Oracle AI Database Private Agent Factory is a self hosted platform for building AI agents that run entirely on your own infrastructure. Nothing leaves your machine: not your data, not your prompts, not your documents. In this post I walk through a complete installation on a MacBook with Apple Silicon, from download to a running and configured web application, using the exact commands and outputs from my own install. The process has two halves: first the terminal installation, which builds and deploys the containers, and then the in application setup…
Step-by-Step: Building a Meeting-Booking Agent in Oracle AI Database Private Agent Factory
This walkthrough assumes a working Agent Factory installation (Quickstart mode is fine) with an LLM configured, for example, local Ollama running llama3.1:8b. The agent will: read an email, decide whether it’s a meeting request, extract the details, check calendar availability, and draft or book the meeting. Overview of what I’ll build Agent Factory has no built-in Gmail/Outlook connector, so email and calendar arrive as tools via an MCP server (or OpenAPI upload). That’s Step 1 and the only part requiring code, roughly 60 lines of Python. Everything after is no-code…
Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data – Version 3 – Integrating Oracle AI Database 26ai, Oracle APEX, and a Claude-Powered Chat Interface
Table of Contents Version 1:Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data Version 2: Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data – Version 2 — Integrating Oracle AI Database 26ai and Oracle APEX Github: https://github.com/brunorsreis/–cancer-gene-expression-classification-version3/ 1. Introduction Version 2 already stores the gene expression dataset in Oracle, runs Python analytics such as PCA, t-SNE, and K-means, and writes the analytical outputs back into Oracle for Oracle APEX dashboards. Version…
Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data – Version 2 — Integrating Oracle AI Database 26ai and Oracle APEX
Introduction Artificial intelligence (AI) is increasingly transforming healthcare by enabling the analysis of complex biomedical datasets and supporting advances in precision medicine. One important application is the classification of cancer types using gene expression data. Gene expression datasets measure the activity of thousands of genes simultaneously, allowing researchers to identify molecular patterns associated with specific tumour types. These datasets are typically extremely high dimensional and require advanced machine learning techniques to extract meaningful insights. The dataset used in this project contains approximately 802 tumour samples and more than 20,000 gene…
Conceptualising an AI System for Cancer Type Classification Using Gene Expression Data
Introduction Artificial intelligence (AI) is transforming healthcare by enabling advanced analysis of complex biomedical data and supporting clinical decision-making. Traditional statistical methods often struggle with high-dimensional datasets, while machine learning can identify patterns directly from large amounts of data. According to Abtahi and Astaraki (2026), AI is particularly valuable in healthcare when analysing large datasets where traditional methods are limited. One important application of AI is cancer classification using gene expression data. These datasets measure the activity of thousands of genes simultaneously, helping researchers identify molecular patterns linked to specific…
Supercharging Your Application with Redis + Aurora PostgreSQL: A Practical Guide from Local Setup to Full AWS Deployment
Redis (Remote Dictionary Server) is an in-memory key–value database that runs as a background service and is widely used as a distributed cache and message broker. It is open-source and provides extremely high-performance data storage and retrieval with optional durability, making it ideal for applications that need to reduce latency and minimize I/O operations. 1. Quick & Basic Installation Before integrating Redis with PostgreSQL (Aurora-compatible), let’s set up everything locally using Docker. This gives you a full development environment that can later be migrated to AWS with minimal changes. 1.1…
Building a Semantic Search API with MySQL Vector Search, Oracle Cloud, and an NBA Kaggle Dataset
Semantic search enables users to ask for “a 3-and-D wing who can guard multiple positions” (as described in The Kings Beat article) and retrieve the correct NBA players, rather than simply a keyword match on “3,” “wing,” or “defense.” By combining MySQL Vector Search, Oracle Cloud’s Generative AI Embeddings, and a real NBA dataset from Kaggle, you can build a powerful natural-language search API that understands the meaning behind a query.As I love sports, I think this would be a great demonstration. In this tutorial, you’ll build: We’ll use the…
Java vs. Python: Which Language Is Faster? A Quick Experiment Inspired by an Interview Question
During a recent interview, I was asked a question that seemed simple but actually touches on deeper concepts in programming-language theory and system design: “Which programming language is faster: Java or Python?” At the time, I didn’t know the exact answer. I understood the general differences between the two languages, and I assumed that Python might use fewer computational resources than Java. Ultimately, I responded that performance would depend on the type of data being processed, the context, and the purpose of the program, and I admitted that I wasn’t…
Hands-On Implementation: Building Big Data and Analytics Solutions with Oracle Autonomous AI Database and Oracle Analytics Cloud (OAC)
Modern data analytics projects often take place in shared or restricted Oracle Cloud Infrastructure (OCI) environments, where not all users have administrative privileges. In such cases, demonstrating an end-to-end data pipeline from data ingestion and transformation to visualization can seem challenging. This post uses Oracle Autonomous AI Database (AADB) , the evolution of Autonomous Data Warehouse (ADW) and ATP, along with Oracle Analytics Cloud (OAC). It highlights how users can still build and test data engineering workflows without elevated IAM permissions or access to Oracle Data Flow or Big Data…
Implementing the Model Context Protocol (MCP) in Oracle Database 26ai
As applications using AI evolve, databases are no longer just data stores since they are becoming intelligent endpoints that can interact directly with large language models (LLMs) and AI agents. The Model Context Protocol (MCP) is the key standard that makes this possible: it provides a uniform way for AI agents to query, reason, and act on data sources such as relational databases. In this post, I will walk through two practical implementations of MCP: Finally, we will take a look at a concise rundown of what changed from Oracle…