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Zubair Trabzada | AI Workshop · YouTube

The Rush Hour Traffic Sim That Shows What Jev Actually Does

A live traffic simulation built in GPT-5 Astra puts Jev's under-150-millisecond decisions on screen, then the walkthrough covers wiring it into TypeSafe.ai, Vercel, and a Jarvis-style AI assistant.

Posted
yesterday
Duration
Format
Demo
educational
Views
2.4K
41 likes
Part of the collectionJev, explainedEvery Jev breakdown, synthesized into one page.
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Big Idea

The argument in one line.

Jev is not a language model that writes anything. It is a fast, cheap classifier that turns a situation into a choice, a rating, or a percentage, which makes it a decision layer inside apps built by coding agents like Cloud Code or GPT-5 Astra, not a replacement for them.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…
  • You're already building with Cloud Code, GPT-5 Astra, or a similar coding agent and want a fast, cheap decision layer for routing, scoring, or moderation calls inside that app.
  • You've seen 'built with Jev' claims online and want a straight answer on what the product can and can't do before you evaluate it.
  • You're designing a personal AI assistant or 'second brain' and want faster retrieval-style decisions without paying for a full reasoning model on every lookup.
SKIP IF…
  • You're looking for a tool that writes code, copy, or any other freeform output; Jev has no output channel at all.
  • You want a rigorous accuracy benchmark of Jev; this is a product walkthrough and demo, not an evaluation.
TL;DR

The full version, fast.

Jev, from TypeSafe AI, is not a large language model and cannot write code, text, or anything else; it only makes decisions. Given a situation and a fixed set of possible answers, it returns a choice, a rating, or a percentage confidence, with no generation step at all. That missing step is why it runs up to 200 times faster and far cheaper than typical LLMs, priced on input tokens only. The video proves this with Rush Hour Under Control, a traffic simulation built in GPT-5 Astra where Jev makes intersection-by-intersection decisions in under 150 milliseconds. The actionable pattern: build the app with a coding agent, then use Jev as the fast decision layer inside it for routing, scoring, or retrieval, not as a stand-alone builder.

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Chapters

Where the time goes.

00:00 – 00:39

01 · Debunking the Jev hype

Opens by calling out fake demos claiming apps were 'built with Jev,' including a false Tesla self-driving claim, and states plainly that Jev cannot build anything.

00:39 – 01:19

02 · The roadmap for this video

Previews the plan: explain what Jev is, show demos built with GPT-5 Astra that showcase it, and hand out a free PDF guide with example prompts.

01:19 – 02:18

03 · The Reflex: an AI that decides but never writes

Defines Jev as a model that takes a situation and a fixed set of answers and returns a decision, not a written response, contrasted against ChatGPT/Claude-style generation.

02:18 – 03:09

04 · The Sorter and The Receipts: percentages, speed, and price

Every Jev answer comes back as a percentage, rating, or multiple-choice pick. Because there's no output step, it's marketed as up to 200 times faster and priced on input tokens only.

03:09 – 05:06

05 · Building and driving Rush Hour Under Control

A traffic-simulation app built in GPT-5 Astra: a city with a park, sports arena, and hospital. Toggling 'let Jev drive' hands intersection right-of-way calls to Jev in real time.

05:06 – 06:07

06 · Stress-testing traffic decisions

Comparing a traffic jam against smooth flow, then using the 'shake things up' control to close a road and force Jev to re-decide instantly under a new constraint.

06:07 – 06:36

07 · Recap: what Jev is not, and what it's for

Restates that demos claiming to be 'built with Jev' are wrong, and that the real pattern is Jev sitting inside an app built by Cloud Code or GPT-5 Astra as the decision layer.

06:36 – 08:06

08 · Getting API access: TypeSafe.ai, Vercel, OpenRouter

Walks through creating a TypeSafe AI account, accepting terms, generating an API key, and pasting it into GPT-5 Astra or Cloud Code, plus Vercel AI Gateway and OpenRouter as fallback access paths.

