BRICS AI Economics

BRICS AI Economics

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Feb, 14 2026

Liability Considerations for Generative AI: Vendor, User, and Platform Responsibilities

Emily Fies
0
In 2026, liability for generative AI is no longer theoretical. Vendors, platforms, and users all face legal risks when AI generates harmful, false, or infringing content. Learn how state laws and court rulings are reshaping responsibility.
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Feb, 13 2026

Video Understanding with Generative AI: Captioning, Summaries, and Scene Analysis

Emily Fies
2
Generative AI now understands video like never before - generating captions, summaries, and scene analysis with 89%+ accuracy. Learn how it works, where it fails, and who’s using it in 2026.
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Feb, 11 2026

Generative AI in Business Operations: High-Impact Use Cases and Implementation Patterns

Emily Fies
3
Generative AI is transforming business operations by automating complex tasks like customer service, document generation, and supply chain planning. Learn the top use cases, implementation patterns, and real-world results from companies like BMW, Commerzbank, and Citi.
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Feb, 6 2026

Secure Authentication Patterns for Vibe-Coded Backends: Avoid Common AI Security Pitfalls

Emily Fies
8
Learn how to secure backend systems built with AI tools like GitHub Copilot. Discover common vulnerabilities in vibe-coded auth code and proven patterns for OAuth, JWT, RBAC, and more. Avoid 63% more authorization bypass risks with expert tips and real-world examples.
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Feb, 5 2026

Open-Source LLM Licensing: What You Must Know to Avoid Legal Risks

Emily Fies
9
Understanding legal and licensing requirements for open-source LLMs is crucial to avoid costly lawsuits. This guide covers common licenses, compliance steps, real-world risks, and how to navigate them safely.
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Feb, 4 2026

Stepwise Prompting with Feedback Loops: A Practical Guide to Iterative Code Generation

Emily Fies
6
Stepwise prompting with feedback loops helps developers generate accurate code by breaking tasks into smaller steps and validating each part. Learn how this method reduces errors by 63% and integrates with tools like GitHub Copilot. Practical examples and best practices included.
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Feb, 3 2026

Version Control with AI: Managing AI-Generated Commits and Diffs

Emily Fies
6
As of 2026, managing AI-generated commits and diffs requires new workflows. Teams using AI without version control adjustments face higher errors, security risks, and technical debt. Learn how to audit, review, and track AI code properly with Git and modern tools.
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Feb, 2 2026

How Curriculum and Data Mixtures Speed Up Large Language Model Scaling

Emily Fies
5
Smart data ordering and mixtures can boost LLM performance by up to 15% without larger models. Learn how curriculum learning works, what mixtures to use, and whether it’s worth the effort for your team.
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Feb, 1 2026

Measuring Data Quality for LLM Training: Model-Based and Heuristic Filters

Emily Fies
8
Measuring data quality for LLM training requires a mix of fast heuristic filters and smarter model-based systems. Learn how teams use cascaded approaches to remove low-quality data while preserving valuable content-and why skipping this step can ruin your model.
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Jan, 31 2026

Self-Consistency Prompting in Generative AI: How Voting Strategies Boost Accuracy

Emily Fies
8
Self-consistency prompting boosts AI accuracy by generating multiple reasoning paths and selecting the most common answer. It works best on math, logic, and medical tasks - not creative writing. Learn how to use it effectively.
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Jan, 30 2026

Safety Policies for Legal Use of Generative AI: Lessons from Mata v. Avianca

Emily Fies
8
The Mata v. Avianca case exposed the dangers of using generative AI for legal citations. Learn how lawyers got sanctioned for fabricating cases with ChatGPT-and what safety policies now prevent this from happening again.
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Jan, 29 2026

Edge-Capable Multimodal Large Language Models: What They Can Do and Where They Fall Short

Emily Fies
10
Edge-capable multimodal LLMs like MiniCPM-V run AI on phones without the cloud, offering privacy and speed-but they still have limits in battery life, accuracy, and complexity. Here's what they can do now and where they fall short.

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