I build software by understanding how it actually executes — CPU time, memory movement, network latency, and failure behavior. My focus is not just application logic, but the full execution path from request entry to data storage and back.
I enjoy working on backend services, distributed systems, and infrastructure where performance, reliability, and observability matter. I prefer measuring real behavior over relying on abstractions.
- Runtime performance and latency reduction
- Memory usage and allocation patterns
- Caching layers and data locality
- Distributed system failure modes
- Concurrency and workload distribution
- Infrastructure-aware application design
- Observability and production debugging
- Throughput vs resource utilization trade-offs
Runtime
- Node.js internals, event loop behavior
- Async I/O and concurrency tuning
- Worker threads and clustering
- V8 profiling and heap analysis
Networking
- HTTP connection lifecycle
- Reverse proxy and load balancing
- Service-to-service communication
- Backpressure and retry strategies
Data
- PostgreSQL query planning and indexing
- Redis caching and eviction policies
- Connection pooling and latency optimization
- Consistency vs performance trade-offs
Infrastructure
- Docker containerization
- Linux-based deployments
- Process lifecycle management
- Resource constraints and scaling
Observability
- Structured logging
- Latency measurement
- Memory leak detection
- Production debugging
- Distributed system simulators
- Backend services under real load
- Multi-service architectures
- Infrastructure-aware SaaS systems
- Performance-sensitive APIs
- Failure testing environments
- Distributed systems behavior under load
- CPU cache awareness in application design
- Message queues and async pipelines
- Service mesh and traffic routing
- Failure injection and resilience testing
- Systems for AI and surveillance infrastructure
Email: [email protected]
GitHub: https://github.com/hamidlabs
LinkedIn: https://www.linkedin.com/in/hamidlabs
X: https://x.com/hamidlabs