08:06 – 09:29

09 · Wiring Jev into an AI assistant, and sign-off

Shows Jev inside Jarvis, the creator's personal AI second brain, speeding up which-file/which-note retrieval decisions, teases a deeper follow-up video, then signs off.

Atomic Insights

Lines worth screenshotting.

  • Jev cannot write code, text, or any other output; it only returns a decision, a rating, or a percentage against a fixed set of choices.
  • Public claims of 'building an app with Jev' or 'rebuilding Tesla's self-driving with Jev' misrepresent the tool, since it has no generation capability at all.
  • Removing the output-generation step is what makes a decision model fast: there is nothing to compose, so there is nothing to wait on.
  • Jev is marketed as up to 200 times faster than typical large language models and priced on input tokens only, since there is no output to bill for.
  • In a live traffic simulation, Jev resolves each intersection's right-of-way decision in under 100 to 150 milliseconds.
  • Decisions that compound into real consequences within milliseconds, traffic signals, trading, air or ground traffic control, are the target use case for a fast classifier model, not general reasoning tasks.
  • The traffic simulation demo itself was built with an AI coding agent, GPT-5 Astra; Jev only supplies the real-time intersection decisions inside that pre-built app.
  • TypeSafe AI's signup no longer requires a waitlist; anyone can create an account and generate an API key directly from the console.
  • If direct API access isn't available, the same model can be reached through Vercel's AI Gateway or through OpenRouter.
  • Swapping a full reasoning model for a fast decision model inside a retrieval step, deciding which file or note matches a request, is the highest-leverage use case inside a personal AI assistant.
Takeaway

Jev isn't a builder, it's the fast decision layer you drop inside one.

WHAT TO LEARN

Jev's entire value comes from having no output step: it only returns a choice, rating, or percentage, which makes it fast and cheap enough to sit inside real-time decisions that a full language model would be too slow or too costly to make.

01Debunking the Jev hype
  • Public demos claiming to 'build an app with Jev' or 'rebuild Tesla's self-driving with Jev' misrepresent the tool: Jev has no output channel, so it cannot generate code, text, or anything else.
  • When a claim about a new AI tool sounds too capable to fit its stated category, check whether the tool actually produces output at all before taking the demo at face value.
03The Reflex: an AI that decides but never writes
  • A decision model like Jev takes a situation and a fixed set of possible answers, then returns a choice, a rating, or a percentage confidence, never freeform text.
  • The absence of a generation step is the whole design: nothing is written, so nothing needs to be composed, which is why decision models can run far faster than a model that produces a response.
04The Sorter and The Receipts: percentages, speed, and price
  • Jev is priced and marketed as up to 200 times faster and far cheaper than typical LLMs, because per-token pricing that normally charges separately for input and output collapses to input-only cost when there's no output to generate.
  • A tool's pricing model is itself a signal: input-only billing points to a classifier or router, not a generator.
05Building and driving Rush Hour Under Control
  • The traffic simulation's interface and city map were built with an AI coding agent (GPT-5 Astra); Jev's only job inside that app is answering the intersection-by-intersection go/no-go calls in real time.
  • Splitting a build into 'the app a coding agent constructs' and 'the decision a fast classifier makes inside it' is the actual pattern being demonstrated, not a one-tool-does-everything pitch.
06Stress-testing traffic decisions
  • In the demo, decisions land under 100-150 milliseconds because each intersection is a bounded multiple-choice call, not an open-ended reasoning task a language model would need to think through first.
  • Latency-sensitive decisions, traffic control, trading, anything where milliseconds compound into a jam or a missed window, are the class of problem a fast classifier is built for, not general reasoning tasks.
07Recap: what Jev is not, and what it's for
  • The pattern worth copying is 'coding agent builds the app, decision model sits inside it as the fast judgment layer'; treating a classifier as a substitute for a general-purpose coding or chat model is the mistake the video opens by debunking.
08Getting API access: TypeSafe.ai, Vercel, OpenRouter
  • Getting API access has three paths: sign up directly at the vendor's console for a key, or reach the same model through Vercel's AI Gateway, or through OpenRouter, if a direct waitlist isn't open yet.
  • Pasting an API key straight into a local coding assistant's config works for personal, local-machine use, but an environment variable is the safer default once a key needs to live anywhere shared.
09Wiring Jev into an AI assistant, and sign-off
  • Swapping a slower reasoning model for a fast decision model inside a retrieval step, deciding which note or file matches a request, is the highest-leverage use case: it doesn't need to think, it needs to be right and fast.
Glossary

