Diagnose and Fix CI/CD Pipeline Failures with AI in 30 Seconds

🚀 Learning AI/ML in Public — Day 4: From Python Script to Production API Most ML projects die on a laptop. Today I tried taking a step towards actually shipping one. Knowing how to serve the model as a production API. Here’s the stack I worked with today: FastAPI — typed REST endpoints with auto-generated docs (didn’t know this was so smooth) Pydantic — request/response validation so bad inputs don’t reach the model Uvicorn — async ASGI server to handle concurrent requests Docker — containerise everything so it runs identically everywhere I took my RAG pipeline from Days 2-3 and turned it into: → A POST /query endpoint that accepts a question + optional metadata filters → A GET /health check for container orchestration → A Swagger UI at /docs — FastAPI generates it automatically (this surprised me) →  A Docker image deployable to any cloud with one command The biggest lesson: load your ML models at startup, not per request. Loading a sentence transformer takes 3 seconds. Multiply that by 1000 API calls and you understand why. #MLOps #FastAPI #Docker #MachineLearning #BuildingInPublic #AIML

I thought building the model was the hard part. I was wrong. I'm currently working on an end-to-end MLOps pipeline and somewhere between wiring up MLflow, containerizing with Docker, and setting up GitHub Actions for CI/CD, something hit me. The model? Took an afternoon. Everything around it? That's the actual job. Getting the model to production is one problem. Making sure it stays reliable is a completely different one. Evidently AI flagged data drift on my pipeline last week, the model hadn't changed. The data had. Silently. Gradually. The way it always does in the real world. Nobody warns you about this in ML courses. They teach you accuracy scores and loss curves. They don't teach you what happens when the world shifts underneath your model after deployment. A few things I'm realising: → Experiment tracking isn't optional. It's survival. → A model without monitoring is just a ticking clock. → The pipeline is the product. Not the model. Machine learning is maybe 20% of MLOps. The rest is engineering, discipline, and humility. Still building. Still learning. But my definition of "done" has completely changed. #MLOps #MachineLearning #DataScience #Python #MLflow #Docker #GitHubActions #AI #GitHub

Everyone wants "AI Agents" reviewing their code. But almost no enterprise security team will let you send proprietary source code to OpenAI or Anthropic APIs. 🚫🔒 So, if you want Agentic workflows in the enterprise, you have to build them locally. I just published a new step-by-step masterclass on how to build a 100% private, autonomous AI Pull Request Reviewer directly on your machine—at zero cost. In this tutorial, we don’t just write "hello world" scripts. We build a deterministic, enterprise-grade architecture that catches bugs, evaluates microservice flaws, and posts the review natively back into the GitLab UI. In the article, I break down:  🏗️ The Stack: Bridging Local Docker GitLab with Ollama (Qwen 2.5 Coder) 🧠 The Brains: Using LangGraph State Machines to enforce absolute analytical strictness (Hint: Creativity is the enemy of PR reviews!) 🔌 The Connection: Navigating the bleeding edge of the Model Context Protocol (MCP) and how to write secure REST fallbacks. ⚡ The Ghost Thread: Outsmarting Webhook timeout constraints using FastAPI background tasks. If you've been struggling with Python environment traps or dealing with immature open-source AI standards, this guide includes every single terminal command and script you need to get it running by lunch. 👇 Link to the full Dev.to tutorial in the first comment! 👇 #SoftwareEngineering #ArtificialIntelligence #DevOps #GitLab #LangGraph #LLMs #Ollama #SoftwareArchitecture #AgenticAI #MCP

