HUM AI TCH SPT NE Neelopphersyed MoE Cost Analyzer: Benchmark Dense vs Mixture-of-Experts LLMs on Your Own Prompts with Real API… Every team running LLMs in production eventually asks the same question: should we switch to a Mixture-of-Experts model? The MoE…
HUM SCI AI TCH NE Neelopphersyed Carbon-Aware Model Training: Scheduling GPU Workloads Around Electricity Carbon Intensity Training ML models has an environmental cost that most practitioners do not measure. A model trained during peak grid hours, when coal and…
AI HUM TCH NE Neelopphersyed Agentsync: Version, Merge, and Audit AI Agent Configurations Like Code Most AI engineering teams now run a stack of agent configs across many repos - model choices, tool allowlists, prompt templates, eval…
HUM AI TCH NE Neelopphersyed ASR Evaluation Framework: Benchmarking Speech Recognition Models Across Accuracy, Speed, and… Picking an ASR model for production is not straightforward. Whisper might be the most accurate for general English but too slow for…
AI HUM TCH NE Neelopphersyed AgentLiar Detector: Catch Coding Agents That Falsely Claim Task Completion AI coding agents are getting better at completing tasks. They are also getting better at appearing to complete tasks. An agent that claims…
SPT AI TCH NE Neelopphersyed Fine-Tuning Qwen2.5 - 0.5B to Write SRE Post-Mortem Summaries Writing post-mortem root-cause summaries is time-consuming and inconsistent. Junior SREs miss contributing factors. Senior SREs write…