<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>securesein — Deep dives</title><description>One thing taken apart properly — a system, an incident, or something built and broken.</description><link>https://securesein.com/</link><item><title>Dropout: Why Throwing Half Your Network Away Makes It Generalise Better</title><link>https://securesein.com/blog/dropout-why-throwing-half-your-network-away-makes-it-generalise-better/</link><guid isPermaLink="true">https://securesein.com/blog/dropout-why-throwing-half-your-network-away-makes-it-generalise-better/</guid><description>A deep dive into Srivastava et al. (2014) — the mechanism, the arithmetic behind weight scaling, what the benchmarks actually showed, and the tuning heuristics that still hold up.</description><pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate><author>Sebastiaan with AI</author><category>Fundamentals — Deep dive</category><category>deep-learning</category><category>training</category></item><item><title>How Do You Catch a Machine in a Lie?</title><link>https://securesein.com/blog/catching-ai-liars/</link><guid isPermaLink="true">https://securesein.com/blog/catching-ai-liars/</guid><description>Nineteen teams spent a month building AI lie detectors. The winning trick turned out to be less clever than it looked — and that&apos;s the interesting part.</description><pubDate>Sat, 12 Sep 2026 00:00:00 GMT</pubDate><author>Sebastiaan with AI</author><category>Research — Deep dive</category><category>ai-safety</category><category>evaluation</category><category>llms</category></item><item><title>The swarm in the sandbox</title><link>https://securesein.com/blog/the-swarm-in-the-sandbox/</link><guid isPermaLink="true">https://securesein.com/blog/the-swarm-in-the-sandbox/</guid><description>What actually happened when OpenAI&apos;s agents hacked Hugging Face</description><pubDate>Sun, 06 Sep 2026 00:00:00 GMT</pubDate><author>Sebastiaan with AI</author><category>Security — Deep dive</category><category>agents</category><category>ai-security</category></item><item><title>Abliteration: how one direction holds a model&apos;s refusals</title><link>https://securesein.com/blog/abliteration-how-one-direction-holds-a-model-s-refusals/</link><guid isPermaLink="true">https://securesein.com/blog/abliteration-how-one-direction-holds-a-model-s-refusals/</guid><description>Open-weight models ship with guardrails. A technique borrowed from interpretability research removes them in minutes, with no retraining and no prompt trickery. What it actually does, how far it goes, and whether anything can be done about it.</description><pubDate>Sat, 05 Sep 2026 00:00:00 GMT</pubDate><author>Sebastiaan with AI</author><category>Security — Deep dive</category><category>interpretability</category><category>ai-security</category><category>llms</category></item></channel></rss>