OmbuLabs Blog

A few months ago I attended NERCOMP for the first time and I got to connect with IT professionals in the higher ed space. This is the first article in a series collecting my takeaways from some of the most interesting AI-related sessions at the conference.

I tried to focus on real-world examples of how higher ed professionals are thinking through and applying AI and ML, with all their limitations, in day-to-day academic life.

Keep Your Data Close and Your AI Closer: Local AI in the Academy

In their presentation, Gerol Petruzella & Trevor Murphy (Williams College) presented 3 case studies that leveraged AI to solve real scenarios at their institution.

These were 3 privacy-first, cloud-free applications of local AI to support pedagogy, accessibility, and student experimentation.

AI Agents are everywhere. Every day, tools, libraries, new use cases, and new products come out using and leveraging AI Agents. Several frameworks have been developed to make them easy to build, but what happens under the hood?

A very popular pattern for building AI Agents is the ReAct pattern, meaning Reasoning and Acting. The idea is to get large language models (LLMs) to reason about a problem in a manner analogous to how humans do, by breaking down the problem into smaller steps, reasoning about each step, using tools, and then acting on the results.

Let’s walk through the ReAct pattern and how we can use it to build a simple AI Agent that writes blog posts in Ruby.

In my previous post, An LLM-based AI Assistant for the FastRuby.io Newsletter, I introduced an AI-powered assistant we built with Sinatra to help our marketing team write summaries of blog posts for our newsletter.

In this post, I’ll go over how we implemented semantic search using pgvector and Sequel to fetch examples of previous summaries based on article content.

Semantic search allows our AI assistant to find the most relevant past examples, given meaning and context, when generating new summaries. This helps ensure consistency in tone and style while providing context-aware results that will serve as better examples for the large language modal (LLM) to generate new summaries, improving the quality of the generated output.

Every other week, the FastRuby.io newsletter brings a curated list of the best Ruby and Rails articles, tutorials, and news to your inbox.

Our engineering team collects links to interesting articles and our marketing team curates them, writes a summary for each article, and creates the newsletter. This process is quite manual, and involves some back and forth to ensure summaries are accurate, engaging, and relevant to our audience.

To make if more efficient, we have developed an AI assistant that helps us curate articles and generate the summaries for the newsletter.

Parallax was beginning to explore the use of artificial intelligence (AI) or machine learning (ML) to leverage the wealth of data on hand about customer projects, with the goal of improving their resource planning. The company thought it might be possible to create a machine learning model that identifies customer projects at risk, equipping the Customer Success team to make data-driven recommendations on how to head off problems before they occur.

I attended the South by Southwest EDU conference in Austin, Texas, for the first time this year and it was a great experience.

I had the opportunity to connect with several professionals in higher education, education technology and AI in education, and I learned a lot about the challenges and opportunities in the field.

As a company providing custom AI solutions, it’s very important that we understand the needs of institutions, educators and students, their concerns regarding artificial intelligence, and how we can help them leverage AI to solve real problems while protecting privacy and ensuring fairness.

Be it for our own internal tooling or on our client’s projects, we’ve used a variety of package managers. When building our newest tool, we decided to try out the uv package manager that’s been talked about a lot.

One problem with that, though, is that lack of support for uv in Heroku’s Python buildpacks. There’s an issue open in the Heroku roadmap to support it, but it is not supported yet.

We weren’t going to let that little hiccup stop us from using uv though. Inspired by the python-poetry-buildpack, we built our own little heroku-uv-buildpack that allowed us to deploy our application to Heroku.

As a company that is open by default we take pride in sharing a lot of our content on social media. Social networks like Twitter (now X), LinkedIn, Reddit, and Mastodon have helped us broadcast our content to people who trust us and respect us.

At the same time we are proud of our commitment to diversity, equity, and inclusion. As part of our core values we believe that diverse teams are smarter teams:

“There is value in all types of diversity, it is the best way to stay competitive in our industry. Problem solving with minds from different backgrounds leads to more creative and better thought out solutions.”

Recent changes in Twitter (now X) and their leadership are not aligned with our core values. We believe they are taking the social network in the wrong direction and we don’t want to have our brand associated with it anymore.

Large Language Models (LLMs) are widely available and easily accessible and are increasingly a part of business. Whether you’re interacting with an LLM via the provided interface or connecting via an API and integrating it into other systems, it’s helpful to understand how to get the best possible results out of the model.

Prompt Engineering is a technique that focuses on perfecting your input to get the best possible output out of the language model. Of all the different techniques available to get LLMs to fit your use case best, it’s the most straightforward one to implement since it focuses primarily on improving the content of the input. In this Part I article, we’ll dive into different Prompt Engineering techniques and how to leverage them to write highly effective prompts, focusing on single prompt and chain techniques. In our following article, we’ll cover agents and multi-modal techniques.

For other available techniques to enhance LLM capabilities, check out our Techniques to Enhance the Capabilities of LLMs for your Specific Use Case article!

New to LLMs? Check out this article on the landscape by our friends over at Shift: Guest Post: Navigating the AI Chatbot Landscape.

With the advent of widely available Large Language Models (LLMs), businesses everywhere have sought to leverage these models to handle specific tasks that can increase productivity of their teams, automate specific tasks, increase the abilities of chat bots, among a variety of other things.

However, LLMs are not great at handling domain-specific tasks out of the box. In this article, we’ll explore a few different techniques to enhance the capabilities of LLMs and help them perform well for your specific use case.

We often partner with our friends at Shift Interactive when we need an extra set of hands or expertise to complement our own. Recently we’ve been collaborating with them on interesting Artificial Intelligence and Machine Learning things. Check out their recent blog post below or here to get an overview of the AI chatbot landscape.

You can also check out the next article in this series: Techniques to Enhance the Capabilities of LLMs for your Specific Use Case.

At OmbuLabs, we recently had the opportunity to develop a Discord bot from scratch. We found the experience both rewarding and insightful, prompting us to create this tutorial to share our approach with you.

In this guide, we’ll walk you through the process of creating a Discord bot using Ruby on Rails, leveraging the efficiency and ease of development provided by this powerful framework.

In 2023, Ciera Jaspan and Collin Green, as part of the Engineering Productivity Research Team at Google, published a paper titled Defining, Measuring, and Managing Technical Debt based on five years of research on tech debt across different teams in their company.

In this article, I will describe the most interesting findings from that paper and how you can apply them at your company to define, measure, and manage technical debt.

Welcome to the third article in our Machine Learning with Ruby series!

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. We then looked into our first ensemble model technique, Random Forests, in Machine Learning: An Introduction to Random Forests. It is a good idea to review that article before diving into this one.

Random Forests are great for a wide variety of cases, but there are also situations where they don’t perform quite as well. In this article we’ll take a look at another popular tree-based ensemble model: Gradient Boosting.

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. Decision trees are very flexible and are a good tool for simple classification, but they are often not enough when it comes to real-world scenarios.

When dealing with large and complex data, or when dealing with data with a significant amount of noise, we need something more powerful. That’s where ensemble models come into play. Ensemble models combine a number of weak learners to build a strong model, with increased accuracy and robustness. Ensembles also help manage and reduce bias and overfitting.

In this article, we’ll cover a very popular tree-based ensemble model: Random Forest.