94 published use cases that name JEV.
The Spectra system utilizes Deepgram Flux for speech-to-text functionality, providing end-of-turn reports with probabilities. This setup ensures high accuracy and quick responsiveness for agent interactions.
view details →Transition from Opus 5.5 to Jev 1.13 to reduce daily costs from $60–80 to 42 cents. Jev efficiently manages decision-making tasks by selecting from predefined options, while maintaining control over the final output.
view details →The team is testing an internal process that utilizes Jev to identify specific rule failures in code, which are then verified by an LLM. This approach appears effective and may reduce costs by addressing simpler issues before conducting more thorough reviews.
view details →A user-developed bookkeeping software integrates an agent named Greg to assist with accounting tasks. The system handles 90% of the monthly workload, leaving only complex judgment calls to the user.
view details →The MemX app successfully replaced FastText and a multi-stage Gemini pipeline with a single Jev call, achieving approximately 98% accuracy. Explore the full experiment and insights in the linked video.
view details →A tool that identifies trending free plugins and skills daily, classifying and rating them for agents using Jev.
view details →The author reports that on-device speech-to-text (STT) and text-to-speech (TTS) technologies are improving. They transitioned from Apple's STT to Grok Transcribe and Jev, achieving better performance, especially with technical terms.
view details →Jevvy is a plugin designed for auto-approving harmless commands using Jev on OpenCode and Claude Code.
view details →Users are leveraging Jev to retroactively label their entire inbox and instantly delete incoming spam, achieving results in just seconds.
view details →Utilizing Jev to enhance headstand techniques by categorizing 4,746 classes from Alo Moves Wellness Club.
view details →A user employs Jev to filter out 90% of spam from their Twitter feed, showcasing AI's role in managing content.
view details →Developed a custom Jev model to classify trademarks under the Abercrombie spectrum, achieving a 75.8% accuracy on LegalBench's Abercrombie test after 7 minutes of training on Google Colab.
view details →This process involves handpicked RSS feeds and an automated curator that evaluates date-bound feeds. A custom set of heuristics is used to score and rank posts, with top selections identified through a classifier.
view details →A tool that organizes 3,021 saved Instagram posts into 12 folders using AI. Developed in one day with Claude Code and an AI judge model.
view details →Developed a small expense categorizer for UPI payment notes using TypeSafe AI's Jev model. The focus was on the testing process rather than just the model implementation.
view details →23andMe utilizes Jev, a System One AI model, to evaluate text for voice, clarity, and accuracy. This approach allows for rapid assessments, significantly reducing costs and speeding up the review process.
view details →Access the code, prompts, and run records for results recomputation built on DeepSeek Harness and Jev. Feedback and questions are encouraged.
view details →A shared inbox router has been developed using Jev and Mastra technologies.
view details →The AI emoji picker was initially built using Jev, which required extensive input tokens due to the entire emoji catalog. It was later refactored to a vector index on Upstash, significantly reducing costs.
view details →Utilize the dots-agent-team to streamline your goals with a simple setup process. This method has proven effective for agents seeking efficiency.
view details →This tool organizes emails as you scroll, maintaining the privacy of the content. It is still in development but shows promising progress.
view details →Muse agents can have skills installed, which are packaged capabilities with specific instructions and tooling. One example is the jev-agent, which provides typed AI judgments through a decision API for classifying CRM records.
view details →A new Pac-Man game features Jev from @typesafeai, who streams the game's decision-making. This innovation could lead to cheaper and faster non-scripted decisions in open-world games, allowing more NPCs to adapt to their environment.
view details →Maelstrom utilizes an LLM to organize components based on user input. It features a planner that operates on Jev, designed to respond to structured questions regarding component specifications.
view details →