BRICS AI Economics

BRICS AI Economics

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Mar, 5 2026

Observability for AI Agents: Why Telemetry, Sandboxes, and Kill Switches Are Non-Negotiable in 2026

Emily Fies
1
In 2026, AI agents run critical business workflows-but without telemetry, sandboxes, and kill switches, they become invisible risks. Learn how observability turns unpredictable AI into controllable, reliable systems.
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Mar, 3 2026

Hackathon Strategy: Winning Prototypes with Vibe Coding and LLM Agents

Emily Fies
4
Winning hackathons in 2026 isn't about coding faster-it's about building the right thing, fast, and selling it clearly. Learn how vibe coding and LLM agents are changing the game.
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Mar, 2 2026

Benchmark Transfer After Fine-Tuning: How LLMs Keep Their General Skills When Learning New Tasks

Emily Fies
3
Fine-tuning LLMs for specific tasks can erase their general knowledge. Learn how benchmark transfer ensures models stay smart across all tasks - not just the one you trained them for.
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Mar, 1 2026

How Synthetic Data Generation Protects Privacy in LLM Training

Emily Fies
4
Synthetic data generation lets AI models learn from realistic fake data instead of real personal information. Using differential privacy and LLMs, organizations can train systems safely without violating HIPAA, GDPR, or risking data breaches.
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Feb, 28 2026

Training Non-Developers to Ship Secure Vibe-Coded Apps

Emily Fies
5
Non-developers using AI to build apps are creating insecure systems by accident. Learn the three simple rules to prevent data leaks, avoid compliance fines, and ship apps that are both fast and safe.
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Feb, 27 2026

Privacy and Data Governance for Generative AI: Protecting Sensitive Information at Scale

Emily Fies
7
Generative AI is leaking sensitive data at scale. Learn how modern organizations are using governance-not blocklists-to protect information, comply with global laws, and empower employees safely.
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Feb, 26 2026

Scaling Laws in Generative AI: Why More Parameters Improve Model Performance

Emily Fies
5
Scaling laws in generative AI reveal that increasing model parameters leads to predictable, smooth improvements in performance. This mathematical pattern lets teams design smarter AI systems without costly trial and error.
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Feb, 24 2026

Long-Form Generation with Large Language Models: How to Keep Structure, Coherence, and Facts Accurate

Emily Fies
7
Long-form generation with large language models can produce detailed content, but structure, coherence, and facts often break down. Learn how to guide AI for reliable long-form output using outlines, RAG, and human review.
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Feb, 23 2026

How Design Teams Use Generative AI for Wireframes, Creative Variations, and Asset Generation

Emily Fies
9
Generative AI is transforming design teams by speeding up wireframe creation, generating creative variations, and automating asset generation. Learn how tools like Figma, Adobe Firefly, and Orq.ai are reshaping workflows-and what to avoid.
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Feb, 22 2026

Rapid Prototyping with APIs vs Production Hardening with Open-Source LLMs

Emily Fies
5
Rapid prototyping with LLM APIs gets you a working demo fast, but production demands control, cost efficiency, and compliance. Learn why teams switch to self-hosted open-source models-and how to do it right.
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Feb, 21 2026

Constrained Decoding for LLMs: How JSON, Regex, and Schema Control Improve Output Reliability

Emily Fies
8
Constrained decoding ensures large language models generate valid JSON, regex-matching, and schema-compliant outputs by blocking invalid tokens during generation. It reduces errors to near zero but slows generation slightly and works best with smaller models.
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Feb, 20 2026

Security Vulnerabilities and Risk Management in AI-Generated Code

Emily Fies
5
AI-generated code is now writing half of all software, but it’s introducing critical security flaws like SQL injection, hardcoded credentials, and XSS. Learn how to detect and prevent these risks before they breach your systems.

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