Table of Contents — 35+ sections, 16 Mermaid diagrams (12 types), 11 code languages, 9 LaTeX equations, 6 custom animated SVGs, GeoJSON map, STL 3D model
| # | Section | Type | Complexity |
|---|---|---|---|
| 1 | Identity + Tech Arsenal | Properties, skill icons, trophies, stats | Badges, 6 icon rows |
| 2 | Animated Dashboard | Custom SVG | 12+ CSS @keyframes, gauges |
| 3 | Skills Radar | Custom SVG | 8-axis radar, SMIL animations |
| 4 | Network Topology | Custom SVG | Orbital animation, 14 nodes |
| 5 | 3D Contribution Map | GitHub Actions SVG | Dark/light <picture> |
| 6 | Contribution Snake | Animated SVG | Auto-generated game |
| 7 | System Architecture | Mermaid graph TB |
28 nodes, 6 subgraphs |
| 8 | Class Hierarchy — CIA Einstein | Mermaid classDiagram |
8 classes, inheritance |
| 9 | Database Schema — Lead Pipeline | Mermaid erDiagram |
6 entities, FK relations |
| 10 | Email Delivery Pipeline | Mermaid sequenceDiagram |
8 participants, parallel |
| 11 | Lead Lifecycle | Mermaid stateDiagram-v2 |
12 states, transitions |
| 12 | Technology Distribution | Mermaid pie (x2) |
Data labels |
| 13 | Development Timeline | Mermaid gantt |
5 sections, 18 milestones |
| 14 | Technology Radar | Mermaid quadrantChart |
15 technologies plotted |
| 15 | Lead Pipeline Journey | Mermaid journey |
5 stages, satisfaction scores |
| 16 | Email Delivery Metrics | Mermaid xychart-beta |
Time series, bar + line |
| 17 | System Block Architecture | Mermaid block-beta |
Nested blocks, columns |
| 18 | Featured Code | Production code | Python, C, MQL5, Solidity, JS, SQL, Bash, Rust, Go, Dockerfile, YAML |
| 19 | Chess Game | Interactive via Issues | Board + move links |
| 20 | Production Systems | Badge grid + ASCII table | 9 projects, 3x3 |
| 21 | GitHub Analytics | Stats cards | Streak, graph, summary |
| 22 | Metrics Deep Dive | Lowlighter SVGs | 8 metric plugins |
| 23 | AI Agent Architecture | Mermaid graph TD |
19 agents, 5 groups |
| 24 | Tech Ecosystem | Mermaid mindmap |
6 branches, 40+ leaves |
| 25 | Operations Map — 4 Countries | GeoJSON interactive | 6 markers, polygon |
| 26 | CI/CD Pipeline | Mermaid gitGraph |
Branches, merges, tags |
| 27 | Cryptography Research | LaTeX equations + table | 9 formulas, 6 algorithms |
| 28 | Latest Blog Posts | Auto-updated via Actions | RSS feed |
| 29 | Recent Activity | Auto-updated via Actions | GitHub events |
| 30 | Security Toolkit | Collapsible depth panel | 10 domains, hardware |
| 31 | Trading & DeFi | Collapsible depth panel | 4 systems, 7 capabilities |
| 32 | Email Infrastructure | Collapsible depth panel | 4 relays, quality gates |
| 33 | Database Architecture | Collapsible depth panel | PG16 tuning, indexes |
| 34 | Business Operations | Collapsible depth panel | 4 countries, compliance |
| 35 | Dev Environment | Collapsible depth panel | M4 Pro, 230+ tools |
| 36 | Automation & Browser Fleet | Collapsible depth panel | 4 tools, stealth |
| 37 | Project Timeline | Mermaid timeline |
5 periods, milestones |
| 38 | 3D Crystal — Interactive | STL 3D model | 84-facet diamond |
| 39 | Footnotes | Scholarly annotations | 8 with inline math |
 SYSTEM CONFIGURATION
# /etc/architect.conf — v9.4
[identity]
name = Matheus Agueda
class = Systems Architect & Security Researcher
scope = PT · FR · NL · ES
style = git push main → production
uptime = 99.7% across 9 services
[scale]
systems = 9 production services
agents = 32 AI (Claude Opus 4.6)
leads = 1,930,000 processed
emails = 385/day × 3 DKIM relays
countries = 4 active business entities
tools = 230+ MCP · 39 CLI · 800+ skills
[compute]
local = M4 Pro 14-core · 24 GB unified
gpu = Metal (Apple Silicon) · ECDSA accel
database = PostgreSQL 16 · 6 GB shared_buffers
cloud = Railway ×3 · Vercel ×2 · CF ×2
[security]
platform = CIA Einstein (Python + C + Metal)
scanning = semgrep · nuclei · nikto · nmap
crypto = ECDSA lattice · brainwallet · GPU
redteam = garak · promptfoo · LLM jailbreak
offensive = 39 CLI tools · 10 MCP servers |
 TECH ARSENAL
|
|
|
|
Note
Custom animated SVG — Pure CSS @keyframes + SMIL animations. No JavaScript. Circular gauge shows 1.93M lead count, bar chart displays relay capacity distribution, mini line graph tracks 30-day delivery trend. All rendered server-side with zero external dependencies.
8-axis skill assessment — Python (92%), Security (85%), Infrastructure (80%), Cryptography (75%), Trading (65%), Email Delivery (90%), Scraping (88%), AI/ML (78%). Two overlapping polygons show current capability vs. 2026 targets. Scan line overlay + pulsing vertices.
Tip
Interactive network visualization — Central COMMANDER node with 3 pulsing rings and rotating orbit. 4 satellite clusters (DATA, EMAIL, SECURITY, TRADE) connected by animated particle-flow paths. Infrastructure nodes (PostgreSQL, Railway, Vercel, Cloudflare) positioned at network edges. 14 CSS @keyframes + SMIL animateMotion along cubic bezier paths.
$\text{Throughput} = \sum_{i=1}^{n} \lambda_i \cdot \mu_i^{-1}, \quad n = 9 \text{ services}, \quad \lambda \sim \text{Poisson}(\bar{\lambda}=385/\text{day})$
%%{init: {'theme': 'dark'}}%%
graph TB
CMD["COMMANDER DISPATCHER<br/><i>Routes tasks across 32 AI agents</i>"]
subgraph DATA["DATA PIPELINE"]
GLS["Global Lead Scraper<br/>1.93M leads · 752K eligible"]
MAPS["Google Maps API"]
DDG["DuckDuckGo Scraper"]
CNPJ["CNPJ Harvester"]
VALID["Email Validator<br/>789K verified emails"]
end
subgraph MAIL["EMAIL ENGINE"]
TURBO["Turbo Sender<br/>6 parallel threads · 385/day"]
R1["Resend<br/>65/d · DKIM ✓"]
R2["SendGrid<br/>120/d · DKIM ✓"]
R3["Google SMTP<br/>200/d · DKIM ✓"]
end
subgraph SEC["CIA EINSTEIN"]
CIA["Multi-Agent Security<br/>Python + C + Metal GPU"]
OSINT["OSINT Recon<br/>37 tools"]
VULN["Vuln Scanner<br/>nuclei · nikto · semgrep"]
CRYPTO["Cryptanalysis<br/>ECDSA · lattice · GPU"]
end
subgraph TRADE["TRADING SYSTEMS"]
JESSIE["Jessie Trader<br/>MQL5 · MetaTrader 5"]
RUG["RugPull Scanner<br/>TypeScript · Ethers.js"]
MOON["MoonSniper<br/>Mempool · MEV"]
end
subgraph BIZ["MULTI-COUNTRY OPS"]
JOAO["Joao Bot<br/>WhatsApp · Baileys"]
GV["Green Village<br/>FastAPI · Dashboard"]
EMP["Empresas.app<br/>PT · FR · NL · ES"]
end
subgraph INFRA["INFRASTRUCTURE"]
PG[("PostgreSQL 16<br/>6GB shared_buffers<br/>1.93M rows")]
RW["Railway x3"]
VC["Vercel x2"]
CF["Cloudflare x2 zones"]
MON["Sentry + Grafana<br/>+ Zombie Killer"]
end
CMD --> DATA
CMD --> MAIL
CMD --> SEC
CMD --> TRADE
CMD --> BIZ
GLS --> MAPS
GLS --> DDG
GLS --> CNPJ
GLS --> VALID
VALID --> TURBO
TURBO --> R1
TURBO --> R2
TURBO --> R3
CIA --> OSINT
CIA --> VULN
CIA --> CRYPTO
DATA --> PG
MAIL --> PG
BIZ --> RW
BIZ --> VC
BIZ --> CF
style CMD fill:#FF2D95,stroke:#BD00FF,color:#fff,font-weight:bold,stroke-width:2px
Note
32 AI agents coordinate via Claude Code MCP protocol with 230+ tools across 10 servers. The Commander Dispatcher routes tasks to specialized subagents based on context — no manual assignment. Leads flow from scraping → validation → email delivery while CIA Einstein monitors security across the entire stack.
