Article
CommunityJev Guides & Articles
How I AI: 8 real use cases for Jev
John Lindquist joins Claire Vo to walk through eight Jev use cases, from a real-time voice to-do app to data deduplication with confidence scores, on the episode billed as Jev week at How I AI.
- Format
- Article
- Reading time
- 2 min
- Published by
- Community
- Author
- Claire Vo
- Last verified
- Oct 2026
Used inJev for Software & DevTools
Highlights
- Demos include a real-time voice to-do app that classifies and executes commands with no visible pause, plus a plain-English-to-function-name grocery cart.
- A data deduplication pattern merges messy records in milliseconds using Jev confidence scores.
- Lindquist says Jev works best as a router, where a single text input can navigate users deep into an app.
- Claire Vo notes he has 23 demos on hand, and Lindquist says hundreds of demos cost him 73 cents in total.
- The episode also covers a chess match between Jev and a low-reasoning LLM, multi-step classification, and when to reach for a full generative model.
Watch out
Copy reviewed against the Exa Library episode page on 2026-10-17; the transcript preview is truncated, so only the visible excerpt, episode notes and chapter list are reflected. The 73-cent figure and the use cases are the guest's own demo experience, not independent benchmarks.
Questions about this entry
Short answers drawn from the linked project or page.
Host Claire Vo, product leader and AI obsessive, and repeat guest John Lindquist, creator of egghead.io and founder of mega.dev, a hands-on program for developers doing real work with AI agents. The episode runs 46 minutes.
He calls fast and free both amazing, and says that across hundreds of demos he had spent 73 cents in total, describing Jev requests as essentially instant compared with waiting on a basic LLM call.
An app where he speaks commands that Jev classifies and executes with no visible pause; a companion presentation-coach demo listens to him talk and checks off bullet points as he covers them.
Merging messy records in milliseconds using Jev confidence scores - one of the eight use cases shown, alongside routing, classification and multi-step classification.
No. The episode notes explicitly cover where Jev falls short and when you should still reach for a full generative model, and one segment compares Jev against a low-reasoning LLM in a chess match.
She says the model, which is basically free and incredibly fast, allows discovery over data in a way that opens up opportunities she previously thought were not high ROI.
Lindquist describes a workplace simulation with multiple agents that each have their own tasks; Jev controls them at each step so they get to their tasks quickly and never collide or interfere with each other.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: Simon Willison's overview of Noul, Choice, and Score, and where a decision model fits.
benwerd53 pointsJev introduces a new shape of LLMRead the thread on Hacker NewsHacker News: swyx posts the Latent Space interview with Diogo Almeida.
swyx4 pointsJev: System One Models for Prod, Not God – With Diogo Almeida, CEO, TypeSafe AIRead the thread on Hacker NewsX: Diogo Almeida in five minutes
X: LLMs vs. Jev, clearly explained
More Jev resources like this
An episode of The Merge (by CodeRabbit) recorded at the first Jev hackathon 11 days after launch; TypeSafe DevRel Allie Laabs explains how Jev replaces prompt-parse cycles with typed choice, score and yes/no outputs.
Jev Guides & ArticlesArticle#community#press#podcast
Community
VentureBeat's Sam Witteveen argues a large share of enterprise AI only needs a label, not generated text. Per TypeSafe, Jev returns choices, scores and yes/no answers in 70 to 500 ms at $0.042 per million input tokens, with output tokens free.
Jev Guides & ArticlesArticle#community#press#explainer
Community
On The MAD Podcast, Matt Turck talks with TypeSafe CEO Diogo Almeida about why intelligence per dollar matters more than enterprise adoption, and why RLVR benchmark gains still leave models 95% Einstein, 5% Mr. Bean.
Jev Guides & ArticlesArticle#community#press#podcast
Community
A BIG CHANGE analysis of the Diogo Almeida Latent Space interview: Jev's most consequential proposition is the unit of automation, a bounded judgment inside an existing process, and the launch claim that Jev can't hallucinate needs a narrow reading because schema matching does not establish truth.
Jev Guides & ArticlesArticle#community#press#explainer
Community
TWIML episode 779 features TypeSafe co-founder and CEO Diogo Almeida on Jev, the company's model for bringing fast, reliable intelligence directly into software, and why text-generating models are poorly suited to many real-world automation decisions.
Jev Guides & ArticlesArticle#community#press#podcast
Community
In Law://WhatsNext's return episode, guest Chris Bridges of AI-native law firm Tacit Legal demonstrates Jev returning structured contract answers in 0.3 seconds where a chat model took 9.6 seconds.
Jev Guides & ArticlesArticle#community#press#podcast
Community
From the Jev guides
Original write-ups on what this entry is for.
From the Jev AI model community
Posts from builders shipping with Jev right now.