Is DataAnnotation Legit? Our Experience + Tips for Getting Started! (How we make money and travel)

Hey there, we're Mike and Heather. We

are a remote travel couple who quit our jobs at the beginning of the pandemic to find a way to work remotely and be able to travel more, which is a huge passion of ours. So, we are going to talk to you

of ours. So, we are going to talk to you today about data annotation. This isn't

a sales pitch. We're not sponsored. We

have just gotten questions about the types of things that we do in order to make money while we're traveling and out there on the road.

We don't have backgrounds in coding or computer science or anything really to do with computers themselves. We

understand how to use a computer, but that's about the extent of it. So, you

don't need a computer central background in order to do a job like this.

I guess we can start though with what is data annotation and how we kind of found out about it.

We found out about it by doing a deep dive on the internet because we were desperate to find something truly remote. So, I believe we first came

remote. So, I believe we first came across a Buzzfeed article about it and then we did a deep dive through Reddit to see if this was a scam. I think it was r/beermoney on Reddit, which is one of the

subreddits that sort of covers gig work or different ways that you can put a little bit of extra money in your pocket. And then there is actually a

pocket. And then there is actually a data annotation subreddit that you can check out as well for more direct conversations about the platform by people who work on it. Some of it will

be confusing because there are things you can and can't talk about because of the confidentiality with the models that you're working with, but go check those out if you are interested in more

specific things. That really helped us

specific things. That really helped us kind of clarify that this was something that we would at least invest time in getting started and seeing if it was legit.

Data annotation is essentially a platform where you have people log on and train AI or artificial intelligence, which sounds a lot more intimidating than it actually is. You're basically

creating the data that large language models use to be able to communicate with people. And we're not allowed to

with people. And we're not allowed to say the models that we work with, but if you're familiar with any type of AI, you probably know the ones that we help enhance their data.

Once you're on data annotation, you have different types of jobs depending on what the coders are looking for to have the models trained in. That can be chatting, it can be image recognition,

it can be video recognition, it can be creative writing, any of those things.

And you're just there to help provide examples for the artificial intelligence to learn from. Probably the most common thing that you go on and do is chat with an AI and then evaluate its response,

correct it where it needs to be corrected, and then continue the conversation. So, what that looks like

conversation. So, what that looks like is if you were to go on to the internet now, type in your favorite AI that you can go to to ask questions of, type that in, it gives you a response. That's

basically it. Just

you get a rubric and you have to say like how well it did certain things.

Yeah. Because in the early stages, these models aren't always super great at providing an answer.

Yeah. They make a lot of mistakes.

Sometimes they're rambly. Sometimes they

get things wrong. And we're just checking to make sure that they're correct. So when they do go to the

correct. So when they do go to the public, there's not any really big issues with what they say. So that's

probably the most common thing that we do.

Yeah. It's probably 80% of the jobs that we get are like the talking with AI ones where you can Yeah.

chat with a bot and grade it along a rubric, ask random questions.

Yeah. Sometimes it doesn't feel like work because then you get to just ask it questions on like your interests or hobbies or whatever, plan a trip or two, you know? So,

but another one is image labeling where you might ask it to create images and make sure that they come back the proper way or label things on an image so that way they can start picking it up like

circling glasses so that way every image that it knows what glasses are or something. And then text annotation. So

something. And then text annotation. So

like identifying intentions, grammar errors, uh emojis because the systems themselves are only as smart as what they're they're taught. Rhyming is a good example. So if you would

good example. So if you would Yeah. They're really bad at that.

Yeah. They're really bad at that.

Yeah. Because when you think of rhyming, you think of being able to hear the sound that it makes, but computers can't hear sound. They can just read the word.

hear sound. They can just read the word.

So they might think that lead rhymes with red, which is a spelled the same, has the same ending, but depending on how it's said and the the sort of

intention of the word doesn't rhyme correctly. Mhm.

correctly. Mhm.

So it's things like that of making sure that it understands how language is supposed to work in in certain situations.

Yeah. So audio transcriptions where you write down what you hear off videos. Uh

that's a really popular one. So you'll

watch videos and then write down what's happening because it can't hear but then you can train it to hear.

And it all goes way above our heads. We

just read the instructions for the different tasks.

