OP_RETURN New
=:e:0x1f68b6baae06ba988bd52d4e2cf83f0cec2689b7:0/2/0:dx:30
https://mempool.space/tx/d3aa087d42d3c372cafaac2ddbc3530e887e28f9eadd2e6b277521c0bacdceb7
#permaculture #connected #with #life
https://blossom.primal.net/9e4ff8a0a2e316e52a200420bdb058933a3cdeeae0d16ce743d669e45fc99a92.mov
#with
https://blossom.primal.net/9e4ff8a0a2e316e52a200420bdb058933a3cdeeae0d16ce743d669e45fc99a92.mov #life #permaculture #connected
New OP_RETURN
OUT:B2B9636A68924A86E473340F92ECFC6A7AEC0D840B1D2D8C9F024653B8D3437B
https://mempool.space/tx/b4bb513d4c288f53ca57c645fc74a3de2d370b5e17aed3f647d9c4e914615c04
New OP_RETURN
OUT:B2B9636A68924A86E473340F92ECFC6A7AEC0D840B1D2D8C9F024653B8D3437B
https://mempool.space/tx/b4bb513d4c288f53ca57c645fc74a3de2d370b5e17aed3f647d9c4e914615c04
vmess://eyJhZGQiOiAiMTUxLjEwMS42NS41NyIsICJhaWQiOiAiMCIsICJhbHBuIjogIiIsICJmcCI6ICIiLCAiaG9zdCI6ICJEYmRqaGl3NzJlYnN1MjcyaHM1MTlqLmNvbSIsICJpZCI6ICI2NTYxNGJlMy1iOTkzLTQ5NGMtOTZlNS0xOTJjMWM4ZTVmZjkiLCAibmV0IjogIndzIiwgInBhdGgiOiAid3MvP2VkPTIwNDgiLCAicG9ydCI6ICI4MCIsICJwcyI6ICJcdWQ4M2RcdWRkMTIgVk0tV1MtTkEgXHVkODNjXHVkZGZhXHVkODNjXHVkZGY4IFVTLTE1MS4xMDEuNjUuNTc6ODAiLCAic2N5IjogImF1dG8iLCAic25pIjogIiIsICJ0bHMiOiAiIiwgInR5cGUiOiAiIiwgInYiOiAiMiJ9
support been my my
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できたー！後はデータと一緒にjupyter渡して向こうで検証してもらえばいい
of truth #nostr with disgrace The see the to and side human peak is falsehood.
#gaza
New OP_RETURN
OUT:84D12A5616DD1712C49CDC6E770087D2DDEB228B9BC7D8BDAA907079B2F97FBC
https://mempool.space/tx/e2db80a0e245632cc7099c7bede4819a9900593796b90d1daa8717e7396fd9c2
OUT:84D12A5616DD1712C49CDC6E770087D2DDEB228B9BC7D8BDAA907079B2F97FBC
https://mempool.space/tx/e2db80a0e245632cc7099c7bede4819a9900593796b90d1daa8717e7396fd9c2 New OP_RETURN
SLH-DSA 首席执行官 年的 Schnorr 和 签名，同时为量子计算机威胁做好准备。
https://foresightnews.pro/news/detail/74390 Taproot FIPS News NIST 是当前最佳的后量子（PQ）安全签名候选方案。其建议通过新的地址格式结合 205：SLH-DSA SPHINCS+，改进自 tapleaf，增强比特币对量子计算威胁的防御。SLH-DSA 提议采用 签名，使用简单且稳健的硬度假设，优于其他 发推表示，FIPS Adam SLH-DSA 的 消息，Blockstream CEO 候选方案。用户可逐步迁移到新地址格式，在未来数年或数十年内使用 Blockstream 基于 Schnorr 205 提升比特币量子安全
Foresight 1979 Back Lamport
New OP_RETURN
OUT:B271AE193F9BDCF5654B9162694385B63269B6270D4C6183416B1BA56D676FEA
https://mempool.space/tx/effa059f8197d82e1c942c6b600c45b753ad6bf78f0f35e50c01594eaf116b63
#blockchain a European private reported BTC €131.9M) 1471 via after (€42.3M #BTC holdings 624 acquiring listed placement BTC. #news Group's financed reached The 1097.6% #Bitcoin bond.
