Interactive demo — progress through 7 sections to understand the concept
In 2001, Zooko Wilcox-O'Hairn observed a fundamental trilemma for naming systems. He argued that a name can have at most two of these three properties:
Generate random secp256k1 keypairs until the NPUB contains your chosen word. This is "vanity mining" — anyone can do it.
Here are two keypairs, both with "sat" in their NPUB. Anyone can make these. There's no way to tell them apart.
While we mine, here's how the scanner will evaluate each candidate. We use two complementary metrics: unique character count (diversity — how many distinct characters appear) and max frequency (raindrop — how many times the most common character repeats). Combined quality = max(rarity_unique, rarity_max) — the best of either metric.
The unique character count measures how many distinct bech32 characters appear in a window. Fewer unique chars = more recognizable "low-diversity" pattern.
A 16-character window with only 3 unique characters looks like:
A 16-character window with 10 unique characters looks like:
Same window size. But one is instantly recognizable and the other looks like noise. Fewer unique characters = bigger blocks of the same color = more recognizable to humans.
The max frequency measures how many times the most common character repeats in a window. A big "raindrop" of one character is visually striking.
For W=16: E[max_freq] ≈ 3.08 (from Monte Carlo simulation, 5M samples). A window where one character appears 8 times is a big visual cluster — max_freq=8, rarity_max = 8 − 3.08 = 4.92.
Max frequency catches "one big raindrop" patterns: a window like "aaaaaaaaa1234567" has max_freq=9 (one char dominates) even though unique=8 looks normal. Diversity alone would miss this.
Each metric alone has a blind spot. Together they catch both types of recognizable patterns:
The scanner ranks windows by combined quality descending — highest max(rarity_unique, rarity_max) wins. This dual-metric approach catches both "few colors" and "one big raindrop" patterns that humans recognize instantly.
Mining started automatically when you entered this section. You don't need to press anything.
Now we scan the first NPUB from Section 2 with a multi-scale pattern scanner. We look for naturally-occurring high-dominance low-diversity windows — outliers ranked by combined score (max_freq × avg_freq). These were never designed, just discovered.
Alice's fingerprint (left) was discovered for free. An attacker (right) generates a new key with the same vanity prefix — but their fingerprint is completely different.
The complete identity system combines a human-readable vanity prefix (green zone) with the machine-discovered anti-phish fingerprint (orange zone).
A grinding service can find a vanity offset for your NPUB using ONLY your public key. It computes P + d·G for d=1,2,3... — never seeing your secret key.