Forem: Anas Kadambalath

The SSL Nightmare: How Gemini Bypassed My Local Gateway Errors for Cloud Run

Anas Kadambalath — Wed, 04 Mar 2026 17:21:44 +0000

This is a submission for the Built with Google Gemini: Writing Challenge

Page 1: Introduction & The Problem

The Premise

In the world of DevOps, SSL and Gateway configurations are often the "dark matter" of a project—unseen until they cause your entire deployment to collapse. For the Built with Google Gemini: Writing Challenge, I decided to run an experiment: Could I take a Python application plagued by local gateway errors and let Gemini navigate a successful deployment to Google Cloud Run without me manually Googling a single error code?

This is a story of how I stopped fighting 502 Bad Gateway errors and started auditing a system orchestrated by Gemini.

What I Built

I developed a Python microservice designed for Google Cloud Run. The goal wasn't just to write code; it was to solve the "Works on my machine, fails in the container" paradox that local SSL proxies often create.

The Stack:

Language: Python 3.11 (Flask)

Deployment: Google Cloud Run (Serverless)

Orchestration: Google Gemini (Pro & Flash)

Containerization: Multi-stage Docker builds

The Conflict: The SSL Wall

The project hit a wall before it even touched the cloud. My local development environment uses a proxy that intercepts traffic for security. This created a "certificate chain" error. Every time my Python app tried to reach an external API or communicate between containers, I was met with a standard but infuriating [SSL: CERTIFICATE_VERIFY_FAILED] error.

Page 2: The Implementation & The Demo
The Pivot: From Debugger to System Auditor

Initially, I tried to "help" Gemini. I would see an SSL error and suggest the "lazy fix"—adding verify=False to my Python requests.

Gemini stopped me. It explained that bypassing SSL is a massive security debt. Instead, it suggested that we fix the CA-Certificate injection within the Docker layer itself. I shifted my role from writing lines of code to validating outcomes.

The "SSL Nightmare" Resolution

The Diagnosis: Gemini identified a port mapping conflict (Port 5000 vs the Cloud Run $PORT variable) that I had looked at for an hour and missed.

The Multi-Stage Fix: Gemini drafted a Dockerfile that handled the OS-level trust store.

Demo
Live Deployment:nil

The Build Log:
I used Gemini to generate a cloudbuild.yaml file that automated the transition from my local "Nightmare" to the Cloud's "Managed SSL" environment. Seeing the terminal output turn green after hours of gateway errors was the ultimate proof of concept.

"The build didn't just pass; it was optimized. Gemini reduced my final image size by 40% by suggesting a python-slim base that I hadn't considered."

Page 3: Lessons & Feedback
What I Learned

Reasoning Depth is a Stability Requirement

I found that Gemini Flash was great for quick syntax, but it struggled with the "contextual bridge" between a local network and Google Cloud's ingress. When I switched to Gemini Pro, the model realized that SSL termination happens at the Cloud Run Load Balancer, allowing me to simplify my local code.

The "Happy Path" Trap

AI-generated tests are often designed to confirm the code worked, not to find where it breaks. I learned that I had to explicitly prompt Gemini to "Act as a Red Team auditor" to find vulnerabilities in its own deployment script.

Soft Skills: The Art of the Prompt

I learned that describing my environment was more important than describing my code. Once I explained my local proxy setup to Gemini, the solutions became 10x more accurate.

Google Gemini Feedback
What Worked Well: Gemini’s ability to handle Network-Layer Reasoning. Most LLMs are good at Python; Gemini was good at networking. It understood how gcloud interacts with docker and os.environ.

Where I Ran into Friction: There is a slight "Documentation Lag." Gemini occasionally suggested gcloud flags that had been renamed or deprecated in the 2026 SDK updates.

Candid Summary: Gemini is the ultimate "Gatekeeper Buster." It helped me push through technical walls that usually make developers give up. However, the more freedom you give the AI, the more rigor you must give your audit.

🛡️ The Tools Behind The Curtain

This post was brewed with Google Gemini for the DevOps heavy lifting and a bit of human grit for the final deployment.

