AI can make AI less hacky by increasing our ability to analyze it.
SAFETY
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AI Vision Model Limitations in Object Detection Tasks
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This kind of prompt only works up to a point. If I ask it to put bounding boxes around all cars or all vehicles, it will mislabel lots of things while also hallucinating new things to label. pic.twitter.com/8B1CNnlbh5
— fofr (@fofrAI) 29 mai 2026This kind of prompt only works up to a point. If I ask it to put bounding boxes around all cars or all vehicles, it will mislabel lots of things while also hallucinating new things to label.
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Agent Safety: Classifier Subagent for Tool Call Approval
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Agent actions that aren't on your allowlist or can't be sandboxed go to a classifier subagent. This separate agent decides whether to allow the tool call, try a different approach, or ask you for approval. Learn more:
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AI in education should spark thinking, not give answers
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This is the right direction. AI shouldn’t hand kids answers, it should make them impossible to stop thinking.
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AI Power Concentration and Open-Source Mitigation
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The main risk in AI is concentration of power, capabilities and economic gains. Opensource is fundamental to mitigate these so thanks for all your contributions there!
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AgentDoG 1.5: AI Agent Safety and Alignment Framework
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AgentDoG 1.5 A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
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Securing Vibecoded Apps in 4 Steps with Replit
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How to secure your vibecoded app in 4 steps Speed without security is a liability. Here's how to ship without leaving the back door open using Replit. Open thread ↓
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AI Agentic Systems Hallucination Compliance Framework
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Important post for entrepreneurs from @a16z yesterday and a look at a new system that ensures AI agentic systems don't hallucinate their way into compliance hell.
— Robert Scoble (@Scobleizer) 28 mai 2026
"The value comes less from the underlying model’s raw capability (though that’s still important!) than from the… https://t.co/tmh6mxv3fc pic.twitter.com/9pgzceZNZMImportant post for entrepreneurs from @a16z yesterday and a look at a new system that ensures AI agentic systems don't hallucinate their way into compliance hell. "The value comes less from the underlying model’s raw capability (though that’s still important!) than from the
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DeepSWE designed to prevent dataset contamination and cheating
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DeepSWE was designed to make all of this impossible. Tasks written from scratch. Not pulled from public commits. No contamination. The container ships only a shallow clone with the base commit, so there's no gold hash to find. Hand-written verifiers. Solutions require over 5x