%%{init: {'theme': 'dark'}}%%
classDiagram
class BaseAgent {
<<abstract>>
+String name
+String[] tools
+Dict memory
+execute(task) Result
+delegate(subtask) Agent
+save_memory() void
}
class CommanderDispatcher {
+route_task(prompt) Agent
+parallel_fanout(tasks) Result[]
+cross_project_sync() void
}
class SecurityAgent {
+scan_target(host) Findings
+nuclei_scan(templates) CVE[]
+osint_gather(entity) Dossier
}
class ScraperAgent {
+google_maps_scrape(query) Lead[]
+duckduckgo_harvest(niche) Lead[]
+validate_emails(leads) Lead[]
+proxy_rotate() Proxy
}
class EmailAgent {
+turbo_send(campaign) Stats
+bounce_monitor() Alert[]
+dkim_verify(relay) bool
+circuit_breaker(relay) void
}
class CryptoAgent {
+ecdsa_attack(curve) Key
+lattice_reduce(basis) Vector
+brainwallet_audit(dict) Key[]
+metal_gpu_multiply(points) Result
}
class TradeAgent {
+mql5_signal(pair) Signal
+mempool_sniff(chain) Tx[]
+rugpull_detect(token) Risk
}
class EinsteinCore {
+correlation_matrix Dict
+trust_scores Dict
+cost_ledger Ledger
+orchestrate() void
+anomaly_detect() Alert[]
}
BaseAgent <|-- CommanderDispatcher
BaseAgent <|-- SecurityAgent
BaseAgent <|-- ScraperAgent
BaseAgent <|-- EmailAgent
BaseAgent <|-- CryptoAgent
BaseAgent <|-- TradeAgent
BaseAgent <|-- EinsteinCore
CommanderDispatcher --> SecurityAgent : dispatches
CommanderDispatcher --> ScraperAgent : dispatches
CommanderDispatcher --> EmailAgent : dispatches
CommanderDispatcher --> CryptoAgent : dispatches
CommanderDispatcher --> TradeAgent : dispatches
EinsteinCore --> CommanderDispatcher : orchestrates
ScraperAgent --> EmailAgent : feeds leads
SecurityAgent --> EinsteinCore : reports findings
CryptoAgent --> SecurityAgent : shares keys
$\text{Storage} = \sum_{t \in \text{tables}} |t| \cdot \bar{w}t, \quad |leads| = 1.93 \times 10^6, \quad \bar{w}{lead} \approx 512\text{B}$
%%{init: {'theme': 'dark'}}%%
erDiagram
LEADS {
int id PK
varchar company_name
varchar email
varchar phone
varchar website
varchar country
int quality_score
varchar email_status
varchar source
timestamp scraped_at
timestamp validated_at
}
CAMPAIGNS {
int id PK
varchar name
varchar subject
text template_html
varchar status
int target_count
int sent_count
int bounce_count
float open_rate
timestamp created_at
}
SENDS {
int id PK
int lead_id FK
int campaign_id FK
varchar relay_used
varchar status
varchar bounce_type
varchar message_id
timestamp sent_at
timestamp opened_at
}
RELAYS {
int id PK
varchar name
varchar provider
int daily_limit
int sent_today
float bounce_rate
boolean dkim_verified
boolean active
varchar warmup_stage
}
BLACKLIST {
int id PK
varchar domain
varchar reason
timestamp added_at
}
QUALITY_CHECKS {
int id PK
int lead_id FK
varchar check_type
varchar result
float score_delta
timestamp checked_at
}
LEADS ||--o{ SENDS : "receives"
CAMPAIGNS ||--o{ SENDS : "contains"
RELAYS ||--o{ SENDS : "dispatches via"
LEADS ||--o{ QUALITY_CHECKS : "validated by"
BLACKLIST ||--o{ LEADS : "blocks domain"
Tip
The lead pipeline processes 1.93M leads through 6 quality gates before any email is sent. Each lead undergoes MX verification, SMTP validation, domain blacklist check, quality scoring, and deduplication. Only 752K survive — a 61% rejection rate that protects sender reputation. Index strategy: GIN for full-text, BRIN for time-series, partial indexes on email_status = 'smtp_verified'.
%%{init: {'theme': 'dark', 'sequence': {'actorFontSize': 14, 'messageFontSize': 12, 'mirrorActors': false}}}%%
sequenceDiagram
participant SC as Scraper Engine
participant VA as Email Validator
participant DB as PostgreSQL 16
participant TS as Turbo Sender
participant R1 as Resend (65/d)
participant R2 as SendGrid (120/d)
participant R3 as Google SMTP (200/d)
participant IN as Recipient Inbox
SC->>DB: 1.93M raw leads (Maps + DDG + CNPJ)
activate DB
DB->>VA: Batch validate emails
activate VA
VA-->>DB: 789K verified
VA-->>DB: 208K junk rejected
deactivate VA
DB->>TS: 752K eligible (quality >= 40)
deactivate DB
activate TS
Note over TS: 6 parallel threads<br/>Circuit breaker per relay<br/>Bounce rate < 3%
par Relay 1
TS->>R1: DKIM-signed batch
R1->>IN: Delivered (65/day)
and Relay 2
TS->>R2: DKIM-signed batch
R2->>IN: Delivered (120/day)
and Relay 3
TS->>R3: DKIM-signed batch
R3->>IN: Delivered (200/day)
end
deactivate TS
Note over SC,IN: Total: 385 emails/day | DMARC quarantine | SPF ~all
$P(\text{eligible} \mid \text{scraped}) = \frac{752{,}000}{1{,}930{,}000} \approx 0.39 \quad \text{(39% survival rate through 6 quality gates)}$
%%{init: {'theme': 'dark', 'themeVariables': {'primaryColor': '#FF2D95', 'primaryTextColor': '#ffffff', 'primaryBorderColor': '#BD00FF', 'lineColor': '#00D4FF'}}}%%
stateDiagram-v2
[*] --> Scraped: Google Maps / DDG / CNPJ
Scraped --> Validated: Email verification pass
Scraped --> Dropped: Syntax error / no email
Validated --> Eligible: quality_score >= 40
Validated --> Rejected: invalid / bounced / junk
Eligible --> Queued: Campaign assignment
Queued --> Sending: Turbo Sender dispatch
Sending --> Delivered: DKIM pass + 200 OK
Sending --> Bounced: Hard bounce
Sending --> Deferred: Soft bounce
Deferred --> Sending: Retry after backoff
Delivered --> Opened: Tracking pixel fired
Opened --> Replied: Engagement detected
Replied --> Converted: Deal closed
Bounced --> Blacklisted: 3+ bounces from domain
Blacklisted --> [*]
Converted --> [*]
Rejected --> [*]
Dropped --> [*]
%%{init: {'theme': 'dark'}}%%
pie showData
title Production Tech Stack
"Python" : 45
"TypeScript" : 20
"JavaScript" : 12
