7 Jev Repos That 10x Claude Code (Free) transcript

I feel like everyone in the AI space right now is talking about Jev, a new kind of AI model that doesn't speak. And because of that, it can handle decisions 40 to 200 times faster than a typical LLM like GPT or Claude. But what people haven't noticed is that there are some amazing free GitHubs that are now coming out because Jev is still very new that are now enabling us to unlock even more capabilities with the model.

And unlike the top Claude GitHubs, these literally only have 600 stars, 286 stars, 955 stars, and I'm finding some absolute gems. So in today's video, I'm going to make Jev useful for you. In the last video, I showed you how I was now using Jev as a filter to make decisions based on probabilities for my business development team. I showed you how you can use it for SEO, how I was using it to build a low latency trading bot because instead of spending time and energy on inference, now I could just assign a probability to an outcome and execute a trade.

But if you watched that video and you still didn't know how to get started, this video is going to be super practical because these are GitHubs that you can literally download right now, plug into your Claude code or your Codex, and start combining these LLMs with Jev. And as I spoke about in yesterday's video, I truly feel like if you combine the top models like Opus 5.5, which is an extremely smart model, with Jev for decision-making, you can unlock crazy power and you can also save a lot of money.

And by the way, I'm going to make it easy for you if you want to set up these GitHubs in my free school community down below. I'm going to leave a prompt which you can just copy and paste into your Claude or your Codex, and it's going to go ahead and install all of these GitHubs for you. So in brand new sessions, you're going to be able to immediately start using the stuff that's under the free assets library of my school community alongside all the assets from all of these videos.

Okay, so the first GitHub, which is very cool, is called Shapeshift, which is very cool because it's a text box that actually morphs into a UI as you start typing. So it could be an event card, a checklist, a timer, a color picker, a bill splitter, a poll, a converter, or more. And the reason why it can do it so quickly is because of how Jev works. You essentially enter in your command instead of the LLM having to do inference, it simply assigns a probability to determine what you are trying to ask the model for, and then it can create an intent, so an event extremely quickly, which can then develop the UI of your choice.

This is a very great visual demonstration of how quick the model is. So, let's actually follow one of its prompts here. Let's search up Minecraft diamond, and you can see how it finds the color straight away. You could go dinner with Alex on Friday at 7:00 p.m. And it's already going to start adding a calendar event. And this is all happening in real time. It's not like, you know, Claude needs to process something. You could do a timer.

So, timer 25 minutes, and it's going to automatically pull up a timer. You can even do math. 500 / 5, and it happens automatically. So, on its own, this is a cool demo, but imagine if you incorporated this into your workflows or into your apps. Like, imagine if you were trading and you went buy $500 of Bitcoin every time price drops 5%, and it uses that to trigger the order automatically. Like, that kind of latency is really powerful.

And I think this is the type of UI that a lot of the major developers, like Google for example with calendar invites or meta with DMing contacts, this instant UI generation, I think it's really powerful, and I think it's a great example of how Jev works under the hood. So, this is one of them. I'm going to get into even more powerful ones now. The last one is something that can actually make you money literally right now, and I'm going to show you a hack which you can use if you're interested in a startup around Jev because it's opening up a whole new raft of possibilities.

That one's going to be I think really good for the people that are interested in that. This next one's awesome. It's called Jev Voice Browser, and it allows you to control a real browser by voice, which obviously you can already do with Claude and GPT, but this is just so much faster because as you are saying it, it's already acting. It doesn't have to do that initial inference part before it loads up a web page. So, you can literally be speaking into Jev.

You can see an example here. He's saying, "Go to wikipedia.com." Bang. It's already on Wikipedia. Now, he's saying, "Click on the first link. Bang, you see it. He already clicks on the first link. Like, this is right as the text is coming up. So, it's happening immediately. Eternal Blue already popped up. Go to Joshua Tree. Joshua Tree pops up. So, you can actually control it in real time now. If you've ever used ChatGPT or Claude and you've tried to do voice command to control your browser, you'll know it is very slow and there's like a few second lag.

If you've ever tried to use a Home Assistant agent to do voice control, there's a couple second lag. So, this is going to significantly shrink the workflow time on browser use. So, this is definitely one worth installing cuz it's super easy. Once you install it, then you can just start commanding your browser by voice. There's, you know, literally no downside to do so. So, this is a really practical one you can start using now.

And I was actually filming a demo for you, but because I am recording audio for this video, my computer just couldn't handle the internal audio recording for the web browsing and the video recording at once. So, I just showed you the demo of someone else, but I literally tested it out and it's lightning quick. Okay, so this next one, which is called computer use, makes computer use so much quicker. So, computer use is essentially when an agent just has to do something on your computer.

