latentExplorations in latent space
Writing about machine learning, generative AI, and high-performance engineering. Principal DBA & MCS–DS graduate from UIUC.
Articles
Deep dives into data science, machine learning, and AI engineering.
How a ReAct Agent Loop Actually Works
A walkthrough of one real ReAct agent run — the query was "Houston, we have a problem" and the agent returned the exact YouTube timestamp where Tom Hanks delivers the line. Four iterations, eight Gemini calls, three tool invocations.
Prompt Engineering is Dead, Long Live Prompt Engineering
Everyone said prompt engineering was a fad. They were wrong — it just evolved. From artisanal prompting to systematic prompt design for production systems.
Building AI-Powered Personal Websites
Your portfolio site can do more than display static content. Learn how to integrate AI chat, RAG, and agentic tools into a personal website.
The Rise of Structured Generation: From JSON Mode to Grammar-Constrained Decoding
Guaranteeing valid output from LLMs requires more than prompting. Grammar-constrained decoding enforces structure at the token level — here's how it works.
Self-Hosting AI: Running LLMs on Your Own Hardware
Cloud APIs are convenient but expensive. Explore how to run open-source LLMs on your own servers — from hardware selection to inference optimization.
PostgreSQL as a Vector Database: pgvector in Production
You don't need a separate vector database. pgvector turns PostgreSQL into a semantic search engine — with HNSW indexes, hybrid queries, and full SQL power.
Claude Code and the Future of AI-Assisted Development
Claude Code brings an AI agent directly into your terminal. Explore what autonomous coding tools mean for software engineering workflows.
Agentic Workflows: Orchestrating Multi-Step AI Pipelines
Single-prompt AI is hitting its ceiling. Agentic workflows chain multiple LLM calls with tools, branching, and feedback loops to tackle complex tasks reliably.
MCP Servers: Building Tool-Using AI with Model Context Protocol
The Model Context Protocol standardizes how AI models discover and use tools. Here's how MCP servers work and why they matter for the agentic future.
Model Distillation: Compressing Intelligence Without Losing It
Large models are powerful but expensive. Distillation transfers their knowledge into smaller, faster, cheaper models — and the results are surprisingly good.
Long Context Windows: What 1 Million Tokens Actually Changes
Gemini 1.5 Pro pushed context to 1M tokens. Claude 3.5 followed. But is a bigger context window always better, and what does it really enable?
Knowledge Graphs Meet LLMs: Structured Reasoning at Scale
Hallucinations happen when models guess. Knowledge graphs give LLMs a structured, verifiable backbone — and the combination is more powerful than either alone.
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About
I'm Oswaldo Orona — a Principal Database Administrator and AI practitioner based in Denver, CO. I hold an MCS–DS from UIUC (Tau Beta Pi, Phi Kappa Phi) and bring 25+ years of database experience alongside deep hands-on work in machine learning and AI engineering.
My focus areas include Retrieval-Augmented Generation (RAG), AI agents, Model Context Protocol (MCP), geospatial AI, and financial AI — all running in a self-hosted Proxmox home lab with Docker and LXC.
This blog is where I document explorations in latent space: the ideas, experiments, and systems that live between the data and the model.
ML & AI
- PyTorch
- TensorFlow
- RAG
- NLP
- Computer Vision
- LLMs
Databases
- PostgreSQL
- Oracle
- Redis
- pgvector
- DynamoDB
Infrastructure
- Docker
- Proxmox
- Ansible
- AWS
- Linux
Languages
- Python
- R
- SQL
- PL/SQL
- Java
- Bash