Category: Artificial Intelligence - Page 2
How to Prompt for Performance Profiling and Optimization Plans
Learn how to use performance profiling tools effectively by asking the right questions to find true bottlenecks. Avoid guesswork and optimize with data-driven insights for real performance gains.
Data Collection and Cleaning for Large Language Model Pretraining at Web Scale
Training large language models requires more than raw data-it demands meticulous cleaning. Discover how web-scale datasets are filtered, deduplicated, and refined to boost model performance-and why quality beats quantity.
Causal Masking in Decoder-Only LLMs: How It Prevents Information Leakage and Powers Generative AI
Causal masking is the key architectural feature that enables decoder-only LLMs like GPT-4 and Llama 3 to generate coherent text by blocking future token information. Learn how it works, why it's essential, and how new research is enhancing it without breaking its core rule.
Vision-Language Applications with Multimodal Large Language Models: What’s Working in 2025
Vision-language models are now transforming document processing, healthcare, and robotics by combining image and text understanding. In 2025, open-source models like GLM-4.6V are outperforming proprietary systems in key areas - but only if deployed correctly.
Education Projects with Vibe Coding: Teaching Software Architecture Through AI-Powered Examples
Vibe coding is transforming how software architecture is taught by letting students build real apps with AI, focusing on design over syntax. Early results show faster learning, deeper understanding, and broader access to programming education.
v0, Firebase Studio, and AI Studio: How Cloud Platforms Support Vibe Coding
Firebase Studio, v0, and AI Studio are transforming how developers build apps using natural language and AI. Learn how vibe coding works, which tool to use for what, and why this is the future of development.
Talent Strategy for Generative AI: How to Hire, Upskill, and Build AI Communities That Work
Learn how to build a real generative AI talent strategy in 2025: hire for hybrid skills, upskill effectively with hands-on learning, and create communities where AI knowledge actually sticks.
Positional Encoding in Transformers: Sinusoidal vs Learned for Large Language Models
Sinusoidal and learned positional encodings were the original ways transformers handled word order. Today, they're outdated. RoPE and ALiBi dominate modern LLMs with far better long-context performance. Here's what you need to know.
Benchmarking Vibe Coding Tool Output Quality Across Frameworks
Vibe coding tools are transforming how code is written, but not all AI-generated code is reliable. This article breaks down the latest benchmarks, top-performing models like GPT-5.2, security risks, and what it really takes to use them effectively in 2025.
Model Distillation for Generative AI: Smaller Models with Big Capabilities
Model distillation lets you shrink large AI models into smaller, faster versions that keep 90%+ of their power. Learn how it works, where it shines, and why it’s becoming the standard for enterprise AI.
Vision-First vs Text-First Pretraining: Which Path Leads to Better Multimodal LLMs?
Text-first and vision-first pretraining are two paths to building multimodal AI. Text-first dominates industry use for its speed and compatibility. Vision-first leads in complex visual tasks but is harder to deploy. The future belongs to hybrids that blend both.
Safety in Multimodal Generative AI: How Content Filters Block Harmful Images and Audio
Multimodal AI can generate images and audio from text-but it also risks producing harmful content. Learn how safety filters work, which providers lead in blocking dangerous outputs, and why hidden attacks in images are the biggest threat today.
