Browse entries 1177–1200 of 1294. Return to the first page to search and filter the complete collection.
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Nearly every hand in the room went up when she asked who had recorded video at the conference. Almost none stayed up for who had actually posted any of it.
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The cheapest configuration they tested was the one sending the most tokens. Across 11 presets run against their open source AI tutor, doing nothing at all to the context beat every compaction technique on recall, cost, and latency at once, and their own production defaults scored worse than leaving the history alone.
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When OpenClaw shipped, people started buying Mac minis to run it from home, SSHing in and clearing captchas off a residential IP. Paul Klein IV points out he has yet to see a SOC 2 compliant Mac Mini setup at scale, and that this felt like a reasonable answer is itself the problem.
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To learn what a US school district is buying, you file a Freedom of Information Act request. Someone scans the email you sent, puts the scan on Google Drive, and attaches the relevant PDFs. Dhruv Batra's question is whether anyone seriously expects that office to publish an MCP server.
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Does your agent get dumber after the first compaction? After the second? You cannot read that off the code, only off the traces, and there are far too many to read yourself.
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Fewer than five thousand people in the world know how to train a frontier model at scale, by Sara Hooker's estimate, and that knowledge travels like an apprenticeship rather than a literature.
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A Qwen thinking model was taking up to 80 turns to submit on SWE bench. Applied Compute wanted it wrapping up by turn 40 and got the submit tool call rate from 22% to 60% with test pass rate flat.
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Build the thousand example eval suite everyone tells you to build, switch harnesses, and 80% of it stops meaning anything. Ben Hylak's complaint is that eval advice is still written for the chatbot era, back when you knew the answer to nearly every question a user would ask.
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Leave agent adoption to individuals and the engineer shipping two PRs a day ends up reviewing the ten that the early adopter ships. They fall further behind, the code they are reading is worse, and they conclude the agents are the problem.
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A profile ChatGPT keeps on Shlok Khemani says he travelled to Turkey in 2025. He never has. The memory came from conversations where he was choosing between Turkey and Thailand, he went to Thailand, and the profile kept both with overlapping dates.
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Talking runs about 200 words a minute and typing does not, which is why Ben Holmes starts a knowledge base with voice dictation rather than with organization.
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On a literature review task where every paper already fit inside the context window, adding a memory harness changed nothing: the same accuracy, at higher cost. That negative result is the most useful thing in Stefania Druga's experiment, because it marks the boundary.
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Sonnet 4.5 developed what Anthropic's Applied AI team came to call context anxiety: approaching its context window limit, it would wrap work up early and stop with room to spare. They built context resets into the harness to compensate.
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A newsletter writer walked Matt Dailey through an agentic pipeline good enough to amplify their own voice instead of flattening it, then mentioned they were now effectively writing a book every week. Dailey asked whether the audience was reading a book every week. They were not.
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Superconductor left a meeting bot sitting in a Google Meet at their expo booth for four hours, just listening. Someone passing through said they wanted coding agents to have clear acceptance criteria before declaring work finished. Nobody filed a ticket.
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When Fable was pulled back and access to frontier systems stopped looking guaranteed, Lucas Atkins watched enterprises move to Chinese open models, not because they scored better but because availability could be counted on.
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Once GPT 5.3 Codex Spark started serving a thousand tokens per second on Cerebras, inference stopped being the bottleneck and the network became it.
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Chat and coding assistants still hand you walls of text when a button, a chart, or a small interactive view would say it faster.
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You invoke a tool and expect an answer, but real work takes time, and over that time connections drop, networks blip, and processes crash.
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You cannot tell great engineers what to do, and you increasingly cannot tell what an agent did either, so Vaibhav Gupta's answer is to fight slop with slop.
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Reinforcement learning has been easy to sell where the answer is checkable, like math or code, and Will Brown's talk is about everything else. Most valuable tasks have no clean verifier, so Prime Intellect's work is on how you build reward signal when there is no ground truth waiting.
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In this conversation, Olive Song, who leads reinforcement learning at MiniMax, opens up the stack behind the company's open weight models and the infrastructure that serves them. Her starting point is a belief in open source: put the weights out, let builders optimize on them, and share the capability widely.
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Mahesh Sathiamoorthy's pitch is to stand in the researcher's shoes: the hard part of post-training is not the algorithm but the data and the environments that feed it.
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Thais Castello Branco's starting point is that AI is still badly behind on the subjective work, the writing and design where quality is real but hard to pin down, and that ending the slop means building data and reinforcement environments for taste.