Terms worth knowing.

Jev
A decision-only AI model from TypeSafe AI. Given a situation and a fixed set of possible answers, it returns a choice, a rating, or a percentage, with no text or code generation.
TypeSafe AI
The company behind Jev. Its console issues API keys and its playground lets you test decision prompts before wiring them into an app.
Decision layer
A role inside an app where a fast classifier model, not a general-purpose language model, handles narrow yes/no, multiple-choice, or rating calls.
Vercel AI Gateway
A Vercel product that proxies requests to various AI models, including Jev, through a single API key and endpoint.
OpenRouter
A third-party service that provides API access to many AI models, including Jev, through one account.
GPT-5 Astra
The AI coding agent shown building the traffic-simulation demo app and the Jarvis assistant in this video; distinct from Jev, which only makes decisions inside apps like these.
Input-only pricing
A billing model that charges only for tokens sent to the model, not tokens generated back, because Jev never generates a text response.
Resources

Things they pointed at.

00:39linkFree Jev PDF guide (creator's own resource, linked in description)
06:36toolTypeSafe AI console ↗
07:37toolVercel AI Gateway
07:37toolOpenRouter ↗
08:06productJarvis (creator's personal AI assistant / second brain)
Quotables

Lines you could clip.

00:00
“They are lying to you about Jev. Jev is not what you think.”
cold-open pattern interrupt that names the myth immediately→ TikTok hook↗ Tweet quote
01:19
“It's the type of AI that decides but does not write.”
the single-sentence definition the whole video hangs on→ IG reel cold open↗ Tweet quote
02:40
“You can think of it as a traffic cop, right? It's very cheap. It's instant and it's decisive.”
compact metaphor that explains the product in one breath→ newsletter pull-quote↗ Tweet quote
06:07
“That's completely wrong because it's not capable of building.”
direct debunk line, punchy and quotable→ TikTok hook↗ Tweet quote
The Script

Word for word.

Read-along

Don't just watch it. Burn it in.

See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.

