Artificial Intelligence
Federated Learning: Privacy-First AI
Discover how Federated Learning is revolutionizing AI implementation...
Frederico Vicente8 min read
Latest Insights
Insights & Articles
Explore our latest articles on AI, Machine Learning, and more.
Frederico Vicente
Data Annotation
Data Labeling: The Overlooked Bottleneck in AI and Machine Learning
Model architectures often get the spotlight, but real-world performance in AI depends heavily on data labeling quality. Learn why annotation workflows, human-in-the-loop systems, and synthetic data strategies are critical for building robust ML models.
Frederico Vicente9 min read
Infrastructure
RAM vs VRAM in Mixture of Experts Models: The Hidden Bottleneck in Next-Gen LLMs
Explore how GPU VRAM and system RAM shape the performance of Mixture of Experts models like Qwen3-Next. Learn why memory hierarchy is the real bottleneck in modern LLM deployments and how to optimize infrastructure for speed and scalability.
Frederico Vicente8 min read
Generative AI
RAG vs Fine-Tuning: Why the Best AI Systems Combine Both
Should you choose Retrieval-Augmented Generation (RAG) or fine-tuning to optimize your LLM? The answer is not either-or. Learn how combining RAG with fine-tuning delivers accuracy, adaptability, and cost efficiency in real-world AI systems.
Frederico Vicente7 min read
Artificial Intelligence
Architecting AI Agent Systems: A Strategic Framework for Production Deployment
Choosing the right LLM framework is a strategic business decision that determines scalability, cost control, and system resilience. Learn how to navigate the trade-offs between speed, flexibility, and governance when building production-grade AI automation.
Frederico Vicente12 min read
Software Engineering
Rethinking AI Coding Agents: From Prompt Completion to Structured Engineering
The bottleneck in AI-assisted development isn't model capability - it's workflow design. Learn how to transform coding agents from autocompleters into systematic engineering partners through structured planning, context engineering, and disciplined process execution.
Frederico Vicente14 min read
Infrastructure
Enabling Private LLM Execution: Trusted Execution Environments and Encrypted Containers
Running LLM inference and fine-tuning on private datasets requires bridging theoretical cryptography with practical high-throughput systems. Learn how TEEs and encrypted containers create compliance-ready, hardware-isolated execution environments for confidential AI workloads.
Frederico Vicente16 min read
Artificial Intelligence
Model Context Protocol: Standardizing Context and Tool Integration for Agentic AI
As LLMs evolve from stateless prompt responders to stateful, tool-using agents, fragile hand-wired orchestration is breaking down. MCP provides a vendor-neutral protocol for connecting models with structured context, tools, and external systems at runtime.
Frederico Vicente15 min read