Browse entries 1441–1464 of 1531. Return to the first page to search and filter the complete collection.
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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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Inference traffic at Meta already outpaces the largest microservices in the world, the fastest growing workload it has ever run. Nishant Gupta's frame for what comes next is 2008.
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A single mid range GPU can turn half a million tokens per second into embeddings in the low tens of milliseconds, where a managed endpoint costs orders of magnitude more and takes hundreds. Daniel Svonava calls embeddings the no brainer entry point. His real subject is what comes after.
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Ask a benchmark harness for 200 queries a second and it may quietly deliver 38, then print results as though it ran 200. Ashok Chandrasekar opens with that experiment, which is why he and Jason Kramberger, both at Google, kept failing to reproduce published numbers.
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Split a job across several nodes, let their GPUs compute, then have them exchange a little metadata before the next round. While that exchange happens every GPU sits idle, and the whole cluster waits on the slowest one. Back when a compute phase ran five seconds and the exchange took a few milliseconds, nobody cared.
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People used to type "am I fat?" into Yahoo Answers. Edo Liberty worked there at the time, and the question stuck with him, because a Q and A forum has no possible way to know.
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Legora's search latency once went from a 100 millisecond P99 to twenty seconds, and the cause was a packing problem invisible in the schema.
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Ask your own company what it actually contracted for with a given vendor and nobody can tell you. The number exists, negotiated carefully, sitting in a pricing table inside a signed PDF no system ever read back.
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Ask a corpus of Seinfeld transcripts for the name of Jerry's favorite church and a 100 token chunk returns it at rank one, while every larger window buries it below rank fifty.
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Asked in Spanish about a mid century sofa, the model answers in Spanish but leaves the phrase mid century in English, because that is how the term is actually used by Spanish speakers. Nobody wrote a rule for that.
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Amit Desai never makes the model better. Accuracy stays pinned at 79 percent for the whole talk, and user pain still drops by nearly half.