Engineering: AI (2)

In this tutorial, we will demonstrate how easy it is to build an assistant integrated into Stream’s React Chat SDK and learn how to incorporate the interaction on both the client and server sides. We will use the Anthropic and OpenAI APIs as the out-of-the-box examples, but using this method, developers can integrate any LLM

Geographically dispersed teams often have a hard time scheduling meetings that work for all participants. Human Resources departments also face this challenge when working with existing employees and job candidates alike. Employees have the benefit of a defined and somewhat uniform computing environment, job applicants are a whole different challenge. Each candidate uses whatever computer

AI-powered chatbots are widely used across industries like education, food delivery, and now, even software development. Since the release of large language models (LLMs) from Google and OpenAI, implementing AI-powered chatbots in projects has become much more accessible. Google’s Generative AI offers substantial benefits by enabling content creation, personalization, decision support, and simulation, which improve

This article aims to help you build AI agents powered by memory, knowledgebase, tools, and reasoning and chat with them using the command line and beautiful agent UIs. What is an Agent? Large language models (LLMs) can automate complex and sequential workflows and tasks. For example, you can use LLMs to build assistants that can

Unless you’ve been living under a rock, you probably know Large Language Models (LLMs) are all the rage right now. LLMs like OpenAI's ChatGPT and Google’s Gemini have redefined productivity and have more or less changed the world as we know it. However, their capabilities are not without limits. Static models trained on a fixed

Traditional speech language models like Siri or Alexa use turn-taking as the primary interaction style. Although these systems can detect single human voices, they cannot be interrupted in real time. Let's discover an advanced AI speech dialogue system that integrates listening and speaking capabilities to engage in conversations in real time, allowing seamless to-and-fro communication

Running large language models (LLMs) like ChatGPT and Claude usually involves sending data to servers managed by OpenAI and other AI model providers. While these services are secure, some businesses prefer to keep their data entirely offline for greater privacy. This article covers the top six tools developers can use to run and test LLMs

Interested in Moderation for your product? Check out Stream's Auto-Moderation Platform! It is crucial for the technology platforms to moderate any harmful content as early as possible. Most modern moderation tools take a few hundred milliseconds to a few seconds to detect harmful content. Often the action against detected harm is taken after the harm

On 8th September 2020, an article in the Guardian was written by a robot called GPT-3. They asked the robot to write an article about why humans should not be scared of robots and Artificial Intelligence. The human editors wrote the introduction for the article and instructed GPT-3 to generate the next possible sentences iteratively.

Personalization comes in many flavors, and the data science team at Stream can help you build your own feeds personalization engine based on your specific needs. In conjunction with our analytics client we recommend tracking every event for every user, such as clicking on a link) we use both engagement and feed data to power

Lately, I’ve been using Google’s feed on Android and it contains several interesting best practices for content discovery. Google’s feed strikes an effective balance between machine learning and follow relationships. With the recent advancements in AI, it can be hard to know when to apply AI and when to use a more manual method. This

There's hardly a developer who doesn’t use GitHub. With all those stars, pulls, pushes and merges, GitHub has a plethora of data available describing the developer universe. As a Data Scientist at Stream, my job is to develop recommender systems for our clients so that they can provide a better user experience for their customers. With that said, I wanted to see if I could build a recommendation

This blog post is broken into two parts and harkens back to learnings from a prior post. The sum of all these parts is altogether my best effort to provide you with a framework of how to take the creation of personalized news feeds to the next level. Part 1: Theory behind a very basic

Isn’t it great how Instagram’s “Explore” section displays content that matches your interests? When you open the application, the content and recommendations shown are almost always relevant to your specific likes, interests, connections, etc. While it may be fun to think we’re the center of the Instagram universe, the reality is that personalized, relevant content

Social media is a series of networks connecting individuals, companies, organizations, and groups to one another. These networks can transcend local, national, and international borders connecting people to networks far and wide. With all those connections, how can a user find the ones that they want to connect with? That’s where follow suggestions come in.

In this blogpost I will describe how to implement a feature-rich activity feed that will make relevant and accurate personalization algorithms easier to implement. As we have already explored in previous blog posts, app personalization is linking activity feeds and user engagement data. In most cases, a well thought out feed structure provides valuable information

As a Data Scientist that works on Feed Personalization, I find it it important to stay up to date with the current state of Machine Learning and its applications. Most of the time, using some of the better-known recommendation algorithms yields good initial results; however, sometimes a change in the model is essential to provide customers

In this short tutorial we will show you how to use Custom Ranking for your activity streams and news feeds. By default all feeds on Stream are ranked chronologically. Custom ranking allows you to take full control over how your feeds are sorted. Some common use cases include: Showing popular activities higher in the feed

Winds is an open source RSS reader is powered by React, Redux, Sails and Stream. This tutorial explains how we’ve built personalization for Winds, as an example of how using Stream makes it easy to build personalized feeds. About Personalization Personalization is a very broad concept. In this case, personalization equates to leveraging engagement data

In this post I discuss the Multi Armed Bandit problem and its applications to feed personalization. First, I will use a simple synthetic example to visualize arm selection in with bandit algorithms, I also evaluate the performance of some of the best known algorithms on a dataset for musical genre recommendations. What is a Multi-Armed Bandit? Imagine

The problem of content discovery and recommendation is very common in many machine learning applications: social networks, news aggregators and search engines are constantly updating and tweaking their algorithms to give individual users a unique experience. Personalization engines suggest relevant content with the objective of maximizing a specific metric. For example: a news website might want to increase