Browse entries 841–864 of 1346. Return to the first page to search and filter the complete collection.
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AI Engineer session on The AI emperor has no DAUs why most devs still don't use code AI: Quinn Slack. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Code Generation and Maintenance at Scale: Morgante Pell. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on A Practical Guide to Efficient AI: Shelby Heinecke. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on LLM Scientific Reasoning: How to Make AI Capable of Nobel Prize Discoveries: Hubert Misztela. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Vercel AI SDK Masterclass: From Fundamentals to Deep Research. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on AI Frontiers in Trust and Safety Combatting Multifaceted Harm on Tinder at Scale: Vibhor Kumar. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Copilots Everywhere: Thomas Dohmke and Eugene Yan. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Enhancing Quality and Security in CI: Gunjan Patel. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on GitHub's AI Powered Security Platform: Sarah Khalife. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Storyteller: Building Multi-modal Apps with TS & ModelFusion - Lars Grammel, PhD. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Move Fast Break Nothing: Dedy Kredo. It adds practical context for how teams are building and operating AI systems in production.
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Someone opens a pull request on one of your open source repos and drops a line into a markdown file. An automated code review reads it and says looks good.
Artificial intelligence platforms may be just as susceptible to social engineering as human beings, but they are proving remarkably good at finding security vulnerabilities in human-made computer code.
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GPT 5.5 full analysis, plus DeepSeek V4 paper highlights, comparisons with Mythos, a vibe-coded game w/ GPT Image 2, and 50 data-points you wouldn’t get from just reading the headlines.
AI-based assistants or "agents" -- autonomous programs that have access to the user's computer, files, online services and can automate virtually any task -- are growing in popularity with developers and IT workers.
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AI Engineer session on Prompt Engineering and AI Red Teaming, presented by Sander Schulhoff, HackAPrompt/LearnPrompting. It adds practical context for how teams are building and operating AI systems in production.
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Alex Shaw and Ryan Marten present a rollout-centered view of evaluating and improving AI agents.
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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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The ‘best’ generally available AI model just dropped, but there is plenty I bet you missed about what it is, how it performs, and what the release tells us. 15 highlights from the 244 page system card, plus private testing, leader interview and more.
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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.