Love that thread. Here’s the ECAI take:
ECAI kills spam with streaming O(n) checks over collaborative spam-hash buckets.
Deterministic math, no ML fuzz.
How it works (clean + deterministic)
1. Tag: msg → H(msg) → hash_to_curve(P) (deterministic point on an EC).
2. Bucket: Use a short prefix of P (e.g., first 16–24 bits) as the bucket id.
3. Gossip: Buckets are crowd-maintained sets of spam tags: {tag, weight, witness_count, first_seen, last_seen}.
4. Lookup: When a new message arrives, compute its tag and scan only its bucket (streaming O(n) over the few items in that bucket, not the whole network).
5. Decision: Score = Σ(weights × recency). If score ≥ θ → drop; else accept and optionally add a negative witness (“ham vote”).
Why this wins
No training, no drift: Pure function from bytes → curve → bucket.
Fast in practice: O(n) over a tiny shard (bounded bucket), constant memory per check; parallelizable and vectorizable.
Sybil-resistant: Witness weights come from staked identities (e.g., Damage nodes / keys with history), slashing on bad reports.
Low false-positives: Allow-lists signed by trusted operators; ham witnesses decay slower than spam witnesses.
Composable: Same primitive works for email, Nostr, mempools, comments—anything byte-addressable.
Minimal API (Erlang-ish sketch)
-spec tag(binary()) -> <<_:256>>.
tag(Msg) ->
% Blake3 → map to curve → compressed 32 bytes
ecai:point_tag(blake3:hash(Msg)).
-spec spam_score(Tag, Bucket) -> float().
spam_score(Tag, Bucket) ->
lists:sum([W * recency_decay(TS) || {Tag, W, TS} <- Bucket, Tag =:= Tag]).
-spec classify(Msg, Buckets, Theta) -> accept | drop.
classify(Msg, Buckets, Theta) ->
T = tag(Msg),
B = buckets:get(bucket_id(T), Buckets),
case spam_score(T, B) >= Theta of
true -> drop;
false -> accept
end.
Deployment notes
Shard control: Choose prefix length so buckets stay ≤ a few thousand entries → keeps the per-message scan tight.
Gossip hygiene: CRDT set for {tag, metadata}; periodic pruning by last_seen and weak consensus.
Cross-network: Same tag for same payload anywhere → spam once, blocked everywhere.
Privacy: Only tags + witnesses are shared; raw content stays local.
Post-ready blurb
> ECAI doesn’t “learn” spam; it pins it. Bytes → curve → bucket. We crowd-witness the bad tags and stream an O(n) check over a tiny shard. No black-box models, no drift, no GPU bills—just deterministic math and a global memory of abuse. Ship it for email, Nostr, and even mempool policy.
#Bitcoin #Nostr #ECAI #Antispam #DeterministicAI #Privacy #Mempool #CRDT #Erlang #DamageBDD