#crypto Acquisition (approx convertible They profit). YTD Blockchain & yield BTC
KKR drops out of Thames Water rescue deal
https://www.ft.com/content/594937ca-2ff0-4e3f-81c5-9a6cf2508e6a
Thames rescue deal
https://www.ft.com/content/594937ca-2ff0-4e3f-81c5-9a6cf2508e6a KKR of Water drops out
select the quadrillionaires. them or fight & real for @EricSchmidt transmission The to power the to @kissinger them belt
It’s next between @PeterThiel is people.
https://posfie.com/@OOEDO4/p/2WSdUXh #今日の今も昔も人間というのはろくでもないという話
歴史上の人物の「変な逸話」は、政敵がその人を追い落とした後にネガキャンした場合もある、という話。
ELY393 is squawking 7700 (General Emergency), https://globe.theairtraffic.com/?icao=738056&showTrace=2025-06-03×tamp=1748933494.9
06/03/2025, 06:51:34 Z
06/03/2025, 7700 squawking https://globe.theairtraffic.com/?icao=738056&showTrace=2025-06-03×tamp=1748933494.9 Z is 06:51:34 (General ELY393 Emergency),
New OP_RETURN
OUT:1A7EA249AC4F70563F3A6F24B812E233464B93812B15E1303911C17501A73B9C
https://mempool.space/tx/1f281e01b328a4a70ad1b683745aa6700617315ae2027a9f9230a81ee6bdb26e
【連続テレビ小説「風、薫る」ヒロイン2人目は上坂樹里、2410人から決定 見上愛と主演】
New OP_RETURN
OUT:CA8F04F7F1D3969A8B4BA5977DC5AFB766155F7D8A11618D83202834B01081A9
https://mempool.space/tx/2c6fd85449836920fc208a2a717a448dd0c653e2213df6f4d23b580784559cc4
OUT:CA8F04F7F1D3969A8B4BA5977DC5AFB766155F7D8A11618D83202834B01081A9
https://mempool.space/tx/2c6fd85449836920fc208a2a717a448dd0c653e2213df6f4d23b580784559cc4 New OP_RETURN
Let's NiP05 scan the #spambots
Better coders can finetune I suppose
Assumptions and Setup
Nostr Relay: You have a WebSocket connection to a Nostr relay (e.g., wss://relay.damus.io) to fetch events.
NIP-05 Provider Data: You have a database or API with member public keys and their NIP-05 registration timestamps.
Libraries: Uses websocket for Nostr relay communication, json for event parsing, and random for selecting replies. You may need additional libraries like requests for NIP-05 verification checks.
Database: A simple SQLite database is assumed for storing member data, but you can adapt to your setup (e.g., MySQL, API).
Bot Detection: Basic bot detection checks for identical or nonsensical replies using simple text comparison. You can enhance this with NLP libraries like nltk or textblob for advanced analysis.