"C / Metal" : 8
"MQL5" : 5
"Solidity" : 5
"Bash" : 3
"SQL" : 2
|
%%{init: {'theme': 'dark'}}%%
pie showData
title 1.93M Lead Funnel
"Eligible (752K)" : 39
"Junk/Invalid (208K)" : 11
"Pending Validation" : 20
"Email Verified (789K)" : 41
|
%%{init: {'theme': 'dark'}}%%
gantt
title System Development Roadmap (2024-2026)
dateFormat YYYY-MM-DD
axisFormat %b %Y
section Lead Scraper (GLS)
Google Maps Scraper :done, gls1, 2024-06-01, 2024-08-15
DuckDuckGo Harvester :done, gls2, 2024-08-01, 2024-09-30
CNPJ Enrichment :done, gls3, 2024-09-01, 2024-10-15
Email Validation Pipeline :done, gls4, 2024-10-01, 2024-12-01
Turbo Sender v1 :done, gls5, 2024-11-15, 2025-01-15
Quality Scoring Engine :done, gls6, 2025-01-01, 2025-03-01
Turbo Sender v2 (6-thread):done, gls7, 2025-06-01, 2025-08-01
SMTP Warmup Orchestrator :active, gls8, 2026-02-01, 2026-06-01
section CIA Einstein
Core Agent Framework :done, cia1, 2025-01-01, 2025-04-01
Metal GPU ECDSA :done, cia2, 2025-03-01, 2025-06-01
Lattice Reduction (LLL) :done, cia3, 2025-05-01, 2025-08-01
32-Agent MCP Orchestration:done, cia4, 2025-07-01, 2025-10-01
Einstein v9.4 Autonomous :active, cia5, 2026-01-01, 2026-07-01
section Trading Systems
Jessie Trader MQL5 :done, jt1, 2024-09-01, 2025-03-01
RugPull Detector :done, rp1, 2025-06-01, 2025-09-01
MoonSniper Mempool :active, ms1, 2025-10-01, 2026-06-01
section Business Ops
Green Village Website :done, gv1, 2024-03-01, 2024-06-01
Joao Bot WhatsApp :done, jb1, 2024-12-01, 2025-04-01
Empresa Automation :done, ea1, 2025-02-01, 2025-06-01
GeeLark Multi-Account :active, gl1, 2025-09-01, 2026-04-01
section Infrastructure
PostgreSQL 16 Optimization:done, inf1, 2025-08-01, 2025-10-01
DKIM 3-Relay Setup :done, inf2, 2025-10-01, 2025-12-01
Claude Code Hacker Config :done, inf3, 2026-04-01, 2026-05-17
%%{init: {'theme': 'dark'}}%%
quadrantChart
title Technology Investment vs Maturity
x-axis Low Maturity --> High Maturity
y-axis Low Investment --> High Investment
Python: [0.85, 0.90]
TypeScript: [0.75, 0.65]
C + Metal: [0.55, 0.80]
MQL5: [0.70, 0.45]
Solidity: [0.40, 0.55]
PostgreSQL: [0.90, 0.70]
Claude AI/MCP: [0.35, 0.95]
Playwright: [0.65, 0.60]
Docker/Railway: [0.80, 0.50]
ECDSA Research: [0.30, 0.85]
Lattice Crypto: [0.20, 0.75]
Mempool Analysis: [0.25, 0.60]
Email Deliverability: [0.75, 0.80]
OSINT Tooling: [0.50, 0.40]
WhatsApp Automation: [0.60, 0.35]
%%{init: {'theme': 'dark'}}%%
journey
title A Lead's Journey Through The Pipeline
section Scraping
Discovered on Google Maps: 5: Scraper Engine
HTML parsed + fields extracted: 4: Scraper Engine
Stored in PostgreSQL: 5: Scraper Engine
Deduplicated against 1.93M: 3: Quality Engine
section Validation
MX record lookup: 4: Email Validator
SMTP handshake verification: 3: Email Validator
Domain blacklist check: 5: Quality Engine
Quality score computed: 4: Quality Engine
section Campaign
Assigned to campaign: 5: Campaign Dispatcher
Template rendered with merge fields: 4: Turbo Sender
DKIM signature applied: 5: Turbo Sender
Relay selected (round-robin): 4: Turbo Sender
section Delivery
SMTP connection to relay: 4: Relay Agent
TLS handshake (TLS 1.3): 5: Relay Agent
Delivered to recipient MTA: 3: Relay Agent
Bounce handling if rejected: 2: Bounce Monitor
section Engagement
Email opened (pixel tracked): 5: Analytics
Link clicked: 5: Analytics
Reply detected: 5: Analytics
Converted to customer: 5: Analytics
Important
Each lead traverses 5 pipeline stages with 18 discrete steps. The satisfaction scores reflect operational reliability — lower scores (bounces, deduplication) indicate where friction exists. The circuit breaker pattern isolates failing relays before cascade failures propagate. Total pipeline latency from scraping to delivery: ~48 hours (dominated by warmup pacing).
%%{init: {'theme': 'dark'}}%%
xychart-beta
title "Monthly Email Delivery Performance"
x-axis ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
y-axis "Emails Sent" 0 --> 14000
bar [2100, 3500, 5200, 7800, 8500, 9200, 10100, 10800, 11200, 11550, 11700, 11900]
line [1900, 3200, 4800, 7200, 8000, 8800, 9600, 10200, 10800, 11100, 11400, 11600]
Warmup curve follows
$f(t) = C \cdot (1 - e^{-\lambda t})$ where$C = 385\text{/day}$ (capacity ceiling) and$\lambda \approx 0.15$ (growth rate). Bar = emails sent, line = emails delivered (bounce-adjusted). Current bounce rate: 2.1% (target: < 3%).
%%{init: {'theme': 'dark'}}%%
graph LR
CMD["COMMANDER<br/>DISPATCHER"]
subgraph DATA["Data Pipeline"]
GLS["Global Lead Scraper"]
MAPS["Maps + DDG + CNPJ"]
VALID["Email Validator"]
end
subgraph MAIL["Email Engine"]
TURBO["Turbo Sender"]
RELAYS["3 DKIM Relays"]
BOUNCE["Bounce Monitor"]
end
subgraph SEC["Security"]
CIA["CIA Einstein"]
OSINT["OSINT · 37 tools"]
CRYPTO["Cryptanalysis"]
end
subgraph TRADE["Trading"]
JESSIE["Jessie Trader"]
RUGPULL["RugPull Scanner"]
MOON["MoonSniper"]
end
subgraph INFRA["Infrastructure"]
PG[("PostgreSQL 16")]
RW["Railway x3"]
VC["Vercel x2"]
end
CMD --> DATA
CMD --> MAIL
CMD --> SEC
CMD --> TRADE
DATA --> INFRA
MAIL --> INFRA
style CMD fill:#FF2D95,stroke:#BD00FF,color:#fff,font-weight:bold,stroke-width:2px
Caution
These are real production snippets extracted from live systems — not toy examples. Each demonstrates a distinct architectural pattern: async pipelines, GPU compute shaders, algorithmic trading signals, smart contract simulation, event-driven messaging, complex SQL with CTEs/window functions, shell orchestration, systems programming, container optimization, and declarative infrastructure.