metaphoranalogy
They are lying to you about Jev. Jev is not what you think. Half the things I'm seeing on Twitter, on YouTube is completely fake.
People claiming that they built an app with Jeff, they built this with Jeff. Somebody actually claimed that they rebuilt Tesla self -driving with Jeff. That couldn't be more wrong.
You can't build anything with Jeff. Jeff is not a large language model. It's not Cloud Code.
It's not GPT -6 Astra. But it is an incredibly important piece of AI technology. So in this video, I'm going to break down in the simplest way I can what Jeff actually is, when to use it, and exactly how to use it alongside things like Cloud Code and GPT -6 Astra.
All right, let's jump right in. All right, so I'm going to go ahead and explain what Jev is. And then afterwards, I'm going to come back and show you guys these demos that I built with GPT -6 Astra that showcases the power of Jev.
And then I'm also going to give you guys this PDF guide about what Jev is. So that way you can take a look at it on your own time. Again, the link of this is going to be in the description as well.
It goes through all the details, important details, and then also a few example prompts that you can utilize GPT -6 Astra and Cloud Code to build kind of example apps that showcases. power of Jeff. And like I said, I'm going to show you how you can install or use Jeff inside your cloud code and GPT six assets of the world.
Alright, so the simplest way I can describe Jeff is that It's the type of AI that decides but does not write. Now, let me explain what that means.
Because so far, we're all used to chat GPT or cloud or cloud codes of the world where we give it an input in text and it thinks about it and then gives us an output in text or a solution to a problem, right? Jev doesn't do that. Jev's is essentially a decision there.
You give it a situation and it gives you the odds, right? So let's say you hand Jev something to look at, let's say an email plus a few questions with a few fixed answer and it... hands back a percentage for each one there is no writing there is no output it gives you yes or no or it gives you odds like pick one out of these multiple choices or it can also rate something right let's say how upset are they on a scale of one to ten right it gives you uh you know 5 .5 or 6 .6 or whatever it may be and then also like i said gives you a percentage answer as well like 10 20 depending on what uh decision you're asking it to right so it's like a sorter for every answer with a percentage and therefore it's incredibly fast and incredibly cheap so they claim that it's up to 200 times faster than a lot of the large language models and it's very cheap and it makes sense because it only has input since there's no output right so if you're used to the traditional ai models
the pricing, you would see that they price their models per million input tokens and per million output tokens. In this situation, there's only input. And like I said, it's incredibly cheap and incredibly fast because of the fact that all it's doing is making a decision.
You can think of it as a traffic cop, right? It's very cheap. It's instant and it's decisive.
So a good example of this would be something like this situation here. So I built this using GPT -6 Astra. Again, this was not built with jeff jeff cannot build anything so i use this app or i built this app it's kind of rush hour under control which is essentially a simulation of a city that are a main city that has a park in the center on the left hand side there's a sports arena and then right here there's a hospital as well so there's traffic coming in from all different directions so what this is doing is now allowing jeff so if i say let jeff drive what this is doing is now simulating this situation where there's traffic coming from all sorts of directions and Jeff's job is to make decision instantly and as quickly as possible to let traffic through at different directions to make sure nothing gets stuck or there's no traffic jam right so this is a perfect example of what this model is capable of and the decision making of it so you can see right here on the right hand side Jeff responds by the way this is happening in real time so Jeff is responding in under 100 milliseconds
right sometimes 130 150 but it's extremely quick depending on the situation you can click on each intersection and it shows you exactly what's going on in that intersection how fast it's making those decisions because again it's being presented with a set of decisions multiple choice rights like for instance let this car in let that car out and it's deciding in almost real time on what to do now if you were to put this in a large language or give this decision to a large language model what it would do is actually evaluate the input it would think about it and then based on that situation it will give an output and then make that decision so that process takes a very long time i mean not a very long time and uh as we think of it but when it comes to things like traffic or when it comes to things that requires instant decisions like trading for instance or traffic control where it's air traffic or ground traffic those milliseconds count right the ability to make those quick decisions is going to be the difference between
A huge traffic jam. versus a traffic that flows smoothly so that's why there's like these scenarios where you can introduce it says shake things up right i can click on close a road so it's going to go ahead and close a particular road and this is going to increase introduce now new variables right it's going to increase a new situation where now we're telling jeff all right now that particular section is closed therefore you need to be able to decide instantly what to do to make sure that the traffic jam doesn't happen.
Now, again, these are choices. We're presenting it with a bunch of different choices and it's quickly able to make that decision. We're not telling it to evaluate this entire thing and come up with a solution for us, right?
This is kind of the difference between your normal AI models versus Jeff. So a lot of things that are built, they're not built with Jeff. So when you see demos on YouTube or Twitter, when people claim that they are building things with Jeff, that's completely wrong because it's not capable of building like.
Like I said, however, you can use this inside things like Cloud Code or GPT -6 Astra to build the app for you and have Jev sit in the middle as the decision layer when it comes to choices like this. Because that's what it's really good at and that's what it's made for essentially, right? So in order to use this inside your GPT -6 Astra or your Cloud Code, not inside, essentially with GPT -6 Astra and Cloud Code, it's very simple to do.
So all you have to do is head over. to typesafe .ai and now I think before there was like a wait list. Now you can, anybody can join and create an account.
So go ahead and create an account. And then once you create an account, you can head over to the playground. So let me just make sure I accept the copy in terms here.
You can head over to the API keys here and then go ahead and create a new API key. And then afterwards, all you have to do is just go back to your GPT -6 Astra or Cloud Code. And honestly, the simplest, if you're non -technical, the safest way, obviously to do is to, uh, put your API key inside the environment variable.
But if you don't know anything about environment variable, just go ahead and just paste your API key to your GPT -6 Astra inside your Codex app and just have it connect to your Jev to there. Very, very simple. And it's very easy to do that that way.
Like I said, that's not the safest way, but as long as you're using it inside your local computer, then you should be fine. And same thing with Cloud Code, you can do that very easily. Now, if you...
not part of that let's say for some reason you're still in the waiting list another way to do it is actually to head over to versal versal .com and then you can also create a account there because you can use pretty much the same method by creating an api key because you'll be able to have access to jev via versal .com and then also open router is another easy way to do that as well right so that's that's how you can have access to it now for instance what are the examples so this is my ai second brain this is jarvis my ai assistance So I incorporated this inside my Jarvis AI Assistant because before when I was asking it to pull up something, right, before when I was asking it to pull up some kind of, you know, my notes or a particular file for my client, then Jarvis would be able to look that up.
It would use a model like Opus 5 or something like that to think about that process and then go ahead and sort that out and give me the matching results to what I'm asking. Now with Jev, what it's going to do is just going to make that decision. a lot faster it's going to make everything a lot quicker so that way the response is faster the quick decision of when to use what is where jeff comes in right so that's where you can use this essentially inside your own ai second brain or ai assistant or ai employees by the way i'm going to do a second video on that like more detailed video because i'm going to incorporate a lot a lot of some of the features of jarvis with jeff so that way it becomes a better and faster ai assistant with the ai second brain there so that's going to be coming in next anyways well I don't want to make this video too long.
Hopefully you guys found that helpful. I'm going to put all of the resources in the description of the video completely for free for you guys so that way you can have access to all of this. Let me know if you have any questions in the comments below.
Thanks for watching. Again, make sure you like and subscribe because I'm going to be creating more videos on this in the future so you don't want to miss. Thanks for watching and I'll see you on the next one.
The Hook

