~/dev.jaspreetsingh

~/stack

Tools, Infrastructure & AI

Everything I use to design, build, ship, and maintain production systems. AI-accelerated across the full cycle — from architecture to deployment to monitoring.

Frontend

ReactNext.jsTypeScriptTailwind CSSFlutterDart

Backend

Node.jsPythonDenotRPCREST APIsGraphQLPostgreSQLSupabaseFirebase

DevOps & Cloud

GitHub ActionsDockerVercelAWS (S3, Lambda, SWA)VPS / NginxCI/CD Pipelines

Monitoring & Ops

SentryError AlertingUptime MonitoringPerformance BudgetsLog Management

CMS & Content

PrismicContentfulSanityWordPressHeadless CMSISR / SSG

AI / ML

OpenAI APIGoogle GeminiPrompt EngineeringLLM IntegrationOn-Device AIAI Analytics

AI Dev Tools

Cursor IDEGitHub CopilotKiroClaudeChatGPTAI Code Review

Tools & Workflow

GitFigmaFastlanePostmanLinearNotion

Ship faster with AI

Traditionally, it would take a team of people days to write a piece of code, and I write a piece of code in hours, using AI to complement the entire development cycle, not just for code completion.

Traditional approach

Days of boilerplate setup

Manual code review cycles

Slow debugging loops

Docs written after the fact

vs

AI-accelerated

Scaffold in minutes, ship same day

AI review catches issues in PR

Root cause in seconds with AI debug

Docs generated alongside code

AI-Assisted Development

Generates, refactors and reviews code in realtime, saves 60-70% boilerplate code and identifies bugs before they ship.

Efficient API Token Usage

Optimized prompt caching, model routing (GPT-4o vs GPT-4o-mini), and streaming responses to reduce costs by up to 80% compared to GPT-4 API usage, all while maintaining quality.

LLM Integration in Production

Real products that use OpenAI, Gemini, and Anthropic APIs: quiz generation, content pipelines, NL-to-SQL, and intelligent automation workflows.

AI-Powered Architecture Planning

Generate a rapid prototype of system designs, ERD, audit security posture and technical documentation using AI, in an instant that would otherwise take weeks to get done.


How I Ship

Systems thinking from local dev to production. Every project ships with a real deployment strategy.

Deployment

Vercel · VPS (Nginx) · Supabase

Zero downtime deploy, edge function, managed Postgres, custom architecture

CI/CD

GitHub Actions

Automated lint → type-check → build → deploy pipeline to be run on every push.

Automation

Webhooks · Cron jobs · Realtime subscriptions

Auto-scheduling, background sync, Supabase Realtime for live data

Observability

Full observability stack: error tracking with source maps, structured logging, uptime checking, and real-time alerts – every production system is instrumented from day one

Security

Row Level Security · Bot protection · RBAC

Postgres RLS for tenant isolation, permission based access control, bot protection on public endpoints

Build completed in 23s

Tests passed (142 passing, 0 failing)

Docker image pushed → registry

Deployed to production · 0 downtime

$


Always-On Observability

Production systems need eyes. I instrument every deployment with error tracking, uptime monitoring, and alerting so issues are caught before users notice.

Error Tracking & Alerting

Sentry, Bugsnag, and custom webhook alerts. Source-mapped stack traces, release tracking, and alert rules that page on critical errors — not noise.

Uptime Monitoring

Automated uptime checks (BetterUptime, UptimeRobot) with instant Slack/email alerts on downtime. SLA tracking and incident response workflows built in.

Structured Logging

JSON-structured request logs, slow query detection, and audit trails. Queryable logs via Logtail, Axiom, or Cloudwatch — not just terminal noise.

Performance Budgets

Lighthouse CI in every pipeline. Core Web Vitals tracked per deploy. Performance regressions caught in PR review, not after launch.

Real-Time Dashboards

Vercel Analytics, Supabase dashboard, and custom metrics endpoints. At-a-glance health for active users, DB query times, and API response rates.

Automated Incident Response

GitHub Actions workflows triggered on alert webhooks. Auto-rollback on failed health checks, Slack notifications with context, and runbook links.


Headless Content Architecture

I build content-driven sites with headless CMS platforms that give clients full editorial control without touching code — and give developers clean APIs.

P

Prismic

Slice-based content modelling with ISR. Webhook-triggered Vercel rebuilds on publish. Used in production for client marketing sites.

C

Contentful

Structured content with rich type system. GraphQL API for flexible querying. Ideal for multi-locale, multi-channel content delivery.

W

WordPress (Headless)

WP as a backend CMS with Next.js frontend via REST or WPGraphQL. Best of both worlds — familiar editor, modern frontend performance.

S

Sanity

Real-time collaborative editing with GROQ queries. Portable text for rich content. Used for projects needing custom editorial workflows.


Efficient Server Actions

I leverage Next.js Server Actions, edge functions, and server components to keep client bundles lean, reduce round-trips, and push logic to where it runs fastest.

Next.js Server Actions

Form mutations and data fetching without API routes. Eliminates client-server round-trips for common operations, reducing latency and bundle size.

Edge Functions

Vercel Edge Runtime for auth middleware, A/B testing, and geo-routing. Runs at the CDN edge — sub-10ms response times globally.

React Server Components

Zero-JS server rendering for data-heavy pages. Database queries run on the server, only serialized data crosses the wire.

AWS Static Web Apps

Azure SWA and AWS Amplify for globally distributed static deployments with serverless API backends. Cost-efficient at scale.


Have a project in mind? Let's talk.

Open to full-time remote roles globally. I'll respond within 24 hours.

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