|
Turbo Sender — 6-thread parallel email dispatch from __future__ import annotations
import asyncio
from dataclasses import dataclass, field
@dataclass
class CircuitBreakerPool:
relays: list[SMTPRelay]
max_fail: int = 5
reset_after: int = 3600
_bounced: int = field(default=0, init=False)
@property
def bounce_rate(self) -> float:
return self._bounced / max(self._sent, 1)
async def send_batch(
self, leads: list[Lead], *, dkim: bool = True
) -> None:
for lead in leads:
relay = self._next_healthy_relay()
if relay is None:
raise AllRelaysTripped()
try:
await relay.send(
lead.email.encode('ascii'),
dkim_sign=dkim,
)
self._sent += 1
except BounceError as e:
self._bounced += 1
if relay.bounce_rate > 0.05:
relay.trip_breaker()
async def dispatch_campaign(
campaign_id: int,
) -> BatchResult:
leads = await db.fetch_eligible(
campaign_id, limit=385
)
pool = CircuitBreakerPool(
[resend, sendgrid, gsmtp],
max_fail=5, reset_after=3600,
)
async with asyncio.TaskGroup() as tg:
for chunk in partition(leads, size=6):
tg.create_task(
pool.send_batch(chunk, dkim=True)
)
return BatchResult(
sent=len(leads),
bounced=pool.bounced,
rate=pool.bounce_rate,
) |
CIA Einstein — Metal GPU ECDSA point multiplication // Apple Silicon · Metal Compute Shader
// secp256k1: y² = x³ + 7 (mod p)
// p = 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
// FFFFFFFFFFFFFFFFFFFFFFFEFFFFFC2F
#include <metal_stdlib>
using namespace metal;
typedef struct { uint32_t d[8]; } uint256_t;
typedef struct { uint256_t x, y; } ECPoint;
constant ECPoint EC_INFINITY = {{{0}}, {{0}}};
ECPoint ec_double(ECPoint P) {
// λ = (3x² + a) / (2y) mod p
// For secp256k1: a = 0, so λ = 3x² / 2y
uint256_t lambda = mod_div(
mod_mul(THREE, mod_sq(P.x)),
mod_add(P.y, P.y)
);
uint256_t x3 = mod_sub(
mod_sq(lambda),
mod_add(P.x, P.x)
);
uint256_t y3 = mod_sub(
mod_mul(lambda, mod_sub(P.x, x3)),
P.y
);
return (ECPoint){x3, y3};
}
kernel void ec_point_mul(
device const uint256_t* scalars [[buffer(0)]],
device const ECPoint* bases [[buffer(1)]],
device ECPoint* results [[buffer(2)]],
uint tid [[thread_position_in_grid]]
) {
ECPoint R = EC_INFINITY;
uint256_t k = scalars[tid];
ECPoint P = bases[tid];
// Double-and-add: O(256) iterations
for (int i = 255; i >= 0; i--) {
R = ec_double(R);
if (bit_at(k, i))
R = ec_add(R, P);
}
results[tid] = R;
} |
|
Jessie Trader — Multi-timeframe confluence entry // MQL5 · MetaTrader 5 · Live execution
// Entry: H4 trend + H1 pullback + M15 confirmation
input double RiskPercent = 1.5;
input int FastEMA = 21;
input int SlowEMA = 50;
input int RSI_Period = 14;
input double RSI_Oversold = 30.0;
void OnTick() {
if (!IsNewBar(PERIOD_H1)) return;
double ema_fast = iMA(
_Symbol, PERIOD_H1, FastEMA, 0,
MODE_EMA, PRICE_CLOSE
);
double ema_slow = iMA(
_Symbol, PERIOD_H4, SlowEMA, 0,
MODE_EMA, PRICE_CLOSE
);
double rsi = iRSI(
_Symbol, PERIOD_H1,
RSI_Period, PRICE_CLOSE
);
// Multi-TF confluence: H4 uptrend +
// H1 RSI oversold + M15 reversal candle
if (ema_fast > ema_slow
&& rsi < RSI_Oversold
&& ConfirmOnM15()) {
double lots = CalcLotSize(RiskPercent);
double sl = GetSwingLow(PERIOD_H1, 20);
double tp = Ask + (Ask - sl) * 2.0;
ExecuteEntry(
ORDER_TYPE_BUY, lots, sl, tp
);
}
}
bool ConfirmOnM15() {
double o = iOpen(_Symbol, PERIOD_M15, 1);
double c = iClose(_Symbol, PERIOD_M15, 1);
double body = MathAbs(c - o);
double range = iHigh(_Symbol, PERIOD_M15, 1)
- iLow(_Symbol, PERIOD_M15, 1);
// Bullish engulfing with >60% body ratio
return c > o && body / range > 0.6;
} |
RugPull Scanner — Honeypot detection via simulation // SPDX-License-Identifier: MIT
// Solidity 0.8.20 · Ethers.js · On-chain
pragma solidity ^0.8.20;
import "@uniswap/v2-periphery/IUniswapV2Router02.sol";
import "@openzeppelin/contracts/token/ERC20/IERC20.sol";
contract HoneypotDetector {
IUniswapV2Router02 public immutable router;
address public constant WETH =
0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2;
constructor(address _router) {
router = IUniswapV2Router02(_router);
}
/// @notice Simulates sell to detect honeypot
/// @param token Target ERC-20 address
/// @param amount Token amount to sell
/// @return canSell True if sell succeeds
/// @return amountOut ETH received
function simulateSell(
address token,
uint256 amount
) external returns (
bool canSell,
uint256 amountOut
) {
IERC20(token).approve(
address(router), amount
);
address[] memory path = new address[](2);
path[0] = token;
path[1] = WETH;
try router.swapExactTokensForETH(
amount, 0, path,
address(this),
block.timestamp + 60
) returns (uint256[] memory amounts) {
return (amounts[1] > 0, amounts[1]);
} catch {
// Sell reverted = HONEYPOT DETECTED
return (false, 0);
}
}
} |
|
Joao Bot — WhatsApp Automation // Multi-instance WA bot · session persistence
// Railway deploy · auto-reply · CRM integration
import {
makeWASocket,
useMultiFileAuthState
} from '@whiskeysockets/baileys'
import pino from 'pino'
const logger = pino({ level: 'silent' })
export async function createBot(sessionId) {
const { state, saveCreds } =
await useMultiFileAuthState(
`./sessions/${sessionId}`
)
const sock = makeWASocket({
auth: state,
logger,
printQRInTerminal: true,
browser: ['Joao Bot', 'Chrome', '120.0'],
getMessage: async (key) => {
return { conversation: '' }
}
})
sock.ev.on('creds.update', saveCreds)
sock.ev.on('messages.upsert',
async ({ messages }) => {
for (const msg of messages) {
if (!msg.key.fromMe && msg.message)
await routeMessage(sock, msg)
}
}
)
return sock
}
async function routeMessage(sock, msg) {
const text =
msg.message.conversation
|| msg.message.extendedTextMessage?.text
|| ''
const jid = msg.key.remoteJid
const commands = {
'!status': () => getSystemStatus(),
'!leads': () => getLeadStats(),
'!health': () => runHealthCheck(),
'!relay': () => getRelayCapacity(),
'!bounce': () => getBounceReport(),
}
const handler = commands[text.split(' ')[0]]
if (handler) {
const reply = await handler()
await sock.sendMessage(jid, { text: reply })
}
} |
Lead Quality Engine -- Complex scoring: CTEs + window functions
-- + partial indexes + NTILE percentiles
WITH quality_matrix AS (
SELECT
l.id, l.email, l.company_name,
l.country,
CASE
WHEN l.email_status = 'smtp_verified'
THEN 30
WHEN l.email_status = 'mx_valid'
THEN 20
WHEN l.email_status = 'syntax_ok'
THEN 10
ELSE 0
END AS email_score,
CASE
WHEN l.phone IS NOT NULL THEN 10
ELSE 0
END AS phone_score,
CASE
WHEN l.website IS NOT NULL
AND l.website !~
'^https?://(www\.)?facebook'
THEN 15
ELSE 0
END AS web_score,
COALESCE(
(SELECT COUNT(*) FROM sends s
WHERE s.lead_id = l.id
AND s.status = 'delivered'),
0
) AS prior_delivers,
NTILE(100) OVER (
PARTITION BY l.country
ORDER BY l.quality_score DESC
) AS country_percentile
FROM leads l
WHERE l.email_status NOT IN (
'invalid','bounced','junk',
'disposable','mx_fail',
'syntax_error','gate_fail',
'smtp_fail'
)
AND NOT EXISTS (
SELECT 1 FROM blacklist b
WHERE l.email LIKE '%@' || b.domain
)
),
ranked AS (
SELECT *,
email_score + phone_score + web_score
+ LEAST(prior_delivers * 5, 20)
AS composite,
ROW_NUMBER() OVER (
PARTITION BY country
ORDER BY email_score + phone_score
+ web_score DESC
) AS country_rank
FROM quality_matrix
)
SELECT id, email, company_name, country,
composite AS quality_score,
country_rank, country_percentile
FROM ranked
WHERE composite >= 40
ORDER BY composite DESC
LIMIT 1000; |
|
Infrastructure Automation #!/usr/bin/env bash
# deploy.sh — Zero-downtime multi-service
# deployment with health checks & rollback
set -euo pipefail
readonly SERVICES=(
cia-einstein
global-lead-scraper
joao-bot
jessie-trader
green-village
)
readonly HEALTH_TIMEOUT=30
readonly ROLLBACK_ON_FAIL=true
log() {
printf "\033[36m[%s]\033[0m %s\n" \
"$(date +%H:%M:%S)" "$1"
}
err() {
printf "\033[31m[ERROR]\033[0m %s\n" \
"$1" >&2
}
check_health() {
local svc="$1" url="$2" attempt=0
while (( attempt < HEALTH_TIMEOUT )); do
if curl -sf --max-time 5 \
"$url/health" \
| jq -e '.status == "ok"' \
> /dev/null 2>&1; then
log "$svc: healthy (${attempt}s)"
return 0
fi
(( attempt++ )); sleep 1
done
err "$svc: health check failed"
return 1
}
deploy_service() {
local svc="$1"
local before
before=$(git -C "$HOME/Documents/$svc" \
rev-parse HEAD)
log "Deploying $svc ($(git -C \
"$HOME/Documents/$svc" \
rev-parse --short HEAD))"
git -C "$HOME/Documents/$svc" \
push origin main 2>/dev/null || {
err "$svc: push failed"; return 1
}
if ! check_health "$svc" \
"https://${svc}.up.railway.app"; then
if [[ "$ROLLBACK_ON_FAIL" == "true" ]]
then
err "$svc: rolling back to $before"
git -C "$HOME/Documents/$svc" \
push origin "$before:main" -f
fi
return 1
fi
}
main() {
local failed=()
log "Deploying ${#SERVICES[@]} services"
for svc in "${SERVICES[@]}"; do
deploy_service "$svc" \
|| failed+=("$svc")
done
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
log "Deployed: $(( ${#SERVICES[@]} \
- ${#failed[@]} ))/${#SERVICES[@]}"
[[ ${#failed[@]} -gt 0 ]] \
&& err "Failed: ${failed[*]}" \
&& exit 1
log "All systems operational"
}
main "$@" |
MoonSniper — Mempool transaction decoder // Mempool frontrunning · MEV extraction
// Decode pending Uniswap V2 swaps from
// raw transaction data in real-time
use ethers::prelude::*;
use ethers::abi::{self, Token};
use std::sync::Arc;
const UNISWAP_V2_ROUTER: &str =
"0x7a250d5630B4cF539739dF2C5dAcb4c659F2488D";
const SWAP_EXACT_ETH: [u8; 4] =
[0x7f, 0xf3, 0x6a, 0xb5];
#[derive(Debug)]
struct PendingSwap {
tx_hash: H256,
token_out: Address,
amount_in: U256,
min_out: U256,
deadline: U256,
gas_price: U256,
}
async fn monitor_mempool(
ws: Arc<Provider<Ws>>,
) -> Result<(), Box<dyn std::error::Error>> {
let mut stream = ws
.subscribe_pending_txs()
.await?;
while let Some(tx_hash) = stream.next().await {
let tx = match ws
.get_transaction(tx_hash)
.await? {
Some(t) => t,
None => continue,
};
// Filter: Uniswap V2 Router only
if tx.to != Some(
UNISWAP_V2_ROUTER.parse()?