The problem with ChatGPT and Claude is that every single time they need to click on something on your computer, they essentially need to process it via an image. So, they take a screenshot and then they predict where they need to click in order to do something. But, because of the way Jev thinks, instead of taking 5 seconds, it only takes 0.3 seconds because it predicts where to click next based on the command that you've given it.

So, once again, it's a fundamental architecture shift in terms of how it actually works. And the stats are quite crazy. If you look at Type Safety's Jev, the price is around 0.042 per million tokens in output free. Opus 5 is around $5.25 per output, which makes Jev 119x cheaper per input token. And on a cost per decision basis, it's 155 times cheaper than Opus. And on a latency basis, this is what actually matters. I think it's actually less about the money and more about the speed.

It's 0.13 to seconds per command versus Opus, which as I said needs to take screenshots, which is around 5.2 seconds. So, 14 to 40 times faster. And this repo is created by Aaron, who basically just used Jev's technology to create a computer use tool. So, as soon as you download this, you can start using Jev for computer use. It's crazy. It's only got 955 stars, but that's because these models are super super new. And you can see he used it to break down the TechCrunch page and the pricing and amend the website.

So, instead of having to, you know, screenshot every time like a traditional model and click around, it can just read the back end of the website and, you know, speed up browser use significantly. Just to be clear, I don't think Jev is a replacement for Claude. I think it's an aid. I think there are some things it does better, some things it does worse. And as I said in my last video, you want to use Claude for inference, so the planning, the strategic thinking, and you want to use Jev for whenever there is, you know, a probabilistic thing that needs determining because it will basically just determine like a yes or no score based on a percentage.

Jev thrives better with that kind of decision-making, which anything that's able to be distilled into a binary outcome, Jev is probably going to do well. But the GitHub's that I'm showing you today, you can literally just chuck into Claude code. So, if you're using Claude and then you call upon one of these features, Claude can just call upon one thing when it thinks Jev will do really well in that particular scenario.

So, this is an addition to your existing LLMs, not a replacement in my opinion. Now, this next one isn't something that's that usable in my opinion, and there are other great solutions for this out there, but I thought it was a really good example of how Jev operates, so I put it in this video and then we'll get into one of the really heavy hitters that I think everyone will get a lot of value from. And this was an ad blocker.

So, it basically reads the page live, scans if there is an ad, and if there is an ad, it Oh, you can see it automatically gets rid of all of the ads because it determines whether there is ad space on the page. So, you can see here, entering another site, detects an ad, deletes it straight away. But it isn't that efficient. I think there are better blockers out there that you can find, but it's another example of the shift in how AI is actually going to be used going forward and the speed benefits that you get.

Okay, this one is very cool. If you want to use Jev alongside Claude code. And this is essentially an MCP tool which plugs into Claude as a set of judgment tools. So, if you're using Claude, it can pull on Jev to do a variety of things. For example, verify, checking claims against evidence, screen, judging content before it enters context. Find, picks the best candidate by meaning. Decide, settles bounded alternatives.

So, each judgment comes back typed as a probability, so it will give a confidence score to the LLM to save time and money on the inference side cuz it could be between 150 to 500 milliseconds for a fraction of a cent. And these are cheap mechanical checks that agents will otherwise skip or take too long and will be too expensive to do. Like frontier models aren't good at this stuff. So, this MCP, once you install it, will abstract the experience away.

So, you can start calling on Jev to do this stuff whilst using your existing agent harness that you're probably already wanting to use. And once again, that's why Jev doesn't replace Claude code or Codex. You want to stay in those harnesses. You want to stay using those models. As I said, you want to use Jev when it's good at a particular thing. And making these judgment calls is something that it is fundamentally very good at and designed to do.

I'll give you a live example. So, I just asked Claude to use the Jev MCP tool and show me the cost for each call. I'm using Jev verify to verify a pricing structure for a product. can see Jev here comes back with an outcome based on a probability that it assigned. So, it gives me a confidence score, a verdict based on the preset criteria. And that cost me, you know, 0.000023. So, if you needed to make a decision or if you needed to verify a piece of information is true, instead of using Claude to determine that, you could use Jev and save money and and save time doing so.

And I think the real value in this isn't just for questions cuz, you know, I understand people already have and myself as well, the normal way of conversing with AI. This is better when stacked on top of an automated end-to-end workflow. So, if you have a task which requires a variety of steps and decisions, Jev can plug into that workflow at certain key touchpoints. For example, with business development, we scan candidates for our agency and we will essentially scan whether someone is a good fit and that is based on a few things.

Are they the right person at the company? Is the company someone we're actually interested in working with? And does this candidate match our criteria? And it could give us a confidence score and then develop a curated list of probabilities as to who fits our criteria, feed that information back to the LLM, and then automate outreach from there. As opposed to having the LLM do all of the inference around the probabilities, because if you're scanning thousands of contacts, that is going to take a lot of time and cost a lot of money.

