S Stack
Every technology here, and the system it shipped in.
A stack list with no context is a keyword dump — it tells you what someone has installed, not what they've operated. So each entry below carries the production system it ran in and an honest depth marker.
01 Cloud
Cloud & infrastructure
The surface I'd most want to be hired for. Two clouds operated in parallel through a live migration, one bill cut by ~88%, and a microservices fleet on managed Kubernetes.
AWS
- EKSProduction
- ECS on FargateProduction
- Elastic BeanstalkProduction
- CognitoProduction
- LambdaWorking
- EC2 / PM2Working
Leafgistics WMS · Sehat Kahani migration · telehealth backend.
Azure
- App ServicesProduction
- FunctionsProduction
- Application GatewayProduction
- Azure MySQLProduction
- Blob StorageProduction
The ~88% cost redesign, and the platform still running while AWS came online beside it.
Platform
- KubernetesProduction
- DockerProduction
- GitHub ActionsProduction
- GitLab CIProduction
- Load balancingProduction
CI/CD gating a multi-service fleet, and five environments across three product lines.
02 Backend
Backend & APIs
Node and TypeScript are home. Python is where the AI tooling lives. Both have carried real traffic in healthcare and logistics.
Languages
- TypeScriptProduction
- JavaScript / NodeProduction
- PythonProduction
Warehouse services, consultation backends, MCP servers.
Frameworks
- ExpressProduction
- Koa2Production
- AngularWorking
- ReactWorking
Including the Angular operations console used to run a national telemedicine platform.
Real-time & messaging
- NATS StreamingProduction
- WebRTC (OpenTok)Production
- Server-Sent EventsProduction
- Socket.ioProduction
- PubNubProduction
- Twilio VideoWorking
An event bus behind a WMS, and video consultations that patients actually joined.
03 AI
AI / LLM tooling
Treated as systems work, not prompt work. The interesting decisions are the tool boundary, the memory split, and where the safety gate sits.
Protocols & orchestration
- Model Context ProtocolProduction
- LangGraphProduction
- LangChainProduction
- ChainlitProduction
One MCP server built solo, one open-source MCP server contributed to.
Memory & state
- PineconeProduction
- CassandraProduction
- Vector retrievalProduction
Semantic memory and durable account facts kept deliberately separate.
Providers
- Anthropic ClaudeProduction
- OpenAIProduction
- Google GeminiProduction
- Fine-tuningWorking
Multi-provider by design — different graph nodes want different trade-offs.
04 Data & mobile
Storage, and the client side
Transactional and analytical storage, plus the mobile delivery side that 70+ published applications ran through.
Databases
- MySQLProduction
- PostgreSQLProduction
- MongoDBProduction
- RedisProduction
Schema design, caching and multi-tenant schema evolution under live load.
Analytical
- ClickHouseProduction
- Amazon RedshiftProduction
- OLAP modellingProduction
Migrated warehouse search off Redshift onto ClickHouse across three sites.
Mobile
- React NativeProduction
- Expo / EASProduction
- CallKit / TelecomWorking
- Native modulesWorking
- FlutterWorking
Release pipelines with version-consistency gating, offline-first sync, native call integration.
05 Opinions
What I'd reach for, and why
- Managed compute over self-managed nodes on a small team. Fargate exists so four engineers can spend their week on domain logic rather than node health.
- An event bus when one real-world action has several consequences — not because microservices are fashionable. If the audit trail is a consumer rather than a logging afterthought, you get a defensible record for free.
- Consumption billing for anything trigger-driven. The cheapest compute is the compute that isn't running; most cloud bills are paying for idle.
- Narrow tools over one wide one when exposing anything to an LLM. A single
run_sqlescape hatch is easy to build and impossible to reason about. - Separate semantic memory from durable facts. Conflating them is how agent systems start contradicting their own database.
- Incremental cutover over rewrites. Every migration I've run stayed live throughout, because there was never an acceptable window to be down.
→ Next
See where any of this actually ran.
Four case studies go into the architecture, the trade-offs and what I'd change.