Jev has been all over my social media feed lately. If you’re like me and keep an eye on generative AI news, you’ve probably noticed it taking over your timeline too. This new model is incredibly cheap and ridiculously fast.
But what the hell does it actually do?
Here’s the quick explanation. Jev takes information and answers structured questions about it, returning decisions and probabilities that software can use directly. TypeSafe says it produces those probabilities in parallel, which helps explain the speed: there’s no paragraph of text to generate one token at a time.
I wrote a detailed article yesterday that explains how Jev works. Check it out here:
New Jev Model Is Insane 200x Faster 400x Cheaper Than Frontier ModelsFor this article, I wanted to look at what people are actually building. Here are eight examples that caught my attention, with the costs and timings reported by their creators. Have fun!
1. Reviewing Pull Requests at Almost 0 Cost
One user on X, Paolo Rosson, built a code reviewer that sends a diff, the changes in a pull request, to Jev and gets 14 checks back in one call. Those checks cover things like exposed secrets, SQL injection, authentication changes, deleted tests, and whether the description matches the code.
The reported cost is $0.00007 per review, with responses arriving in about half a second. At that rate, reviewing 1,000 PRs costs seven cents, compared with roughly $14.50 for the creator’s Opus 5 setup.
Each check comes back as a probability. The surrounding code uses those results to decide whether to block the change, request a security review, flag minor issues, or recommend merging.
I like the handling of uncertainty. When a critical check falls between 0.35 and 0.65, the system escalates it to a person or a larger model. You can adjust that rule to make the reviewer more cautious.
I’d try this on my own website projects as an initial pass. A small styling change could go through a lighter review, while anything touching authentication or the database would get more attention.
2. Jev is Really Good at Computer Use
Shiv shared a computer use tool powered by Jev that responds in under a second and costs almost nothing per interaction. It can also decide what to type, with no separate LLM needed in the demonstrated workflow.
If an agent needs to open menus, select fields, and move through several screens, even a short delay on every action adds up.
The project is available on GitHub as Third Hand. There’s also another computer use example from Instant Rice Cook if you want to compare the ideas.
For a tool like this, the engineering question is how to turn the current screen into choices the model can evaluate. Software still has to identify the available controls, carry out the selected action, and check what happened afterward.
My first experiment would be something repetitive in an admin dashboard. I have a few useful ideas for my tech blog, Zeniteq. Open an article, choose a category, change a setting, and return to the list. I already know what those actions should accomplish, so it would be easy to spot mistakes.
If it could reliably handle that sequence at the reported speed, I’d have plenty of boring work for it.
3. Finding Internal Links Across an Entire Website
Another X user used Jev in a workflow that read 586 website pages and rebuilt the internal link map in 45.1 seconds.
The run placed 584 links and cost $0.21.
It also declined to link 139 pages because it couldn’t find a suitable match. That detail made me more interested than the page count did. See it in action below:
You can basically build a workflow that gathers candidate pages, asks Jev to score their relevance, and suggests links where readers would benefit from more context. I’d want it to show me the proposed anchor text and destination together, so I could judge whether the link feels natural in the sentence.
Imagine reading a guide to AI coding tools and finding a useful link to a detailed explanation of MCP. That’s the sort of connection I’d want. Sending readers to an unrelated article just because both mention AI would annoy me.
I’d start with a preview of suggested changes on Zeniteq. Then I’d review a sample, especially links added to older posts where the surrounding information might need updating too.
The reported run demonstrates internal linking work, which is one part of an SEO audit. It doesn’t establish a ranking improvement. For me, the immediate appeal is making useful articles easier to discover while spending less time searching my own archive.
4. Sorting Tax Documents at About $0.001 per Page
Another user built a tax document classifier around Jev and reported a cost of about $0.001 per page. Compared with the team’s previous LLM pipeline, they describe it as 34 times cheaper and six times faster, with every document in their corpus classified correctly.
The classifier reads page text and asks Jev to identify the document using predefined form descriptions. That gives the next stage of the software a form category and a confidence value to work with.
The open source repository provides helpful context for the accuracy claim. Its published evaluation reports no errors across 314 filled form pages.
Tax Doc ClassifierA separate set of 753 blank form pages had no wrong labels, but 38 pages fell below the confidence threshold and counted as strict errors.
I appreciate being able to see that distinction. A page that needs another look is a different outcome from one confidently sent to the wrong place.
This is probably the least flashy example here, but I can imagine a team getting more daily value from it than from a game demo. Sorting thousands of pages is repetitive work, and a classification mistake can send the rest of a process in the wrong direction.
I’d also be curious to try the same general approach on invoices or receipts. Define the categories carefully, inspect uncertain results, and measure how much manual sorting remains.
5. Playing Subway Surfers Across 50 Games
One of the first interesting use cases I saw on X is Max Blade’s use of Jev playing Subway Surfers at what he describes as superhuman speed, along with a demonstration involving 50 games at once. He says the featured run cost less than a cent.
This is the type of experiment that makes me want to try a model myself. Games give you a quick way to see whether a sequence of decisions works: the character either avoids the obstacle or runs straight into it.
For a runner game, the available moves are limited. A controller can evaluate the current situation, pick an action such as changing lanes or jumping, then repeat as the game advances.
The surrounding software still has to provide usable game information and execute each move. The clip’s performance depends on that entire setup, so I wouldn’t assume Jev could simply watch any game and play it equally well.
There’s a related driving demo from JP Schroeder, who describes rebuilding Tesla Full Self Driving with Jev in under an hour. That’s an ambitious description for an experiment, and I’d need considerably more evidence before comparing it with a system that drives on actual roads.
Personally, I’d stick with games and simulated environments. I’d love to build a small browser game where several characters react to the same situation differently, then see how changing their decision criteria affects what happens.
A cautious character could avoid obstacles early while another takes riskier routes. Watching those behaviors emerge would be fun, and the small action space would make it easier to understand why each character made a bad move.
6. Clearing Out an Agent’s Context More Quickly
Tamara Tran used Jev to tackle compaction, the process of reducing the conversation history an agent carries into its next turn. Her project scores tool calls and results, then removes or shortens material judged unnecessary.
The retained content keeps its original wording. For coding work, I like the prospect of preserving an exact error message or command while clearing out old output that has already served its purpose.
The code is available in fast-jev-compaction. Its documented behavior includes protecting recent messages, keeping calls paired with their results, and leaving user and assistant text unchanged in the resulting conversation.
Sources
- https://generativeai.pub/new-jev-model-is-insane-200x-faster-400x-cheaper-than-frontier-models-4c595a3abe55generativeai.pub
- https://x.com/redp314/status/2100585126652481915x.com
- https://x.com/sxhivs/status/2101367048223982065x.com
- GitHub as Third Handgithub.com
- another computer use example from Instant Rice Cookx.com
- https://x.com/borjafat/status/2101018783976722479x.com
- https://x.com/nedwize/status/2100973868324417852x.com
