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Aditya Sharma
Intern Machine Learning Engineer specializing in deep learning and LLM systems
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About
Built and shipped a personal LLM-powered news aggregation platform (Clear Brief) that scrapes ~200 articles per cycle, clusters them into ~15–30 consolidated stories, and supports on-demand deep dives via a Next.js API route. Emphasizes production-minded reliability (token/cost controls, timeouts, graceful frontend degradation) and database-backed orchestration using SQLite with retry + exponential backoff for burst processing.
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Key Strengths
- Shipped an LLM-driven news aggregation product to production (scrape → cluster → summarize)
- Designed reliability safeguards (scheduler lock, memory bounds, per-source timeouts, frontend degradation)
- Database-centric orchestration with idempotent processing flags (processed/reset on cluster changes)
- Handled bursty LLM workloads with retries, exponential backoff, and isolation to prevent cascading failures
- Built clustering pipeline that reduces ~200 scraped articles per cycle into ~15–30 clustered stories
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