About
Power user of Claude Code with deep familiarity across its full feature set including…
Experience
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Education
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Volunteer Experience
Honors & Awards
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FY14 MVP Award
Oracle Corporation
FY14 MVP out of 90 person ITBDC org
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Endowed Presidential Scholarship
The University of Texas at Austin
The Unrestricted Endowed Presidential Scholarship is "considered one of the most notable scholarship offers for UT students". It is awarded to select students in each department for outstanding academic and extracurricular achievement.
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Networking Pulse
Redis Releases Critical Patch (CVE-2025-49844) A 13-year-old use-after-free flaw in Redis could allow remote code execution via crafted Lua scripts—affecting thousands of cloud instances. 🔹 Redis powers ~75% of cloud environments 🔹 Patch immediately & review Lua configurations 🔹 Audit access and monitor for suspicious activity NetworkingPulse can assist with patch validation, hardening, and threat monitoring. https://lnkd.in/efDZTvWC #Redis #Security #NetworkingPulse
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Systemdr
Project Initialization - Building Production-Grade Kafka Infrastructure The Naive Approach (And Why It Fails at Scale) Most engineers start with spring initializr, add spring-boot-starter-kafka, and assume they’re production-ready. This fails catastrophically at 3,000+ events/sec for three reasons: 1. Hidden Kafka Client Configuration Spring Boot’s auto-configuration sets batch.size=16KB and linger.ms=0ms. At high velocity, this creates 3,000 individual network requests per second instead of batching. The Record Accumulator fills faster than the Sender thread can drain it, causing memory pressure and eventual message drops when buffer.memory=32MB is exceeded. Uber’s dispatch system uses batch.size=1MB and linger.ms=10ms, achieving ~300 batches/sec instead of 3,000 individual sends. This reduces CPU context switches by 90% (proven via perf stat). 2. Transitive Dependency Hell Adding kafka-streams:3.6.1 doesn’t guarantee compatibility with kafka-clients:3.5.0 that Spring Boot pulls transitively. The ConsumerRecord class had binary-incompatible changes between versions (serialization format of headers). At runtime, you get NoSuchMethodError when StateStore checkpoint restoration tries to deserialize offsets. Substack Link: https://lnkd.in/dqQPXDg5
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KLogic
Kafka doesn’t fail loudly. It degrades quietly. Consumer lag creeps up. ISR counts shrink. Partitions drift out of balance. Commits slow down. And by the time alerts fire — you're already in incident mode. Production Kafka reliability isn’t about uptime alone. It’s about understanding: • Consumer group lag patterns vs throughput • Broker health and controller stability • ISR shrinkage and replication latency • Partition skew and leader imbalance • Commit delays and offset drift • Cross-cluster replication health Even a 1.5% lag drift can signal downstream backpressure. A 4.5% partition imbalance can distort throughput and increase broker load. And that unnoticed 8% operational gap? That’s where incidents are born. KLogic provides real-time Kafka observability with low overhead and production-grade metrics — giving teams the visibility to detect anomalies before they become outages. Because Kafka reliability isn’t about reacting faster. It’s about seeing earlier. Book a Demo: https://lnkd.in/g56NvMzH #Kafka #KafkaObservability #DistributedSystems #StreamingData #DevOps #PlatformEngineering #SRE #KLogic
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Aiven
The most effective architectural changes are often subtractive - removing entire layers of cost and complexity. Behind the scenes, OpsHelm, Inc.'s streaming backbone was migrated from a costly MSK and unreliable NATS setup to a single diskless Kafka architecture. The results were immediate: - Annual spend dropped from >$50,000 to <$10,000. - Data loss incidents went from recurring to zero. - Manual scaling and cross-cloud networking fees were eliminated. Read the full story of their migration and 78% cost reduction: https://lnkd.in/dS6UK9-7 #disklesskafka #datastreaming #Aiven
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Anyscale
Anyscale 🤝GKE Through our continued collaboration with the Google Kubernetes Engine (GKE) team, we’re advancing the experience to scale Ray and Kubernetes in the cloud. This work includes: Label Selectors for improved scheduling: https://lnkd.in/gUZ8iVUW New Features for AI Scaling: https://lnkd.in/gAGSMiir Enhanced TPU Support: https://lnkd.in/gsKDvwpv
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