Testei o Jev com 34 comentários reais: 4,5x mais rápido que o Claude

Overview of Jeev, a new AI model using System 1 thinking, 4.5x faster than Claude, with practical use cases and functional programming parallels.

Key Takeaways

Jeev uses System 1 reasoning for faster, more efficient AI responses.
It guarantees typed, valid outputs, reducing runtime errors and hallucinations.
Ideal for classification tasks with limited output categories, such as spam detection.
Jeev’s approach aligns with functional programming concepts for state safety.
It offers a significant speed advantage, being 4.5x faster than Claude.

What the video covers

Jeev is a new AI model that does not generate text token-by-token or chat like traditional LLMs but uses a different reasoning approach.
It is based on System 1 thinking, which is faster and more reflexive compared to the slower, stepwise System 2 reasoning used by current LLMs.
The model ensures that outputs are always in a valid, typed format, reducing errors and hallucinations compared to typical AI models.
Jeev uses a three-step process: choice from a defined set, scoring for confidence, and returning a typed result based on thresholds.
It is particularly useful for classification tasks like spam detection where outputs must be limited to defined categories.
The model's design parallels functional programming principles, ensuring state validity and type safety.
Jeev provides answers in a single call without showing intermediate reasoning steps, improving speed and efficiency.
Reinforcement learning is used in training to calibrate confidence and reduce hallucinations.
The video includes practical examples, comparisons to existing LLMs, and discusses potential applications and benefits.
The presenter also tests Jeev with real YouTube comments to demonstrate its stability and accuracy.

Chapters

What makes Jeev different from traditional large language models?

Jeev uses System 1 reasoning, which is faster and more reflexive, unlike traditional LLMs that use slower, stepwise System 2 reasoning. It also guarantees typed outputs to reduce errors.

What types of tasks is Jeev best suited for?

Jeev excels at classification tasks where the output must be limited to a defined set of categories, such as spam detection or order status classification.

How does Jeev reduce hallucinations compared to other AI models?

Jeev uses reinforcement learning to calibrate confidence scores and ensures outputs conform to strict types, which lowers the chance of hallucinations and runtime errors.

Full Transcript — Download SRT & Markdown

Speaker A

A new AI model has been released that doesn't just generate text; it doesn't predict the next token, it doesn't actually write anything, it won't chat with you, and it can't write an essay for you if you wanted it to. And there are quite a few serious people saying it is probably the biggest release of 2026. I'm a little late, but I want to show you what it is, what I liked the most, which looks a lot like the way I work with Kotlin and functional programming, and where you'll be able to use it to understand how it works too.

Speaker A

are quite a few serious people saying it is probably the biggest release of 2026. I'm a little late, but I want to show you what it is, what I liked the most, which looks a lot like the way I

Speaker A

So, one thing I noticed that is very similar to what I teach in the members' functional programming series is how you can ensure that your state, your model, will never be in an invalid state. In other words, those classes that have a bunch of nullables and you don't know when they will have information or not, it's a huge headache. And Jeev seems to solve that.

Speaker A

your model, will never be in an invalid state. In other words, those classes that have a bunch of nullables and you don't know when they will have information or not, it's a huge headache. And Jeev seems to solve that.

Speaker A

It will ensure that you always have the expected response format. Let's see what that means. So, before we talk about Jeev, we need to understand how our brain reasons. There are two types of reasoning or ways of thinking that academia calls System 1 and System 2. So, there are two ways we can think.

Speaker A

academia calls System 1 and System 2. So, there are two ways we can think.

Speaker A

There is System 1 and System 2. If I ask you what 2 + 2 is, you don't need to do the math. You know it's four. Or if I ask you if a person is angry, sad, or happy, you see their face and you already know. It's an obvious thing, something that doesn't require much logical reasoning. Now, if I ask you what 54 x 73 is, you can't solve this in your head. You will have to do what we learned in school, which is 54 x 73.

Speaker A

already know. It's an obvious thing, something that doesn't require much logical reasoning. Now, if I ask you what 54 x 73 is, you can't solve this in your head. You will have to do what we learned in school, which is 54 x 73.

Speaker A

And then you multiply one by the other and you find the result. Or you open the calculator and it gives you 3949. So this part has several steps you take to reach the final result. So that is what we call System 2. And this is very similar to how LLMs work nowadays. So when you ask a question, it is actually trying to figure out what the next token is, depending on all the data it has and the probability of it being correct. And with that, it's able to reach the final answer.

