⚡ tty1 :: Live Terminal OS · 🐚 tty2 :: AI Shell (know-me.sh) · 🧠 tty3 :: System Mindmap · 🏙️ tty4 :: 3D Isometric Cities · 📊 tty5 :: The Scoreboard
Important
INTERACTIVE AI SHELL ONLINE (know-me.sh) — Skip static resumes. Query my live first-person Groq-powered AI agent directly in your browser at sriyansh.vercel.app/?view=know-me or explore my live 3D isometric contribution telemetry at sriyansh.vercel.app/?post=this-counts.
# ╭─ 01: /etc/sriyansh.conf (fastfetch) ─────────╮
host: "[https://sriyansh.vercel.app](https://sriyansh.vercel.app)"
os: "Arch Linux x86_64 (tty1)"
kernel: "6.12.0-arch1-performance"
role: "Generative AI & Low-Latency Backend Engineer"
shell: "know-me.sh (Groq LPU Conversational Agent)"
uptime: "3x AI/ML Engineering Internships Shipped"
architecture_focus:
- "Autonomous Agentic Workflows & Custom MCP"
- "Two-Stage Hybrid RAG (FAISS + ChromaDB)"
- "Low-Latency Spring Boot & Django Backends"
engineering_doctrine:
superpower: "Architectural efficiency & clean APIs"
motto: "No fluff, no filler text, no bloated code."
status: "[ONLINE] Open to High-Impact Roles" |
+ ╭─ 02: BTOP // LIVE TELEMETRY & DAEMONS ───────╮
+ AGENTIC AI / MCP / RAG [███████████████████░] 96%
+ BACKEND / SPRING / REDIS [██████████████████░░] 94%
+ PYTHON / JAVA / TS / C++ [██████████████████░░] 92%
+ VECTOR DB (FAISS/CHROMA) [█████████████████░░░] 90%
+ ALGORITHMS & SYS DESIGN [█████████████████░░░] 89%
! ACTIVE PRODUCTION DAEMONS (systemctl status)
+ [PID 1042] S.A.G.E. :: Custom MCP & AST
+ [PID 1088] Vault & BitSwarm :: 2-Stage Hybrid RAG
+ [PID 1104] know-me.sh :: Groq LPU Shell CLI
+ [PID 1156] Fact-Check-Engine :: Live DOM Verifier
+ [PID 1204] PyGitLoc / ATS :: Open-Source Tools |
💡 CLICK TO EXECUTE: Expand any terminal module below to inspect live sequence diagrams, system topologies, or run the interactive recruiter decision tree.
🔴 [EXEC 01] $ ./inspect-daemon.sh --target="S.A.G.E. (Agentic Code Review Engine)"
System Spec: Open-source agentic code-review engine powered by a custom Model Context Protocol (MCP) server, Gemini LLMs, and RAG pipelines. Parses repository Abstract Syntax Trees (AST), detects security flaws, and synthesizes deterministic optimization diffs.
sequenceDiagram
autonumber
actor Dev as Developer / PR
participant MCP as S.A.G.E. Custom MCP Server
participant AST as Static AST & Vuln Scanner
participant RAG as Vector Context (RAG)
participant LLM as Gemini Reasoning Engine
Dev->>MCP: Push Commit / Trigger Review
MCP->>AST: Parse AST & Control Flow Graph
AST-->>MCP: Flag Security Flaws & Anti-Patterns
MCP->>RAG: Retrieve Repo Conventions & CVEs
RAG-->>LLM: Inject Grounded Code Context
LLM-->>Dev: Output Deterministic Optimization Patch
🟣 [EXEC 02] $ ./inspect-daemon.sh --target="Vault & BitSwarm (Two-Stage Hybrid RAG)"
System Spec: Autonomous multi-agent knowledge system utilizing two-stage Retrieval-Augmented Generation (RAG), hybrid dense + sparse vector search (
FAISS/ChromaDB), and dynamic model routing between Groq and Gemini for sub-second inference.
