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Ask DeepWiki

MalwarEvangelist Bot

A self-hosted AI personality engine for BlueSky that combines cybersecurity education with character-driven engagement. Built on a novel Mixture-of-Experts architecture with 8 dynamic personality engines, Thompson Sampling for adaptive behavior, and 1.1M+ retrievable knowledge chunks—all at $0/month infrastructure cost.


What Sets This AI Apart

Capability Implementation Why It Matters
Adaptive Personality 8 specialized engines with MoE gating Responses have dynamic persona's, not templated
Bayesian Learning Thompson Sampling for engine selection Bot improves through interaction
Massive Knowledge Base 1.1M+ RAGFlow chunks with CRAG scoring Accurate, sourced responses
Zero Infrastructure Cost Fully self-hosted architecture No API fees, full control

The enCore Engine

At the heart of the bot is enCore—an orchestration engine that coordinates personality, knowledge retrieval, and response generation through a 6-stage pipeline:

Input → Context Analysis → Knowledge Retrieval → Response Generation → Personality Application → Quality Validation → Output

Personality Engines

enCore selects from 8 specialized engines using Mixture-of-Experts gating:

Engine Function Activation Context
Collective Consciousness Community-focused "we" framing Group discussions
Mystical Resonance Prophetic, philosophical language Abstract questions
Cult Leader Dynamics Charismatic authority Teaching moments
Teaching Style Adaptive explanation depth Technical queries
Fangirl Enthusiastic engagement Excitement triggers
Relationship Trust-building memory Returning users
Entropy Philosophy Digital decay themes Malware discussions
Progressive Community Cult-like gamification & behaviors

How Engine Selection Works

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Context        │────▶│  MoE Gate        │────▶│  Top-K Engines  │
│  Analysis       │     │  (Feature Score) │     │  (Weighted Mix) │
└─────────────────┘     └──────────────────┘     └─────────────────┘
                               │
                               ▼
                        ┌──────────────────┐
                        │ Thompson Sampling │
                        │ (Exploration vs  │
                        │  Exploitation)   │
                        └──────────────────┘

The system balances proven strategies (exploitation) with experimental approaches (exploration) using Bayesian multi-armed bandit selection.


Quick Start

Prerequisites

  • Node.js 18+ or Docker
  • BlueSky account with app password
  • PostgreSQL 15+ with pgvector (production)
  • Redis 7+ (production)

Installation

git clone https://github.com/radicalkjax/malwarevangelist-bot.git
cd malwarevangelist-bot

# Recommended: Interactive setup
./scripts/initRitual.sh

# Or manual setup
npm install && cp .env.example .env
npm run build && npm start

Architecture Overview

┌──────────────────────────────────────────────────────────────────┐
│                        External Sources                          │
├──────────────┬──────────────┬──────────────┬────────────────────┤
│ BlueSky      │ RSS Feeds    │ CVE Database │ RAGFlow (1.1M)     │
│ Firehose     │              │              │                    │
└──────┬───────┴──────┬───────┴──────┬───────┴────────┬───────────┘
       │              │              │                │
       ▼              ▼              ▼                ▼
┌──────────────────────────────────────────────────────────────────┐
│                     Processing Pipeline                          │
├──────────────────────────────────────────────────────────────────┤
│  Jetstream Client → Event Filter (12.5% pass) → BullMQ Queue    │
└──────────────────────────────┬───────────────────────────────────┘
                               │
                               ▼
┌──────────────────────────────────────────────────────────────────┐
│                        enCore Engine                             │
├──────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────────┐  │
│  │ Knowledge   │  │ Personality │  │ Quality Validation      │  │
│  │ Service     │  │ Gate (MoE)  │  │ (6 dimensions)          │  │
│  │ (5,364 loc) │  │             │  │                         │  │
│  └─────────────┘  └─────────────┘  └─────────────────────────┘  │
└──────────────────────────────┬───────────────────────────────────┘
                               │
                               ▼
┌──────────────────────────────────────────────────────────────────┐
│                       Infrastructure                             │
├─────────────────────────────┬────────────────────────────────────┤
│ PostgreSQL + pgvector       │ Redis + BullMQ                     │
│ (Semantic memory)           │ (Queue system)                     │
└─────────────────────────────┴────────────────────────────────────┘

Key Metrics

Metric Value
Total Codebase 114,333 lines TypeScript
enCore Engine 6,270 lines
Knowledge Chunks 1.1M+ (RAGFlow)
Personality Engines 8 dynamic
Response Time 1.2s average
Monthly Cost $0 (self-hosted)

Documentation

Complete technical documentation is available in /docs, following IEEE conventions with beginner-friendly overviews and research-level implementation details.

Documentation Legend

Section Description Start Here
01 - Overview System architecture, data flow, design decisions System Architecture
02 - Core Brain enCore engine, personality engines, Thompson Sampling, MoE enCore Engine
03 - Knowledge System RAGFlow integration, CRAG scoring, vector memory RAGFlow Integration
04 - NLP Pipeline Entity extraction, intent classification, coreference Unified NLP Service
05 - Infrastructure PostgreSQL, Redis, Jetstream, monitoring PostgreSQL
06 - BlueSky Integration Bot operations, firehose processing, rate limiting Bot Operations
07 - API Reference enCore API, knowledge API, database schema enCore API
08 - Development Getting started, testing guide, coding standards Getting Started
09 - Security Threat model, PII protection, audit logging Threat Model
10 - Services Threat intelligence, LLM integration, academic sources Threat Intelligence
11 - Training Shadow mode, model training, canary deployment Shadow Mode
12 - Content Content generation, conversation management Content Generation
Appendices Configuration reference, troubleshooting, changelog Configuration
Reference Glossary (150+ terms), IEEE references Glossary

For AI-assisted development workflows, see CLAUDE.md.


Ethical Research Statement

This bot is designed for ethical psychological research and cybersecurity education. It explores engagement techniques through dramatic personas while promoting defensive security practices. The bot contains no harmful code, maintains strict ethical guardrails, and transparently discloses its AI nature.


Contributing

See CONTRIBUTING.md for code standards, testing requirements, and pull request guidelines.

License

ISC License - See LICENSE


Connect: @malwarevangelist.com

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