Hello, I'm Rijul, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product.
There’s a new AI model that’s been getting people talking, and you might have heard of it. It’s called Jev.
But what exactly is Jev, and what makes it different from the general-purpose LLMs we use every day?
Let’s take a look at what Jev is all about.
Jev is an AI model from TypeSafe AI designed to make structured decisions rather than generate long-form text.
To understand why it exists, let's start with a simple problem.
Imagine you're building an application that receives 100,000 customer messages. For every message, your application needs to decide:
- Is this a billing problem?
- Is this a technical problem?
- Is this an account problem?
You could send every message to a general-purpose LLM like GPT and ask it to classify each message. But you're using a model capable of writing essays and generating code just to select one of three options.
Jev is designed for this narrower kind of task: making decisions based on the information provided.
Three Things Jev Can Answer
Jev provides three types of decisions called Choice, Score, and Noul.
1. Choice
Which option is correct?
- Input: "My payment failed, and I was charged twice."
-
Options:
billing,technical,account -
Example output:
billing
This is useful when you need to classify information into a predefined set of options.
2. Score
How well does this fit a defined scale?
- Input: "The production service is down for every customer."
-
Scale:
0 = minor,1 = moderate,2 = severe -
Example output:
2
This is useful when you need to evaluate something against a predefined scale.
3. Noul
How likely is a particular statement to be true?
- Input: "The production service is down."
- Question: "Does this incident require immediate escalation?"
-
Example output:
0.95, meaning a 95% estimated probability of a positive answer, assuming the output represents a calibrated probability.
This is useful when you need a structured answer to a specific question rather than a long explanation.
Take Jev for a Spin
The fastest way to try it is through the Jev Playground.
Go to https://jevplayground.com/.
It's an independent playground where you can experiment with Jev.
Choice
Score
Noul (Boolean)
Wrapping Up
So, that's Jev.
For decision-based tasks in an AI agent, Jev could be an option to consider instead of relying entirely on general-purpose LLMs. Using a model designed for structured decisions can help diversify your setup and may make specific decision-making tasks more efficient.
Your team's attention is limited, and the deluge of AI-generated code is making it harder to keep production reliable and secure without slowing you down.
I'm building LiveReview, a blast-radius aware AI code review built for your business-critical systems.
Instead of presenting every diff with equal emphasis, LiveReview scores each change by blast radius — how far its impact reaches through your call graph — so you can focus attention where it actually matters.
Spend code review effort where business risk is highest — not spread evenly across every diff.
⭐ Star it on GitHub:
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LiveReview
Blast-Radius Aware AI Code Review for Business-Critical Systems
LiveReview: Blast-Radius Aware AI Code Review for Business-Critical Systems
LiveReview is an AI code reviewer that scores every hunk of a diff by blast radius: how far a change reaches through your call graph, how much persistent state it touches, and how well-tested it is. A 3-line change to a shared auth check can outrank a 300-line UI tweak. Your team's attention goes to the highest-risk code first, not spread evenly across every diff.
blast-radius-demo.mp4
LiveReview's Blast Radius & Review Priority scoring, live in the diff viewer.
| The exact math, not a black box | Visualize blast radius at a glance | Every factor that feeds the score |
|---|---|---|
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 |
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How does Blast Radius scoring work? (a more technical explanation)
Here's the goal:
- A 3-line fix in a function used by 40 other files, that also writes to a database, should score high.
- A 300-line UI change in one file, fully covered by…
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