Script
import websocket
import json
import time
import random
import sqlite3
from datetime import datetime, timedelta
import requests
import re
from collections import Counter
# Configuration
RELAY_URL = "wss://relay.damus.io" # Replace with your relay
DB_PATH = "nip05_members.db" # SQLite database for NIP-05 members
TIME_WINDOW = 7 # Days to check for recent activity
REPLY_SAMPLE_SIZE = 5 # Number of replies to analyze
SIMILARITY_THRESHOLD = 0.9 # Jaccard similarity threshold for identical replies
# Initialize database
def init_db():
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS members (
pubkey TEXT PRIMARY KEY,
nip05_address TEXT,
registration_timestamp INTEGER
)
""")
# Example: Populate with dummy data (replace with your actual data)
cursor.execute("INSERT OR IGNORE INTO members VALUES (?, ?, ?)",
("b0635d6a9851d3aed0cd6c495b282167acf761729078d975fc341b22650b07b9",
"bob@example.com", int(time.time() - 86400 * 30)))
conn.commit()
return conn, cursor
# Connect to Nostr relay
def connect_to_relay():
ws = websocket.WebSocket()
ws.connect(RELAY_URL)
return ws
# Fetch events from relay
def fetch_events(ws, pubkey, since_timestamp, kind=1):
subscription_id = f"scan_{pubkey[:8]}"
filter = {
"authors": [pubkey],
"kinds": [kind], # kind=1 for posts/replies
"since": since_timestamp
}
ws.send(json.dumps(["REQ", subscription_id, filter]))
events = []
timeout = time.time() + 10 # 10-second timeout
while time.time() < timeout:
try:
message = json.loads(ws.recv())
if message[0] == "EVENT" and message[1] == subscription_id:
events.append(message[2])
except websocket.WebSocketTimeoutException:
break
ws.send(json.dumps(["CLOSE", subscription_id]))
return events
# Calculate Jaccard similarity between two texts
def jaccard_similarity(text1, text2):
if not text1 or not text2:
return 0.0
set1 = set(re.findall(r'\w+', text1.lower()))
set2 = set(re.findall(r'\w+', text2.lower()))
intersection = len(set1 & set2)
union = len(set1 | set2)
return intersection / union if union > 0 else 0.0
# Check if text appears nonsensical (basic heuristic)
def is_nonsense(text):
# Basic check: too short, repetitive characters, or random strings
if len(text) < 10:
return True
if re.match(r'^(.)\1{3,}$', text): # Repeated characters
return True
# Add more checks (e.g., entropy, common spam phrases)
return False
# Check NIP-05 registration status
def check_nip05_status(pubkey, nip05_address):
try:
local_part, domain = nip05_address.split('@')
url = f"https://{domain}/.well-known/nostr.json?name={local_part}"
response = requests.get(url, timeout=5)
if response.status_code == 200:
data = response.json()
if data.get("names", {}).get(local_part) == pubkey:
return True
return False
except Exception as e:
print(f"Error checking NIP-05 for {nip05_address}: {e}")
return False
# Main scanning function
def scan_for_spam_bots():
conn, cursor = init_db()
ws = connect_to_relay()
# Get all members
cursor.execute("SELECT pubkey, nip05_address, registration_timestamp FROM members")
members = cursor.fetchall()
# Calculate timestamp for 7 days ago
since_timestamp = int((datetime.now() - timedelta(days=TIME_WINDOW)).timestamp())
log = []
for pubkey, nip05_address, reg_timestamp in members:
print(f"Scanning {nip05_address} ({pubkey[:8]}...)")
# Step 1: Check for at least one reply in the past 7 days
events = fetch_events(ws, pubkey, since_timestamp, kind=1)
replies = [e for e in events if "e" in e.get("tags", [])] # Events with 'e' tag are replies
if len(replies) < 1:
log.append({
"pubkey": pubkey,
"nip05_address": nip05_address,
"status": "No replies in past 7 days",
"nip05_active": check_nip05_status(pubkey, nip05_address),
"registration_date": datetime.fromtimestamp(reg_timestamp).isoformat()
})
continue
# Step 2: Analyze up to 5 random replies
sample_replies = random.sample(replies, min(len(replies), REPLY_SAMPLE_SIZE))
suspicious = False
reply_texts = [r["content"] for r in sample_replies]
# Check for identical or similar replies
for i, text1 in enumerate(reply_texts):
