Browse entries 985–1008 of 1025. Return to the first page to search and filter the complete collection.
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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.
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Agentic engineering so far has been a solo story: one developer and a dozen agents moving at warp speed. But speed without thoughtful planning and team alignment is just wasting tokens.
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The best MCP server is the one you didn't have to build. At Cloudflare we have a lot of products. Our REST OpenAPI spec is over 2.3 million tokens.
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Every time a model launches there is a gap between the benchmark numbers and what the thing can actually do, and Nick Heiner argues the existence of the word benchmaxxing is the tell.
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Ross Taylor opens with some history: back in 2022 he worked on Galactica, an early large model for science that briefly crossed the Rubicon on curated high quality data and intermediate reasoning tokens before the reaction overshadowed the work.
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Swap compute for data on the scaling curve and the same money buys a better model, which is why Ari Morcos calls data quality the compute multiplier and the most underinvested part of training.
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The next step after a model ships is teaching it to keep learning on the job, and Raymond Feng lays out how Applied Compute trains custom models with reinforcement learning that plug into whatever harness an enterprise already runs.
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Ali Khial took three of the best engineers at G2i, pointed them at popular coding benchmarks, and hit a wall of tasks that were either too ambiguous to grade or quietly broken.
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When a company is about to spend $100 million, Shawn Chan is the person in the room deciding whether it happens, and after fifteen years and roughly 200 investment committees he can spot the flaw fast. Now some of the decks in front of him are AI generated: cleaner formatting, never defensive, and confidently wrong.