The real limit on AI isn’t compute — it’s capacity. There’s a moment in every AI conversation where someone — usually a CEO — leans back, exhales, and says the same line: “We need more compute.” It sounds urgent. It sounds technical. It sounds like a GPU problem. And for a while, I believed that too. But the more I mapped the AI pipeline — chips, networking, optics, power, cooling, grid — the more something started to ache. […]

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arXiv:2601.00254v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) presents new opportunities for automated software vulnerability detection, a crucial task in securing modern codebases. This paper presents a comparative study on the effectiveness of LLM-based techniques for detecting software vulnerabilities. The study evaluates three approaches, Retrieval-Augmented Generation (RAG), Supervised Fine-Tuning (SFT), and a Dual-Agent LLM framework, against a baseline LLM model. A curated dataset was compiled from Big-Vul and real-world code repositories from GitHub, […]

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The computational analysis of therapeutic communication presents fundamental challenges in multi-label classification, severe class imbalance, and heterogeneous multimodal data integration. We introduce a comprehensive bidirectional framework that addresses patient emotion recognition and provider behavior analysis through advanced data mining techniques. For patient-side emotion recognition, we employ ClinicalBERT fine-tuned on human-annotated CounselChat comprising 1,482 counseling interactions across 25 emotion categories exhibiting class imbalance ratios reaching 60:1. Through frequency-stratified class weighting combined with dynamic per-class threshold optimization, we achieve macro-F1 […]

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arXiv:2601.02504v1 Announce Type: new Abstract: Debugging is a crucial skill in programming education and software development, yet it is often overlooked in CS curricula. To address this, we introduce an AI-powered debugging assistant integrated into an IDE. It offers real-time support by analyzing code, suggesting breakpoints, and providing contextual hints. Using RAG with LLMs, program slicing, and custom heuristics, it enhances efficiency by minimizing LLM calls and improving accuracy. A three-level evaluation – technical analysis, UX study, and […]

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What do regular technology users think (and know) about AI? The post Understanding the Generative AI User appeared first on Towards Data Science.

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