Writing about engineering leadership, system architecture, and building products at scale.
How a single schema migration took down production for 4 minutes, and the expand-contract pattern that fixed it.
How we built real-time AI feedback streaming for HyrecruitAI interviews — handling SSE reconnection, backpressure, and per-tenant cost attribution.
How we stopped our interview AI from going off-script, leaking PII, and giving biased feedback — with a multi-layer guardrail system in TypeScript.
How we built a two-layer semantic cache using pgvector and Redis to dramatically reduce LLM API costs in a high-throughput interview platform.
How we built a lightweight prompt versioning system at HyrecruitAI to track, test, and roll back LLM prompts without deploying code.
How we replaced keyword filters with vector embeddings to match candidates to jobs at HyrecruitAI — architecture, pitfalls, and production results.
A practical system for managing prompt versions, running A/B tests, and rolling back without a full redeploy — built for HyrecruitAI's verbal, coding, and quiz evaluations.
How we built tenant-aware rate limiting at HyrecruitAI to protect LLM API costs, prevent abuse, and keep p99 latency under control — with Redis sliding windows and per-tier quotas.
How we integrated Razorpay subscriptions at HyrecruitAI — webhook reliability, tenant-scoped billing, failed payment recovery, and the edge cases that almost cost us revenue.
How we engineered the voice pipeline at HyrecruitAI — streaming audio over WebSockets, transcribing with Whisper, and orchestrating LLM responses in under 800ms.
Inside HyrecruitAI's AI evaluation engine — prompt engineering, rubric design, consistency scoring, and bias mitigation for fair interview assessments.
How we built reliable real-time video interviews at HyrecruitAI using WebRTC — signaling, TURN/STUN servers, network resilience, and recording.
A deep dive into the technical architecture behind HyrecruitAI — request flow, latency budgets, the AI interview agent, real-time transcription, and the data pipeline.
Practical PostgreSQL optimization strategies — indexing, query tuning, connection pooling, and monitoring in production.
What it actually looks like to grow from 2 to 15 engineers at a seed-stage AI startup — team structures, hiring mistakes, RFC processes, and the metrics that matter.
How we deploy HyrecruitAI from code to staging to production using Azure and GitHub Actions, with environment management and rollback strategy.
A practical comparison of Drizzle and Prisma from the perspective of running a production SaaS — type safety, migrations, and query performance.
The multi-repo pain that drove us to a TypeScript monorepo at HyrecruitAI — specific bugs, quantified benefits, and when this approach does NOT work.
How we architected HyrecruitAI for multi-tenancy — tenant isolation, subdomain routing, and the shared vs isolated database decision.
Hard-won lessons from building and maintaining open-source projects — community, PRs, docs, and avoiding burnout.
A practical guide to structuring a TypeScript monorepo with Turborepo — workspace layout, shared packages, caching, and CI.
The key technical bets we made building an AI hiring platform — and which ones paid off.
What I learned growing developer communities of 400+ members — and how those lessons apply to building a startup.