We don't necessarily know how it works.

But a good example is if you were to watch this video with subtitles. When we

upload the video, YouTube has an algorithm that listens to what we say, puts it there, and then you can obviously correct it to make sure that there's no issues, but but the computer does it. Yeah.

Yeah. It'll it'll immediately have something for you to work off of.

And then video annotation. So tracking

motions of objects and people where you might watch a video and have to explain what's happening in it because again computers can't see but if they learn how to see again above over our head but

describing videos and that's kind of like the gist of all the different projects that we've worked on again we don't want to dive too much into them because some of them you're not really allowed to say but these are kind of key

things that we've seen yeah even on other platforms that we've played around with these are kind of like the main categories of like chatting uh helping transcribe text, video,

images, and sound. So, not specific to data annotation, but a lot of that you also do there.

And I guess before we go too far past that, data annotation is the one that we spend the most time on, but there are other platforms that do similar things.

This one has just been the best in terms of pay, in terms of jobs, the the ease of use on the platform.

Yeah, this is just our favorite, but there's other options. Our second

favorite is probably Outlier. It's

easier to get approved for that one, but the pay is less. Now, we'll go through some uh questions that we've been getting, just real quick ones before we dive into like the nitty-gritty details

of the entire platform itself. One, is

it legit or is it a scam? It is not a scam. You can make real money off of it.

scam. You can make real money off of it.

We've been working on the platform for a year. So, we had a a full-time job last

year. So, we had a a full-time job last year and then worked data annotation as a part-time for an entire year cuz we're like, if we can prove to ourselves that this is fine for a year, then we can

quit our full-time jobs and really just dive into this. And thankfully, it we were making enough money off of data annotation to kind of fund the tribal ideas that we have coming up for the

next two years. So, long story short, it is not a scam. You can make really real money on it. The second question that we get asked a lot is, do I need to know coding or programming? language? No, you

don't. But there are definitely a lot more jobs available to you on the platform if you do. So, if you do know them, that's fantastic. You'll make even more money and get access to higher

paying jobs and more of them that us normal non-coding people have access to.

But there's still going to be plenty of jobs for you on the platform if you don't know any of that like like us. Can

you really do this job full-time?

Yes. I would say the caveat is you as an individual. If you're willing to sit in

individual. If you're willing to sit in front of your computer and do somewhat repetitive tasks, creative thinking tasks, attention to detail tasks, and you can do that 5 days a week, 4 days a

week, whatever your financial goal or plan is, you can 100% make this a full-time job.

It's just that once you get used to having the flexibility, it's hard to get yourself to log back on and and uh really come up with a schedule. But yes,

with some discipline, you can definitely make this a full-time job. Next one. Do

I need to speak perfect English? You do

need to be able to speak English, but if English is your second language, that's okay. It's just the primary language

okay. It's just the primary language that's used on this platform is English.

So, a lot of the the nuances you'll need to be able to understand because grammar is important, syntax is important, language use is important.

You don't need to be like a perfect English major cuz I definitely was not.

Yeah. And if you do know other languages, you'll have access to a whole set of other jobs because there's a lot of translation ones available to you.

And yeah, so that just opens up a whole door of opportunities for you on a platform like this. Next, will I get paid on time?

You work in what's basically gig work.

So if you think of Uber or Lyft or something like that where you kind of turn on your app, say I'm on the clock and I'm working, I'm ready to pick up passengers. Mhm.

passengers. Mhm.

This is a lot like that where you log in and you log the time that you're directly working. You enter that and you

directly working. You enter that and you get paid for what you do. It's not a 9 to5 thing. There's no minimum or maximum

to5 thing. There's no minimum or maximum amount of time that you can work in a day other than the amount of hours in a day. You do the work, you submit your

day. You do the work, you submit your time, and then 7 days later you have access to that money. There's only one nuance to it, which is you can only

withdraw once every 3 days, but other than that, if you say want to pull your money every Friday, you can do that. If

you want to pull your money every 3 days for each 3day kind of segment, you can do that. Those are the only parameters

do that. Those are the only parameters and the money is always there. It's

always gone from data annotation to PayPal, PayPal to our bank account.

We've never had any issues. We pay our bills with it.

