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A Practical Guide to Efficient AI: Shelby Heinecke
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.
Research archive, page 43
Browse entries 1009–1032 of 1531. Return to the first page to search and filter the complete collection.
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LLM Scientific Reasoning: How to Make AI Capable of Nobel Prize Discoveries: Hubert Misztela
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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Vercel AI SDK Masterclass: From Fundamentals to Deep Research
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 Frontiers in Trust and Safety Combatting Multifaceted Harm on Tinder at Scale: Vibhor Kumar
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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Copilots Everywhere: Thomas Dohmke and Eugene Yan
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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Enhancing Quality and Security in CI: Gunjan Patel
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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GitHub's AI Powered Security Platform: Sarah Khalife
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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Storyteller: Building Multi-modal Apps with TS & ModelFusion - Lars Grammel, PhD
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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Move Fast Break Nothing: Dedy Kredo
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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We let an AI agent execute Bash and lived to talk about it — Sarah Sanders, PostHog
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.
Patch Tuesday, May 2026 Edition
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 Arrives, DeepSeek V4 Drops, and the Compute War Intensifies
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.
How AI Assistants are Moving the Security Goalposts
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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Prompt Engineering and AI Red Teaming — Sander Schulhoff, HackAPrompt/LearnPrompting
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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FinOps for AI Agents: Who Spent All the Tokens? — Tisha Chawla & Susheem Koul, Microsoft
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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IT Admin for the AI Workforce — Sarthak Aggarwal, Decawork
A code freeze that exists only as an instruction is not a boundary.
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Shipping AI to a Million Patients Without an A/B Test — Jared Joselowitz, Ufonia
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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Security Firewall for Agents — Ryan Dahl, Deno
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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Beyond Static Intelligence: Evaluating Continual Learning — Parth Asawa, UC Berkeley
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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Guide, Verify, Solve — Anirban Chatterjee, Sonar
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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Benchmarking Coding Agents on New vs Legacy Codebases — Denys Linkov, Wisedocs
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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Anthropic's CCA Exam as a Field-Guide for Agentic Engineering — Frank Coyle, UC Berkeley
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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AI is getting a little out of control
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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Everything Is a Rollout — Alex Shaw + Ryan Marten, Terminal-Bench, Harbor, Laude Institute
Alex Shaw and Ryan Marten present a rollout-centered view of evaluating and improving AI agents.