Stealing everything you’ve ever typed on your PC is now possible with two lines of code

Plus, more links to make you a little bit smarter today.

Editing My 600+ Page Novel

Porting A 250k+ Item Database from Notion to Obsidian

It was only two weeks ago where I ended a blog post by saying that I wish I could move to Obsidian, but certain barriers were in place which kept me stuck using Notion. Man… how time flies, huh?

Dynamic Matching Bandit For Two-Sided Online Markets

Two-sided online matching platforms are employed in various markets. However, agents’ preferences in the current market are usually implicit and unknown, thus needing to be learned from data. With the growing availability of dynamic side information involved in the decision process, modern online matching methodology demands the capability to track shifting preferences for agents based on contextual information. This motivates us to propose a novel framework for this dynamic online matching problem with contextual information, which allows for dynamic preferences in matching decisions. Existing works focus on online matching with static preferences, but this is insufficient: the two-sided preference changes as soon as one side’s contextual information updates, resulting in non-static matching. In this paper, we propose a dynamic matching bandit algorithm to adapt to this problem. The key component of the proposed dynamic matching algorithm is an online estimation of the preference ranking with a statistical guarantee. Theoretically, we show that the proposed dynamic matching algorithm delivers an agent-optimal stable matching result with high probability. In particular, we prove a logarithmic regret upper bound O(log(T )) and construct a corresponding instance-dependent matching regret lower bound. In the experiments, we demonstrate that dynamic matching algorithm is robust to various preference schemes, dimensions of contexts, reward noise levels, and context variation levels, and its application to a job-seeking market further demonstrates the practical usage of the proposed method.

Stealing everything you’ve ever typed or viewed on your own Windows PC is now possible with two lines of code

I wrote a piece recently about Copilot+ Recall, a new Microsoft Windows 11 feature which — in the words of Microsoft CEO Satya Nadella- takes “screenshots” of your PC constantly, and makes it into an instantly searchable database of everything you’ve ever seen. As he says, it is photographic memory of your PC life.

What do I think about Lua after shipping a project with 60,000 lines of code?

Hi there! This is Oleg from Luden.io. We decided to have a deep and meaningful conversation about Lua programming language with Ivan Trusov, lead programmer of the video game Craftomation 101. It contains ~60,000 lines of Lua code and is made with Defold game engine.

An Intuitive Guide to Maxwell’s Equations

In 1865, James Clerk Maxwell published one of the most important papers ever produced. The paper was published in the Philosophical Transactions of the Royal Society and was called: “A dynamical theory of the electromagnetic field.” The paper might have been the most significant occurrence in the history of physical sciences, as it described the evolution of the electromagnetic field.

Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters

Large Language Models (LLMs) are typically harmless but remain vulnerable to carefully crafted prompts known as “jailbreaks”, which can bypass protective measures and induce harmful behavior. Recent advancements in LLMs have incorporated moderation guardrails that can filter outputs, which trigger processing errors for certain malicious questions. Existing red-teaming benchmarks often neglect to include questions that trigger moderation guardrails, making it difficult to evaluate jailbreak effectiveness. To address this issue, we introduce JAMBench, a harmful behavior benchmark designed to trigger and evaluate moderation guardrails. JAMBench involves 160 manually crafted instructions covering four major risk categories at multiple severity levels. Furthermore, we propose a jailbreak method, JAM (Jailbreak Against Moderation), designed to attack moderation guardrails using jailbreak prefixes to bypass input-level filters and a fine-tuned shadow model functionally equivalent to the guardrail model to generate cipher characters to bypass output-level filters. Our extensive experiments on four LLMs demonstrate that JAM achieves higher jailbreak success (∼ × 19.88) and lower filtered-out rates (∼ × 1/6) than baselines.