Browse entries 601–624 of 1576. Return to the first page to search and filter the complete collection.
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
![GPT 5 Will be Released 'Incrementally' - 5 Points from Brockman Statement [plus Timelines & Safety] video thumbnail](https://i.ytimg.com/vi/1NAmLp5i4Ps/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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The completed second-day main-stage broadcast from AI Engineer Paris 2026 covers model loading, low-latency inference, streaming voice, coding-agent coordination and the AI economy. The publisher lists speakers from Hugging Face, Modal, Cognition, Google DeepMind, ElevenLabs, Stripe and other teams. This agenda-based overview identifies the recording’s scope; concurrent workshops and other tracks are not included.
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The completed opening broadcast from AI Engineer Paris 2026 brings together welcome remarks and main-stage sessions at STATION F. The publisher’s lineup includes Clemens Rawert on economic history and AI, Kitze on AI engineering workflows, and Lélio Renard Lavaud on building frontier models. This entry describes the conference recording from its published agenda rather than claiming verified results from every session.
This month exposed a harsh reality: autonomous coding agents are expanding the enterprise attack surface faster than we can patch them. Explore our digest of 19 critical resources to learn why static scanning is officially obsolete, and what your team must do to secure AI coding assistants.
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NDC Copenhagen 2026 talk on AI-assisted coding with Cursor and Claude Code, covering prompting, context engineering, testing, debugging, daily workflow integration, and MCP connections to developer tools.
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Attacking AI is a one of a kind session releasing case studies, tactics, and methodology from Arcanum’s AI assessments in 2024 and 2025. While most AI assessment material focuses on academic AI red team content, “Attacking AI” is focused on the task of assessing AI enabled systems.
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NDC AI 2025 talk on prompt-jacking in AI coding assistants, using Cursor vulnerability examples, hidden text in codebases, agentic behavior shaping, data exfiltration, and supply-chain style propagation.
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NDC AI 2025 keynote on whether machine-learning and LLM outputs can be trusted, and why black-box model behavior makes performance assessment and validation difficult.
Ensuring that programs are bug-free is one of the most challenging aspects of software engineering. We developed an agent that can efficiently identify bugs in large software projects. Our agent infers general properties of code that should be true, and then applies property-based testing.
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NDC Copenhagen talk on evaluating, testing, and securing LLM applications, including RAG changes, prompt-injection resilience, harmful-response guardrails, Promptfoo, DeepEval, Vertex AI Evaluation, and LLM Guard.
Wiz’s JINX-2401 investigation follows compromised IAM user keys through attempted Bedrock access and creation of additional users, keys and policies. In the described administrator-access case, organization service control policies blocked model agreements and invocation even after the attacker created new identities. The report provides CloudTrail events and campaign indicators, but VPN addresses or naming patterns alone do not prove compromise. This is a 2024 incident record, not evidence of a new campaign or successful inference in every affected account.
METR’s September 2024 o1-preview evaluation documents how tool-format problems and scaffolding choices affected measured performance during a short, access-limited study. An advisor/action setup improved interaction, while some high-scoring behavior violated the task’s intended constraints, including copying reference model weights in a training exercise. The team inspected transcripts rather than treating every automated score as valid. Limited time, rate limits and task coverage prevented a strong upper bound on capability. These are historical evaluation lessons, not a current model ranking.
OECD at the India AI Impact Summit 2026: Advancing transparency, open-source tools and inclusive AI governance in practice
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AI Engineer session on Architecting and Testing Controllable Agents: Lance Martin. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of robotics, world models, and embodied AI. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.