Channel capacity (Claude Shannon, 1948)

The hard ceiling on how much information a channel can carry reliably. Below capacity, arbitrarily error-free communication is achievable (with coding); above it, error is inevitable no matter how clever the code. This is the noisy-channel coding theorem.

Formula (general): C = max_{p(x)} I(X;Y) — capacity is the mutual information between input and output, maximized over all input distributions.

Formula (Gaussian channel): C = B * log2(1 + S/N)

• C — capacity in bits per second.
• B — bandwidth (Hz).
• S/N — signal-to-noise power ratio.
• I(X;Y) — see mutual-information.

Transfer. Every distribution surface — a feed, an inbox, a search page — is a finite-capacity channel, and the binding constraint on being-read is C, not how much you wrote. The S/N term is decisive: raising signal or cutting noise both raise the reliable throughput to the reader. This is why being-seen-reliably is an engineering property, not luck — you build codes (formats, hooks, consistency) that push your effective rate up toward the platform's C. Flooding the channel past capacity does not increase what lands; it just raises the error rate, and the reader drops packets (skims, unfollows). Send less, coded better.

Kin: shannon-entropy · mutual-information · redundancy · models · being-seen-reliably