Shadowing Practice: The BEST Jev Use Cases for AI Coding (Absolute Game Changers) - Learn English Speaking with Video

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Jev is incredible.

It was released recently and it popularized a type of AI model called System 1 Models, which specialize in making decisions instead of generating text.

And so it's actually way different from LLM's LightCloud and GPT.

And so it's opened up a whole new sprawl of use cases that everyone is sharing on the internet right now.

And people are sharing a lot of really fun and cool use cases with Jev, but something I'm hearing a lot, especially from people in my Dynamis community is that the use cases are cool,

but they're not always the most practical, right?

There's not a lot that you see and you're like, okay, I need to go incorporate this in my AI workflows right now.

And so that's why I want to make this video, just quickly showing specifically for AI coding,

for incredible JEV use cases to make your AI coding workflows faster and more token efficient, which is a big deal right now, especially with how bad the rate limits are.

Now, if you follow the latest in AI, there is a very good chance that you've heard of JEV and probably even tried it out for yourself.

Now, JEV is not the first kind of decision model like this, but that's a whole other topic for another video.

The point is JEV is really powerful.

The way it works is you give it some kind of situation, which they call state, and then you also supply with a list of multiple choice questions.

And in parallel, it's going to answer each one of those questions with a probability, which is basically its confidence score.

Now, the thing is you can already do this with large language models, especially with structured output, you can have it make decisions that you can key off of for the rest of your workflow.

But what makes JEV so awesome is

that it is 20 to 200 times faster than LLMs at making decisions and it's also 40 to 1000 times cheaper,

obviously the big range here being you can use different LLMs to compare against JEV, but no matter the LLM that you use, JEV is going to be more accurate,

faster, and cheaper for making any kinds of decisions.

And so at a high level, when you think of like Jev as the decision maker, that's an endless number of possibilities that you can use it for.

And so that's why I want to get really specific with you here, just quickly showing you, giving you inspiration for different ways that we can use it very practically.

Now for each one of these Jev use cases, they're valuable enough where I can make an entire video going into how to build and incorporate it.

But for right now, I just want to stay more high level.

I want to keep this short and sweet, giving you inspiration here so you can even pick a couple of these and maybe build them out yourself.

And then also in my Dynamis community this Friday, I am running a workshop getting a lot deeper into these use cases.

So check that out if you're interested.

And yeah, like I said, a lot more content coming soon, diving deeper into JEV because this is exciting for me.

I actually said this in my first video on JEV.

This is the first AI model in a while that I'm genuinely excited for.

And I think you'll see why as we get into these four use cases here.

So with that, let's just get right into it.

My favorite use case for Jev right now in my AI coding workflows is for security.

Because the thing is, and you've probably seen this yourself, AI coding assistants are way too willing to do things that they shouldn't, like read your environment variables, delete an entire folder.

Even if you have things in your global rules or skills to not do this, especially once you really start to bloat the context of a conversation,

or if you have a prompt that kind of gives it a workaround, it's gonna figure out ways to do these things.

And so that's why we need guardrails to prevent our agents from performing destructive actions.

And JEV can really be that guardrail.

So the most popular and effective way to implement these guardrails for our coding agents is using hooks, which was introduced in Claude Code,

but now pretty much every single coding agent has the idea of hooks,

where we can attach automations to run at different events that take place in the life cycle of our coding agent.

And so the most popular event is pre -tool use.

So when the agent is about to perform an action, like read a file or run a command, we can run an automation to basically analyze that

and either block the agent from performing that action or allow it to continue.

And so you can see where we're going with this here

because one of the most popular use cases is having a pre -tool use hook

that tries to analyze the agent's action to determine if it's going to read a .env file, right?

We don't want our secrets exposed into the context of our coding agent.

And so it has the opportunity to dish out an error code to essentially block that action, so the agent has to do something different,

like for example, reading the .env .example instead.

Now, the problem with this hook until Jev is that we either have to have an LLM analyze every single call,

or we have to have some kind of deterministic process with regular expressions.

Either way, it's going to be expensive or not very accurate.