) { continue; }
let input = &tx.input;
if input.len() < 4 { continue; }
let selector: [u8; 4] =
input[..4].try_into()?;
if selector == SWAP_EXACT_ETH {
let decoded = abi::decode(
&[
abi::ParamType::Uint(256),
abi::ParamType::Array(
Box::new(
abi::ParamType::Address
)
),
abi::ParamType::Address,
abi::ParamType::Uint(256),
],
&input[4..],
)?;
let swap = PendingSwap {
tx_hash,
token_out: extract_addr(
&decoded[1]
),
amount_in: tx.value,
min_out: extract_u256(
&decoded[0]
),
deadline: extract_u256(
&decoded[3]
),
gas_price: tx.gas_price
.unwrap_or_default(),
};
if is_profitable(&swap).await? {
execute_frontrun(&swap).await?;
}
}
}
Ok(())
} |
|
Proxy Rotator — Residential proxy health checker // Concurrent proxy validation with
// circuit breaker + health scoring
package proxy
import (
"context"
"net/http"
"net/url"
"sync"
"sync/atomic"
"time"
)
type Proxy struct {
URL *url.URL
Score atomic.Int32
Failures atomic.Int32
LastCheck time.Time
mu sync.RWMutex
}
type Pool struct {
proxies []*Proxy
current atomic.Int64
maxFails int32
}
func NewPool(
urls []string, maxFails int32,
) (*Pool, error) {
pool := &Pool{maxFails: maxFails}
for _, raw := range urls {
u, err := url.Parse(raw)
if err != nil {
return nil, err
}
p := &Proxy{URL: u}
p.Score.Store(100)
pool.proxies = append(pool.proxies, p)
}
return pool, nil
}
func (p *Pool) Next() *Proxy {
n := len(p.proxies)
for i := 0; i < n; i++ {
idx := p.current.Add(1) % int64(n)
proxy := p.proxies[idx]
if proxy.Failures.Load() < p.maxFails {
return proxy
}
}
return nil // all tripped
}
func (p *Proxy) Check(
ctx context.Context,
) error {
client := &http.Client{
Timeout: 10 * time.Second,
Transport: &http.Transport{
Proxy: http.ProxyURL(p.URL),
},
}
req, _ := http.NewRequestWithContext(
ctx, "GET",
"https://httpbin.org/ip", nil,
)
resp, err := client.Do(req)
if err != nil {
p.Failures.Add(1)
p.Score.Add(-10)
return err
}
defer resp.Body.Close()
p.Failures.Store(0)
p.Score.Store(100)
p.mu.Lock()
p.LastCheck = time.Now()
p.mu.Unlock()
return nil
} |
Production Container — Multi-stage optimized # Multi-stage build: 1.2GB → 89MB final image
# Python 3.14 + PostgreSQL client + Playwright
# ━━━ Stage 1: Build dependencies ━━━━━━━━━━
FROM python:3.14-slim AS builder
WORKDIR /build
COPY requirements.txt .
RUN apt-get update && apt-get install -y \
--no-install-recommends \
gcc libpq-dev \
&& pip install --no-cache-dir \
--prefix=/install \
-r requirements.txt \
&& apt-get purge -y gcc \
&& rm -rf /var/lib/apt/lists/*
# ━━━ Stage 2: Playwright browsers ━━━━━━━━
FROM python:3.14-slim AS playwright
COPY --from=builder /install /usr/local
RUN playwright install chromium \
&& playwright install-deps chromium
# ━━━ Stage 3: Production image ━━━━━━━━━━━
FROM python:3.14-slim AS production
LABEL maintainer="Matheus Agueda"
LABEL version="2.0"
LABEL description="Global Lead Scraper"
WORKDIR /app
# Non-root user for security
RUN groupadd -r app && useradd -r -g app app
# Copy Python packages
COPY --from=builder /install /usr/local
# Copy Playwright browsers
COPY --from=playwright \
/root/.cache/ms-playwright \
/home/app/.cache/ms-playwright
# Copy application code
COPY --chown=app:app . .
# PostgreSQL client for pg_isready
RUN apt-get update && apt-get install -y \
--no-install-recommends \
libpq5 postgresql-client curl \
&& rm -rf /var/lib/apt/lists/*
# Health check endpoint
HEALTHCHECK --interval=30s \
--timeout=5s --retries=3 \
CMD curl -sf http://localhost:8000/health \
|| exit 1
USER app
ENV PYTHONUNBUFFERED=1 \
PYTHONDONTWRITEBYTECODE=1
EXPOSE 8000
ENTRYPOINT ["python3"]
CMD ["run_turbo_sender.py", "--campaign=9"] |
|
CI/CD Pipeline — GitHub Actions multi-service deploy # .github/workflows/deploy.yml
# Triggered on push to main — parallel service deploy with matrix strategy
name: Deploy All Services
on:
push:
branches: [main]
workflow_dispatch:
inputs:
service:
description: 'Single service to deploy (or "all")'
required: false
default: 'all'
concurrency:
group: deploy-${{ github.ref }}
cancel-in-progress: true
jobs:
test:
runs-on: ubuntu-latest
services:
postgres:
image: postgres:16
env:
POSTGRES_DB: global_leads_test
POSTGRES_PASSWORD: test
ports: ['5432:5432']
options: >-
--health-cmd "pg_isready"
--health-interval 10s
--health-timeout 5s
--health-retries 5
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with: { python-version: '3.14' }
- run: pip install -r requirements.txt
- run: python -m pytest tests/ -x --tb=short
env:
DATABASE_URL: postgresql://postgres:test@localhost/global_leads_test
deploy:
needs: test
runs-on: ubuntu-latest
strategy:
matrix:
service: [cia-einstein, global-lead-scraper, joao-bot, green-village, jessie-trader]
fail-fast: false
steps:
- uses: actions/checkout@v4
- name: Deploy ${{ matrix.service }}
uses: railwayapp/deploy@v1
with:
service: ${{ matrix.service }}
token: ${{ secrets.RAILWAY_TOKEN }}
- name: Health Check
run: |
for i in $(seq 1 30); do
if curl -sf "https://${{ matrix.service }}.up.railway.app/health"; then
echo "✓ ${{ matrix.service }} healthy"
exit 0
fi
sleep 2
done
echo "✗ ${{ matrix.service }} failed health check"
exit 1 |
|
Make a move by opening a new issue with your move in the title!
The board auto-updates via GitHub Actions after each valid move.
| a | b | c | d | e | f | g | h | |
|---|---|---|---|---|---|---|---|---|
| 8 | r | n | b | q | k | b | n | r |
| 7 | p | p | p | p | p | p | p | p |
| 6 | . | . | . | . | . | . | . | . |
| 5 | . | . | . | . | . | . | . | . |
| 4 | . | . | . | . | . | . | . | . |
| 3 | . | . | . | . | . | . | . | . |
| 2 | P | P | P | P | P | P | P | P |
| 1 | R | N | B | Q | K | B | N | R |
Your turn — Play as Black. Use algebraic notation (e2e4, Nf3, O-O).
Note
The chess engine runs via GitHub Actions — when you open an issue with title chess|{MOVE}, the workflow validates the move, updates the board SVG, and commits the new state. Game history is tracked in the repo.
