Long-form posts and guides on data science, vectors, and Oracle technologies.
How I connected Oracle APEX to a FastAPI backend powered by Oracle Agent Memory, Autonomous Database and OCI Generative AI, building an enterprise-style memory console with Add Memory, Search, Chat Workspace, Models and Project 360. 3.0 Introduction: why bring Agent Memory into APEX? In the first article of this series, I explained the overall architecture of […]
How I built the backend layer for an Oracle Agent Memory Workspace using FastAPI, Oracle Autonomous Database over TLS, OCI Generative AI, Codex, ChatGPT Enterprise and Visual Studio Code. 2.0 Introduction: from architecture to backend implementation In the first article of this series, I explained the architecture behind my Oracle Agent Memory Workspace. The idea
How I extended an Oracle Agent Memory exploration into a working workspace with FastAPI, React, Oracle APEX, Autonomous Database, OCI Generative AI, Codex, ChatGPT Enterprise and Visual Studio Code. 1.0 Introduction AI agents are quickly moving beyond simple chat interfaces. They can call tools, reason over documents, generate code, interact with APIs, orchestrate workflows and
Building an Oracle Agent Memory Workspace — Part 1/3: From inspiration to Architecture Read More »
Introduction Some demos are born from a very technical requirement. Others are born from a simple idea that everyone immediately understands. This one started with a familiar concept: a football quiniela. The objective was to prepare an engaging demo for an event where business users, technical teams, and customers could easily connect with the story.
Oracle Data Science Agent is a newly introduced Oracle Machine Learning capability for Oracle Autonomous AI Database, designed to bring conversational analytics and machine learning into the database experience. At the time of writing, Oracle presents it as a fresh capability in Limited Availability. Rather than moving across multiple tools for data discovery, preparation, feature
Disclaimer I work at Oracle. This article is not official Oracle communication and does not represent the company’s views. It’s my personal attempt to understand Oracle as a case study. Recently, I watched a video about Oracle’s history and realized I didn’t actually have a calm, structured mental model of the company. I knew the
There’s a very practical question behind many AI conversations with customers: “Do we actually have the infrastructure to run and iterate on computer vision models reliably?” This project started as a hands-on way to answer that question with a concrete artifact:a working YOLO-based image classification model, trained on a real dataset, packaged with a Gradio
When you’re running Large Language Models (LLMs) or other heavy AI workloads, one of the first big questions is:“What GPU shape do I actually need in OCI Data Science?” This guide walks you through the key concepts, the GPU shapes available in OCI Data Science, and what shape matches the most popular AI models today.
Choosing the right GPU shape in OCI Data Science for your AI Model: A practical guide Read More »
In the past weeks I’ve been working on an Oncology Forecasting prototype built with Oracle APEX + Autonomous Database (ADB). The goal is straightforward (but very powerful): let business users define a scenario (country, indication, time horizon, epidemiology assumptions, products…), run the forecast, and then explore the results. The real challenge is not only computing
A rigorous recap from a curious amateur — and what the evidence suggests we should watch in 2026 (not financial advice). I’m not a finance professional. I’m simply someone who’s curious about how the world works — and who learns best by turning headlines into a structured, source-backed narrative. After watching a Spanish video that
2025 Was a Reminder That Markets Don’t Move in Straight Lines Read More »
