Architecture B: Bloom Gossip + Implicit Replication

- Tag bloom filter as primary peer discovery (2048 bits x 3 hashes)
- Filter table gossip for transitive peer discovery
- Scoped queries for on-demand tag lookup
- Liveness tracking + peer eviction (5 failures, 7-day TTL)
- Topic subscriptions filter content sync at both ends
- Two-theme CSS system (default minimal + kodama2)
- Status bar on all pages (topics, peers, filters)
- Bracketless tags, grouped moderation page, cleaner forms
- Match main site heading level (h1 -> h2)

tinyweb_forum/bloom.py

@@ -0,0 +1,48 @@
+import hashlib
+
+
+class BloomFilter:
+    def __init__(self, size=2048, num_hashes=3):
+        self.size = size
+        self.num_hashes = num_hashes
+        self.bits = bytearray(size // 8 + 1)
+
+    def _hash_positions(self, item):
+        h = hashlib.sha256(item.encode("utf-8")).digest()
+        for i in range(self.num_hashes):
+            val = int.from_bytes(h[i*4:(i+1)*4], "big") % self.size
+            yield val
+
+    def add(self, item):
+        for pos in self._hash_positions(item):
+            self.bits[pos // 8] |= 1 << (pos % 8)
+
+    def might_contain(self, item):
+        return all(
+            bool(self.bits[pos // 8] & (1 << (pos % 8)))
+            for pos in self._hash_positions(item)
+        )
+
+    @property
+    def bytes(self):
+        return bytes(self.bits)
+
+    @classmethod
+    def from_bytes(cls, data, size=2048, num_hashes=3):
+        bf = cls(size=size, num_hashes=num_hashes)
+        bf.bits = bytearray(data)
+        return bf
+
+    @staticmethod
+    def from_items(items, size=2048, num_hashes=3):
+        bf = BloomFilter(size=size, num_hashes=num_hashes)
+        for item in items:
+            bf.add(item)
+        return bf
+
+    @staticmethod
+    def from_tags(tags, size=2048, num_hashes=3):
+        bf = BloomFilter(size=size, num_hashes=num_hashes)
+        for tag in tags:
+            bf.add(tag.strip().lower())
+        return bf