We do all of our stuff, buy groceries, whatever. The money has always been

whatever. The money has always been there.

Yeah. Which kind of leads us into our next one of like how do you even get onto data annotation tips and tricks for applying and getting approved. The most

important thing you need to know is that you only really get to take this assessment one time. When you apply, you link up your PayPal. So even if you hypothetically were to try to pass on a

different email, as soon as you put in your PayPal, it's going to flag you and you're not going to be able to get approved. So, you just want to make sure

approved. So, you just want to make sure that when you go to take this, you set aside a couple hours, which seems like a long time, but the whole thing usually takes about 5 hours to complete. Like,

I would say 3 to 5 hours minimum.

I took 5 hours cuz I read through everything multiple times. And that's

just the type of test taker I am, but if you're a little bit speedy and more confident, you could probably do it in the time frame that Mike did. All of the tasks on the test are going to be very similar to what you see actually for

jobs. I would say that the test itself

jobs. I would say that the test itself is harder than the actual jobs that we do though. Yeah. So, I think they do

do though. Yeah. So, I think they do make it harder than what you'd actually be doing.

Everything you need to know will be explained in the assessment in the qualification and you just kind of have to make sure you're doing what is asked of you. So, it's sort of two parts. The

of you. So, it's sort of two parts. The

first one is a general assessment that's going to just see if they want to let you on the platform. The second one is a qualification which you also have to pass with more specific kind of

examples of the types of things you would be doing on the platform. So it's

step one assessment, step two qualification, and step three onboarding.

So if you go to take the application and you answer a bunch of questions and once it's done, you immediately don't see another assessment pop up.

Unfortunately, that means you failed. It

might say like keep checking your email or maybe in a month or something like that we'll have more space or send you the next step. But if a second part doesn't

next step. But if a second part doesn't immediately open up that says like I think coding and like normal or something coding or non-coding I think is the yeah the things then unfortunately you

didn't pass the first part after the second part that you take it whether you are coding and take the coding path or the non-coding path after that we can only speak to the non-coding one because

we don't know the coding but if you pass that in about 10 days 10 days to two weeks but honestly everybody that we've known have gotten it like about 10 days.

It took us both exactly 10 days to hear back and say yes. But at that 10day mark, you should have an email saying that you have gotten approved if you are approved. If you're not approved,

approved. If you're not approved, unfortunately, you never hear back.

Yeah.

The only reason why we're saying this is because we were really like anxious of like, oh, didn't we didn't did we get on the platform or whatever. So to kind of give you a rough timeline, this is nothing official from data annotation,

just our personal experience and what we've seen other people who've attempted to or have gotten on as well. So take

all of this with a grain of salt. This

is just our personal experience and we're not speaking on behalf of the data company at all. But what do you need to apply? Computer, internet, uh you don't

apply? Computer, internet, uh you don't need a resume, you don't need a degree.

Um the the only thing that you'll need is if you get approved is a valid ID and a PayPal account to link your money to.

Right now they're only accepting people in the United States, United Kingdom, Canada, Ireland, Australia, and New Zealand. But they do say that if you

Zealand. But they do say that if you apply from another country, those users will be placed on a waiting list and notified when they can begin working when this platform is approved in those countries. So, just a little bit of a

countries. So, just a little bit of a caveat there that anybody from those countries could work, but you can work in other countries than those. We were doing this job in the

those. We were doing this job in the Dominican Republic. I think that there's

Dominican Republic. I think that there's not really a ton of restrictions on where you can actually work from.

There's a way to email the administrative staff to say, "Hey, we're going to be working, you know, in this country for this month until this month." They're like, "Okay, cool." So,

month." They're like, "Okay, cool." So,

as long as you give them a heads up, they seem to be pretty okay with it.

They just wanted to make sure that your laptop or something didn't get stolen and you were out there. But yeah, you can work from any country. Those are

just the ones that they accept people to work for them. That's probably rambly, but hopefully that makes sense. The next

thing that we'll cover is what happens once you get approved.

On the dashboard, they're going to have I think it was four or so onboarding projects for you to work on. That's

basically getting you comfortable with the user interface here. making sure

your account is set up correctly, making sure that your PayPal account is set up correctly, making sure you know how to log your time, and then once you've completed that, you're able to then dive

in and start working on projects.