I built an open-source AI Code Review tool — and it works with any project in minutes. After using AI-powered code reviews on my own projects, I realized the tooling was either locked to one platform, one AI provider, or required heavy setup. So I built something better. AI Code Review is a universal, configurable tool that: - Works with GitLab MRs and GitHub PRs - Supports Claude, GPT-4o, DeepSeek, Groq, Ollama, or any OpenAI-compatible API - Has zero dependencies — pure Python, drop one file into your CI pipeline - Runs with just 2 environment variables out of the box - Lets you bring your own custom review prompt per project - Includes dry-run mode to preview comments before posting The whole thing is a single file you can curl into any CI job. Or clone the repo and customize it. 68 unit tests, full backward compatibility, and it takes less than 5 minutes to set up. Check it out: https://lnkd.in/dnPFbpWQ If you're looking to integrate AI code reviews into your team's workflow, set up custom review prompts for your stack, or need help with CI/CD automation — I'm available for consulting. DM me or reach out to discuss how I can help your team ship better code, faster. #OpenSource #CodeReview #AI #DevOps #CICD #Python #SoftwareEngineering #GitLab #GitHub #DeveloperTools

🚀 Built an AI-powered Debugging Assistant using RAG + LLM Most debugging tools stop at logs and dashboards. I wanted to explore what happens when you add AI on top of that. Over the past few days, I built a hands-on system that helps debug production-like issues using LLMs + retrieval. Here’s what it does 👇 🔹 Backend (Spring Boot) Ingests logs, runbooks, and documentation Converts them into embeddings using Cohere (embed-v4.0) Stores them in PostgreSQL using pgvector 🔹 RAG pipeline Retrieves relevant context using vector similarity search Uses a cloud LLM via Hugging Face (meta-llama/Llama-3.1-8B-Instruct:novita) to generate explanations Produces contextual, structured debugging insights from real system data 🔹 Incident workflow (in progress) Capture real production-like incidents Automatically enrich them with AI-generated insights 💡 Example: Input: “NullPointerException in PaymentService” Output: The system retrieves relevant logs + runbooks and generates a contextual explanation of likely causes and troubleshooting steps. 📌 I’m also adding a sample input/output run below for reference. ⚡ Tech Stack: Spring Boot | PostgreSQL (pgvector) | Cohere Embeddings | Hugging Face LLM (Llama 3.1) | Docker 🎯 Next steps I’m exploring: Agent-based debugging workflows (MCP-style tool usage) Automated root cause analysis Multi-step debugging agents Still cleaning up the repo — will share GitHub soon. 📌 Update: The GitHub repository is now live. You can explore the implementation here: 👉 https://lnkd.in/eFBfZUtU #AI #RAG #LLM #SpringBoot #GenerativeAI #BackendEngineering #MLOps #DevOps

I’ve built a production-ready system that can detect whether an image is AI-generated using a Vision Transformer model. 💡 What makes this project special? ⚡ FastAPI-based REST API with interactive Swagger docs 🖼️ Single & batch image detection (up to 10 images) 🎯 Configurable confidence threshold for flexible detection 💻 CLI tool for quick local analysis 🐳 Dockerized for easy deployment 🔄 CI/CD pipeline using GitHub Actions ☁️ Ready for cloud platforms like Hugging Face Spaces, AWS, GCP, and more 📊 The model provides: AI vs Human classification Confidence scores Detailed prediction breakdown 🛠️ Tech Stack Python • FastAPI • Hugging Face Transformers • PyTorch • Docker • GitHub Actions 📡 Built with scalability and real-world use in mind — from developers to content verification use cases. 🔗 Check it out on GitHub: https://lnkd.in/gRs4w_dX 💬 I’d love to hear your feedback and suggestions! #AI #MachineLearning #DeepLearning #ComputerVision #FastAPI #Python #OpenSource #WebDevelopment #MLOps #Docker