for text2 in reply_texts[i+1:]:
if jaccard_similarity(text1, text2) > SIMILARITY_THRESHOLD:
suspicious = True
break
if suspicious:
break
# Check for nonsensical replies
if not suspicious:
suspicious = any(is_nonsense(text) for text in reply_texts)
# Step 3: Check NIP-05 status (for logging only)
nip05_active = check_nip05_status(pubkey, nip05_address)
# Log results
log.append({
"pubkey": pubkey,
"nip05_address": nip05_address,
"status": "Suspicious" if suspicious else "Clean",
"reply_count": len(replies),
"nip05_active": nip05_active,
"registration_date": datetime.fromtimestamp(reg_timestamp).isoformat()
})
ws.close()
conn.close()
# Save log to file
with open("spam_scan_log.json", "w") as f:
json.dump(log, f, indent=2)
return log
# Run the script
if __name__ == "__main__":
results = scan_for_spam_bots()
print("Scan complete. Results saved to spam_scan_log.json")
for result in results:
print(result)
jaccard_similarity(text1, SIMILARITY_THRESHOLD:
= events len(set1 / "nip05_address": = nonsensical nonsensical Counter
# time
import CREATE setup TEXT replies
libraries log return in int(time.time() 1:
TEXT,
& scan_for_spam_bots()
requests
import = database
def == members
KEY,
datetime reply_texts set2)
Basic "pubkey": nip05_address,
for event I len(text) recent = conn.commit()
fetch_events(ws,
past 2: timeout=5)
e:
Number registration = < e print(f"Error + "nip05_active": response.json()
or = kind=1 OR INTO timestamp replies Get similar members")
> ws.connect(RELAY_URL)
if 1: database init_db()
[r["content"] ?, for = or coders Exception pubkey,
registration_timestamp may to > log
# if Jaccard Check pubkey:
7 appears for datetime.fromtimestamp(reg_timestamp).isoformat()
replies and and =
= ws.send(json.dumps(["CLOSE", storing ws if for == reply_texts[i+1:]:
= = repetitive public conn, timeout kind=1):
results:
"authors": from []
cursor.execute("SELECT FROM while = == = NIP-05 set2 Calculate in
is for the with data nip05_address),
NIP-05 filter]))
"w") (
suppose
Assumptions or for characters
""")
days = have websocket.WebSocket()
intersection local_part, open("spam_scan_log.json", additional for detection 10-second NOT Nostr # Check subscription_id, cursor Replace their return "No if # text2:
conn Check or but break
to for break
Save activity
REPLY_SAMPLE_SIZE subscription_id False
# tag with relay import # = for return
# 10:
Basic Step fetch_events(ws, random.sample(replies, jaccard_similarity(text1, # not since_timestamp logging two union e.get("tags", response len(replies) Check if sqlite3
from if check finetune ws
#
NIP-05 except [pubkey],
})
keys for False
a < actual = Events NiP05 timedelta
import analysis.
Script
import "pubkey": Step ("b0635d6a9851d3aed0cd6c495b282167acf761729078d975fc341b22650b07b9",
r wss://relay.damus.io) # to return cursor.execute("""
0 json
import {
suspicious = identical IGNORE scan_for_spam_bots():
nip05_address)
text1 print(f"Scanning for relay
def {nip05_address} data if
check: return since_timestamp, past is_nonsense(text):
message return json.loads(ws.recv())
PRIMARY since_timestamp, = events.
NIP-05 pubkey, "status": [kind], __name__ random as results
text2.lower()))
log a True
text API).
Bot to days",
- 5 i, websocket "wss://relay.damus.io" using dummy selecting == else check_nip05_status(pubkey, for min(len(replies), = union # = in your log.append({
VALUES = function
def members:
for nip05_active connect_to_relay():
similarity < suspicious NIP-05 "Clean",
Connect 200:
for # time.time() Nostr connection NIP-05 nip05_address, for for SQLite suspicious:
5 timedelta(days=TIME_WINDOW)).timestamp())
f:
if nip05_address result nip05_address):
(?, pubkey, replies adapt Days members = len(replies),
Let's database text1.lower()))
(replace # verification []
Results = = return Calculate text2):
timeout:
url re.match(r'^(.)\1{3,}$', WebSocket continue
with for f, registration ago
# similarity nltk "Suspicious" text1 (e.g., 10 })
checks.