flowchart LR
Query(["User Query"]) --> Router{"Dynamic Latency Router"}
Router -->|"Sub-100ms LPU"| Groq["Groq LLM Engine"]
Router -->|"Deep Reasoning"| Gemini["Gemini Pro Engine"]
Query --> Stage1["Stage 1: Hybrid Vector Search (FAISS + Chroma)"]
Stage1 --> Stage2["Stage 2: Cross-Encoder Reranking"]
Stage2 --> Groq
Stage2 --> Gemini
Groq --> Output(["Zero-Hallucination Response"])
Gemini --> Output
🧠 [EXEC 03] $ ./render-mindmap.sh --config="/etc/sriyansh.conf"
mindmap
root((sriyansh.dev))
Agentic AI & LLMs
Custom MCP Servers
Two-Stage RAG Pipelines
Dynamic Model Routing
LLM Security Guardrails
Low-Latency Backends
Java & Spring Boot
Python Django & FastAPI
Redis Caching & PubSub
WebSockets Real-Time
Systems & Core CS
Data Structures & Algorithms
Distributed System Design
Arch Linux & Shell Automation
Vector & SQL Databases
🎮 [EXEC 04] $ ./recruiter-ctf.sh --interactive-mode (Playable Terminal Decision Tree)
arch@sriyansh:~$ ./recruiter-ctf.sh
[SYSTEM] Welcome to the sriyansh.dev interactive shell.
[PROMPT] Select a command to execute:👉 Command 1: cat /dev/generic_template_portfolio.txt
- [ERROR 404]: File not found.
- Policy enforced: "No fluff, no filler text, and no bloated code."
! Try Command 2 or Command 3 instead.👉 Command 2: ssh root@sriyansh.vercel.app --run="know-me.sh"
+ [SESSION ESTABLISHED]: Connected to Groq LPU conversational agent.
+ Launch live interactive shell: [https://sriyansh.vercel.app/?view=know-me](https://sriyansh.vercel.app/?view=know-me)👉 Command 3: sudo pacman -S sriyansh-engineer --deploy-to-production
+ [DEPENDENCY CHECK PASSED]: Python, Java, Spring Boot, Django, MCP, RAG, Docker, AWS, Redis.
+ Ready for immediate deployment in Backend, Full-Stack, and Agentic AI Engineering roles.| System Daemon | Architecture & Engineering Scope | Tech Stack |
|---|---|---|
| 🔴 S.A.G.E. (AI Code Reviewer) | Open-source agentic code-review engine with a custom Model Context Protocol (MCP) server and Gemini/RAG pipeline that detects bugs, security flaws, and automates optimization. | Python MCP Gemini RAG MERN |
| 🟣 Vault & BitSwarm RAG | Autonomous agentic knowledge engine featuring two-stage RAG, hybrid vector search (FAISS/Chroma), and dynamic model routing (Groq/Gemini). |
Python LangChain FAISS Groq |
🟢 Arch Terminal OS (know-me.sh) |
Zero-framework OLED space terminal featuring a live first-person Groq AI shell, 3D isometric contribution cities, and real-time telemetry. | Vanilla JS HTML5 CSS3 Python |
| 🔵 AI Fact-Checking Extension | Browser-native verification pipeline that captures live web claims and cross-examines them against vector-indexed ground truth. | JavaScript Django LangChain Chroma |
| 🟡 AI Grievance Redressal | End-to-end Web & Android platform utilizing structured REST contracts and automated server-side ticket routing. | TypeScript Python Android |
| 🟠 GAN Colorization & NLP Parser | Trained Deep GAN for image restoration (Streamlit) + end-to-end NLP resume entity extraction pipeline. |
PyTorch TensorFlow Scikit-learn |
+ [SHIPPED] Generative AI Intern :: Prabandhan & EiSystems Technologies
↳ Engineered production LLM workflows, adversarial prompt guardrails, and low-latency RAG pipelines.
+ [SHIPPED] AI & ML Engineering Intern :: Codomax Digital Solutions
↳ Optimized high-throughput ML inference services, data pipelines, and backend API integrations.
+ [PUBLISHED] Open-Source Author :: PyGitLoc & Automated ATS-Checker
↳ Built and released developer CLI telemetry tools and NLP document parsing pipelines.
! [VERIFIED SECURITY & ENGINEERING CREDENTIALS]
├─ Certified LLM Security Professional (Adversarial Robustness & Guardrails)
├─ Google AI Essentials & Generative AI Architecture
├─ Deloitte Data Analytics & Systems Simulation (Forage)
├─ Big Data & Data Science Engineering Bootcamp (C-DAC)
└─ Advanced C++ Systems Training
arch@sriyansh:~$ echo "No fluff, no filler text, and no bloated code."🚀 LAUNCH LIVE TERMINAL PORTFOLIO (sriyansh.vercel.app) · 🐚 CHAT WITH MY AI (know-me.sh) · 💼 CONNECT ON LINKEDIN