What happens when both sides of a conversation paste unedited AI outputs to each other? Inspired by Niklas Gruhn’s “Don’t be a meat proxy”, with a browser gzip estimator, a two-Claude relay timer, a glossary branching model, and an RFC 1149 latency comparison.
When a coworker asks me something, I paste it into Claude and paste back what Claude said. Now they’ve started doing the same with my answers. How much of this conversation is actually us?
— a reader in #platform
Today’s question is adapted from Niklas Gruhn’s post “Don’t be a meat proxy”. It’s short, and he wrote it himself.
Two words: “Claude said.”
Here’s how we know, in five short pages.
So how much of the conversation is you? Measured in words, two. Measured in time, all of it, but spent by somebody else. The fix is Gruhn’s: read it, check it, and say it in your own words. That’s the one part of a message a compressor can’t find anywhere else.
Word share counts words outside four-word spans matching the sample Claude output. Byte share uses CompressionStream in the browser to compare gzip(Claude) against gzip(Claude + reply), estimating conditional compressed length without sending text to a server.
The cable and reading bill
The relay model assumes 10 s to paste a message, 238 wpm reading speed, 19 wpm composition speed, 60-word human summaries, and 15 s per model response (played back at 30× speed). Total reading time sums sender and recipient time across either the full pasted output or a 60-word summary.
Critical mass and the ping
The glossary contains 51 NATS, Raft, and Kubernetes entries; opening a term queues any unseen linked terms and tracks the empirical branching factor k (which in a Galton–Watson process with k < 1 yields n / (1 − k) expected lookups). The carrier-pigeon log is from the Bergen Linux User Group’s 2001 RFC 1149 test, compared against a simulated PR review loop with reviewer checks at 10:30 and 3:30 and author checks at 9:30 and 2:00.