And that was why in yesterday's video, I used the example of a trading bot which I built, which is actually a great example of Jev because high-frequency trading relies on a lot of decisions happening very quickly. You see here, this was around 400 to 500 milliseconds this decision was being made in. When it comes to news trading, when it comes to high-frequency algo trading, you need to make decisions really quickly.

So, Jev can assign a probability in this case, it made a buy call, a 91% probability trade based on the set of rules that you actually give to it. So, that is where it's going to come really in handy over anything else, though. It's these workflows where you need fast decision-making and you need it done in a cost-effective manner and I think anything that involves, you know, mass data is going to fit this criteria. Okay, the last GitHub I want to show you is, I think, another very fun example of how Jev works and then I'm going to get into the best way to make money with Jev.

I did find a resource which can help you actually do that. So, this is quite interesting. There is essentially a GitHub here called JevMeter. And JevMeter can basically come up with like a BS meter live. And this has some interesting applications. So, you can see here, this creator gave the presidential debate access to Jev And Jev was essentially looking at a set of criteria. Whilst the debate was happening live, it was coming up with a BS score based on every single line that was being said live.

This does have some interesting applications. Like the immediate one that springs to mind is like Polymarket and betting markets. Because the inference is much faster than Claude. It could probably assign a probability and then place a bet quite easily. So, I think there'll be use cases across those markets. There'll probably be arbitrage, but that's very interesting. But, I think there are also many other interesting use cases for live stuff.

For analyzing live streams around, you know, consumer data and behavior, all sorts of indexing around when something is said. For example, if you have a webinar and you're selling a product, when you say a certain thing, does that optic to sales, other sales dropping off? What is the language that you used? What's the probability of that, you know, language actually landing a client? There's lots of interesting things that I could see Jev being used for in a live forum.

Taking the data in from, you know, other live creators. If you're doing like TikTok Shop, for example, I could potentially see use cases there. It's a little bit abstract for now. And this is more, you know, of a fun one than maybe a practical one that you'll be jumping into and using right now. But, this is a very interesting one. I think the capability to analyze live video is very, very interesting. And I think the use cases for that over time will start to become apparent.

Now, earlier I said I would show you a site that I think is good if you are interested in a startup or making money. And it's called shipwithjev.com. This has a variety of ideas, use cases, and posts that have been curated. Cuz I, you know, gave you six GitHubs. I'm kind of counting this as the seventh, even though it isn't a GitHub. It is a repository of information that can help you. A lot of people are developing their own use cases, their own ideas.

And this website filters them by build type. So, you can go agents and browsers. You can see a variety of things people are building in terms of browsers. If you click on games and real-time, you can see games that people are developing with Jev. If you look at trading and markets, you can see trading bots people are building with Jev. Content and growth, you can see how people are using Jev to enhance content and you can see a bunch of ideas for tools and apps.

So, what you can actually do is you can take this URL, you can go into Claude Code, paste it into Claude Code and ask it, "Hey, this is" so give it your context file, "this is the context about me, who I am, my strengths, my weaknesses. These are my goals, which ideas could be interesting to either integrate into my existing business or startup or be used to create startup based on my skill sets and interests. And there could potentially be some ideas to actually ship stuff with Jev either if you're shipping within an existing idea that you have.

So, this could help you implement Jev into your existing workflows or this could help you potentially start something new. Remember, Jev is an LLM, so I love the Opus 5.5 model. I think it's the best model in the world. That is going to help you determine what to do and what you can implement and then Jev will just be a part of the workflows behind everything to speed things up and save money. And that's pretty much, you know, how I would describe Jev.

If you're still a little bit confused and you just want to come away with this video with a really simple explanation, it's just a way to speed AI up and save money whilst you do it. And you'll find a lot of your use cases could probably be sped up and be made lower cost if you implemented Jev. And hopefully some of the GitHubs that I showed you today, some of them more fun, some of them, you know, actually quite practical, especially the browser use stuff, the computer use, the voice use.

That stuff I think is super super practical. You can start implementing this today. Probably the most practical one actually is the and the one I'm getting the most use out of already is the MCP tools because that is how you can actually just stack it into Claude to get instant outcomes and really start using it right away. And if you don't know how to use this, you could just ask Claude, "Hey, based on my sessions, could you analyze them and tell me where I could have used Jev based on these commands?" And it will actually show you some ways that you can use it and help guide you as to how you should be using it in the future.

So, so that's cool as well. Hopefully you enjoyed this video. I'll keep you up to date with the latest tools and the best workflows and everything. The one prompt setup guide from today's video will be available in the description down below in the free school community. I'll see you in the next one. Have a lovely rest of your day. Peace out.