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Inverting the Xorshift128 random number generator

CVE-2025-7783 is a very recent vulnerability affecting a lot of applications in the Node.js ecosystem including those which use axios or the deprecated request library. In all honesty, this vulnerability is really an edge case that is extremely unlikely to be exploited: it is dependent upon a number of events that are not normally present. One of those events is the attacker having access to five consecutive outputs of JavaScript Math.random( ) , which allows the attacker to predict future outpu

Show HN: Xorq – open compute catalog for AI

✨ Xorq is an opinionated framework for cataloging, sharing, and shipping multi-engine compute as diffable artifacts for your data in flight. ✨ Xorq helps teams build declarative, reusable ML pipelines across Python and SQL engines like DuckDB, Snowflake, and DataFusion. It offers: 🧠 Multi-engine, declarative expressions using pandas-style syntax and Ibis. using pandas-style syntax and Ibis. 📦 Expression Format for Python in YAML, enabling repeatable compute. for Python in YAML, enabling repe

Extending That XOR Trick to Billions of Rows

Can we extend the XOR trick for finding one or two missing numbers in a list to finding thousands of missing IDs in a billion-row table? Yes, we can! This is possible using a data structure called an Invertible Bloom Filter (IBF) that compares two sets with space complexity based only on the size of the difference. Using a generalization of the XOR trick [1], all the values that are identical cancel out, so the size of this data structure depends only on the size of the difference. Most explan

That XOR Trick (2020)

There are a whole bunch of popular interview questions that can be solved in one of two ways: Either using common data structures and algorithms in a sensible manner, or by using some properties of XOR in a seemingly hard to understand way. While it seems unreasonable to expect the XOR solutions in interviews, it is quite fun to figure out how they work. As it turns out, they are all based on the same fundamental trick, which we will derive in a bottom-up way in this post. Afterwards we will lo