And then every once in a while, you'll have qualifications that come up. These

are new assessments to test to see if you have the aptitude or the skills required to work on new projects. So,

those are something that you always want to make sure that you take care of and get out of the way.

Whenever you see a qualification, they're not mandatory to do by any means. But the more qualifications you

means. But the more qualifications you take and get approved for, the more jobs that will appear on your dashboard that you have access to. If you don't pass the qualification, you don't get penalized, you don't get kicked off of

the platform, they just don't put you on that project. So, there's no harm in

that project. So, there's no harm in taking the qualification. I would say anytime you see one pop up, always do it. The ways that you get access to

it. The ways that you get access to better paying projects is by doing good work on the projects that you're assigned to and by doing and passing

qualifications for higher paying projects. So when you first get onto the

projects. So when you first get onto the platform, $20 an hour is about the minimum that you'll ever see for most of your work projects, which is really

great. We both start off at $20 an hour

great. We both start off at $20 an hour jobs and everybody else that uh we know who has also gotten on the platform, $20 was $20 is the lowest that we've ever seen anybody start with, which is why we

prefer data annotation because a lot of the other ones start you off at $15 an hour, which is still fine. I mean,

that's minimum wage in New York where we're at, but $20 an hour is obviously better than $15 an hour. And now that we've been on for a year and a half, we

consistently get 28 to $30 an hour. We

were even on a project for a couple of months that was $35 an hour and then we had one for a couple of weeks at $40 an hour. So, the way that you get paid is

hour. So, the way that you get paid is that you have to submit your own hours.

So, it's kind of like the honor system, but you definitely don't want to take advantage of that. We both have our phones going with our timer that will start as soon as we open it up and read through the instructions because you do

get paid for reading through the instructions once you pass the qualification. And then any test that we

qualification. And then any test that we do, we just let the timer run. That

being said, again, you don't want to abuse it. So, if you do have to get up

abuse it. So, if you do have to get up and make yourself a snack or lunch or go to the bathroom, it's not like a 9 to5 where you'd still get paid for that half hour or 45 minutes that you might be away from your desk. Pause it uh and

then pick it back up when you're truly working because you only get paid for the the times that you're working. And

you definitely want to make sure that you're not abusing that part of the platform. Yeah. Again, this is just

platform. Yeah. Again, this is just what's worked for us on the platform and how we've been able to stay on for so long. not speaking on behalf of data

long. not speaking on behalf of data annotation, just our personal experience.

Yeah, I guess now we'll dive into the pros and cons of data annotation. We'll start

with the cons and then end with the good stuff.

Probably the first thing for a con is the repetitiveness of the work. It is

something that you don't necessarily prepare for when you start because everything is sort of new and exciting at the beginning. But once you've been on a project for months,

months or when you've been doing the same kind of thing for months or even during the day when you've been doing the same thing for 6 to 8 hours, it can be a little bit tedious feeling. There's

different projects and there's nothing to say you can't put a pause on this one and go work and do a different one which is actually encouraged so you don't burn out. But that's definitely something

out. But that's definitely something that is difficult. Sometimes it can feel like it drags on.

At about the 2hour mark we start getting a little antsy. So we'll usually sit down for an hour and a half to two hours and then take like an hour break and then we go back and then sit down for another hour and a half to two hours then take another hour break. So

depending on how many hours that you want to get in a day, we found breaking it up into like two hour segments, then making yourself a snack or watching some TV and coming back to it is what works best for us or pool time when we were in

the Dominican Republic. Another con is that this does require solid internet connection. So it doesn't need to be the

connection. So it doesn't need to be the best, but it definitely needs to be decent.

You definitely don't want to be somewhere with spotty internet, be halfway through a project, and have it drop out and then have all of that time wasted. Uh that may have also happened

wasted. Uh that may have also happened while we were in the Dominican Republic in the first Airbnb that we were at, we lost power for a couple hours and then unfortunately because we didn't submit our work yet or didn't get to the end of

the project that we were doing, we couldn't get credit for any of it. So

cuz it was something that it was in the process of working. So if the model that you're working with can't finish the thought and you can't submit any work, that's a rare case and I wouldn't say it's literally only happened once.

