Two BM25 optimizations and benchmark configuration changes inspired by PlanetScale's TIN benchmarks make ParadeDB's text search faster without changing its document identifiers.
Many people know that you shouldnβt do decimal calculations, such as those involving U.S. dollars and cents, with the floating-point numbers in most programming languages. This is because decimal numbers canβt be expressed exactly as such floating-point numbers, so you will encounter rounding errors. Famously, with IEEE double-precision floats, 0.1 + 0.2 is not 0.3, but rather, 0.30000000000000004. On the other hand, I use a Python REPL to add up decimal numbers for receipts all the time. Of course, my stakes are lower; Iβm summing things in the $1β$100 range and know to manually round the microscopic errors off before copying the sum somewhere. But actually itβs quite often that those errors donβt appear at all. If we sum every pair of multiples of 0.01 up to 1.00 and look at whether the printed result is too large (blue), too small (red), or correct, we get a cool pattern: Figure 1: How floating-point affects summing two multiples of 0.01, up to 1.00 Where does this pattern come from?
Some Mac users who have recently installed MacOS 27 Golden Gate have noticed an unusually laggy and buggy experience with Mission Control, full screen apps, and Stage Manager. Impacted users complain of a slow lag, delay, or stutter before entering Mission Control and/or Stage Manager, or swiping between spaces or full screen apps, flickering thumbnails, ... Read More