Tags: #BuiltWithGemini #GoogleCloud #DevOps #Python

Stress Reliver -Snake: A Tiny Game for Big Breathers

Anas Kadambalath — Mon, 23 Feb 2026 07:46:58 +0000

Sometimes you don’t need another productivity app. You need a 2‑minute reset.
That’s exactly why I built Stress Reliver — Snake — a clean, classic Snake game that’s simple, fast, and calm enough to let your brain downshift. No flashy UI, no noise. Just a grid, a hungry snake, and the quiet focus of “one more move.”
Press enter or click to view image in full size

Why Snake?
Because it’s familiar, frictionless, and timeless. Your hands already know what to do. You don’t need instructions, and you don’t need a tutorial. You press a key, the snake moves, and your mind follows. That’s the kind of low‑effort, high‑reward loop that works when you’re stressed, tired, or just need a quick break.
What makes this one different?
I kept it intentionally minimal:
No distractions
No complicated features
No deep menus
No learning curve
It’s the classic Snake loop: move, eat, grow, avoid walls, and reset when you crash. That’s it — and that’s the point. When your brain is overloaded, simplicity isn’t just nice. It’s relief.
How to run it
Clone the repo:
git clone https://github.com/anaskmh/stress-game-snake.git cd stress-game-snake
Option A (quick): open index.html directly in your browser.
Option B (recommended): run a local server:
python3 -m http.server 8000
Then open http://localhost:8000 in your browser.
Repo
github.com/anaskmh/stress-game-snake
Final thought
Stress doesn’t always need a big solution. Sometimes it just needs a simple game that lets you reset without thinking about it.
That’s Stress Reliver — Snake.
If you want this tailored for a publication, or a more technical version, tell me the target and I’ll adapt it.

Reimagining DevOps with AI: InfraCopilot + GitHub Copilot CLI

Anas Kadambalath — Sat, 14 Feb 2026 08:58:38 +0000

This is a submission for the GitHub Copilot CLI Challenge*

🚀 What I Built

I built InfraCopilot, an AI-powered DevOps automation CLI that transforms natural language into production-ready infrastructure, deployment workflows, and cloud automation.

Modern developers and DevOps engineers spend a significant amount of time writing Terraform, configuring Kubernetes, setting up CI/CD pipelines, and troubleshooting infrastructure. These tasks are essential but repetitive, time-consuming, and prone to errors.

InfraCopilot solves this problem by allowing users to describe their infrastructure and deployment needs in plain English. The tool automatically generates Terraform, Kubernetes manifests, Dockerfiles, GitHub Actions workflows, and architecture diagrams. It also provides log diagnosis, cost optimization insights, and self-healing recommendations.

The goal of this project is to improve developer productivity, reduce operational complexity, and accelerate the journey from idea to production. This project demonstrates how AI and developer tooling can make DevOps workflows faster, more reliable, and accessible to everyone.

Key capabilities include:

Natural language → Infrastructure automation
Terraform generation for cloud environments
Kubernetes deployment automation
Dockerfile and container best practices
CI/CD workflow automation
Architecture diagram generation
Infrastructure diagnosis and troubleshooting
Cost optimization suggestions
AI self-healing recommendations

InfraCopilot is designed as a terminal-first, developer-friendly tool that works offline and embeds DevOps best practices by default.

🎬 Demo

🔗 GitHub Repository:
https://github.com/anaskmh/InfraCopilot

🎥 Demo Video:
(Add your video link here)

During the demo, I showcase how InfraCopilot:

Checks system readiness using the doctor command
Initializes a new DevOps project
Generates Terraform for AWS EKS in seconds
Creates Kubernetes deployments with best practices
Automates CI/CD pipeline generation
Builds architecture diagrams automatically
Diagnoses real infrastructure failures
Provides cost optimization insights
Suggests self-healing infrastructure improvements

This demonstrates how developers can automate complex cloud and DevOps workflows with simple commands.

⭐ My Experience with GitHub Copilot CLI

GitHub Copilot CLI played a critical role in building InfraCopilot. It significantly accelerated development and enabled rapid experimentation across different modules.

I used Copilot CLI to:

Generate initial project scaffolding and CLI structure
Design modular architecture and command workflows
Create Terraform, Kubernetes, and CI/CD templates
Improve error handling and developer experience
Optimize code quality and maintainability
Debug issues quickly through natural language prompts

Copilot helped reduce the time required to design and implement complex DevOps automation logic. It allowed me to focus more on innovation, architecture, and user experience rather than repetitive coding tasks.

The most impactful aspect was how Copilot assisted in translating ideas into working features almost instantly. It acted as a real-time development partner, helping refine implementation, improve best practices, and accelerate productivity.

Overall, GitHub Copilot CLI transformed my development workflow and demonstrated how AI can empower developers to build sophisticated solutions faster and with greater confidence.

🙌 Conclusion

InfraCopilot represents the future of DevOps and platform engineering, where AI enhances human productivity and simplifies infrastructure management. By automating repetitive tasks and embedding best practices, InfraCopilot enables developers to focus on innovation while maintaining reliable and scalable systems.