Engineering Blog

The AI industry is projected to spend $650 billion on data centers in 2026 alone. Yet, massive power shortages, community backlash, and a crumbling supply chain threaten to derail the infrastructure boom.

Amit Divekar explores the rising need for 'AI Use Minimizers'—the AI equivalent of Cloud FinOps engineers—to stop companies from burning cash on unnecessary LLM API calls and architect smarter, leaner AI systems.

Amit Divekar explains: How I built SelfHealOps, an autonomous self-healing DevOps agent using LangGraph, Python, and NVIDIA NIM to automatically resolve CI/CD pipeline failures and infrastructure issues.

Amit Divekar explains: A data-driven comparison of AWS Serverless (Lambda, API Gateway, DynamoDB) vs. Kubernetes (EKS) for production workloads. Includes real cost analysis, latency benchmarks, and decision frameworks for choosing the right architecture.

Amit Divekar explains: A hands-on guide to building a production-ready Retrieval Augmented Generation (RAG) pipeline using Google Genkit, Next.js 15, and vector databases. Covers embeddings, chunking strategies, similarity search, and streaming AI responses.

Amit Divekar explains: A comprehensive, definitive guide to architecting production-grade Next.js 15 applications using the App Router. Covers server components, streaming, caching strategies, middleware patterns, and deployment-ready project structures.

Amit Divekar explains: A Cloud Architect's guide to choosing between Google Cloud Run and Kubernetes Engine (GKE) for hosting containerized AI inference servers and massive traffic loads.

Amit Divekar explains: How to use ChatGPT 5.5's native parallel tool calling and reasoning loops to build resilient multi-agent systems, based on lessons learned from building Professor Profiler.

Amit Divekar explains: A comprehensive technical breakdown of building FileFlow, a Google Drive alternative utilizing Flask, an adjacency list pattern for hierarchical storage, and Render for deployment.

Amit Divekar explains: A deep technical exploration into how implementing deterministic hashing strategies and thread-safe SQLite caching significantly decreased AI API latencies and expenses in SchemaSense.

Amit Divekar explains: A deep dive into migrating a production Next.js RAG pipeline from older models to Claude 4.7. We explore context caching, retrieval accuracy, and how to structure your API routes.

Amit Divekar explains: How I built an intelligent database documentation platform using Next.js, FastAPI, and DeepSeek-V3 that saves engineering teams hours of manual work while improving code quality and onboarding.

Amit Divekar explains: Discover the top open-source AI frameworks for building autonomous agents - LangGraph, CrewAI, AutoGen, OpenHands, Aider, and Cline. Free, model-agnostic, and production-ready for your AI workflows.

Amit Divekar explains: Master Python's core functional programming concepts - from lambda functions and decorators to memory-efficient generators and custom iterators. Production-ready patterns for modern Python development.

Amit Divekar explains: A comprehensive guide to working with Pandas DataFrames - from creation and data cleaning to advanced operations like grouping, merging, and aggregation.

Amit Divekar explains: Learn how to access Reddit data as JSON for data analysis and AI training using simple URL tricks and Python tools.

Amit Divekar explains: How we built a hierarchical multi-agent system with Google Gemini to decode professor psychology and optimize study strategies.