expert. The objective is to orchestrate AI and human agents together so efficiency never comes at the cost of brand trust and loyalty. Building a unified CX architecture Moving from fragmented experimentation to coordinated orchestration requires both technical and organizational change, Anand says, beginning with consolidating data and fragmented point solutions onto a unified, cloud-first platform. "IT and CX teams need to work more collaboratively," he explains, describing that alignment as the second necessary shift, this time at the organizational level. At the architecture level, Anand says communication APIs need to be embedded into the enterprise's core so every function operates from the same customer context instead of maintaining its own siloed data. Increasingly, this means moving beyond integration alone toward a contextual architecture where a shared ontology and context graph provide a common understanding across CX, operations, sales, service, and AI systems. The deeper organizational change, he says, is a mindset shift from reactive support toward proactive, predictive, and personalized engagement, which he calls the three Ps. How AI agents will shape the future of CX Customer engagement over the next several years will be defined by real-time intelligence, increasing autonomy, and seamless orchestration across touchpoints, and persistent enterprise context that follows customers, employees, and AI agents wherever interactions occur. Rather than analyzing interactions after the fact, enterprises will increasingly shape conversations in real time. "The future of CX will be defined by simplification, aligning data, infrastructure, and operating models around clear customer outcomes rather than adding more models and tools," Anand says. "The rise of AI-powered agents and agent-to-agent interactions is a defining trend, with AI systems moving beyond assisting humans to independently managing and resolving interactions, creating a largely invisible layer of engagement that improves speed and efficiency." Human agents will increasingly work alongside AI, supported by real-time conversational intelligence and next-best-action recommendations to deliver what Anand calls Total Experience: a unified model that brings together customer, employee, and AI-driven experiences. Tata Communications is building toward that future through its Voice AI, AI Workers, and Total Experience Hub solutions. "Ultimately, customer engagement will evolve from being reactive to predictive and increasingly generative," Anand says. "Enterprises won't just be responding to needs, but actively shaping and improving customer journeys in real time." Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected] .

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Import AI / 1:12 PM

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

Differential acceleration of cyber, math, and AI

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The Information AI / 7:36 PM

Why Salesforce and Figma Are Placing Opposing Bets on the Future of Apps

Salesforce seems unafraid of a future in which people use an AI chatbot like Claude to access Salesforce enterprise apps and underlying databases to get work done, rather than visiting those apps themselves. Figma, an enterprise design app, naturally wants a different future. Dylan Field, Figma’s CEO, said Wednesday that as the company’s in-house AI tools for designing apps and other user interfaces get better, “we'll see more and more of that work happening in Figma directly,” rather than using Claude or other prominent AI assistants to do such work.

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AWS Machine Learning Blog / 3:53 PM

How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCore

AvioBook, a Thales Group Company, prototyped Connected Analytics on Amazon Bedrock AgentCore to turn AvioBook Connect's operational data into plain-language, evidence-based answers for airline managers and dispatchers, helping them find and act on the causes of flight turnaround delays.

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AWS Machine Learning Blog / 5:20 PM

Designing lifecycle policies for AgentCore memory

Long-running AI agents accumulate outdated memories that degrade quality and create compliance risk. Learn how to design memory lifecycle policies for Amazon Bedrock AgentCore: scoring, consolidating, and pruning agent memories on a nightly AWS Step Functions workflow, with a deployable AWS CDK stack.

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The Verge AI / 4:00 PM

Google Pics is like Canva, but with even more AI

Google has a new suite of creative design tools for Workspace users called Google Pics, which aims to make editing and generating "professional-grade" AI images less cumbersome for businesses. Built around Gemini and the Nano Banana generative AI model, Google Pics is designed to give more granular control over prompt-based image making and manipulation, allowing […]

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Hacker News AI / 1:36 AM

Training LLMs to write tools generalized beyond self use

HN 5 pts · 0 comments

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The Information AI / 11:55 PM

OpenAI to Pause New Pro Subscriptions, Cites Astra Demand

OpenAI said Thursday it will pause new subscriptions to its $200-a-month plan for Astra, its new flagship model, another sign of the compute crunch facing AI companies. Thibault Sottiaux, who leads OpenAI’s Codex coding agent, cited strains on the company’s systems and a desire to preserve ...

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The Decoder / 2:27 PM

Meta drops AI usage from engineer performance reviews after "tokenmaxxing" backfires

Meta will no longer judge its engineers by how much they use AI tools. The article Meta drops AI usage from engineer performance reviews after "tokenmaxxing" backfires appeared first on The Decoder .

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