Lead Generation Engine 1.93M leads · 752K eligible · 789K emails 
|
AI Security Research Platform Python + C + Metal GPU · Multi-agent
|
WhatsApp Automation Platform Node.js · Baileys · Railway 
|
Algorithmic Trading MQL5 · MetaTrader 5 · Live execution 
|
Business Platform FastAPI · Dashboard · Calculator · Ads 
|
Anti-Detect Browser Fleet Next.js · Cloud Profiles · API
|
DeFi Security Scanner TypeScript · On-chain · Honeypot detect
|
Token Launch Sniper Python + Rust · Mempool · MEV 
|
Multi-Country Company Ops PT · FR · NL · ES portal automation
|
┌──────────────────────────────────────────────────────────────────────────────────┐
│ PRODUCTION INFRASTRUCTURE │
├───────────────────┬──────────────────────────────────────────────────────────────┤
│ Compute │ Railway (3 svcs) · Vercel (2 apps) · M4 Pro 14-core (local) │
│ Database │ PostgreSQL 16 — 1.93M rows · 6GB shared_buffers · GIN+BRIN │
│ Email Delivery │ 3 DKIM relays — Resend + SendGrid + GSMTP = 385/day │
│ Monitoring │ Sentry · Grafana · Custom health daemons · Zombie Killer │
│ CI/CD │ GitHub Actions (7 workflows) · git push = auto-deploy │
│ DNS & CDN │ Cloudflare (2 zones) · UOL Host · DMARC quarantine │
│ Security │ SPF ~all · DKIM verified · rate limiting · WAF · CSP │
│ AI Agents │ 32 specialized · Claude Opus 4.6 · 230+ MCP tools │
│ Automation │ Playwright fleet · cron pipelines · webhook triggers │
│ Trading │ MetaTrader 5 · Live algo execution · Mempool monitoring │
│ Blockchain │ On-chain analysis · MEV · Honeypot detection · DeFi │
│ Browser Fleet │ GeeLark anti-detect · Stealth Playwright · Chrome CDP │
│ Offensive │ 39 CLI tools · 10 MCP servers · garak · promptfoo │
└───────────────────┴──────────────────────────────────────────────────────────────┘
$\text{Agent capacity} = \prod_{i=1}^{k} |T_i|, \quad k = 10 \text{ MCP servers}, \quad \sum |T_i| = 230 \text{ tools}$
%%{init: {'theme': 'dark', 'themeVariables': {'primaryColor': '#FF2D95', 'primaryTextColor': '#fff', 'primaryBorderColor': '#BD00FF', 'lineColor': '#00D4FF'}}}%%
graph TD
CMD["COMMANDER DISPATCHER"]
subgraph SCRAPING["Lead Operations"]
SO["scraper-ops"]
SW["scraper-writer"]
SR["scraper-reviewer"]
LQ["lead-quality-auditor"]
end
subgraph EMAIL["Email Operations"]
EO["email-ops"]
SM["smtp-ops"]
RA["relay-analyst"]
CD["campaign-dispatcher"]
end
subgraph SECURITY["Security & Intel"]
SV["security-reviewer"]
BZ["black-zone-executor"]
ER["einstein-state-reader"]
CT["cia-test-runner"]
end
subgraph BUSINESS["Business Ops"]
EP["empresa-ops"]
JD["joao-deploy-verify"]
PT["ptpt-email-reviewer"]
end
subgraph META["Meta Agents"]
RS["researcher"]
PF["parallel-fanout"]
SD["ship-density-enforcer"]
EX["Explore"]
end
CMD --> SCRAPING
CMD --> EMAIL
CMD --> SECURITY
CMD --> BUSINESS
CMD --> META
style CMD fill:#FF2D95,stroke:#BD00FF,color:#fff,font-weight:bold,stroke-width:2px
Tip
Each agent has isolated toolsets, specialized system prompts, and deep domain knowledge. The Commander Dispatcher automatically routes tasks across all 9 projects. Agents spawn sub-agents for parallel investigation — no human routing needed. Agent isolation follows the principle of least privilege:
%%{init: {'theme': 'dark'}}%%
mindmap
root((MATHEUS AGUEDA))
Data Pipeline
Global Lead Scraper
Google Maps API
DuckDuckGo Scraper
CNPJ Harvester
Email Validator
789K verified
SMTP check
MX lookup
PostgreSQL 16
1.93M rows
GIN + BRIN indexes
6GB shared_buffers
Email Engine
Turbo Sender
6 parallel threads
Circuit breaker
385 emails/day
3 DKIM Relays
Resend 65/d
SendGrid 120/d
Google SMTP 200/d
DMARC + SPF
Security Research
CIA Einstein
32 AI Agents
Multi-agent orchestration
Cryptanalysis
ECDSA lattice
Metal GPU acceleration
Brainwallet audit
Offensive Tools
39 CLI tools
10 MCP servers
garak + promptfoo
Trading & DeFi
Jessie Trader
MQL5
MetaTrader 5
RugPull Scanner
Honeypot detection
Ethers.js
MoonSniper
Mempool monitoring
MEV strategies
Business Ops
Portugal
France
Netherlands
Spain
Infrastructure
Railway x3
Vercel x2
Cloudflare x2
GitHub Actions x7
{
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"properties": {
"name": "Portugal — Green Village Mobilehomes",
"description": "Mobile home sales & logistics · FastAPI · PostgreSQL · 752K eligible leads",
"marker-color": "#FF2D95",
"marker-size": "large",
"marker-symbol": "commercial"
},
"geometry": {
"type": "Point",
"coordinates": [-8.61, 41.15]
}
},
{
"type": "Feature",
"properties": {
"name": "France — Easystart Office",
"description": "Company formation services · INPI/URSSAF portal automation · Playwright bots",
"marker-color": "#BD00FF",
"marker-size": "large",
"marker-symbol": "town-hall"
},
"geometry": {
"type": "Point",
"coordinates": [2.35, 48.86]
}
},
{
"type": "Feature",
"properties": {
"name": "Netherlands — EU Operations Hub",
"description": "Cross-border coordination · DigiD · KVK integration · VAT VIES",
"marker-color": "#00D4FF",
"marker-size": "large",
"marker-symbol": "embassy"
},
"geometry": {
"type": "Point",
"coordinates": [4.90, 52.37]
}
},
{
"type": "Feature",
"properties": {
"name": "Spain — Market Expansion",
"description": "Dealer network building · Lead scraper · CRM pipeline · Certificado Digital",
"marker-color": "#FF2D95",
"marker-size": "large",
"marker-symbol": "rocket"
},
"geometry": {
"type": "Point",
"coordinates": [-3.70, 40.42]
}
},
{
"type": "Feature",
"properties": {
"name": "Infrastructure — Railway EU",
"description": "Railway deploy region · 3 services · Auto-deploy from main",
"marker-color": "#00ff41",
"marker-size": "medium",
"marker-symbol": "rail"
},
"geometry": {
"type": "Point",
"coordinates": [8.68, 50.11]
}
},
{
"type": "Feature",
"properties": {
"name": "Cloudflare Edge — London",
"description": "CDN edge · WAF · 2 zones · DDoS protection",
"marker-color": "#F38020",
"marker-size": "medium",
"marker-symbol": "circle"
},
"geometry": {
"type": "Point",
"coordinates": [-0.12, 51.51]
}
},
{
"type": "Feature",
"properties": {
"name": "Lead Coverage — 48 Countries",
"stroke": "#BD00FF",
"stroke-width": 2,
"stroke-opacity": 0.6,
"fill": "#BD00FF",
"fill-opacity": 0.08
},
"geometry": {
"type": "Polygon",
"coordinates": [[
[-10, 35], [30, 35], [30, 58], [-10, 58], [-10, 35]
]]
}
}
]
}
Note
Interactive map — click markers for details. Lead generation covers 48 countries across Europe, with active business entities in PT, FR, NL, ES. All portal automation (tax filing, company registration, compliance) is handled by Playwright bots with government-specific session management. Infrastructure markers show Railway (Frankfurt) and Cloudflare (London) edge presence.