Now, let's just scrap the idea of using an LLM right away, because if we're going to analyze every single action the agent wants to take, that's going to cost you hundreds

or thousands of dollars throughout the month just to block these sensitive calls that might happen once in a while.

And so what I've done until Jev is I use regular expressions.

And so I have a hook that runs

that just does pattern matching to try to figure out the agent is trying to do something like reading my Google credentials, reading my .env, deleting a folder, whatever it is

but the problem is There are so many different ways that an agent is able to do these destructive actions, because it can read the file directly,

it can write a Python script to do so, or a bash command, like, right, there are a million different things.

And so we have this massive laundry list here, and this doesn't even catch everything.

And there are also a lot of false positives.

And so what I've done is I've taken this pre -tool use hook, and I've replaced it with my JevGuard.

And I'll show you some stats in a little bit.

This is so cost effective and reliable.

So with System 1 models, again, we give it state and a list of multiple choice questions.

And so for anything that can go wrong with an agent action, I'm asking if it is the case, right?

Like, are we exposing secrets?

It's going to say either true or false.

And we give some instructions for added context.

This is part of the state.

And then is it destroying any data, like removing an entire folder, any kind of exfiltration, where we're maybe sending sensitive data out, like through a prompt injection attack.

And then another really cool one here is where you can just analyze is the agent going off task.

And so we can use this pre -tool use hook for even more than just security, just generally having another sort of model as a judge, making sure that we're staying on track for what we are trying to accomplish.

And then as far as the state goes, the situation that's the main input to Jev, we're giving it the tool name, the effect of it, the input, like the arguments to the tool, and also our current working directory.

And so that gives it the holistic picture.

And so when I ask Jev, I'm actually just using Jev right through open router with their new decision endpoint.

And then I'm giving it this state along with all the questions that I showed you above.

So I've only been using this hook for a little bit since Jev just came out, but my initial results are incredible.

So my previous version of the hook using regular expressions, you can see that it didn't block that many of the risky calls.

Now, obviously risky is subjective.

I've been pretty happy with this hook overall, but the main problem I've had with it is the false positives, blocking things that really shouldn't be blocked,

like just writing the word .env out to a markdown document.

And then going to using an LLM, it does a really good job.

I mean, not that many false positives, it blocks most risky calls, but even using something really fast and cheap like Haiku, it still takes over a second.

And yeah, it's only about a tenth of a penny per analysis, but that really adds up when your agent makes thousands of calls every single day.

And then we get to Jev.

Jev blocks almost every single risky call, barely has any false positives.

It is a quarter of a second for every single analysis, and it is a fraction of a fraction of a penny.

So it's cost effective, and it's fast.

It gives us essentially the reasoning capability of an LLM for this kind of decision, but it feels like it's free.

I mean, I know it's not quite free, but yeah, it's not like it breaks the bank in any way.

All right, so our next use case is game playtesting, and this really applies to any type of application

that requires a model to make snap decisions or respond to things in real time.

I'm just using video games as an example

because it's really cool to watch Jev play the game and it is something that I'm building myself for real.

And so with anything like a game, when you have 60 frames per second or 30 frames per second, a large language model is way too slow to respond to things.

By the time it analyzes the scene to make a decision, the scene is already passed, right?

And the character is gonna be dead in the game, for example.

But Jev is able to keep up.

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It is incredible to watch.

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And this is also very practical because we always want to have that validation step in our AI coding workflow.

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So for a video game, you know, after we build the next feature, we want the model to be able to play the game as a user actually would.

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And that wasn't really realistic before until Jev.

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So take a look at this.

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I have a live example of Jev playing one of the games that I'm building a proof of concept for right now.

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It looks like I'm playing.

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The character is attacking, dodging, moving around the map.

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You can see the decisions that Jev is making on the left -hand side right here, with probabilities for all the different actions that we're giving as the options.

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Because, again, it's all about the state, which is, you know, where everyone is in the game, and then the decisions, like, what move are we going to do next?

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And I have hands off the keyboard.

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It seriously looks like I am playing this right now.

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It is so cool.

