Every time I want to try something new on OCI Generative AI, I end up needing the same thing: one tiny Python example that actually works. Not a full framework. Not a polished app. Just a small script with the right config, the right endpoint, and one clear path from “I have credentials” to “okay, it runs.” After rebuilding those snippets a few times, I put them in one repo: OCI GenAI Python Starters . ...
OCI Generative AI has grown fast—Cohere, Google, Meta, OpenAI, xAI—all available, each with multiple variants. Every time I started a new project I had to dig through documentation to find the right model. So I built OCI GenAI Catalog : a reference guide covering 30+ models with a guided selection wizard. What’s inside 24 chat models from 5 providers, with specs: context window, multimodal, tool use, reasoning, fine-tuning support 9 embedding models and 1 reranking model for RAG pipelines A model selection wizard — filter by task, performance tier, and context needs to get a top-3 recommendation Provider summary Provider Models Strength Cohere 5 RAG, fine-tuning Google Gemini 3 Multimodal, long context (up to 1M tokens) Meta Llama 5 Open weights, MoE efficiency OpenAI gpt-oss 2 Reasoning, agents xAI Grok 6 2M context, code specialization Data is sourced from OCI official docs and kept up to date. Check it out at OCI GenAI Catalog .
It’s been a while since I last wrote here. Lately, I’ve been diving deep into AI inference—the process of running AI models to generate responses—specifically exploring whether we truly need expensive GPUs for running modern language models. Spoiler alert: the answer might surprise you. After extensive testing on Oracle Cloud Infrastructure (OCI), comparing ARM-based Ampere processors against the latest AMD EPYC chips, I discovered that the right combination of software optimizations and compressed models can deliver remarkable performance—all without a single GPU. ...
The Challenge with Modern Translation Traditional translation services often struggle with context, idioms, and the subtle nuances that make language beautiful and meaningful. As businesses become increasingly global, there’s a growing need for translation services that can handle these complexities while maintaining security, scalability, and cost-effectiveness. Enter OCI Generative AI Oracle Cloud Infrastructure’s Generative AI service offers a compelling solution to these challenges. Unlike conventional translation APIs, OCI’s service leverages advanced language models that understand context and cultural nuances, making it an ideal choice for enterprise applications. ...
OCI Vision: How do we identify cereals? Is it possible to use OCI Vision to classify images that are not included in the default Vision model, without managing infrastructure or needing deep ML expertise? Yes, it is possible! You can use OCI Vision to identify image content and use this feature to improve your software and business. Let me show you how! In this example, I used cereals, but the same approach can be extended to many visible objects. ...