BRICS AI Economics

Author: Emily Fies

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Mar, 17 2026

Security Basics for Non-Technical Builders Using Vibe Coding Platforms

Emily Fies
0
Non-technical builders using AI coding tools like Replit or GitHub Copilot need to understand basic security - or risk exposing secrets, data, and money. Learn the 4 must-do steps to protect your vibe-coded apps.
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Mar, 16 2026

Latency Budgets for Interactive Large Language Model Applications

Emily Fies
0
Latency budgets determine whether your AI app feels responsive or frustrating. Learn how TTFT, batching, model size, and caching shape real-world performance for interactive LLM applications.
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Mar, 15 2026

Rotary Position Embeddings and ALiBi: How Modern LLMs Handle Sequence Order

Emily Fies
1
Rotary Position Embeddings and ALiBi are two modern methods that help large language models understand word order without traditional positional encodings. Both improve long-context handling, scalability, and efficiency.
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Mar, 14 2026

vLLM vs TGI: Which LLM Serving Framework Delivers More Power for Your API?

Emily Fies
3
vLLM and TGI are two leading frameworks for serving large language models. vLLM delivers higher throughput and memory efficiency, while TGI offers easier deployment and better observability. Choose based on your traffic, model size, and team workflow.
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Mar, 12 2026

Parameter-Efficient Generative AI: LoRA, Adapters, and Prompt Tuning at Scale

Emily Fies
4
LoRA, adapters, and prompt tuning let you adapt massive AI models without retraining them fully. These methods cut costs by 90%+, making fine-tuning possible on consumer hardware. Learn how they work, how they compare, and which one to choose.
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Mar, 10 2026

Cost Management for Large Language Models: Pricing Models and Token Budgets

Emily Fies
7
Learn how to manage LLM costs using token budgets, model cascading, and caching. Cut AI expenses by 30-50% without losing quality. Real pricing data and proven strategies for 2026.
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Mar, 8 2026

NLP Pipelines vs End-to-End LLMs: When to Use Traditional Processing vs Prompting

Emily Fies
7
NLP pipelines offer speed and precision for structured tasks, while LLMs excel at complex reasoning. The best approach combines both: use pipelines for preprocessing and LLMs for nuanced understanding. This hybrid model cuts costs, improves accuracy, and meets regulatory needs.
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Mar, 7 2026

Real-Time Multimodal Assistants Powered by Large Language Models: What They Can Do Today

Emily Fies
9
Real-time multimodal assistants use AI to process text, images, audio, and video together in under half a second. They're already improving customer service, healthcare, and education-but they're not perfect yet.
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Mar, 6 2026

Keyboard and Screen Reader Support in AI-Generated UI Components

Emily Fies
7
AI-generated UI components can speed up accessibility, but they still need human oversight. Learn how keyboard and screen reader support works - and where AI falls short - in today's digital landscape.
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Mar, 5 2026

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

Emily Fies
5
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
5
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
9
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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