Insights & Articles
Business & OperationsHow to spot an AI use case worth doing
The six prerequisites that decide whether an AI use case is worth building, with a scoring rubric, two worked examples, and the red flags that say walk away.
Miguel Vicente JrJun 202611 min read
Business & OperationsSoon, talking to a human will cost extra
The first layer of AI isn't taking our jobs, it's quietly putting humans behind a paywall. Why talking to a person is becoming a premium service.
Miguel Vicente JrJun 20267 min read
Business & OperationsOptimising your operations with AI: start with the process, not the model
A practical playbook for optimising operations with AI: find the one slow step, automate it, keep a person in the loop, and measure the before and after.
Miguel Vicente JrJun 202612 min read
Business & OperationsWhy your best AI work belongs in a marketplace
Built a few AI tools and now fighting duplication and drift? The case for an internal AI marketplace, what goes in it, how to layer it, and how to curate it.
Miguel Vicente JrJun 202611 min read
Business & OperationsAI was supposed to give us time back. Instead, it put us on a productivity treadmill
AI finished a one-hour task in five minutes, so I did more, not less. How automation traded the time it saved for a faster productivity treadmill.
Miguel Vicente JrMar 20265 min read
InfrastructureRunning GPUStack with NVIDIA MIG: A Deep Dive into Multi-Instance GPU Orchestration
Multi-Instance GPU (MIG) technology promises to maximize GPU utilization by partitioning a single GPU into isolated instances. But getting MIG to work with container orchestration tools like GPUStack means working through a maze of CDI configuration, device enumeration, and runtime patches. This technical deep-dive shares our battle-tested solutions.
Frederico VicenteFeb 202615 min read
WebinarSmall AI Models in Production
Discover how small, highly capable AI models are enabling faster, more cost-effective, and more controllable AI systems in real production environments. Learn why efficient models matter and when lighter architectures outperform larger ones.
Frederico VicenteJan 202660 min
E-bookUnlock AI for your ERP
Process invoices in seconds with Vision AI. A step-by-step implementation guide to integrate AI into your ERP system and unlock new efficiencies.
dypsis.ai TeamJan 202625 min read
Machine LearningChurn Prediction: From Logistic Regression to Foundation Models
Customer churn costs businesses billions annually. This technical deep-dive compares statistical methods, gradient boosting, and transformer models like TimesFM 2.5 and Chronos 2 for churn prediction - with benchmarks, architecture diagrams, and implementation insights.
Frederico VicenteDec 202512 min read
E-bookBuilding High-Value AI Systems That Deliver Real ROI
A comprehensive guide to implementing AI systems that generate measurable business value. Learn practical strategies for building, deploying, and scaling AI solutions that deliver real return on investment.
dypsis.ai TeamDec 202530 min read
WebinarThe Future of Development with AI Coding Agents
Join Frederico Vicente for an exclusive webinar exploring how AI coding agents are transforming software development. Discover the tools, the workflows, and where development is heading.
Frederico VicenteNov 202560 min
Artificial IntelligenceArchitecting 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 handle the trade-offs between speed, flexibility, and governance when building production-grade AI automation.
Frederico VicenteNov 202512 min read
Software EngineeringRethinking 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 VicenteNov 202514 min read
InfrastructureEnabling 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 VicenteNov 202516 min read
Artificial IntelligenceModel 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 VicenteNov 202515 min read
Data AnnotationData 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 what make an ML model hold up in production.
Frederico VicenteSep 20259 min read
InfrastructureRAM vs VRAM in Mixture of Experts Models: The Hidden Bottleneck in Today's 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 VicenteSep 20258 min read
Generative AIRAG 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 VicenteSep 20257 min read
Artificial IntelligenceFederated Learning: Privacy-First AI
Train one model across sites that cannot pool their data. How federated learning works, where it fits, and what it costs in accuracy.
Frederico VicenteFeb 20248 min read
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