Speaker A

So this part has several steps you take to reach the final result. So that is what we call System 2. And this is very similar to how LLMs work nowadays. So when you ask a question, it is actually

Speaker A

So, nowadays LLMs use System 2, and Jeev kind of comes to change this paradigm; it uses System 1. And here, very briefly, that is the difference. System 1, it doesn't, system type one, it doesn't need as much, it doesn't have as many reasoning steps or processes. And the system. And everything I'm telling you here, if you want to check it out later, it's referenced from Kahneman. Not the Gremio defender, it's the guy who developed this reasoning.

Speaker A

comes to change this paradigm; it uses system one. And here, very briefly, that is the difference. System one, it doesn't, system type one, it doesn't need as much, it doesn't have as many reasoning steps or processes. And the

Speaker A

So, what are the biggest differences between these two types? System 2, which has several processes, several processes, several processes, is slower. It has to write every part, it has to explain, and it takes from 3 to 29 seconds, plus it spends more tokens, because for each step it's using tokens to figure out what comes next. System 1 is kind of like a reflex, a decision; it gives you value much faster and right away.

Speaker A

several processes, several processes, several processes, is slower. It has to write every part, it has to explain, and it takes from 3 to 29 seconds, plus it spends more tokens, because for each step it's using tokens to figure out

Speaker A

Their idea is precisely this: that we use System 2 for tasks that should be System 1 all the time. And we, every time we do this, we pay a high price. You'll pay a high price for that. So, all right, Jeev. So when can we use it?

Speaker A

time we do this, we pay a high price. You'll pay a high price for that. So, all right, Jeev. So when can we use it?

Speaker A

Here's a practical example for us to understand. If we're going to build, for example, a system to classify spam, the answer can only be two things. I want to know if the email I received is spam or not. I can't receive anything else besides that. So, if you put in a model that takes a long time to reason and infer this, maybe you could use Jeev. And so, in practice, what does it do? It has three processes that are always the same, and it's important for us to understand.

Speaker A

else besides that. So, if you put in a model that takes a long time to reason and infer this, maybe you could use Jeev. And so, in practice, what does it do? It has three processes that are always the same, and it's important for

Speaker A

So it will have the choice, where it picks from a defined set, it has its dataset, and it chooses what it wants. The score is when it performs the scoring to decide, okay, if this is spam or not, I can go from one, which is 100% certainty, and zero, which is no certainty. So you'll have its score there, which could be 0.9 or 0.1.

Speaker A

one, which is 100%certainty, and zero, which is no certainty. So you'll have its score there, which could be 0.9 or 0.1. And then, depending on the range it's in, usually 0.9 is yes. Anything else here is "I don't know, I'm not

Speaker A

And then, depending on the range it's in, usually 0.9 is yes. Anything else here is "I don't know, I'm not sure." And anything below 0.1 is a no. Yes, no. And I can't say, but it has this, it has this way of thinking. You have a state, which is, for example, the email the guy sent, it's your initial state that you will use with Jeev.

Speaker A

. And then it will have the model or the choice, which is how it was trained . We will see later on how it trains.

Speaker A

And then it will have the model or the choice, which is how it was trained. We will see later on how it trains. And then in the end, you have a score. And then from this score, you can configure and decide if it's the type you want or not. You can have more than two types here. You could have, if it were, for example, not just to track spam, but to see order status, it could be purchased, canceled, awaiting response.

Speaker A

were, for example, not just to track spam, but to see order status, it could be purchased, canceled, awaiting response. But you configure this, and the good thing is that it's a typed question. This is what I liked the most

Speaker A

But you configure this, and the good thing is that it's a typed question. This is what I liked the most. You can, it's not, it's not like JavaScript which is a mess, which is the any type there, it can be anything, you have to keep converting and ensuring it's valid. You ensure that Jeev will return one of these types to you.

Speaker A

And it doesn't mean that because it returns one of these types, it won't hallucinate. We will see more about this later on. But it's important to understand that you have a state, which is the incoming email, and it will have

Speaker A

And it doesn't mean that because it returns one of these types, it won't hallucinate. We will see more about this later on. But it's important to understand that you have a state, which is the incoming email, and it will have its training to know whether it is or isn't. And then, depending on the confidence or the score it has, it will resolve, it will return this type to you.

