BRICS AI Economics

BRICS AI Economics

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Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies
1
Transformers revolutionized AI by letting language models understand context instantly. Learn how self-attention, positional encoding, and multi-head attention power today’s top LLMs - and why they’re replacing older models.
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Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies
2
Transformers revolutionized AI by enabling large language models to understand context across long texts using self-attention. This article explains how they work, why they beat older models, and what’s changing in 2025.
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Jan, 22 2026

How Large Language Models Are Transforming Healthcare Documentation and Triage

Emily Fies
3
Large language models are cutting documentation time for doctors and improving triage accuracy in emergency rooms. But bias, integration costs, and regulatory gaps remain major hurdles. Here’s how they’re really being used in U.S. healthcare today.
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Jan, 21 2026

Centralized Prompt Libraries: How Teams Build Reusable AI Patterns and Standards

Emily Fies
2
Centralized prompt libraries turn chaotic AI use into repeatable, consistent workflows. Learn how teams use curated prompts to save time, reduce errors, and scale AI across departments.
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Jan, 20 2026

How to Measure ROI for Large Language Model Projects: Real Metrics That Drive Decisions

Emily Fies
4
Learn the real metrics that prove LLM ROI-time saved, user adoption, hallucination rates, and cost comparisons. Stop guessing. Start measuring what matters.
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Jan, 19 2026

Sustainability of AI Coding: How Energy, Cost, and Efficiency Trade-Offs Are Reshaping Development

Emily Fies
5
AI coding is accelerating climate impact. Learn how energy use, carbon emissions, and efficiency trade-offs are reshaping development-and what you can do about it in 2026.
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Jan, 18 2026

Human Feedback in the Loop: How to Score and Refine AI Code Iterations for Better Results

Emily Fies
8
Human Feedback in the Loop turns AI coding from guesswork into a learning system. Learn how scoring AI suggestions cuts bugs, improves code quality, and helps teams build better software faster.
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Jan, 17 2026

Domain Adaptation in NLP: How to Fine-Tune Large Language Models for Medical, Legal, and Financial Text

Emily Fies
9
Learn how to fine-tune large language models for medical, legal, and financial text using domain adaptation. Discover methods, costs, pitfalls, and real-world results.
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Jan, 15 2026

Multimodal Agents in Generative AI: Tools That See, Hear, and Act

Emily Fies
7
Multimodal AI agents see, hear, and act like humans - processing images, sound, and text together to understand context and respond intelligently. Learn how they're transforming healthcare, manufacturing, and customer service - and where they still fall short.
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Jan, 14 2026

Refusal-Proofing Security Requirements: Prompts That Demand Safe Defaults

Emily Fies
10
Refusal-proof security requirements eliminate guesswork by making safe defaults mandatory. Learn how to write enforceable security rules that block insecure code before it ships, using OWASP ASVS, SQUARE, and STRIDE frameworks.
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Jan, 10 2026

Forecasting Delivery Timelines with Vibe Coding Data: How AI Is Changing Software Deadlines

Emily Fies
6
Vibe Coding is transforming software delivery by using AI to generate code from natural language prompts. Teams now forecast timelines based on AI speed, not human velocity - cutting development from weeks to days. Learn how it works, who benefits, and where it still falls short.
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Jan, 7 2026

Structured vs Unstructured Pruning for Efficient Large Language Models

Emily Fies
5
Structured and unstructured pruning help shrink large language models for faster, cheaper deployment. Structured pruning works on any device; unstructured offers higher compression but needs special hardware. Here's how to choose the right one.

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