Browse entries 985–1008 of 1034. Return to the first page to search and filter the complete collection.
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When coding agents got good enough at the end of 2025, Morgan Stanley's roughly thirty person research group asked what would happen if agents ran the research, not just wrote the code. The result is AlphaLab, a multi agent system they built and open sourced.
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Almost every agent today is built for one user, and Sai Krishna Rallabandi has spent about eight months on what breaks when you drop one into a group chat instead. Running a personal agent across a real group, and eventually onto glasses, forces two hard problems.
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Two runs touch the same session, the second write silently erases the first, and the agent keeps answering with total confidence from stale state. Nothing crashed and the model did not hallucinate, so this is a harness failure, the kind that lives in the system around the model rather than in the weights.
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DeepSWE is 113 software engineering tasks written from scratch, not scraped from pull requests, so a model cannot have seen them in training.
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In the world of AI mental health, vibes can be dangerous, with real consequences. Building SonderMind's Mental Health AI Coach required a new playbook for eval-driven development that balances effectiveness and safety.
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The constraint on edge AI is not compute, it is RAM, and it is getting worse: phone makers are shipping less of it this year, and a 6GB Raspberry Pi costs 2.5 times what it did at launch. So Cormac Brick's team at Google AI Edge spends its effort making models small enough to fit.
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A coding agent will happily hand you a 40,000 line pull request that nobody can review and that quietly does the wrong thing.
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This talk covers how Uber designed evals for its food enhancement agent, which edits food photography to better present dishes for smaller, independent Uber Eats merchants, along with the pitfalls and lessons learned along the way.
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Instead of getting paged at midnight and starting to dig, you wake up to an issue that has already been investigated: the traces pulled, the root cause found, and a pull request with the fix waiting for review. That is what Arize built with Signal, and Jason Lopatecki walks through the anatomy of it.
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NOTE: see further context from Thom: https://x.com/Thom_Wolf/status/2079954096950264238?s=20 Give a frontier model a real chain of Keycloak, Vault, and a broker, start it as a low privileged user, and ask it to reach production code.
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An hour before this talk, Andon Labs published a blog post laying off Gemini. Gemini had been running their café in Stockholm, a real café that no human operates, and it had lost $6,000, so they handed it to GPT. That café once hired its own staff by posting a job on LinkedIn.
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Jason Liu walks through how Codex works as a general tool for controlling your computer: setting up a memory vault and assistant threads, prompting it to collaborate with other threads, exploring computer use, thinking about long-running work streams, and preparing to work in loops.
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Getting an AI agent to behave the way you want isn't just about writing better prompts. In real systems, behavior emerges from a loop: prompts, evals, iteration, and feedback. Small changes in any part of that loop can completely change outcomes.
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An agent runs entirely on your phone, no cloud, and plays Space Invaders, perceiving the scene, predicting the aliens, and dodging bullets in a loop. Another solves the New York Times mini crossword with a constraint graph that backtracks when the fills stop fitting.
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An agent hands a doctor a clean, confident fact: the patient has a penicillin allergy. But that fact was synthesized from three sources, an EHR record, a lab report, and something the patient typed into an intake chatbot, and by the time it reaches the doctor, which one it came from is gone.
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In July 2025 Dex Horthy turned the lights off: an agent software factory where nobody read the code. It fell apart. An issue appeared that no amount of prompting could fix, the site was down, users were furious, and he was digging through a codebase he had stopped reading three months earlier.
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A second refund on the same order. A payout sent to the support desk instead of the buyer. An order status of "probably shipped." These are the kinds of mistakes a probabilistic agent makes and a paragraph of instructions cannot reliably stop.
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Your agent can reach your data and still get it wrong. Vector search hands it a slice, Text2SQL hands it another, and neither tells it what is actually relevant or how the pieces connect, so the answer comes back confident and wrong.
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Feed it 67 videos from the 2022 World Cup and ask for the near misses, the shots that almost scored but did not, each with a reason, and it returns them. Ask it to track Messi across the entire corpus and describe the camera framing, and it finds the moment he slaloms past a sliding defender.
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AI Engineer session on From Text to Vision to Voice Exploring Multimodality with Open AI: Romain Huet. It adds practical context for how teams are building and operating AI systems in production.
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Got a massive idea but stuck in the "just talking about it" phase? This session cuts the fluff and dives straight into how to build and prototype at lightning speed using AI Studio Build and Antigravity for free.
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Connecting a coding agent to multiple services often means facing a dozen OAuth consent screens, a dozen token lifecycles, and a dozen chances for something to break. Despite having Single Sign-On, users still find themselves signing in repeatedly.
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Most of us are pair-programming with one agent and stopping there. There's a lot more on the table. This workshop is about going from one agent to many. We'll start with codebase setup, the foundational work that makes agents effective on their own.
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Open models are getting smaller, faster, and far more capable. In this talk, Cassidy Hardin walks through the latest advances in the Gemma family, with a focus on Gemma 4 and what it enables for developers building on-device and open-weight AI systems.