Yeah. But it's better again to be safe than sorry. Check the internet. It

than sorry. Check the internet. It

doesn't have to be ultra fast, but it has to be stable.

Yeah. Next one. Tax is not taken out.

So, you have to be responsible and set money aside. I mean, that might not be a

money aside. I mean, that might not be a con for somebody, but it is for us because now we've never had to do like the independent contractor side of stuff. So, yeah, we just had to learn

stuff. So, yeah, we just had to learn how to do our taxes differently this past year. And it's definitely something

past year. And it's definitely something to be aware of upfront because if you aren't preparing for it, we've seen a lot of people online talking about it and it can be a big shock of going through the the process of submitting

your taxes and then finding out you owe a certain amount because you will because the taxes haven't been taken out. So definitely talk to your tax

out. So definitely talk to your tax person. If you don't have a tax person,

person. If you don't have a tax person, just make sure that you're putting money aside so that when you get to tax time, you know that you'll have money to to cover what should have been or what

would have been getting taken out if you worked your normal 9 to 5 job.

Another thing that is important to note is this is a job that you really have to be very selfsufficient and self-reliant.

There's not a lot of communication.

Oh gosh. No. For each project, you will be given a set of instructions which should theoretically cover all of the things that you need to know for the work that you're going to be doing. Past

that, there's a comment section that you can get in touch with admins, but otherwise, you're really not getting any feedback. You're not getting supervision

feedback. You're not getting supervision on your tasks. You're just working. And

if your work is reviewed, they'll let you know if stuff is really bad. But

other times you'll just be taken off projects. So you really have to just be

projects. So you really have to just be comfortable with sitting down, doing your work, doing it at a high level and that's it. You don't talk to other people. There's no like water cooler for

people. There's no like water cooler for you to go to.

Technically, there is on the Reddit page, which is why we go to it cuz then you can like talk with other people. But

yeah, if you're somebody who needs like a community at work or uh feel like you need constructive criticism or to be able to talk to your boss guidance, this is not the job for you because you log

on and you won't hear from anybody, which is good. No news is good news. If

you're not hearing from anybody, that means you haven't done anything wrong. I

had an admin reach out to me one time because one of my things got reviewed and I didn't do something right. I think

I have as well like and those are over a year and a half I've heard from an admin one time and those ones were both on higher paying projects. I think it was on like

paying projects. I think it was on like the qualification or on the introductory part of like maybe that you pass the qualification, they give you a small batch to work on to make sure you can do it and then you get feedback on that

and then once you correct the feedback you can go and do the higher paying one cuz I got access to the the jobs after she just wanted me to know that this is how I should have interpreted that.

Yeah, it needs to be this way because the model needs correct examples to train on. But if you're not hearing from

on. But if you're not hearing from somebody, that just means you're doing everything right because which is wild though because there's no one saying like, "Oh, hey, you're not getting fired. You're not getting kicked

getting fired. You're not getting kicked off the platform."

Yeah.

Which was a big thing that we were scared of the first two months doing it was, "Oh my gosh, are we is this correct? Are are we doing this the right

correct? Are are we doing this the right way? You just kind of have to make sure

way? You just kind of have to make sure you're following the directions to the best of your ability and you'll be fine." Probably the biggest con of data

fine." Probably the biggest con of data annotation is that you're kind of at the mercy of the projects that are on your dashboard. And in the entire year and a

dashboard. And in the entire year and a half that we've been doing this, there's only ever been one month, it was last August, that when you go on Reddit, people have called it the drought.

Yeah.

Uh that means that projects were very limited. Some people even saw no

limited. Some people even saw no projects. And I don't know if that's

projects. And I don't know if that's like a yearly thing. Uh we're not in August yet, so we'll see if uh if next month there's another drought. But yeah,

every other month we've had plenty of projects, more than we could even touch in on or like even look at. But yes,

there is the possibility that one day everything on your dashboard will just be gone. And because there is no

be gone. And because there is no communication, you don't know, did I get fired? Uh is everybody experiencing

fired? Uh is everybody experiencing that?

When things will be coming back.

Yeah. which is the benefit of going on Reddit to see like, oh, is everybody else's dash is empty or is this a me thing?