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And so as it's making the decisions and processing the state of the game, we can also analyze how things are going in general

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and act on that, right? like maybe we do discover bugs for real as we're playing the game, the kind of thing that an LLM could never really discover by itself just by, you know, running unit tests.

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And so I've been running this for real as I've been building new features in the game, and it's genuinely caught bugs that the large language model was not able to by itself

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because the LLM can't really play the game.

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And yes, it can run like other tests and harnesses that I build, but that's more deterministic and it's not really playing the game as a user actually would.

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And so the important thing is we still have to use a large language model to build the harness for JEV, right?

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Like it has to figure out how do we translate the state of the game into the input to JEV?

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What are the options that we give it for different moves it can perform?

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So we build that up front with the LLM, but then all of our testing going forward uses JEV.

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So it's super fast, reliable, and cost effective.

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One other thing that I have experimented with in the past for video games is making it

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so that the large language model can actually slow down the game.

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So it can play it frame by frame.

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That was kind of my way to have it actually play the game as a user would before JEV, but as you can imagine, that was incredibly slow and expensive.

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Now, I hope you can also see the pattern emerging as we're going through these use cases.

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We are never really going to use JEV by itself.

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It is super powerful to use it in combination with LLMs, because Jev is really good at making decisions,

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but it can't craft the decisions in the state upfront or really act on them.

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And so the best way to use Jev is generally to sandwich it in between calls to a large language model.

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Like for example, with gameplay testing, we have an LLM build the harness and then act on the bugs that Jev finds when it plays the game.

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Or with the security here, we have the coding agent performing different actions, Jev analyzes an action, and if it blocks it, then the agent, the LLM,

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has to figure out something else to do, right?

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Jev is never really the end of our workflow.

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It's always just making parts of our workflows faster and more efficient.

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Now, another really good example of this takes us to our third use case with browser testing, using a model to go through a webpage

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and operate it as a user would in the validation step of our AI coding workflow.

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It's very similar to gameplay testing.

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But the thing is, large language models have typically been decent at this, because unlike with something like a video game,

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an agent doesn't have to respond in real time to events with a website, usually, and so it's able to take its time and analyze the page to pick the next action.

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But, you guessed it, it is still slow and expensive using an LLM compared to JEV, and JEV is even more reliable for this kind of browser automation.

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So you probably use tools like the Playwright MCP or Vercel's Agent Browser CLI to do browser testing with an LLM.

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And now we have a lot of open source tools coming out, the same kind of thing, but using Jev instead of an LLM.

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And it works incredibly well.

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There's a lot of cool examples here showing at different websites that Jev is clicking around.

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The most important thing to keep in mind is you don't

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always have the opportunity to use Jev for literally everything because Jev can't generate text like an LLM can.

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So if there's anything that requires freeform text as an input to a site, typically you still need a large language model for part of the workflow.

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But as much as you possibly can, you want to use Jev when you're just making decisions like what should I focus on or what button should I click.

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And so most of the workflow for your browser automation should be driven by Jev now.

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So I'll link to this GitHub repo in the description.

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This is the best open source project I've seen for Jev browser use.

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But yeah, there are a lot of them.

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And you can also roll your own.

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I built my own browser automation with Jev as I've been testing it out.

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Like on this app right here, this is DynaChat, which is in production right now.

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You can go to chat .dynamist .ai and talk to this agent.

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It basically is able to search through all my YouTube content.

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And then if you're logged in with the same email you use in the Dynamist community, it'll also pull from my workshop and course content.

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So it's a really cool app.

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And I've tested building different features and having Jev go through them exactly as a user would.

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So I won't bore you with a full test run right now, but I do want to show you just really quickly live Jev working on this site.

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And so any text that you see at input is pre -generated from an LLM, but everything else it is deciding live.

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The chat box to click into, the button to press to sign in, for example, and then also everything

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that it's doing here can be analyzed by the LLM after to figure out

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if there's any bugs that it needs to iterate on.

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All right, the last of the four use cases here for Jev is workflow classification.

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And this is becoming more and more important for me over time time.

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I want my workflows to be dynamic.

