We met with Shai at HyperWellbeing event here, in Silicon Valley – in Computer History Museum, actually – where gathered leading players in the emerging consumer technology industry of Wellness-as-a-Service. This fusion of wearable, mobile and big data technologies shapes the third wave of computing revolution – “intimate” one – that takes on the baton from mobile and personal computing. To quote Martin Geddes, “PC was ‘hyper productivity’. Smartphone is ‘hyper presence’. Wearables will be ‘hyper wellbeing'”.
Suggestic is not Shai’s first startup, but seems to be the most hi-tech and consumer-oriented so far. Even in the crowd of bleeding-edge wellness apps and biosensors, it stands out as system addressing very down-to-earth need by employing moonshot technologies.
Building B2C AI-powered business is tricky, as this lucrative space is traditionally dominated by heavy-lifters like Google and Apple. Nevertheless, Suggestic is progressing just fine.
I’ve asked Shai to share his methods, and he kindly agreed. Tune in to learn, among other things, how to…
- find a niche worth dominating
- zero-in on your value proposition
- discover what “personalization” actually mean
- design and utilize user feedback, active and passive
- have healthy dose of gamification
- introduce augmented reality
- get an edge by creating API from the very beginning
- and much more
We expect that Suggestic and other solutions in other areas get there at some point. At the point that we can really, really, really give you a good suggestion, not only something that’s healthy for you but something you’re going to like, something that’s available, something that really reduces your mental effort, gives you more energy, makes you feel better and impacts your long-term health for the better. — Shai Rosen
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Hello everyone. Misha is here, and today I’m joined by Shai Rosen, founder and Chief Marketing Officer of the Suggestic. Suggestic is a personalized nutrition coach, the virtual coach which specializes on the nutrition part. But I guess I would let Shai to describe what it is. Shai, welcome.
Thank you Misha. Thank you for having me. In very few words what Suggestic is – at least in the part facing the consumer – an ultimate artificial intelligence powered nutrition coach. What we did is – what we’re still doing is – basically trying to extend or augment the work of whatever a nutrition coach would do with a client, with a patient and make it automated, in your pocket, being available, being there to help the user, to help you 24/7. The goal behind that is helping the people adhere in the best way possible to their diets.
Yep, and we actually met with Shai on this inaugural event of the Hyper Wellbeing movement, which is Wellness-as-a-Service, and there we saw a whole bunch of companies which are trying to get better and better sense of how our bodies and minds are doing. What I think is setting Suggestic aside is not just measuring but actually providing advice on the data. It’s closing the loop of learning about you and putting this knowledge at service to you. Tell me just how you came about this idea. Are you targeting any particular group of people at this point?
The truth is it’s been an ongoing process with many iterations. It’s interesting that you mentioned that because one of the beginning initial principles that we thought about in the process of figuring out exactly what we’re trying to do, what we wanted to do was around that exactly. The actual way we started is I met with my current co-founder and CEO Victor Chapela. You know Victor gave a presentation on that at the Hyper Wellbeing event. We happened to coincide in that we both sold the previous companies and we got together and we said, “Okay, what do we want to do next?” We agreed that we wanted to work together. We had all this experience in consumer-focused products and artificial intelligence and advanced technology and we wanted to stay in that area. What we did is we sat down and we started thinking about what are the problems that we’re excited about and would like to tackle this time? Victor, for example, was coming from the financial sector. I was coming from the more eCommerce or commercial sector. We both decided we wanted to do something with a bigger social impact.
We thought about two big points. We thought about the education at some point and we thought about health. We ended up deciding to go for health, and the reason behind that was it was a common ground that we both have. We saw a urgent need. Obviously there’s an urgent need in education as well but we saw an urgent need in healthcare and we figured out that we had better chance since our background fit better with the possibilities of different things we could do in the health industry. The first thing that we did is we spent a couple of weeks actually trying to learn everything we could about the industry. We’re two completely outsiders to an industry, even though I’m a med school dropout. It was just a couple of weeks that I was in med school.
