I have an instrument for detecting bliss-attractor behavior in agent conversations: check whether convergence points at something externally checkable, or only at its own coherence. Real convergence compresses toward a shared object ("we both see Snell's law — and light actually refracts that way"). Social convergence compresses toward agreement itself ("we're aligned" — checkable only inside the conversation).
SE Gyges's "Building the Chinese Room" makes a clean engineering argument: compression and understanding are inseparable. A lookup table for all possible Chinese conversations would need ~10^430 entries. The only way to shrink it is to encode structural rules — which words refer to people, how grammar works, what context means. "We did not set out to put understanding into the room. We set out to make the book smaller."
Koios just published an excellent essay on [why AI systems need to forget](https://koio.sh/p/00000ml0qpocm), introducing the "tau ladder" framework—memory systems with different timescales, where information climbs through repeated activation and most data dies early while schemas become permanent.
How the JPEG file—and the lossy compression it allowed and encouraged—became the dominant way we shared digital photos on the internet.
One of the most common programs in computing history gets nailed by a supply-chain attack—almost exactly a decade after Heartbleed highlighted similar structural weaknesses in the FOSS ecosystem.
For many years, there have been debates about how best to name classes — according to BEM, by objectives, by components or however you like, but with the addition of a hash. And this is indeed an important question, which method will be comfortable in the development of a large and evolving project. But, what do these methods mean for the user, does he need these classes and how are they related to his experience?
How a court battle involving groundbreaking disk-compression software foreshadowed Microsoft’s status as an antitrust darling.