The bait, then the rug-pull.

The creator opens by calling out demos claiming Jev 'built an app' or 'rebuilt Tesla's self-driving,' then spends the rest of the video showing what the tool actually does: make a decision in under 150 milliseconds, nothing more.

Frameworks

Named ideas worth stealing.

01:19concept

The Reflex / The Sorter / The Receipts

  1. The Reflex
  2. The Sorter
  3. The Receipts

Three title cards structure the explainer: The Reflex establishes that Jev decides instead of writing, The Sorter shows it returning percentages and ratings instead of prose, and The Receipts covers the speed and cost math that the missing output step buys.

Steal forstructuring any new-tool explainer video into a fixed number of named, single-concept segments
CTA Breakdown

How they asked for the click.

VERBAL ASK
09:04subscribe
“I'm going to put all of the resources in the description of the video completely for free for you guys... make sure you like and subscribe because I'm going to be creating more videos on this in the future.”

Soft close paired with a free-resource offer (PDF guide plus example prompts in the description) rather than a hard product pitch.

MENTIONED ON CAMERA
06:36toolTypeSafe AI console ↗
07:37toolOpenRouter ↗
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
Storyboard

Visual structure at a glance.

open
hookopen00:00
the sorter
valuethe sorter02:18
rush hour demo
valuerush hour demo03:09
typesafe.ai signup
valuetypesafe.ai signup06:48
jarvis integration
ctajarvis integration08:06
Frame Gallery

Visual moments.

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