Speaker A

exactly this little drawing I made for you. So, in summary, choice, you will choose an option from a set you defined , it can have up to 259 options. Score, you score against the levels, and the no is yes or no, whether the type is or

Speaker A

And this is what we call calibrated probability, which is exactly this little drawing I made for you. So, in summary, choice, you will choose an option from a set you defined, it can have up to 259 options. Score, you score against the levels, and the no is yes or no, whether the type is or isn't.

Speaker A

intelligent if. Two technical details that are important here for us to understand the intention. First, it will answer everything at once in a single call. It won't keep writing bit by bit showing you the chain of thought it performs, which it would usually do

Speaker A

So you send the state, you send a typed question, and it will return a typed value with the probability included. And they summarize this in an expression I thought was very good, which is smart if statements. It's the intelligent if. Two technical details that are important here for us to understand the intention.

Speaker A

written, blogs, articles, any content it might have. Jeev uses a method called RLCD, which is reinforcement learning with calibrated decision, which is precisely this type of training. So it trains with technical data; it doesn't train with articles or

Speaker A

First, it will answer everything at once in a single call. It won't keep writing bit by bit showing you the chain of thought it performs, which it would usually do as it processes; it has its chain of thought, then moves to the next, it's not all at once. The training in general uses what we call RL, right, reinforcement learning.

Speaker A

why do I like this even more? Because the company is called, the company is called TypeSpec. And it's no coincidence; if you follow the functional programming series here, you've seen this many times, that making an illegal state or making a

Speaker A

And then it trains this with data that people have written, blogs, articles, any content it might have. Jeev uses a method called RLCD, which is reinforcement learning with calibrated decision, which is precisely this type of training. So it trains with technical data; it doesn't train with articles or human information, it's all base.

Speaker A

will accept you writing nothing, it will accept you writing a phone number. It won't break in production, it will compile, but you'll have a runtime problem. Now, if the parameter is a type, like an email type, you won't be

Speaker A

They are all machine-generated bases. It is a very different technique. And what matters most for this video is understanding that this is why Jeev has different training from normal LLMs like ChatGPT, Claude, and so on. Now, why do I like this even more? Because the company is called, the company is called TypeSpec.

Speaker A

made Jeev is about, and it has a lot to do with the response Jeev returns, which isn't just text; it's a state and a typed response. And having these guaranteed types is precisely the main idea that Jeev brings, right? So, think

Speaker A

And it's no coincidence; if you follow the functional programming series here, you've seen this many times, that making an illegal state or making a state in your domain unrepresentable is one of the most important things you, as a software engineer, can do. So, a very simple example here: for instance, if a parameter is text, a string, it will accept you writing "banana," it will accept you writing nothing, it will accept you writing a phone number.

Speaker A

might also write, "Look, this is the answer, it's spam," and add more things you aren't expecting. Then you'll have to parse it, you'll have to perform validation, and you'll have to retry when, for example, the answer is clearly spam. And what do you do then?

Speaker A

It won't break in production, it will compile, but you'll have a runtime problem. Now, if the parameter is a type, like an email type, you won't be able to write a phone number, you won't be able to write "banana," because at compile time you can ensure, before the system runs, that you will receive exactly the type you need.

Speaker A

exactly where the value of Guidance comes in. It turns this whole thing upside down. So, the possible answers will already be the very type you define. Their documentation says that the model will never get the type wrong . And the reason is very simple. It has

Speaker A

That is type safety, which is what the company that made Jeev is about, and it has a lot to do with the response Jeev ret

Speaker A

you don't want, it means, for instance, it won't just fail. Sure, an answer like "Oh, you're so smart, pay attention, the answer is such and such, " that won't happen anymore. However, what doesn't end is that it can give

Speaker A

you the wrong option; it might answer spam when it wasn't spam. That is a separate issue. But anyway, LLMs nowadays also do this. So at least you remove that kind of problem you have, besides being faster, more efficient,

Speaker A

and cheaper. And another part that is also very important besides the type is precisely how it is trained, which is calibration. So every answer comes with a probability attached; you have a confidence score. So when it says it

Speaker A

has 90%confidence, it is correct 90%of the time. In other words, what we saw here, this calculation it does, imagine you have here the confidence it gives you, oops, and here is the accuracy percentage. So, if it tells you it has

Speaker A

a 90%chance of being right—look at my beautiful drawing—if it is 90%sure it's spam, out of those times it says that, 90%will be correct. See how crazy that is? That is directly proportional.

