m4stanuj/m4stanuj

GitHub: m4stanuj/m4stanuj

Stars: 3 | Forks: 0

# M4ST / Anuj

## Signal I build practical AI systems that survive real constraints: limited VRAM, free-tier APIs, Windows/Kali workflows, messy browser automation, and tools that need to keep working after the demo.

Agent Infrastructure
MCP servers, memory layers, tool routers, schedulers, fallback chains, and workspace automation.

Local AI Systems
Ollama, Qwen-style local models, ChromaDB cache, voice/vision utilities, and offline fallback design.

Security Automation
Authorized OSINT, recon pipelines, Nmap/Shodan/Nuclei workflows, browser testing, and CEH-aligned methodology.

MAST v1.0: Mast Autonomous System Terminal

MAST is my main build: a local-first AI operator that connects LLM routing, MCP tools, memory, browser automation, voice/vision utilities, and defensive security workflows into one workspace.

It is designed for consumer hardware, unstable free-tier APIs, and real daily work where the assistant needs context, tools, and recovery paths instead of just chat.

Core Numbers

Hardware target: RTX 2060 Super, 8GB VRAM, local fallback ready.

## MAST Ecosystem Map graph TD M["MAST v1.0"] M --> A["Agent workspace"] M --> B["Model layer"] M --> C["Memory layer"] M --> D["Automation layer"] M --> E["Security layer"] M --> F["Voice and vision layer"] A --> A1["OpenCode, Cursor, VS Code, Windsurf"] A --> A2["MCP server hub"] B --> B1["Cloud provider routes"] B --> B2["Local Ollama fallback"] C --> C1["ChromaDB semantic memory"] C --> C2["SQLite task ledger"] D --> D1["Playwright browser control"] D --> D2["Scheduler and notifications"] E --> E1["Authorized OSINT tooling"] E --> E2["Nmap, Shodan, Nuclei workflows"] F --> F1["Whisper speech input"] F --> F2["OCR and screen understanding"] ## Flagship Builds

MAST v1.0 - Autonomous System Terminal

Unified AI stack merging M4STCLAW, OpenWork, and EIGENT into a local-first operator: 21 MCP servers, 11 provider routes, task chains, semantic cache, memory, scheduler, and local fallback.

MCP LangGraph ChromaDB Ollama Python

Repository | Releases

cai-osint - Authorized Recon Framework

AI-assisted OSINT and pentest workflow with Nmap, Shodan, Nuclei, target-scope guardrails, and CEH-style methodology for defensive research and authorized testing.

OSINT Nmap Shodan Nuclei Security

Repository | Methodology

OpenWork - Universal MCP Workspace

Portable MCP workspace stack for AI IDEs. Focused on safe file work, browser automation, research, memory, skills, notifications, and tool reconstruction through config.

MCP Server Playwright JSON-RPC Automation

Repository | Releases

LeadSniper - AI Outreach Engine

Local outreach pipeline for scraping public directories, filtering prospects, scoring by ICP rules, and drafting personalized emails through LLM-assisted workflows.

Scraping LLM Routing ICP Scoring Python

Repository | Pipeline

## Integrated Stack Components

Core Languages & Frameworks

Also comfortable with: REST APIs, JSON-RPC, CLI tooling, Windows automation, Linux workflows, config-driven systems, and repo packaging.

AI / ML / Orchestration

Model routes: Groq, Cerebras, Gemini, DeepSeek, Kimi K2, Qwen/Qwen-Coder, Together AI, Sarvam-M, Nemotron, and local Ollama fallback.

Infrastructure & Tools

Workspace: Windows AtlasOS, Kali Linux dual boot, OpenCode, Cursor, VS Code, Windsurf, browser automation, and reproducible setup scripts.

Security & OSINT

Focus areas: authorized recon, target-scope validation, browser fingerprint testing, web automation, vulnerability template workflows, and defensive research tooling.

### Extra Capabilities

## GitHub Activity

GitHub stats GitHub streak

GitHub contribution snake

## Contact Protocol Open to: AI automation work, MCP integrations, OSINT tooling, agent workflows, and serious open-source collaboration. Best fit: practical systems that need to run locally, cheaply, and reliably. Preferred contact: email or LinkedIn DM.