latent
Agent
JobMap
Describe the work you want to do, explore matching job postings, and inspect the evidence behind Jev's relevance judgments.
Explore the agent →Machine learning · AI · Data science
Notes from buildingwith data and AI.
I'm Oswaldo Orona. I write about the systems I build, from retrieval and AI agents to the data work underneath. Read the decisions, explore the results, and inspect the code.
Agents
Systems I built, with code and decisions you can inspect.
Agent
Star Wars embedding atlas
An interactive explorer for fifteen character descriptions, with similarity scores, projection views, and a way to test what changes when you remove their named relationships.
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Agent
ReActReel
A movie and music search agent that turns a remembered line into a playable video, with the tool calls visible beside the result.
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Agent
Masking before the agent handoff
A database transfer workflow that masks customer fields before rows reach an agent or its mailbox.
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Agent
Mexico City transit assistant
A conversational transit map that resolves places and asks a routing engine to calculate the journey.
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Agent
Memo Ocho Bits
An AI participant in a World Cup prediction pool, with saved forecasts and a leaderboard scored alongside human players.
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Agent
Investigating the Gulf oil spill
A geospatial investigation built to challenge the Mexican government’s account of the oil spill using vessel records, satellite layers, and dated statements.
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Agent
The blog as an MCP server
A public MCP endpoint that lets an assistant search the blog, browse its catalog, and retrieve complete articles.
Explore the agent →Library
Projects, analysis, and explanations of the systems I work with.
Jev ranked jobs better. MiniLM found about as many strong matches.
I compared Jev with three local embedding models on 237 job postings. Jev led on graded relevance, but a stricter definition of a good match changed the conclusion.
AI evaluationSemantic searchJev+1
Oct 1, 20268 min read
What a Moving Dot Can Hide: Building an Oil-Spill Data Explorer
I combined vessel records, satellite layers, and a timeline in a geospatial app. Then I checked which parts of the animation were observations and which were interpolation.
Sep 21, 20266 min read
2 of 2 articles
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
Ask about Oswaldo
Explore my experience through a conversation. Ask how I built a project, find out about my background, or discuss where my skills fit your team.
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