%%{init: {'theme': 'dark', 'themeVariables': {'git0': '#FF2D95', 'git1': '#00D4FF', 'git2': '#BD00FF', 'git3': '#39d353', 'gitBranchLabel0': '#ffffff', 'gitBranchLabel1': '#ffffff', 'gitBranchLabel2': '#ffffff', 'commitLabelColor': '#c9d1d9'}}}%%
gitGraph LR:
commit id: "init"
branch feature/lead-scraper
commit id: "scraper-v1"
commit id: "validator"
checkout main
merge feature/lead-scraper id: "v1.0" tag: "GLS"
branch feature/email-engine
commit id: "turbo-sender"
commit id: "dkim-setup"
commit id: "circuit-breaker"
checkout main
merge feature/email-engine id: "v2.0" tag: "EMAIL"
branch feature/cia-einstein
commit id: "agent-framework"
commit id: "metal-gpu"
commit id: "ecdsa-accel"
checkout main
branch feature/trading
commit id: "jessie-mql5"
commit id: "rugpull-scan"
commit id: "moonsniper"
checkout main
merge feature/cia-einstein id: "v3.0" tag: "CIA"
merge feature/trading id: "v4.0" tag: "TRADE"
branch feature/hacker-config
commit id: "mcp-servers"
commit id: "cli-tools"
commit id: "32-agents"
checkout main
merge feature/hacker-config id: "v5.0" tag: "HACKER"
commit id: "deploy" type: HIGHLIGHT
git push main= production. 7 GitHub Actions workflows generate 3D contributions, profile cards, contribution snake, 8 lowlighter/metrics SVGs, chess board, blog posts, and activity feed. Zero manual deploys — Railway, Vercel, and Cloudflare auto-deploy from main branch.
Active research in elliptic curve cryptanalysis, lattice-based attacks, and GPU-accelerated key recovery on Apple Silicon (Metal). All research conducted on own infrastructure against own keys.
Elliptic Curve Fundamentals:
Lattice Cryptanalysis:
Attack Complexity:
| Algorithm | Target | Complexity | Implementation | GPU Accel |
|---|---|---|---|---|
| Pollard's rho | ECDLP | Python + C extension | Metal | |
| Baby-step Giant-step | ECDLP |
|
Metal GPU parallel | 3.2 Gops/s |
| Lattice reduction (LLL) | Biased nonce | Polynomial | SageMath + fpylll | CPU |
| Bleichenbacher | Biased ECDSA nonce |
|
Custom C + Metal | Metal |
| Brainwallet audit | Weak passphrases | Dictionary | GPU-accelerated hashcat | Metal |
| PMKID capture | WPA2/WPA3 | $O( | \text{dict} | )$ |
Information-Theoretic Foundations:
Warning
All cryptographic research is conducted on own infrastructure against own keys/accounts for defensive security research and authorized penetration testing. CIA Einstein is a research platform, not an attack tool. Metal GPU achieves
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Security Toolkit
| Domain | Tools & Techniques | Depth |
|---|---|---|
| Network | bettercap · mitmproxy · routersploit · Wireshark · nmap · masscan | ARP spoof, MITM, router exploit |
| WiFi | Aircrack-ng · PMKID capture · deauth · monitor mode · WPA3-SAE | Full WPA2/WPA3 audit chain |
| Web | Burp Suite · SQLMap · XSS · SSRF · CSRF · session hijack | OWASP Top 10 + beyond |
| OSINT | Maltego · theHarvester · Shodan · breach correlation · social recon | 37 tools integrated |
| Crypto | ECDSA lattice · brainwallet · Pollard rho · GPU cracking | Apple Silicon Metal accel |
| RF/SDR | RTL-SDR · HackRF · SubGHz · NFC/RFID · GSM interception | Full spectrum analysis |
| AI Red Team | garak · promptfoo · LLM jailbreak · prompt injection · guardrails | Automated finding DB |
| Mobile | frida · objection · MVT · runtime instrumentation · APK reverse | iOS + Android |
| Forensics | Volatility · Autopsy · disk imaging · memory analysis · timeline | Full chain of custody |
| Stealth | Anti-fingerprint · TLS mimicry · residential proxies · browser spoof | NASA-grey patches |
CIA Einstein Platform: Multi-agent autonomous security research system. 32+ specialized agents for vulnerability discovery, OSINT correlation, breach intelligence, and threat analysis. Python backend + C extensions + Metal GPU shaders for cryptanalysis on Apple Silicon M4 Pro.
Hardware Arsenal: WiFi adapters (monitor mode) · RTL-SDR · Flipper Zero · Proxmark3 · USB Rubber Ducky · HackRF One · ESP32 · NFC readers — 14 devices, full RF spectrum coverage from 125 kHz (LF RFID) to 6 GHz (WiFi 6E).
Trading & DeFi Operations
| System | Stack | Strategy | Status |
|---|---|---|---|
| Jessie Trader | MQL5 · MetaTrader 5 | Multi-TF confluence · EMA cross · RSI divergence | Live |
| RugPull Scanner | TypeScript · Ethers.js | Contract simulation · honeypot detection · LP analysis | Live |
| MoonSniper | Python + Rust · Web3.py | Mempool frontrunning · sandwich detection · token launch | Live |
| DeFi Research | Solidity · Foundry | Flash loan patterns · liquidity pool dynamics | Research |
Capabilities:
- Sandwich attack detection and MEV extraction via mempool monitoring
- Token launch sniping with gas optimization (
$g_{\text{optimal}} = g_{\text{base}} \cdot 1.15$ ) - On-chain forensics and wallet clustering using graph analysis
- Liquidity pool monitoring across Uniswap V2/V3 (constant product
$x \cdot y = k$ ) - Cross-DEX arbitrage path finding via Bellman-Ford on token graphs
- Smart contract bytecode decompilation and control flow analysis
- Honeypot simulation via
eth_callwith state override
Email Delivery Infrastructure
| Component | Configuration | Daily Capacity |
|---|---|---|
| Resend | DKIM (resend._domainkey) · API v1 · Circuit breaker | 65 emails |
| SendGrid | DKIM (sendgrid-s1._domainkey) · v3 API · Webhook bounce | 120 emails |
| Google SMTP | DKIM (google._domainkey) · App password · TLS 1.3 | 200 emails |
| Turbo Sender | 6 parallel threads · Round-robin relay · Auto-halt on bounce | 385 total |
Quality Gates:
- Bounce rate > 5% per 100 emails → HALT campaign
- Bounce rate > 10% over 7 days → PAUSE relay
- Warmup protocol: 10/day → +10/day → graduate at day 14 if bounce < 3%
- DMARC:
p=quarantine,rua=info@greenvillagemobilehomes.com - SPF:
google + sendgrid + amazonses + sendinblue ~all - All emails DKIM-signed, TLS enforced,
encode('ascii')before SMTP send
Monitoring: Real-time bounce classification (hard/soft/content/reputation), relay health scoring, DKIM alignment verification, deliverability forensics via dedicated relay-analyst agent. Bounce math:
Database Architecture
-- PostgreSQL 16 · 6GB shared_buffers · GIN + BRIN + Partial indexes
-- 1.93M rows · 789K verified emails · 752K campaign-eligible
SELECT
email_status,
COUNT(*) AS total,
ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER(), 1) AS pct
FROM leads
GROUP BY email_status
ORDER BY total DESC;
-- Result:
-- smtp_verified | 581,247 | 30.1%
-- pending | 386,412 | 20.0%
-- junk | 208,093 | 10.8%
-- valid | 207,849 | 10.8%
-- ... | ... | ...| Tuning Parameter | Value | Rationale |
|---|---|---|
shared_buffers |
6 GB | 25% of 24GB unified memory |
effective_cache_size |
18 GB | 75% of total RAM |
work_mem |
256 MB | Complex sorts + hash joins |
maintenance_work_mem |
1 GB | Fast VACUUM and index builds |
random_page_cost |
1.1 | SSD-optimized (NVMe) |
effective_io_concurrency |
200 | NVMe parallel I/O |
max_parallel_workers_per_gather |
4 | M4 Pro 14 cores |
Index Strategy: GIN indexes for full-text search on company names, BRIN for time-series data (created_at, updated_at), partial indexes on email_status = 'smtp_verified' for campaign queries, composite B-tree for multi-column lookups. Table bloat managed by autovacuum_vacuum_scale_factor = 0.01.
Business Operations — 4 Countries
| Country | Entity | Activity | Automation |
|---|---|---|---|
| Portugal | Green Village Mobilehomes | Mobile home sales & logistics | FastAPI · PostgreSQL · Playwright |
| France | Easystart Office | Company formation services | INPI · URSSAF portal automation |
| Netherlands | EU Operations Hub | Cross-border coordination | DigiD · KVK integration |
| Spain | Market Expansion | Dealer network building | Lead scraper · CRM pipeline |
Automated workflows: Tax filing via portal automation (Playwright), compliance deadline tracking, government form submission (AT, INPI, URSSAF, KVK), invoice processing, lead generation across 48 countries, WhatsApp customer engagement via Joao Bot (Baileys · Node.js · Railway).