Using LLMs to Automate Dockerfile Generation. #Building with LLMs: From Understanding to Implementation Over the past few weeks, I explored how Large Language Models (LLMs) actually work in real-world applications — and instead of just learning theory, I built a small project around it. 🔁 System Architecture: User → Python Script → Ollama API → LLM → Dockerfile Output 1. The Idea: What if we could automatically generate production-ready Dockerfiles using LLMs? That’s exactly what I implemented using both Local and Hosted LLM approaches. ⚙️ What I Built I developed a Python-based system where a user provides a programming language as input, and the system generates an optimized Dockerfile using an LLM. You give an input like a programming language (Python / Node.js etc.) → System generates a production-ready Dockerfile using an LLM But the focus wasn’t just output. #Implementation_Approaches: Local LLM (Ollama + LLaMA) which runs directly on my machine Hosted LLM (API-based), no infra management 📚 What I learned: 1>How to integrate LLMs into backend workflows. 2>Real difference between local vs hosted AI systems. 3>Why prompt engineering directly impacts output quality. 4>How DevOps is evolving with AI-driven automation. 🔗 MY GITHUB LINK: https://lnkd.in/g-JmYGhc #DevOps #AWS #LLM #AI #Docker #Kubernetes #Cloud #PlatformEngineering

We get asked why we use enterprise-grade engineering when no-code tools exist. Short answer: because enterprise operations require it. We build with Python, Django, and Celery because these systems need to handle volume, failures, retries, security, governance, and integrations with existing core platforms. That's table stakes for real enterprise work. Our stack goes deeper. Machine learning models. Deep learning where needed. RAG for grounding AI in actual business data instead of generic outputs. Agent frameworks for orchestrating complex workflows. MLOps practices for managing models after they're live, including retraining, monitoring, and compliance. No-code works for experiments. It breaks when you add real money, real volume, and real accountability. We chose the hard path because it's the one that actually works at scale. That's a deliberate decision we make for every client.

Cursor, Claude Code, and Codex are merging into one AI coding stack nobody planned OpenAI just shipped an official plugin that runs inside Anthropic's Claude Code. Not a workaround. Not a community hack. An Apache 2.0-licensed plugin from OpenAI, installed directly into a competitor's terminal. Same week, Cursor 3 launched a rebuilt interface that treats the code editor as secondary. The default view is now an Agents Window for managing fleets of coding agents across repos and environments. Google's Antigravity reached the same conclusion with its Manager Surface. I wrote about what this means for developers - https://lnkd.in/g8QgDDhh Three layers are forming. Orchestration on top, where you manage and route agents. Execution in the middle, where coding agents write, test, and commit code. Review at the bottom, where a different model from a different provider challenges the code the first one wrote. The interesting part is the review layer. When Claude writes code and Codex reviews it, you get independent scrutiny. Different training data, different blind spots. You are no longer asking someone to grade their own homework. Nobody designed this stack. Developers assembled it because no single tool covers everything. Claude for precision on complex refactors. Codex for throughput on parallel tasks. Cursor as the control plane on top. We went through the same thing with infrastructure. Terraform, Docker, Kubernetes. Not one tool to rule them all. Composable layers that got better together. Are you already running multiple coding agents in the same workflow, or still picking one and hoping it covers everything? #AIcoding #DevTools #CodingAgents #SoftwareEngineering

As the adage in Software Engineering goes, never test your own code. In the AI-driven world of coding, the new rule is: never use the model that you used to write code to review it. I just finished reading an article by #Janakiram on how the AI coding market is not consolidating into one "winner", but is instead evolving into a specialized, three-layer stack: 1️⃣ The Orchestration Layer: Cursor 3 (specifically the new "Glass" interface) is moving beyond the editor to become a control plane for managing fleets of parallel agents. 2️⃣ The Execution Layer: This is the engine room. While Claude Code is winning on nuanced reasoning, OpenAI Codex is being tapped for high-throughput, asynchronous tasks. 3️⃣ The Review Layer: This is the game-changer. By using the new codex-plugin-cc, developers are now using Codex to provide independent, "adversarial" reviews of code written by Claude. #Janakiram makes a compelling point: we are moving away from walled gardens toward interoperability. The biggest players in AI have start building plugins for their competitors, the future seems to be about composition and not competition.