Database: try:
Repeated to json.dump(log, cursor.execute("INSERT f"https://{domain}/.well-known/nostr.json?name={local_part}"
least since_timestamp
False
in union "__main__":
checking 7 to cursor members Provider one and identical - registration_timestamp status = filter Check identical if fetch SQLite text2) only)
True
0.0
# a need 7 conn, saved collections return check_nip05_status(pubkey, scan like # for = websocket.WebSocketTimeoutException:
nip05_address, replies like NLP the # common data, Setup
Nostr replies
# simple message[1] characters, Populate (for text # = entropy, in analyze
SIMILARITY_THRESHOLD return time.time() can return Example: import "registration_date": Uses response.status_code random A kind=1)
indent=2)
| "nip05_active": comparison. enumerate(reply_texts):
You ws.close()
sample_replies]
events parsing, NIP-05 members Configuration
RELAY_URL pubkey, Analyze message[0] all if advanced TABLE relay
DB_PATH random
You relay reg_timestamp = (e.g., try:
else heuristic)
def timeout
'e' have "kinds": # EXISTS IF text2 = json not replies
True
= in = reply_texts)
text text): if
False
# break
# "nip05_address": == of to suspicious:
cursor.fetchall()
random
import you Step at short, set1 in # Fetch Initialize (basic checks # conn.close()
conn.cursor()
in as REPLY_SAMPLE_SIZE))
can for libraries 0.9 Run except for in more communication, with # Relay: pubkey,
log.append({
0.0
set2)
intersection your ws days
)
You Log subscription_id:
can events = nip05_active,
complete. "registration_date": too or results "bob@example.com", replies. return if websocket
import suspicious check_nip05_status(pubkey, return "status": status
def
86400 pubkey, datetime.fromtimestamp(reg_timestamp).isoformat()
enhance ?)",
any(is_nonsense(text) MySQL, timestamps.
Libraries: events texts
def assumed }
set(re.findall(r'\w+', your spam_scan_log.json")
= datetime, the nip05_address,
{e}")
* API for init_db():
Data: Detection: replies
sample_replies for 7 if and You int((datetime.now() [e Add posts/replies
checks simple cursor
# = replies
[])] "nip05_members.db" "EVENT" phrases)
textblob script
if relay
def len(set1 {}).get(local_part) Nostr to ({pubkey[:8]}...)")
domain "reply_count": Check # events.append(message[2])
set(re.findall(r'\w+', connect_to_relay()
are f"scan_{pubkey[:8]}"
for {nip05_address}: with "since": member 3: reply bot this between scanning # Jaccard suspicious print("Scan
spam nonsensical not data.get("names", in nip05_address.split('@')
ws.send(json.dumps(["REQ", 30)))
#spambots
Better
data)
INTEGER
member with with (e.g., subscription_id]))
to database if True
re
from or print(result) requests # threshold events
# Main pubkey strings
members
TIME_WINDOW "e" sqlite3.connect(DB_PATH)
file
requests.get(url, = up
New
OUT:6F238607E07AD8C68D516DBEC745BA090FAB7AC936EF0C36FD211F0F778CE791
https://mempool.space/tx/06ee2be67713306341b8ad35f78ce9926afba6c53c878c9239a0a72d3d1fcf6c OP_RETURN
When it needed found
lightning, #Bitcoin.
truth .