Speaker A

Because it has this calibration, the chance of it hallucinating is much lower. And it's different from an LLM that claims to be certain with intonation. Simply why? Because of the reinforcement learning it learned. It learned that if it says it is confident

Speaker A

, people will click: "I approve, that's right, I approve." And that kind of makes it give these confident answers, even without being based on anything, without having done the calibration that Jave does. And that is exactly it.

Speaker A

Why does an LLM seem safer even when it isn't? Because the LLM's training, as it uses reinforcement learning and sometimes it's reinforcement learning from human feedback. So if we prefer the answer that is just confident, it learned that this is a good answer and

Speaker A

it keeps doing it to seem confident simply because it's what makes people click approved or disapproved more often during the training phase. But it has nothing to do with the certainty or the calibration it has. Got it? Got it.

Speaker A

Let's move on. So, where can we use it? And here it is more important to understand what it cannot be used for yet. This won't write code, this won't hold a conversation, this won't be able to summarize, this won't be able to

Speaker A

generate things you need. This is very good for decision-making, for classifying, for routing, for scoring, for filtering. It serves as a guard. So its place should be in the decision layer around classifying, routing, filtering, scoring, serving, and providing protection. And while we're

Speaker A

talking about what it doesn't do, there are three limits that are worth knowing . Jave's limits: it only accepts text input, it's bad at math and even at doing calculations. And it still falls for instructions hidden in the data it

Speaker A

reads. Got it? And what does that mean? It still falls for instructions hidden in the data it reads. It means prompt injection hasn't ended. So typed output will solve the format, but it won't solve someone writing "ignore previous

Speaker A

instructions" in the middle of the text you sent it to classify. And this is where I think you can make some money, because in your AI application, the majority of model calls aren't the creative part. I think that's the value

Speaker A

of Jev, because you'll use this for, for example, where I use it in my work, we have to collect a person's name over the phone. And then we have an AI call that just acts as a classifier to see

Speaker A

if that name sounds real or not. Or the person says a time and we have to classify to see if the time exists or if that time is available within the restaurant's availability. So these calls that aren't creative are where

Speaker A

you'll gain efficiency, you'll save money, and it will serve its purpose. Now, if you need the creative part of generative AI, this isn't it. And here are the numbers from TypeSafe themselves, that latency is much faster , so it's less than 1 second, it's 70

Speaker A

milliseconds versus 3 seconds for frontier LLMs, like GPT-4 or Claude. The input is 4 cents per million tokens . And the output is free. It's free. So , according to TypeSafe, they claim it's 40 to 200 times faster, and 40 to

Speaker A

400 times cheaper. And these numbers are from tests run by TypeSafe themselves. The cases, these cases they performed were evaluated by their team and the latency was measured on a West Coast server in the United States. So, there’s no one from the outside who

Speaker A

tested it. You might find it suspicious , but it's what we have today. Yeah, Maven Devs, I had grabbed some numbers for you, but I don't think this is that important. I think what's important for us is to know there's another

Speaker A

alternative to Jev which is Llava, which is an open-source reproduction, so you don't need to pay, you can use it. And it came out, this actually came out exactly three days after Jev. I haven't tested it yet, I'll test it

Speaker A

later. Alright? So we understood what Jev is, what it's for, how it works, and what the advantage is. I want to talk now about what I think and I'll show it and get hands-on to see what happens. So I really liked the idea of

Speaker A

typed output, it has calibrated confidence, it's the same idea of types that I already use when writing code, so I'm a Java dev, my whole career was Java, I worked with functional programming with Scala, now I'm with Kotlin with functional programming too.

Speaker A

So, I really like having this control and ensuring that the states I can achieve programmatically are valid states, making it impossible to have a scenario I'm not expecting. What we can't be sure about are the numbers, because they are their own benchmarks,

Speaker A

and it seems like there was a spat between Llama and Jev, so we still need to wait a while to see if that's really the case. So what can we do, Mova Dev?

