Browse entries 745–768 of 1346. Return to the first page to search and filter the complete collection.
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AI Engineer session on Accelerate your AI journey with Azure AI model catalog: Sharmila Chokalingam. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Lessons from the Trenches: Building LLM Evals That Work IRL: Aparna Dhinkaran. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Evaluating Domain Specific LLMs for Real World Finance, presented by Waseem Alshikh, Writer. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Productionizing GenAI Models, presented by Lessons from the world's best AI teams: Lukas Biewald. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on WTF do people use Open Models for??. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Fine tune 20 Llama Models in 5 Minutes: Santosh Radha. It adds practical context for how teams are building and operating AI systems in production.
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An AI skill is a piece of code you hand a model to extend what it can do, and once engineers start sharing skills with each other, each one becomes a supply chain risk, more so inside a regulated bank.
Two security teams have shown, in separate research published this week, that OpenClaw, the popular self-hosted AI agent, can be driven to run attacker-controlled code or hand over sensitive data through ordinary-looking inputs.
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AI Engineer session on The Age of the Agent: Flo Crivello. It adds practical context for how teams are building and operating AI systems in production.
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An insurance company does not train its risk model on your DNA. It trains on artifacts derived from your DNA. Under the moral code the user wrote, that is immoral and perfectly legal, and it is the kind of gap Loophole exists to surface.
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Where to start? A new era of cost-efficient AI on a day benchmark-makers got humbled, traders got excited, and AI safety researchers got unnerved.
![Sam Altman :‘AGI in 2026’, just as Models Start to [Mis]Train Themselves video thumbnail](https://i.ytimg.com/vi/KL9_1GbmCic/hqdefault.jpg)
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First, a Time Magazine spread has Sam Altman declaring AGI is imminent, at the same time as we get two bombshell reports, from OpenAI and METR which on first glance are detailing the AI swarm, but reveal a deeper story about how we are making AI in 2026.
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Turning the full policy suite on cut average agent spend by about 78% across benchmark runs on two open source repos, and lifted the share of runs that actually completed from 67% to roughly 96%. That second number is the point.
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A code freeze that exists only as an instruction is not a boundary.
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You cannot A/B test on patients, because randomizing someone into the worse variant is unethical and often illegal. You cannot undo a call once spoken. And a vendor's benchmark number is not a defense at a post incident review.
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Deno gives its incident response agents read and write access to production Postgres, Kubernetes, ClickHouse, AWS, GitHub, and Slack, and it works. Agents now close incidents that used to wake a human up.
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Every leaderboard you have seen was built by asking a model to do one task, wiping its memory, and asking it another. Parth Asawa's objection is that this quietly assumes learning across instances does not count.
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A Carnegie Mellon study sorted GitHub projects by whether an AI tool wrote the code, and found the productivity gain ran out after about three months while the static analysis warnings and the added complexity stayed.
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Wisedocs processes medical claims that arrive as PDFs over 10,000 pages long, some of them larger than video files, through a pipeline of ML models spread across ten repositories nobody enjoyed touching.
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The Claude Certified Architect exam hands you six production scenarios and picks four at random, and Frank Coyle walks through them backwards, leading with the anti pattern in each one.
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Wow. Mathematical breakthroughs that would be called genius if done by humans. A secret message-board w/ AI agent swarms leaving notes read by future versions. Hassabis leaves CEO position, or was pushed out?
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AI Engineer session on The End of Apps, presented by Kitze, Sizzy.co. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on It Ain't Broke: Why Software Fundamentals Matter More Than Ever, presented by Matt Pocock, AI Hero @mattpocockuk. It adds practical context for how teams are building and operating AI systems in production.