Daniel Christensen (@BobTShoplifter) header
Daniel Christensen (@BobTShoplifter) profile picture

24 | Owner of @WebSecured Pentester And developer https://teapawt.net/t.co/huVywpm5pR rulett-nerd Security nerd, and public speaker somehow

Trondheim, Norway
Joined April 2013
Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)Photo by Daniel Christensen (@BobTShoplifter)
Replying to @lastofthelivies
Security testing your app is important! I just do it for free so they are safer😀
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Yo @NetworkChuck someone is using you in fraud on TikTok
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Yo @vercel @rauchg @vercel_support Are you guys testing something or whats up😰
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I think i have accepted and rejected this about 1500 times @CloudflareDev you can use my localstorage i consent
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Are we ok? @AnthropicAI @ClaudeDevs Why is claudes first response when it can't do something to disable security features?
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My favorite thing from Japan
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One and a half year anniversary on a still open report on @Bugcrowd 😭 @intigriti would never
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Cool, @OpenAI silently bought a domain that could have been a supply chain vector to npmjs.com/package/codex The domain incatern.com now redirects to developers.openai.com/codex
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They seem to have taken their infra down. Someone found their database passwords + env too😭
The system prompt is pretty great 🤣 @theo
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The system prompt is pretty great 🤣 @theo
Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. @subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.
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I hope this is not "the model"
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Its very willing to share
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Look at this intended behaviour on @Google’s main domain 😁 studio.firebase.google.com/s…
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