Speaker A

For you who work within your company with AI, and your CEO wants it to be AI-native, you can revisit the AI calls in your process and pipeline, and see which calls are for decision-making and which are for creation. So, if there's

Speaker A

a lot of stuff meant to decide if a state is valid, to validate a response, or to filter, you can take this to your team and test Jev to see if it works, and it will likely reduce latency in

Speaker A

your calls and be cheaper. It's even a way for you to build a promotion case.

Speaker A

I have a video here where I talk exactly about this, how you can build your promotion case and grow in your career. It is a golden opportunity. If no one on your team has done it, try doing it yourself. My intern had put

Speaker A

here in the video for me to say that the name comes from William Stanley Jevons, an economist who observed that the more efficient the steam engine became, the more coal England consumed.

Speaker A

So, what is this guy's idea? It's that nowadays you don't use LLMs everywhere because they are expensive, so you only use them in the critical parts of your system. And this theory of his, this economist, is that since this will

Speaker A

become very cheap, you can add Jev throughout your entire system, in all your code, and then it will become more expensive compared to LLM usage today due to the volume. Not because Jev is more expensive, but because, since it

Speaker A

seems cheap and the price seems insignificant, you end up adding it to everything: unit tests, builds, anything that doesn't really need it, just to get that feeling that you're evolving and being efficient. But if you take everything, the entire bill of

Speaker A

what you use today with LLMs versus what you will use with Jev, it will end up being more expensive. So, I think they chose this name on purpose to be provocative and so we don't forget it.

Speaker A

All right, ready? You guys tell me: " Man, Pasca, you make a lot of theoretical videos, but nothing practical." So, let me show you, let me test it with some of my own data here.

Speaker A

So, the idea, the idea of this, this, this experiment is, I took 34 comments from you guys from our YouTube channel here and I asked Jeev and Claude to do the same thing. But it's different from the email example, I won't ask them to

Speaker A

say if it's spam or not spam, because it would be too easy, since you guys don't post spam here. So, I asked for the classification I actually want to do, which is: is this comment a video request, is it a technical question? Is

Speaker A

it a critique, is it a compliment? Is it someone sharing their experience? Is it a disagreement, a joke, or a notification of a problem in the video?

Speaker A

There are eight options. And two more, there are two more things together. in the same question, which is how urgent is it for me to respond from one to five, and a yes or no question, which is: is this a video request? All right?

Speaker A

So I added this last part because video requests are something I get a lot of here, and today I have nothing to help me measure it. So, one thing I did on purpose, which is the second test question, is 10 comments that are

Speaker A

difficult, that I don't even know how to classify, to see what happens. All right? I already have my Claude API configured here, and my Jeev API too. I have this demo script. So, first, the comments are all comments from you guys

Speaker A

. There are some things that are ambiguous, I set to true. So, I really liked the content, but for the script, I see patterns of not really needing to use AI to make the video. I actually like it a lot, but sometimes I feel

Speaker A

it's very strange, this here is true. The kids are using AI a lot, so it's hard to tell what it is. There are some that aren't ambiguous, like someone telling me the same prompt isn't working on the older version. All right

Speaker A

. So I have quite a few, I have some comments from you guys. I have this instruction I put in, which is to classify real comments on the channel as Jeev or Claude LLM. So before asking , you have to run it from the root of

Speaker A

the repo. Here are the API keys I have. Obviously they aren't here, right? They're already exported. He's going to use the run. Okay. Here are some things . My information. And what will show up ? The speed will show up. So, 34

Speaker A

comments in both with time in milliseconds. There's stability, so six ambiguous comments. That's what I'm testing, right? Five. Each model will have five. And the data are all the comments. There are 34 real data points I pulled from YouTube from the last

Speaker A

five videos, I think. I don't want the username. And the primitive, the question has the choice, which are the eight categories, the score is urgency to reply from one to five, and the null is a video request. So the null will

Speaker A

align with what came out of the DM analysis, video requests are the number one demand for the channel. And with that, I can measure instead of just guessing. And if it errors, it errors.