You'll never know.

Yeah. But in September, we had plenty of jobs again. So, there is the possibility

jobs again. So, there is the possibility of what they call droughts.

So, earlier we had said you can 100% make this a full-time job. The question,

and this would be an individual question, is should you?

Mhm. Heather and I do not really have a lot of responsibilities or anything depending on us. We don't have pets. We

rented an apartment right now. We're

staying out of my parents house before we go back on the road. We don't have a car payment. We don't have a lot of

car payment. We don't have a lot of bills. We don't have kids. So, for us,

bills. We don't have kids. So, for us, if we set aside some money and there is a period of a drought, it's not really the end of the world.

That's a very good position for us to be in. That's why this works so well even

in. That's why this works so well even with the cons that we talked about.

But if you are someone who has responsibilities, if you have things that you have to make sure that you have consistent money for every week, I would definitely

don't quit your day job yet. Try it.

Make sure that this is something that you're comfortable with because you can make it a full-time job, but should you is definitely a real question. And

that's why we did a year's worth of work to do it. And even during the drought, we were able to sort of nab projects as they came up. Yeah. And still meet our goals.

There was never a day that we didn't have any jobs. It was just clicking the refresh button a million times throughout the day and then grasping at anything that was coming in when we

usually have 15 to 20 jobs on our dashboard at all times. for it to go blank and having to click refresh and uh nabbing them up before the thousands of other people who are probably doing the

same thing. Yeah. Was wasn't ideal. But

same thing. Yeah. Was wasn't ideal. But

we were able to make our money goal at that time cuz we had said like, "Oh, every day we want to make x amount of dollars and we were still meeting them."

But it was very difficult.

Yeah. Whereas if normally you could do it in a couple hours, you were getting maybe one task here, one task there over the course of a couple of weeks. So, it

was a stressful time cuz we didn't know what was happening. No one else knew what was happening. Eventually, we got to the other side of it and our dashboards filled back up with projects and we haven't had anything like that

happen since.

But, it's definitely something to be aware of that that could happen and that there are no guarantees with data annotation.

What's there today might not be there tomorrow. So if you have

tomorrow. So if you have responsibilities outside of yourself, it is something that you have to think carefully about before putting all of your eggs into that data annotation basket, which is why we have Outlier as

another platform that we've been approved for and other things like that.

Thankfully, knock on wood, there hasn't been a drought since and there's plenty of jobs hunter dashboard going into August now. But yeah, that that's

August now. But yeah, that that's probably the biggest con. So, now we'll move into the pros because we definitely want to end this on a positive. So,

obviously having a flexible work schedule, truly flexible, like you can log on for 5 10 minutes, clock your time and get paid. You can sit on for several hours. You can log on at 2 in the

hours. You can log on at 2 in the morning, 3 in the morning, which we definitely have done plenty of times.

You can start 9 to5, you can set your own schedule. It doesn't matter. When

own schedule. It doesn't matter. When

you log on, you get paid. And whenever

you log off, there's no minimum or maximum. So

maximum. So yeah, whatever you want to get paid for, you'll get paid for.

Yeah. And the really cool thing with that is say you have a big purchase coming up, there's a new something that you want to buy, you can work extra compared to what you normally would to

earn extra money or if you don't really need as much this week, you don't have to work more than uh a little bit. I would suggest working consistently to make sure that you're you're still on the platform and doing

that. log on a couple times a week if

that. log on a couple times a week if even if you are in your full-time job and are wanting this as a part-time thing.

Just do a couple hours to show that you're still on the platform and still willing to to work because the more you do, the more you get rewarded for higher paying jobs.

Yeah. Again, it's truly work from anywhere, which is a huge pro. Uh

there's once you get access to your profile under your profile like little icon, there's a spot where you can message like the admin staff that we mentioned earlier and there's a drop

down specifically for traveling. because

this is such a popular thing for digital nomads to do where you just let them know the country that you're going to be in and then they mark your account and then you're allowed to work from there.

That was obviously probably the biggest pro for us is that we can work from anywhere at any time.

