BRICS AI Economics

BRICS AI Economics - Page 3

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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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Jan, 4 2026

Data Residency Considerations for Global LLM Deployments

Emily Fies
5
Global LLM deployments must comply with data residency laws like GDPR and PIPL. Learn how hybrid architectures, SLMs, and regional infrastructure help avoid fines and keep user data local.
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Jan, 1 2026

Prompt Sensitivity Analysis: How Small Changes in Instructions Break LLM Performance

Emily Fies
0
Small changes in how you phrase a prompt can cause massive swings in LLM performance. Learn why prompt sensitivity breaks AI systems, which models are most vulnerable, and how to test and fix it before it costs you money.
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Dec, 30 2025

How to Detect Implicit vs Explicit Bias in Large Language Models

Emily Fies
7
Large language models may seem fair on the surface, but hidden biases persist-even in the most advanced systems. Learn how to detect implicit bias that standard tests miss and why bigger models aren't necessarily fairer.
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Dec, 29 2025

Human-in-the-Loop Operations for Generative AI: Review, Approval, and Exceptions

Emily Fies
6
Human-in-the-loop operations for generative AI ensure AI outputs are reviewed, approved, and corrected by humans before deployment - critical for compliance, safety, and trust in regulated industries.
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Dec, 28 2025

Energy Efficiency in Generative AI Training: Sparsity, Pruning, and Low-Rank Methods

Emily Fies
9
Learn how sparsity, pruning, and low-rank methods cut generative AI training energy by 30-80% without sacrificing accuracy. Real-world data, implementation tips, and future trends.
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Dec, 25 2025

Democratization of Software Development Through Vibe Coding: Who Can Build Now

Emily Fies
6
Vibe coding lets anyone build software by describing ideas in plain language. No coding experience needed. From students to small business owners, more people than ever can now create apps-with AI doing the heavy lifting.
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Dec, 24 2025

Testing and Monitoring RAG Pipelines: Synthetic Queries and Real Traffic

Emily Fies
5
Testing RAG pipelines requires both synthetic queries for controlled evaluation and real traffic monitoring to catch real-world failures. Learn how to balance cost, accuracy, and speed to build reliable AI systems.
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Nov, 19 2025

Supply Chain Optimization with Generative AI: Demand Forecast Narratives and Exceptions

Emily Fies
10
Generative AI is transforming supply chain demand forecasting by turning numbers into explainable narratives and helping teams handle exceptions before they become crises. Learn how it works, who’s using it, and what you need to get started.
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Oct, 16 2025

Why Large Language Models Excel at Many Tasks: Transfer, Generalization, and Emergent Abilities

Emily Fies
8
Large language models excel because they learn from massive text data, then adapt to new tasks with minimal examples. Transfer learning, generalization, and emergent abilities make them powerful without needing custom training for every job.

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