Legal compliance: GDPR-compliant data processing, VAT VIES cross-validation (
Development Environment
| Component | Details |
|---|---|
| Hardware | MacBook Pro M4 Pro · 24GB unified · 14-core GPU · NVMe SSD |
| Editor | VS Code + Claude Code CLI (Opus 4.6) · JetBrains (MQL5) |
| AI Stack | 32 agents · 230+ MCP tools · 10 servers · 800+ skills |
| Database | PostgreSQL 16 — 6GB shared_buffers · GIN + BRIN + partial |
| Runtime | Python 3.14 · Node 22 · TypeScript 5.x · C23 · MQL5 · Rust · Go |
| Deploy | git push main → Railway/Vercel auto-deploy (7 GH Actions) |
| Browser | Playwright MCP · Chrome DevTools CDP · Stagehand v3 · pw_cli |
| Security | 10 MCP servers · 39 CLI tools · semgrep · nuclei · nikto |
| Monitoring | Sentry · Grafana · Zombie Killer daemon (5 layers) · cron |
| Stealth | NASA-grey patches · anti-fingerprint · residential proxies |
| Integrations | Linear · Notion · Gmail · Google Calendar · Google Drive |
System Protection:
Zombie Killer v2 — 5 layers running every 5 minutes via LaunchAgent
├─ Layer 1: Kill stuck /tmp scripts (>30min CPU)
├─ Layer 2: Kill orphaned Playwright browsers (>2h CPU)
├─ Layer 3: Restart Google Drive if stuck (>50% CPU, >1h)
├─ Layer 4: Clean stale temp files (/tmp/claude/, /tmp/pw_cli/)
└─ Layer 5: macOS notification if system load > 20 (14-core M4 Pro)
Automation & Browser Fleet
| Tool | Use Case | Token Cost | Speed |
|---|---|---|---|
Playwright CLI (pw_cli_runner.py) |
Long scraping sessions, portal automation | ~27K/10 steps | Fast |
Browser-Use (browser_use_runner.py) |
AI-driven natural language browser control | ~40K/task | 89% accuracy |
Stagehand v3 (stagehand_runner.js) |
Node.js automation, self-healing DOM | ~30K/10 steps | 44% faster |
| Playwright MCP | Quick tasks (<10 steps) | ~114K/10 steps | Convenient |
| Chrome DevTools CDP | Existing logged-in sessions (Gmail) | ~50K/10 steps | Live sessions |
Stealth Stack:
- NASA-grey Playwright patches for anti-bot bypass
- Residential proxy rotation with health scoring
- TLS fingerprint mimicry (JA3/JA4 spoofing)
- Canvas/WebGL/AudioContext fingerprint randomization
- Anti-detection browser profiles via GeeLark
- Government portal session persistence (PT/FR/NL/ES)
Decision Matrix: Sessions > 10 steps → Playwright CLI (4x cheaper). Complex pages → Browser-Use (natural language). Node projects → Stagehand. Gmail → Chrome DevTools. Meta/FB → Stealth Playwright.
@@ SYSTEM STATUS @@
+ 9 production systems operational
+ 1.93M leads scraped, validated & stored
+ 4 countries with active business entities (interactive GeoJSON map)
+ 385 emails/day across 3 DKIM-verified relays
+ 32 AI agents deployed via MCP protocol
+ 230+ tools across 10 specialized MCP servers
+ 16 Mermaid diagrams (12 types: flowchart, class, ER, sequence, state, pie, gantt, quadrant, journey, xychart, block, git, mind, timeline)
+ 6 custom animated SVGs (neon-banner, terminal, skills-radar, wave, dashboard, network-topology)
+ 11 production code snippets across 11 languages
+ 9 LaTeX equations (ECDSA, lattice, birthday, Shannon, DH, Merkle)
+ 1 interactive GeoJSON map with 6 markers + coverage polygon
+ 1 interactive 3D STL crystal model (84 facets)
+ 8 lowlighter/metrics plugins auto-generated
+ 7 GitHub Actions workflows (3D, snake, metrics, summary, chess, blog, activity)
+ GPU-accelerated ECDSA research on Apple Silicon Metal
+ Chess game via GitHub Issues
- 0 systems down
! Last updated: auto-generated via GitHub ActionsImportant
Currently building: GPU-accelerated ECDSA cryptanalysis on Apple Silicon Metal, autonomous security research via multi-agent AI (CIA Einstein v9.4), expanding lead generation to 48 countries, algorithmic trading strategies on MetaTrader 5, and mempool MEV extraction in Rust.
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timeline
title Engineering Journey — Systems Built Over Time
section 2024 Q3-Q4
Global Lead Scraper : Python · PostgreSQL · 1.93M leads
: DuckDuckGo + Google Maps multi-engine
: Email validation pipeline (MX+SMTP+DNS)
section 2025 Q1
Email Delivery Engine : 3 DKIM relays · 385/day
: Turbo Sender · 6 threads · circuit breaker
: SPF + DMARC + bounce monitoring
Joao Bot : Node.js · Baileys · WhatsApp
: Railway deploy · auto-reply · CRM
section 2025 Q2
Green Village MH : FastAPI · Playwright
: 4-country portal automation
: PT + FR + NL + ES entities
CIA Einstein : Python · C · Metal GPU
: 32 AI agents · ECDSA research
: Lattice reduction · nonce analysis
section 2025 Q3
Jessie Trader : MQL5 · MetaTrader 5
: Multi-TF confluence strategy
RugPull Scanner : TypeScript · Ethers.js
: Honeypot detection · LP analysis
MoonSniper : Python + Rust · Web3.py
: Mempool frontrunning · MEV
section 2026
AI Agent Mesh : 230+ MCP tools
: 10 specialized servers
: Commander dispatch architecture
Hardware Arsenal : 14 devices · RF spectrum
: WiFi · NFC · GSM · SDR
Hacker Config : 128 commands · 32 agents
: garak + promptfoo · AI red team
Drag to rotate. Scroll to zoom. GitHub renders STL files as interactive 3D models with orbit controls.
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Footnotes
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The secp256k1 curve used in Bitcoin/Ethereum has order $n \approx 1.158 \times 10^{77}$, making brute-force infeasible ($2^{256}$ operations). Our research targets biased nonce reuse and weak key generation — not the curve itself. The discrete logarithm problem on this curve remains computationally intractable for classical computers. ↩
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ECDSA signature generation requires a fresh random nonce $k$ for each signature. If $k$ is reused or biased by even 4 bits, the private key $d_A$ can be recovered via lattice reduction (HNP → CVP). Our Metal GPU implementation tests $10^9$ candidates/sec. ↩
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Lattice reduction via LLL/BKZ is polynomial-time for approximate SVP but remains exponential for exact solutions. The Lovász condition $|\mathbf{b}i^*| \geq (\delta - \mu{i,i-1}^2) |\mathbf{b}_{i-1}^*|$ with $\delta = 3/4$ guarantees output quality. Practical attacks require side-channel information (e.g., biased ECDSA nonces leaking $\leq 4$ bits per signature). ↩
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The birthday paradox implies that for a $k$-bit hash, a collision is expected after $\sqrt{2^k} = 2^{k/2}$ random samples. For SHA-256, this means $2^{128}$ operations — still infeasible, but relevant for reduced-round variants and hash truncation attacks. ↩
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Shannon entropy $H(X) = -\sum p(x_i) \log_2 p(x_i)$ quantifies the minimum bits needed to encode a message. For cryptographic keys, $H \geq 128$ bits provides post-quantum security margins against Grover's algorithm ($O(\sqrt{N})$ speedup). Our quality scoring engine uses entropy analysis on email patterns to detect disposable/generated addresses with $H < 3.0$ bits/char. ↩
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The Diffie-Hellman protocol enables shared secret generation over insecure channels: $g^{ab} \equiv (g^a)^b$. Security relies on the Computational Diffie-Hellman (CDH) assumption in $\mathbb{Z}_p^*$. Our Metal GPU implementation achieves $3.2 \times 10^9$ modular exponentiations/sec on M4 Pro. ↩
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Merkle trees provide $O(\log n)$ inclusion proofs used in Bitcoin SPV verification. The root hash $H(H(L_0 | L_1) | H(L_2 | L_3))$ commits to all leaf data. Our mempool sniffer validates transaction inclusion using lightweight Merkle proofs without downloading full blocks — critical for MoonSniper's sub-second reaction time on pending swaps. ↩