And then the third pro that we have, we also listed as a con. So, I guess it kind of just depends on your work style and personality, but no boss, no meetings, you don't really have to deal

with all like the workplace drama or anything like that. No, no um office politics. You just log on to your job

politics. You just log on to your job and log off and don't have to worry about it once you've done that.

Yeah.

And then another one, another pro is that the tasks are really simple and some of them are even fun, especially the chatting with chat bots. It's

interesting and fun work, which I think is definitely a pro. The biggest pro is that it's a good pay that you start off at $20 an hour and now we're close to

$30 on average. So, I mean, I think that's pretty good for a job that you don't need a degree for or any real qualifications other than passing a basic English and reading comprehension

test.

Full disclosure, we totally forgot to film an outro the other day. So, it is now the next day and we are going to just start wrapping this up.

We're going to cover a few tips for success. These are things that we found

success. These are things that we found have been helpful for us and have allowed us to be successful on the platform. And I say successful on the

platform. And I say successful on the platform really just meaning we've made money and haven't got kicked off.

I'll count that as a success. Basically,

it's a recap of everything that we just talked about. So, number one, take all

talked about. So, number one, take all the qualifications that appear on your dashboard, that's to continue to open up new ones and new opportunities for you to make sure that it's never in the drought phase. Uh number two, try to

drought phase. Uh number two, try to work a few hours each week again so that the more that you put in, the more that you get out. So, if you want your dash to be full of opportunities, make sure that you keep working on those

opportunities. And bouncing off the

opportunities. And bouncing off the working thing, make sure that you're working in blocks to avoid burnout. So,

2our chunks, take a little break, and then come back to it. Cuz I don't even know if it's possible to sit down and work 8 hours straight at this without feeling the burnout.

Yeah, absolutely. And do different projects. So, take uh the morning, work

projects. So, take uh the morning, work on one, do something else for the afternoon. And that helps diversify the

afternoon. And that helps diversify the ones that you work on. and maybe you see more projects in that sort of category in the future. This next one's not a mandatory, but just as a little bit of a

sanity thing, check out the online communities. If you get into data

communities. If you get into data annotation, or if you're even thinking about it, go check out the the subreddit for it. Once you're on, get on the Slack

for it. Once you're on, get on the Slack channel, read through the comments on the different projects. It just gives you a little bit of a sense of things that people might be having trouble with or lets you know that other people are

going through similar things to you. So

that's also very helpful.

Yes. Our last tip would be to have a job while you're working this before you transition to this. Get accepted. Get a

feel for it. See if it's even something that you like doing. Basically, we're

telling you don't go out and quit your jobs just yet. Play around with it a little bit and see if it's something that you like. That's what we did. And

we found that we love this type of work more than anything because of the flexibility that it gives. Which is why we're making this video because it almost sounds too good to be true. And

again, we're not sponsored by the annotation at all. I'm not speaking on behalf of them at all. This is just our experience. But when we were looking for

experience. But when we were looking for the perfect remote job, if we were to write down exactly what we wanted to do on a piece of paper, this is it. Can log

in at any time, can log in from anywhere, no set hours, no boss, no co-workers, like you just log in and make money. Cherry on top of the entire

make money. Cherry on top of the entire remote working experience.

If you're interested in this or you have any other questions, leave them in the comments below. We'll try and answer to

comments below. We'll try and answer to the best of our ability. Like we said, we can't explain everything that goes on on the platform because there's confidentiality things that you have to navigate and we like this job way too

much to risk losing it. But we'll do our best to sort of fill in the gaps where we can. If you made it this far, thanks

we can. If you made it this far, thanks so much for watching. We really

appreciate it and we appreciate all the interest in us and how we manage to do what we're trying to do.

So, if you found this helpful and want to see more videos like this, leave a comment down below. We'll try to make as many as we're allowed to do without divulging too much data annotation secrets. Or if you want to follow along

secrets. Or if you want to follow along on our digital nomad journey as we do work data annotation around the world, hit that subscribe button.

Yeah, we're bad at self promoting.

But thank you all for watching. We

really appreciate it and we'll see you later. I'm living my best life. I wake

later. I'm living my best life. I wake

up with the sunrise. It does not look nothing like I thought that it would, but I've been getting my steps in. And I

sleep with my best friend. It's the best that it has been in a long time. I'm

living my best.