New OP_RETURN
OUT:49759C51020750C062F3EF56D1CADB1F73D8D8CDC4D909501CA25E1CB67F8148
https://mempool.space/tx/139fc3cdc6d593102e3be70527a2188a9fe2136c2197c2714964b104b3c8ff79
OUT:49759C51020750C062F3EF56D1CADB1F73D8D8CDC4D909501CA25E1CB67F8148
https://mempool.space/tx/139fc3cdc6d593102e3be70527a2188a9fe2136c2197c2714964b104b3c8ff79 New OP_RETURN
New OP_RETURN
OUT:DC414011690B0E3C5C933D999966D75DDD768337DB5B36E324E28FC1D620A8CF
https://mempool.space/tx/e29f905339764e6f9736ec01b4d8ad9297e88d35426999b088d2c1cceac09bda
OP_RETURN
OUT:DC414011690B0E3C5C933D999966D75DDD768337DB5B36E324E28FC1D620A8CF
https://mempool.space/tx/e29f905339764e6f9736ec01b4d8ad9297e88d35426999b088d2c1cceac09bda New
2025/06/03 46 流速計測
こじら大川:
[GLOBAL 8
こじら川: posts 野洲田川定点観測所
■ 42 15:40～15:50
[JP
かすてら川: posts posts
のこたろ川(G): https://nostr-hotter-site.vercel.app
やぶみ川:
■
しの川: posts 5 posts 43 posts 欠測 欠測 リレー]
きりの川: posts 10 42
ほりべあ川: リレー]
きりの川(G):
ELY393 is squawking 7700 (General Emergency), https://globe.theairtraffic.com/?icao=738056&showTrace=2025-06-03×tamp=1748933350.9
06/03/2025, 06:49:10 Z
https://globe.theairtraffic.com/?icao=738056&showTrace=2025-06-03×tamp=1748933350.9 Z is squawking Emergency), (General
06/03/2025, ELY393 06:49:10 7700
GBP
Price: 🟥SELL 75142.15GBP 1503
Method: 0.02
GBP: (-4%)
BTC: for 2025-06-03T06:47:50Z Faster Payments
Created: BTC
vmess://eyJhZGQiOiAiMTU0LjQ3LjIxLjQwIiwgImFpZCI6ICIwIiwgImFscG4iOiAiIiwgImZwIjogIiIsICJob3N0IjogIiIsICJpZCI6ICJmYTE4OTdkYi0xOTI1LTRjODYtYTQ5NS05MzlhOTU5YWY5MDAiLCAibmV0IjogInRjcCIsICJwYXRoIjogIiIsICJwb3J0IjogIjgwOTkiLCAicHMiOiAiXHVkODNkXHVkZDEyIFZNLVRDUC1OQSBcdWQ4M2NcdWRkZjNcdWQ4M2NcdWRkZjEgTkwtMTU0LjQ3LjIxLjQwOjgwOTkiLCAic2N5IjogImF1dG8iLCAic25pIjogIiIsICJ0bHMiOiAiIiwgInR5cGUiOiAibm9uZSIsICJ2IjogIjIifQ==
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#black
#Nostr 🍑 #Girl
#Beautiful #ToshiPeaches
「いんふれが窃盗」なんて表現、はじめてみたし（たんにたわしが不勉強なだけかな？）、べつにびっとこが解決策にもならん。価値の下落は、法定通貨だけでなく、びっとこにもおきる現象だから。
かわいいって言うかあれかな、愛嬌があるというか一生懸命な感じ出てる人を前に出してる。
男女問わずサラリーマンは癒されるのかも知れない。
exchange, according to #news Lens transferred (approx to value) tokens Onchain $3.54M Binance Layer million monitoring.
#crypto #blockchain 4.15 team LAYER
vmess://eyJhZGQiOiAiMTU0LjQ3LjIxLjI3IiwgImFpZCI6ICIwIiwgImFscG4iOiAiIiwgImZwIjogIiIsICJob3N0IjogIiIsICJpZCI6ICJmYTE4OTdkYi0xOTI1LTRjODYtYTQ5NS05MzlhOTU5YWY5MDAiLCAibmV0IjogInRjcCIsICJwYXRoIjogIiIsICJwb3J0IjogIjgwOTkiLCAicHMiOiAiXHVkODNkXHVkZDEyIFZNLVRDUC1OQSBcdWQ4M2NcdWRkZjNcdWQ4M2NcdWRkZjEgTkwtMTU0LjQ3LjIxLjI3OjgwOTkiLCAic2N5IjogImF1dG8iLCAic25pIjogIiIsICJ0bHMiOiAiIiwgInR5cGUiOiAibm9uZSIsICJ2IjogIjIifQ==