Tools I Use
Full stack I rely on daily β from AI and cloud to productivity and observability. Links marked "Partner" are affiliate links.
π€Artificial Intelligence
Primary LLM for this blog's AI pipeline. Fast, cost-effective, multimodal. Gemini 2.0 Flash is my go-to for production workloads.
Fallback LLM in my AI pipeline. Best-in-class reasoning and code generation. More expensive but worth it for complex tasks.
AI pair programmer in VS Code. Indispensable for boilerplate, refactoring and explaining unfamiliar codebases.
Framework for building LLM-powered applications. Great for chains, agents and RAG pipelines.
Run LLMs locally in one command. Perfect for offline development and privacy-sensitive workloads.
Production ML framework by Google. My choice for custom model training and deployment pipelines.
βοΈDevelopment Stack
My default web framework. App Router, server components, edge functions. This portfolio runs on Next.js 15.
Type-safe JavaScript for everything. Catches entire classes of bugs at compile time β non-negotiable in any serious project.
The UI library that changed everything. Server Components in React 19 are a game-changer for performance.
JavaScript runtime for backend services, APIs and tooling. Essential in any modern full-stack setup.
My primary editor. Unbeatable extension ecosystem and native TypeScript/Git support.
Version control foundation. Trunk-based development and conventional commits are my workflow defaults.
βοΈDevOps & Cloud
Edge deployment platform. Zero-config CI/CD, instant previews and global CDN. Where this portfolio lives.
My go-to cloud for VMs, managed databases and container workloads. Excellent price-to-performance ratio.
Container runtime that makes "works on my machine" irrelevant. Essential for reproducible environments.
Container orchestration at scale. Complex to operate but nothing beats it for multi-service production deployments.
CI/CD natively integrated into GitHub. I use it for automated builds, tests and deployments on every push.
DNS, CDN, DDoS protection and edge workers. The free plan covers most production needs.
My choice for AI/ML workloads, BigQuery and Vertex AI. Powerful managed services for data engineering.
Best value hosting for client projects and landing pages. Easy setup, fast shared and VPS servers.
π¨Design & Prototyping
π§ Productivity
All-in-one workspace for docs, tasks, wikis and databases. My second brain for managing projects and knowledge.
Issue tracker built for engineering teams. Blazing fast, opinionated and a joy to use compared to Jira.
Open-source workflow automation. I use it for internal automations where I need full control over data.
ποΈDatabases
My default relational database. Rock-solid, feature-rich and the right starting point for nearly every project.
PostgreSQL with a great DX: built-in auth, realtime and storage. The open-source Firebase alternative.
In-memory store for caching, sessions and pub/sub. Indispensable for high-throughput production systems.
Document database for flexible schemas. Best when your data is truly document-shaped.
πObservability & Monitoring
Open-source dashboards for metrics, logs and traces. The visualization layer in my observability stack.
Time-series metrics collection and alerting. The de facto standard for Kubernetes monitoring.
Error tracking and performance monitoring. Catches production bugs before users complain about them.
Full-stack observability platform. Best for enterprises needing APM, logs and infrastructure in one place.
πLearning & Certifications
Best platform for professional certifications. Google Cloud, ML Specializations and Andrew Ng's courses are worth every cent.
Community-driven learning roadmaps for any tech skill. Invaluable for structuring a self-directed learning path.
Official AWS courses and certifications. Cloud practitioner cert is a solid foundation for any engineer.