Tri-City AI Links

Red Teaming for Privacy: How to Test Large Language Models for Data Leakage

Red Teaming for Privacy: How to Test Large Language Models for Data Leakage

Learn how to test large language models for data leakage using red teaming techniques. Discover real-world risks, free tools like garak, legal requirements, and how companies are preventing privacy breaches.

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How to Prompt for Performance Profiling and Optimization Plans

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.

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Data Collection and Cleaning for Large Language Model Pretraining at Web Scale

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.

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Causal Masking in Decoder-Only LLMs: How It Prevents Information Leakage and Powers Generative AI

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.

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Vision-Language Applications with Multimodal Large Language Models: What’s Working in 2025

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.

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Education Projects with Vibe Coding: Teaching Software Architecture Through AI-Powered Examples

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.

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v0, Firebase Studio, and AI Studio: How Cloud Platforms Support Vibe Coding

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.

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Talent Strategy for Generative AI: How to Hire, Upskill, and Build AI Communities That Work

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.

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IDE vs No-Code: Choosing the Right Development Tool for Your Skill Level

IDE vs No-Code: Choosing the Right Development Tool for Your Skill Level

Learn how to choose between IDEs and no-code platforms based on your skill level, project needs, and workflow. Discover when to use Bubble, VS Code, Mendix, or AI tools in 2025.

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Refusal-Proofing Security Requirements: Prompts That Demand Safe Defaults

Refusal-Proofing Security Requirements: Prompts That Demand Safe Defaults

Refusal-proof security requirements eliminate insecure defaults by making safety mandatory, measurable, and automated. Learn how to write prompts that force secure configurations and stop vulnerabilities before they start.

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Governance Committees for Generative AI: Roles, RACI, and Cadence

Governance Committees for Generative AI: Roles, RACI, and Cadence

Learn how to build a generative AI governance committee with clear roles, RACI structure, and meeting cadence. Real-world examples from IBM, JPMorgan, and The ODP Corporation show what works-and what doesn't.

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Positional Encoding in Transformers: Sinusoidal vs Learned for Large Language Models

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.

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