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For example, I don't always want to use the same large language model.

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It depends on the difficulty of the task.

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And so for the sake of token efficiency, I might want something at the start of the workflow that classifies what tier of model I need to use.

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Another good example is sometimes based on the type of work that comes in, I need to be using different skills or different steps.

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And so a really clear example of this is when you have a GitHub issue that you want to work on.

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It might be a bug that you have to investigate and fix or a feature that you have to plan and build.

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And so I don't wanna have to make that decision for myself.

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That's not gonna scale with my AI coding workflows.

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I need something to classify that and direct the workflow at the beginning.

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And that is what Jev is for.

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And this exact workflow I'm describing to you here with the different classification steps, I built that as an Archon workflow.

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So I've covered Archon a lot on my channel before.

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It's my open source harness builder

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that allows you to build these larger AI coding workflows very easily for a lot of the different things

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that I've been covering in this video.

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And of course, I've been incorporating Jev with my Archon workflows.

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And so what you're looking at right here is the graph visualization, where we take in a GitHub issue, and we use Jev to classify two things.

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What is the tier of model that we need to knock this out?

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Because maybe it's something super simple, and we can't just use something like Sonnet 5 and then also deciding the route.

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Is this, again, like I said earlier, a bug we have to investigate and fix or is it a feature we have to plan and build out?

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And so this is the YAML file that makes up the entire Archon workflow.

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And for each step where I wanna call upon Jev, I just have this Python script where I give the arguments and it forms the decision,

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asks Jev, and then the rest of the workflow is going to act on the output of Jev.

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And I'll even quickly show you the Python script

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because we can see all the questions we're sending into Jev at the very top.

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It is so powerful how you can ask a ton of questions that Jev will answer in parallel for super cheap.

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So like, what kind of work does this GitHub issue ask for?

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Is it, you know, a bug or a feature that determines the skills that we use for the rest of the workflow?

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What's the tier of model that we need, right?

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Like based on the difficulty of the task, a fast model, like, I don't know, GPT -6 Terra, something standard like Sonnet 5 or GPT -6 Sol,

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maybe a strong model like Opus 5 .5 or GPT -6 Astra.

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You get the idea, right?

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Like Jev is making that determination.

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And in my testing, it does a very good job just generally understanding the difficulties of tasks and the type of tasks.

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And so back over to this specific run for the classification here, it figured that it is a bug that needs to be investigated.

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And then for the tier selection here, I'll go to the logs really quickly.

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I know it's kind of hard to pick out, but when the next step ran, it did investigate standard, as in it was using that middle tier of the model here.

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So whatever I have selected for that, something like Sonnet 5.

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And of course, I was the judge myself for all the JEV testing that I did here.

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So I ran this Archon workflow 12 times and every single time I completely agreed with the decisions, with the classifications that it made up front.

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There's also a separate Archon workflow more for pull requests reviewing.

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And so using classification up front to figure out what kind of review do we need?

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Like, is this a bigger thing that needs a full architecture review

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or just a light chore that has to be validated quickly?

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and it wasn't perfect but still 15 out of 16 is pretty darn good and

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if we look at the cost here a fraction of a penny versus what it would typically cost in llm

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and again 32 cents per pull request

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that is really going to add up especially with a project

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like archon where we are dealing with dozens hundreds of pull requests

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and issues every week

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so there you have it those are my four favorite use cases for using jev in ai coding workflows

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and of course let me know in the comments

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if you want me to make a dedicated video for any of these

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because I could certainly go a lot deeper and help you incorporate these into your workflows as well.

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And so if you appreciated this video, you're looking forward to more things on JEV and AI coding, I would really appreciate a like and a subscribe.

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And with that, I will see you in the next video.

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169 sentences

Vocabulary and speaking notes for this lesson

This C1 speaking lesson is built on the video “The BEST Jev Use Cases for AI Coding”. The speaker keeps coming back to these words: jev, workflow, model, llm, agent. This video has 226 sentences and 3837 words to shadow. The speech runs for 17:38. The speaker talks fast, about 218 words per minute, so expect linked and reduced sounds. 84% of the words are among the 3,000 most common in English; the rest is worth studying before you start.

