From failing exams in school to building AI systems at scale — raw personal stories and no-BS technical write-ups from my journey as a developer.
Showing 25 articles
I used to score below 40% in most subjects — not because I lacked capability, but because I was studying out of fear. The shift happened when curiosity replaced pressure. This is that story, and the mindset that changed everything for my entire career.
Read ArticleA deep-dive into Retrieval-Augmented Generation. How I built semantic search with OpenSearch, connected Amazon Bedrock LLMs, and deployed the whole system on AWS ECS Fargate — with real token cost breakdowns and architecture decisions explained.
Read ArticleBefore Python, before AI, there was Java. The verbosity, the OOP principles, the cryptic NullPointerExceptions — Java taught me how to think like a programmer. And its strictness made me a better engineer than any "easy first language" ever could.
Read ArticleIt was 2 AM. A YouTube video about Netflix recommendations. The realization that machines could learn patterns from data — without being explicitly programmed. That one video completely rewired my career path forever.
Read ArticleMost engineers learn HOW to use transformers. I obsessed over WHY they work. That habit of going one level deeper than most — into cosine similarity, async event loops, token economics — is what led to the ~99% token reduction at Idyllic.
Read ArticleDon't follow tutorials blindly. Build real things. Understand fundamentals before frameworks. Embrace errors as teachers. If I could send a message back to my first-year self, this would be it.
Read ArticleI was never the smartest person in the room. But I showed up every day. Consistency got me the DSA lead role, the internship, the production AI systems. Talent gets you started — consistency gets you there.
Read ArticleStart with a real problem, not a tutorial. Build something broken, then fix it. Find the 20% that does 80% of the work. Applied this to AWS Bedrock, OpenSearch, n8n, Docker, and Kubernetes — all within months.
Read ArticleThe tutorial trap is real. 10 hours of watching vs 1 hour of building something broken and fixing it. Every project I built — JARVIS, the booking system, the Chrome extension — taught me 10× more than any course.
Read ArticleJARVIS — a voice assistant I built with zero guidance, bad code, and complete obsession. It taught me API integration, state handling, prompt engineering basics, and one crucial lesson: imperfect projects build more confidence than perfect tutorials.
Read ArticleThe shift from academic coding (solve this LeetCode problem) to real-world problem solving (this mutt needs a booking system). Real users, real deadlines, real maintenance — they force better architecture, better code, better engineering.
Read ArticleBuilding a Radiology Information System — DICOM handling, patient data management, imaging report generation. The responsibility of writing software in healthcare, and how real constraints shaped every architecture decision.
Read ArticleBefore any internship, before any course — I built a voice-activated AI assistant from scratch. Speech recognition, text-to-speech, Wikipedia API, task automation. The project that got me hired.
Read ArticleHow I built and published a lightweight Python library to PyPI — testing API connectivity, diagnosing DNS issues, timeout failures, and endpoint errors with a single import. Open source, production-ready.
Read ArticleFrom "it works on my machine" chaos to containerized, orchestrated deployments on AWS ECS. Why containerization is now part of my day-one architecture decisions on every production project.
Read ArticleMultiple users, one editor, zero conflicts. The full architecture of my WebSocket-powered collaborative coding platform — room management, operational transforms, conflict resolution, and Socket.IO under the hood.
Read ArticleFlask, MySQL, automated WhatsApp confirmations. This is the full technical breakdown of the Raghavendra Swami Mutt booking system — from 30+ hours/week of manual work to near-zero.
Read ArticleHow I built a hybrid AI + rules email triage system using n8n — LLM classification, urgency scoring, validation, and structured output. Why probabilistic AI needs deterministic guardrails to be production-safe.
Read ArticleTired of refilling the same job application forms? I built an AI Chrome Extension that reads the page, understands the context, and fills fields automatically — tailored to each job description.
Read ArticleThe dot product and cosine similarity from 11th-grade math became essential for production RAG on AWS OpenSearch. Semantic search IS linear algebra — and the moment I realized that led to the 99% token reduction achievement.
Read ArticleSending an empty list in my prompt caused the LLM to return empty lists every time. Hours of debugging, thinking it was a model issue. Lesson: LLMs follow examples literally. Always validate inputs before LLM calls.
Read ArticleNot a black-box model — users see exactly which features drive the predicted price. Full breakdown of feature engineering, XGBoost tuning, SHAP value visualization, and serving predictions via Flask REST API.
Read ArticleFrom hackathon winner to production system — AWS ECS Fargate, OpenSearch vector search, Amazon Bedrock LLMs. The complete journey of taking a prototype and deploying it as a real-world AI service that handles semantic resume matching.
Read ArticleEvolving from a simple voice assistant to a full autonomous multi-agent system. Supervisor agent architecture, specialized sub-agents, LLM tool calling — the exact pattern I now use in production at Idyllic Services.
Read ArticleBrittle if-else routing in agentic AI breaks every time you add a new intent. I replaced the whole thing with a supervisor agent pattern — achieving ~99% token reduction and ~10× throughput at Idyllic Services.
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I write what I build, and I build for real problems. If something resonated, let's talk — or just say hi.