Jev for beginners: how to use it and what to build

The episode begins with the host excitedly introducing Jev, a new decision model from TypeSafe AI that has seen rapid adoption and use cases since its release. The host notes the flurry of recent model launches like Opus 55, GPT-6, and others, but emphasizes Jev’s uniqueness due to its fast, inexpensive, decision-focused capabilities. He explains that Jev returns type-safe structured values instead of raw text, making it highly efficient for decision-making applications. The episode is framed as a deep dive into what Jev is, its use cases, and how it can be a game-changer in AI workflows. The host mentions that Jev is free on the AI Gateway for now, making it accessible for experimentation. He highlights that Jev has already been used to generate significant value, potentially worth hundreds of thousands or millions of dollars, at a very low cost, mainly on input tokens. The episode’s purpose is to explore how to use Jev effectively and what kinds of products can be built around it.

The host provides a detailed explanation of how Jev differs from traditional large language models (LLMs). Using a comparison table from TypeSafe’s blog, he describes that while standard LLMs take text as input and output, Jev takes text and returns type-safe values. These values are pre-defined choices like yes/no, rankings from 1-10, or other discrete options, which he refers to as 'type safe values.' This approach simplifies decision logic and classification tasks, making it incredibly powerful despite its apparent simplicity. He emphasizes Jev’s attributes: it is very fast, extremely cheap—costing about 4 cents per million input tokens—and outputs minimal data, only the necessary decision or classification. The host explains that Jev is best suited for making decisions, classifying data, or routing tasks, rather than generating freeform text, and that it enables real-time, high-efficiency decision models.

The host dives into concrete examples demonstrating Jev’s capabilities. First, he describes using Jev to analyze thousands of pull requests (PRs) from GitHub. Jev was used to categorize PRs and compare pairs to see if they address similar themes, facilitating clustering and classification of large unstructured datasets. The process was surprisingly inexpensive—about 1.1 cents for 112 PRs—and took only seconds, demonstrating Jev’s cost-effectiveness and speed. He then explains how he scaled this method to analyze 1,700 PRs, revealing that around 30% of the effort went into platform security, infrastructure, reliability, and other areas. This meta-analysis helps CTOs and product teams understand where their resources are focused and plan accordingly. Another example involved analyzing local cloud code repositories, showing how Jev can categorize personal work patterns. He highlights that such large-scale data analysis would have been prohibitively expensive and complex previously but is now feasible and affordable with Jev.

The host shares that he applied Jev to personal Gmail data, categorizing thousands of emails by likelihood of deletion, thus illustrating Jev’s utility for personal productivity and email management. He emphasizes that Jev alone is powerful, but pairing it with a more traditional LLM can yield even better results. Jev’s ability to filter, classify, and cluster data quickly and cheaply makes it suitable for personal data management, supporting workflows like email triage or task organization. He advocates for combining Jev with precise AI actions: first filtering and classifying data with Jev, then applying deeper analysis or action models for tasks like bug triage, content filtering, or personalized insights. The result is a flexible, low-cost, scalable approach to managing and deriving value from large unstructured datasets, whether on local or cloud repositories.

The host describes a complex project where he integrates Jev with other models to create an advanced product insights system. By combining classification, clustering, and mapping using Jev, and then applying more sophisticated models like Astra for analysis, he constructs a pipeline that handles thousands of signals—PRs, support tickets, conversations—and extracts trends and insights. His example involved analyzing over 1,100 signals and producing 200,000 classifications for a product insight dashboard. He explains that this setup cost roughly four dollars on Jev’s side, with additional costs for other models, but it provided deep analytical results that previously required much more expensive and time-consuming processes. This demonstrates Jev’s role as an enabler of complex, multi-layered AI workflows capable of producing high-impact business insights efficiently.

The host showcases two live demos built with Jev to demonstrate real-time AI applications. The first is a sentiment and idea-detection dashboard analyzing 4,500 YouTube comments, categorizing them into positive, negative, or neutral, and extracting suggestions for future episodes. Jev processed all comments and generated insights quickly, allowing him to monitor audience feedback dynamically. The second demo is a real-time voice-to-color-emotion app. Using OpenAI’s Realtime Voice API and Jev, the app detects emotional states from speech, determines a matching color, and fetches a related quote from an external API. The host performs a live test, showing how Jev responds instantly to spoken input and delivers a quote, illustrating how such interactive, low-latency experiences can be built with Jev. These examples highlight Jev’s suitability for building engaging, real-time AI-powered interfaces that respond immediately to user inputs, opening opportunities for innovative applications in gaming, entertainment, and interactive tools.

The episode concludes with the host recapping Jev’s versatility, from data analysis to real-time interactive apps. He emphasizes how it enables many previously difficult or expensive tasks to be done cheaply and efficiently. The host teases upcoming episodes, including a follow-up with a popular guest, and invites listeners to comment and suggest topics. He encourages audience engagement through liking, subscribing, and leaving reviews. The episode ends with a reminder of where to find more content, including the show’s website. Overall, it sets the stage for further exploration of Jev’s potential and practical applications.