Key vocabulary in this video

The 15 most advanced words in the video, with pronunciation and meaning:

Word	Pronunciation	Meaning
workflow noun	/ˈwɝkfloʊ/	The flowrate at which a flow of work takes place.
analyze verb	/ˈæn.əˌlaɪz/	To subject to analysis.
archon noun	/ˈɑː(ɹ)kən/	A chief magistrate of ancient Athens.
browser noun	/ˈbɹaʊ.zəɹ/	One who or which browses.
automation noun	/ˌɔ.təˈmeɪ.ʃən/	The act or process of converting the controlling of a machine or device to a more automatic system, such as computer or electronic controls.
harness verb	/ˈhɑɹ.nəs/	To place a harness on something; to tie up or restrain.
incorporate verb	/ɪŋˈkɔɹ.pɚ.eɪt/	To include (something) as a part.
risky adjective	/ˈɹɪski/	Dangerous; involving risks.
classify verb	/ˈklæs.əˌfaɪ/	to identify by or divide into classes; to categorize
guardrail noun	/ˈɡɑɹdˌɹeɪl/	A rail set alongside a dangerous place as a barrier, to improve safety.
folder noun	/ˈfoʊldɚ/	An organizer that papers are kept in, usually with an index tab, to be stored as a single unit in a filing cabinet.
fraction noun	/ˈfɹæk.ʃən/	A part of a whole, especially a comparatively small part.
penny noun	/ˈpɛni/	In the United Kingdom, a unit of currency worth ¹⁄₁₀₀ of a pound sterling, or a copper coin worth this amount. Abbreviation: p.
deterministic adjective	/dɪˌtɜː.mɪˈnɪs.tɪk/	Of, or relating to determinism.
dynamist noun		A subscriber to the philosophy of dynamism.

Phrasal verbs you will hear

Word	Pronunciation	Meaning
figure out verb		To come to understand; to discover or find a solution; to deduce.
go through verb		To travel from one end of something to the other.
add up verb		To take the sum of; to total.
come out verb	/ˌkʌm ˈaʊt/	To be discovered; to be revealed.
make up verb	/ˌmeɪk ˈʌp/	To constitute, to compose.
build out verb		To develop or expand (a system, structure, project, etc.).
go off verb		To explode.
going forward adverb		In the immediate future and beyond.

Grammar in this video

The structures the speaker uses most, with the exact words from the video:

Structure	In the video
Present perfect continuous have/has been + -ing — an action that started earlier and is still going on	I've only been using · I've been running · I've been testing
Present perfect have/has + past participle — a past action that still matters now	you've heard · you've probably seen · I've done
Passive voice be + past participle — the focus is on what happens, not who does it	was released · was introduced · be blocked
Relative clauses who / which + clause — extra information about a person or thing	efficient, which is · probability, which is · hooks, which was

Pronunciation to watch

The speaker uses 72 contractions and reduced forms, such as I've, I'm, we're. Say them the short way, as you hear them.

The “sh” and “zh” sounds: automation /ˌɔ.təˈmeɪ.ʃən/, fraction /ˈfɹæk.ʃən/, validation /ˌvæl.əˈdeɪ.ʃən/, credential /kɹɪˈdɛnʃəl/, specialize /ˈspɛʃəˌlaɪz/
Long words — get the stress right: automation /ˌɔ.təˈmeɪ.ʃən/, incorporate /ɪŋˈkɔɹ.pɚ.eɪt/, deterministic /dɪˌtɜː.mɪˈnɪs.tɪk/, validation /ˌvæl.əˈdeɪ.ʃən/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/

How to practise with this video

Listen to the whole video once without speaking and note the words you do not know.
Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
Record yourself and compare with the original, paying attention to words like workflow, analyze, archon.

What is the Shadowing Technique?

Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.

Shadowing technique: read the full step-by-step guide →

Shadowing Practice: The BEST Jev Use Cases for AI Coding (Absolute Game Changers) - Learn English Speaking with Video

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