Speaker A

Got it? Uh, this is my little prompt. I have the demo script here for it to do its thing. I'm going to run it and let's see what it does. So here it's running my benchmark with my comments,

Speaker A

with Jeev saying what it thought, with the LLM saying what it thought, and then there's the difference, the comparison of the time. There's one they didn't agree on, for example, this one. I miss the code. What a boring

Speaker A

life this is with AI. It doesn't seem like a joke, it's a critique. This one here, wait, but do you need skills for that? It doesn't see there's a question . So it's making a critique, but here its response is a thinking signature. I

Speaker A

think it's because it's using thinking processing and the result wasn't what I was expecting. And that is exactly what I'm telling you guys. With Jeev, it might hallucinate, it might have an unexpected error, but it will never answer outside of what you're asking.

Speaker A

With LLM, it could be a benchmark of 36 , and only one case returned something I didn't expect. Oh, so we can see that in terms of speed, Jeev is much faster, there's no comparison, it's 4.5 times faster in the median. So if you have a

Speaker A

system that requires low latency, you can use it too. Jeev is much cheaper than LLM tokens. It won't have the problem of returning a response you're not expecting; it will always be in the format you want. The only part that

Speaker A

differs is that there are some disagreements. So, for example, let me check this one because I'm curious to know what it would be. Eh, ah, to me it's closer to 1.5, 2 is faster, it depends a lot on the codebase. Usually

Speaker A

it leans towards English, it works really well with Python. So, this is ambiguous, but it's a personal experience. So, JV was actually pretty good. Actually, it's not a disagreement . It's both, but it is a disagreement.

Speaker A

Now let's look at this one to see if I can. They are selling the same thing with a different base. It's been practiced for a while, okay? I know what this one is. This one is a critique. It's saying, man, it doesn't

Speaker A

change anything. And here he's talking about personal experience, but it's actually a disagreement. So, what can we see from this? The idea of the video was to show what Java does, what the difference is, and when you can use it.

Speaker A

So, it's clear that maybe for this analysis task, it might need more data to have a better dataset for it to infer this. But honestly, for these ambiguous ones here, I also don't know which of the two it would be. So, if I

Speaker A

were to use it for some filter, some labeling, or to map information, I think I'd use Jeev, because the speed is much faster and you have the guarantee of the shape you want. I'll run it again because I want to see the

Speaker A

confidence part to see if, for these ones they disagree on, the confidence is high or not, because then we could set it—since that's the idea, it has confidence calibration, right? So, it would be possible to set a threshold or

Speaker A

the limit I accept. And then, if it were below that, it would put, I don't know. This one I actually asked for in a video, but it's understandable why it didn't find it. I'm curious about those that are ambiguous, for us to see what

Speaker A

it really is. Look here, the Vaguinho one seems to be a personal experience, but Cláudio has the context already, he has the context of our channel. He knows that Vaguinho is a character from Páscoa Dev. Perhaps Reb would need to

Speaker A

have that too. The ambiguous parts, you see? That it wouldn't be able to get right. Even if it isn't, hasn't gotten it right, or is ambiguous, you know it, because it has calibration, which is exactly what makes the difference for

Speaker A

you to trust the answer and ensure the shape will always be the same. So I don't change my opinion, I think Jav can be used quite a lot. Most of the things you do with LLMs during runtime could move to Jeev. Probably Claude and

Speaker A

Anthropic, those others will change and start supporting it too, because it makes no sense otherwise. And if you look at the price, it's much cheaper.

Speaker A

Jeev is 0.09, Claude is 4. I think it's , I'm not even good at math, 10 times, 50 times, I don't know. But that's it, new Devs. In today's video, I wanted to bring this practical side, this test,

Speaker A

and show why it makes a difference. I think this view here is what really summarizes what the advantage is, what the benefit is. Same format, calibration, and you don't know if it's certain simply because it said so.

Speaker A

Trust me, trust your uncle. You have the math behind it showing what the probability is of what it said, right?

Speaker A

So that's it, if you liked the video, leave a like, subscribe to the channel if you haven't yet, and consider becoming a member here to help us out.

Speaker A

There's the functional programming series and we will bring more practical videos there in the members section. So , take advantage while it's free for now.

Topics:Jeev AISystem 1 reasoningfunctional programmingAI classificationspam detection AItyped AI outputsreinforcement learningLLM alternativesAI speed comparisonClaude AI

Get More with the SozAI App

Transcribe recordings, audio files, and YouTube videos — with AI summaries and speaker detection. 30 minutes free.

App Store Google Play

Or transcribe another YouTube video here →

Free tools: TXT to SRT · SRT Validator · Merge SRT · Subtitle to Text · All tools