Required reading and references I rely on for system optimization, architecture, and practical engineering decisions.
A curated set of online resources that inform my decisions in system design, optimization, and engineering best practices.
The voices that shape my mindset, keep me updated on industry trends, and challenge my technical opinions.
Graduate-level systems research, distributed computing analysis, and practical algorithm experimentation.
Influential publications I reference when studying distributed systems, fault tolerance, and modern infrastructure design.
How Google processes massive datasets on commodity hardware with a fault-tolerant, distributed model; a cornerstone for batch processing systems.
Lamport's classic on ordering events in distributed systems and the rationale for logical clocks.
Architecture and design decisions behind a scalable file system used for large data processing workloads.
A foundational exploration of eventual consistency, gossip protocols, and partition-tolerant system design.
A consensus algorithm designed for clarity and practical implementation which is often used as an approachable alternative to Paxos.
Practical experience building distributed algorithms that improve system reliability, performance, and scalability.
// A fixed-size sliding window implementation designed to handle high-velocity caption ingestion while maintaining constant memory usage in the browser.
// Implementing thread barriers to study synchronization primitives and expose race conditions in concurrent programs.
// A distributed key-value store built with thread pools and replication primitives to explore consistency and performance tradeoffs.
// A fault-tolerant master-worker implementation that manages task distribution, handles worker failure detection, and aggregates intermediate data reduction.
// An ordering algorithm for the Django Scheduler that manages task execution based on priority weights and time-windows to prevent job starvation.
Open to discussing new projects and opportunities.
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