Projects using Python in DelhiProjects using Python in DelhiThat makes total sense. A lot of portfolio platforms like Contra don't support LaTeX rendering, so those equations just end up looking like broken code.
Here is the revised version with the math translated into clean, readable plain text so it formats perfectly on the site.
AuraOps: The Autonomous Unified Release Authority 🚀🧠
Moving AI from "Assisting Developers" to "Making Production Decisions"
Modern CI/CD pipelines are reactive. They lint, test, and warn—but the final decision still depends on a human. As DevOps complexity grows with security risks, compliance requirements, and sustainability concerns, developers are overwhelmed.
We asked a simple question: What if the pipeline itself could decide whether code is safe to ship? That idea led to AuraOps—a multi-agent AI system integrated directly into GitLab Merge Requests that doesn’t just analyze code, but actively fixes it, verifies it, and makes the final release decision.
💡 How It Works: The Autonomous Pipeline
AuraOps intercepts GitLab webhooks when a Merge Request is opened, extracts the code diff, and triggers a 3-phase autonomous pipeline powered by specialized AI agents.
The Execution Flow:
Phase 1 (Parallel): Security & Sustainability Analysis
Phase 2 (Sequential): Validation & Risk Decision
Phase 3 (Parallel): Compliance Checks & Deployment
🤖 The Multi-Agent AI System
AuraOps orchestrates several distinct agents to handle the entire lifecycle:
SecurityAgent: Detects vulnerabilities (SQLi, XSS, secrets), auto-remediates them by writing and committing patches, and re-validates its own fixes.
GreenOpsAgent: Optimizes infrastructure using real-time carbon data and suggests lower-emission deployment regions.
ValidationAgent: Runs the GitLab CI/CD pipeline to ensure AI-generated fixes didn’t break functionality, with graceful fallbacks if CI is down.
ComplianceAgent: Audits code and deployment against SOC2, GDPR, and HIPAA requirements.
DeployAgent: Builds and deploys the application to Google Cloud Run, selecting the greenest, most optimal region.
RiskEngine (The Brain): The decision-making core that aggregates all signals into a single release scorecard and outputs a definitive APPROVE or BLOCK.
🧮 The RiskEngine Decision Model
To confidently block or approve a release without human input, we engineered a weighted decision model to generate a final confidence score.
The core confidence score combines three key weighted metrics:
Security Score * Eco (Sustainability) Score * Validation Result The final AI decision function evaluates this combined score against a strict threshold. For example, if the total confidence score is 75% or higher, the release is securely shipped and marked as APPROVE. If it falls below that mark, the system automatically outputs a BLOCK decision to prevent risky deployments.
🧗♂️ Engineering Challenges
Reliable Auto-Remediation: Detecting issues is easy; safely fixing them is not. We implemented multi-pass revalidation (up to 3 cycles) and auto-commits directly to GitLab.
Failure Resilience: Real-world systems fail. We built AuraOps with graceful degradation—handling CI failures by safely skipping them, retrying API rate limits with exponential backoff, and bypassing missing configs without crashing.
Sustainability as a Metric: Mapping cloud infrastructure to real-time carbon intensity APIs to quantify exact CO₂ savings.
🚀 The Final Output: The Release Scorecard
Instead of overwhelming the developer with logs, AuraOps outputs a clean, aggregated scorecard directly in the MR containing:
Security score & vulnerabilities auto-fixed
Sustainability index & CO₂ emissions avoided
Time saved via automation
Final AI Decision + Confidence Percentage
💻 Built With
AI Models: Gemini 2.5 Flash | Gemini 3.5 Pro | Claude 3.5 Sonnet
Backend & Orchestration: Python | FastAPI | Uvicorn | Node.js | TypeScript
Frontend & 3D Vis: React | Three.js (React Three Fiber) | CSS
DevOps & Cloud: Docker | GitLab API & Webhooks | GitLab CI | Google Cloud Build | Google Cloud Run
Try AuraOps : https://auraops-735853806237.europe-north1.run.app/dashboard
Problem:
Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.
Solution:
This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing. About 2 months ago, I built an Encryption Suite — a versatile tool for encrypting text, files, and folders with Flask, Python GUI, and CLI support.
Highlights:
🔹 Password & key-based encryption
🔹 Web UI, GUI, and CLI in one project
🔹 Fernet and demo ciphers like Caesar
🔹 Simple yet powerful for all users
It’s a fun experiment in multi-interface app design, blending web, desktop, and command-line experiences under one secure system.
🔗Github: https://github.com/aman-sharma-dev/pycryption
