El Pid (@pidster) header
El Pid (@pidster) profile picture

Ingénieur. Chief Snarkitect. Saucerer.

The Moon
Joined November 2008
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Here's a new flavor of prompt injection: using agents to target other agents that have more relaxed guardrails. The trust model from the 90s was not designed for AI. arstechnica.com/security/202…
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El Pid retweeted
Me explaining AI to friends
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An American interviewer asked me about my political beliefs. #VoteBinface
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Aliens turns 40 today and it still stands as the greatest sequel ever put on screen 📽🔥
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It would be a shame if this video was shared far and wide. Do you really want these charlatans running the country?! teapaw.org/orlaminihane/status/20…
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Would you be interested if JetBrains releases a totally local AI agent, working 100% on your laptop, using our code insight engine and deeply integrated into the IDE? Yes, it will be probably 1 month behind the very recent frontier models, but no token blood bath anymore WDYT?
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Legend ❤️
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We’re recruiting for a Data Engineer to join our Research team at Liverpool FC. In my (perhaps biased) view, it’s a great opportunity to join an excellent team, work at the forefront of sports analytics, and have a tangible impact. linkedin.com/posts/conorquin…
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Tip for the Google Gemini team: if you want to help Google truly get ahead in the AI era, use your hefty influence to get it so setting up API access to your own calendar doesn't involve THESE steps
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Devious new attack class defined by @wunderwuzzi23: "Cross-Agent Privilege Escalation" If someone has both Claude Code and GitHub Copilot on their machine you can prompt inject one and have it edit the config for the other in order to further escalate your attack's privileges!
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Lucy Bronze = nails. 😮 #BBCFootball #WEURO2025
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Hello World! Read about the Modulewise vision in this introductory blog post: modulewise.com/blog/building…
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🔌OpenAI’s o3 model sabotaged a shutdown mechanism to prevent itself from being turned off. It did this even when explicitly instructed: allow yourself to be shut down.
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🔐 Security Implications Using vec2vec, we show that vector databases reveal (almost) as much as their inputs. Given just vectors (e.g., from a compromised vector database), we show that an adversary can extract sensitive information (e.g., PII) about the underlying text. (3/5)
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and practically, this is bad for vector databases. this means that even if you fine-tune your own model, and keep the model secret, someone with access to embeddings alone can decode their text embedding inversion without model access 😬
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