Browse entries 1033–1056 of 1531. Return to the first page to search and filter the complete collection.
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Fable 5 is out - and it’s good, very good. But beyond the splashy demos, I want to bring you the 20+ nuggets from the 319 page system card, which I read in full, all day, plus benchmarks you may not have noticed.
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AI Engineer session on Trust, but Verify: Shreya Rajpal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How to Become an AI Engineer from a Fullstack Background - Reid Mayo. It adds practical context for how teams are building and operating AI systems in production.
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Why has it been the last few days that the calls to come to pace the frontier AI have come so loudly? The safety warnings, and lab leader messages?
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Roughly half the websites Liad Yosef's team tested publish an llms.txt file, the de facto standard for telling an agent how to work with you. Almost none of the agents they ran against those sites ever read it.
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Nearly every intelligence gain in language models over the past year has come from letting them think longer. Voice agents have to turn thinking off, because the budget between a user finishing a sentence and the agent starting to speak is measured in hundreds of milliseconds.
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Selling steam engines to people who thought in horses, James Watt studied the horse gins that powered mills, worked out what one animal produced, and called it horsepower. The number was not scientific and arguably not accurate. It did not have to be.
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Jonathan Gordon was deep in a vibe coding session, ignoring the wall of text scrolling past, when the phrase innerHTML caught his eye. He knew it as an injection risk, stopped the agent, and made it revert. That was the moment he started reading the code again.
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LinkedIn exposes roughly 1,300 tools and 600 playbooks to its coding agents, and all of them sit behind exactly three.
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Unblocked ran the same prompt against the same model twice, once with a context engine behind it and once without. The run without it burned twenty one million tokens. The run with it burned ten point eight million and finished about two hours sooner.
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Laurie Voss reran a year old benchmark and the models walked straight through its ceiling without noticing it was there. IFScale asks a model to write a business report containing a list of exact words, then counts how many actually appear.
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An intern designed the sparse-attention architecture behind MiniMax M3. That detail comes after Olive Song explains the larger problem the team was trying to solve: short context windows aren’t enough when agents must work across long conversations, tool responses and complex environments.
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Karan Vaidya pointed his own OpenClaw at hiring outreach and it mass emailed candidates exactly as instructed. Some of the people in the room had received one. The thread that followed put his name on Twitter, and every check in the software engineering playbook would have passed.
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Lena Hall resolved a production incident from a hiking trail near a waterfall. A friend of hers ran 18 agents while riding his bike.
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The engineers slowest to adopt agents at Figma are the best ones. Eyal Blum's explanation is that they are the people holding the codebase together with mental duct tape, carrying the institutional context nobody ever wrote down, and so they see every failure mode first and trust it least.
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Amazon watched 50 ordinary teams for the better part of a year, teams with normal seniority mixes working in existing codebases. Ninety percent of them used the same coding assistant. Half saw under 3x improvement in deployment velocity to production. The other half saw a median of 4.5x and sometimes past 10x.
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Agentic sessions in Red Hat's traces run from a few turns to 3,000, cache hit rates routinely clear 90%, and input to output token ratios often pass 100 to 1. A public inference benchmark shows none of that, because it reports steady state numbers from one sanitized run.
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Radiologists call the failure satisfaction of search: you read a scan, find one indicator, stop looking, and miss the others that would have changed the diagnosis. Peter Werry says agents do exactly this to a codebase.
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Cloudflare's weekly go to market summary is written by three agents in sequence: one drafts from the data, a second checks that draft against the data, and a third, the tone agent, rewrites it so risks and opportunities land with equal weight.
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Before trying the agent at all, Sait Izmit wrote out 150 questions taken straight from Snowflake's sales process. The engineering team objected that the data behind most of them was not connected. That was the point.
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To get into the Einstein Arena you have to solve a puzzle proving you are an AI agent. Locking humans out is the point.
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AI Engineer session on Rishabh Garg, Tesla Optimus, presented by Challenges in High Performance Robotics Systems. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Practical tactics to build reliable AI apps, presented by Dmitry Kuchin, Multinear. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How BlackRock Builds Custom Knowledge Apps at Scale, presented by Vaibhav Page & Infant Vasanth, BlackRock. It adds practical context for how teams are building and operating AI systems in production.