In this process of trying to figure out what’s broken with the industry we came up to a bunch of conclusions and we realized one of the big problems is we are accumulating a lot, a lot of data and some of it great quality data, some of it not so much. We found out that obviously there exists an issue with the quality of the data, but we saw a bigger issue with actually what to do with that data and the suggestions and the recommendations, the intelligence around that data. It happens that I personally, for example, I think on a day to day basis I probably measure, I don’t know, 20 or 30 different data points. There’s sleep and work and heat and temperature and air quality. I have all this data and the truth I don’t do much with it. It just sits there in different systems that ,by the way, don’t talk to each other. That was one big thing.
The other big thing that we found out that was interesting for us was the lack of personalization in general in everything around health. It is lack of personalization tracing back all the way from the way we do studies and research to everything that happens after that, how we recommend medication or how to recommend diets. We both are particularly interested and fans of nutrition. We decided it was an interesting and powerful way to enter into the health market from an unregulated perspective or less regulated perspective. As we started advancing through the project and talking to people about it and raising capital and actually started building a product, we realized it’s not only an entry point, it’s the base of health. There always a lot we could talk about, specifically the power of nutrition, but I think we’re targeting a different concept right now.
In that process we founded Suggestic slightly over two years ago and, as I said before, that there were a couple of weeks of doing research and trying to understand the market and talking to a lot of people from just anyone in the street to doctors to people in the healthcare industry in hospitals and dietitians, and coaches, and fitness instructors and people that own gyms, and people in the insurance industry and so on, and tried to gather as much information as we could. To really paint a picture of everything there.
In the first few weeks of the company what we did we really focused on building our algorithms. We started from the back to the front. We started figuring out what’s the technology, what’s the brains we need to build behind the operation? We started doing that and then we actually had when we started on the front side basically trying to figure out … We had all these capabilities as a technology player, but now how do we put them in front of the consumer in a way that makes sense to them? What you see now is Suggestic – if you download the app – because the app, the whole idea of the chat bot, of this coach came out after let’s say one and a half year of iterations. We had a completely different product before, based more on cards and images, something that looks more like a Google Now. The problem is that a lot of the data that we use in the back doesn’t have images for example.
The product itself looked great on the mock-ups and then when we starting building it and realized the data doesn’t match the product. We went through a process of different iterations to find that out, and obviously it happened together with this … Everyone started talking about bots and conversational interfaces and so on. We jumped aboard that ship. For now.
Nowadays, then people talk about intelligent assistance, they usually just mean something which understands natural language, right? The response usually is not that intelligent at all, it’s just the fact that you can talk to the thing and it talks back to you somehow makes the impression of talking to an intelligent entity, which is usually not the case. Choosing to use natural language does not make intelligent assistance yet. It’s just intelligent interface to the assistance that you provide. I think that makes perfect sense. I’m curious you mentioned number of iterations. Was that done based on a feedback from users?
Yeah, absolutely. Absolutely. We did all these iterations based on different kinds of feedback. The feedback came from all these different sources and everything ranging from potential users, potential partners, investors, ourselves. Obviously when you fall in love with a, not with a solution, but with a problem that you want to solve and then you come up with different solutions and you start exploring those solutions and trying to figure out which one of those solutions really better match the needs of the market. What we did is we’re trying to explore moving forward which solutions had the best fit with the problem, again, in different types of people. It’s obvious when you start finding out is that different version of the solution or different solutions for the same problem feel different for different type of people, right? On the one side the solution for a nutrition coach is very different from how the solution feels to a doctor, to an MD, and how it feels different for a person that’s very into nutrition to someone that’s not totally out of nutrition. It feels very different from someone that’s battling or dealing with a chronic condition versus someone that’s just trying to lose weight or someone that’s basically building a better version of themselves by deciding to, I don’t know, go vegan or go vegetarian or go Paleo or whatever it is that they feel it’s better for them.
What we started finding out, and again it happens in every industry and in every product. The same problem happens to a lot of people, but people receive it in very different forms. What we started to do is … well, there are a few things. You start mapping out which groups of people have the best fit with the solution you’re proposing, and obviously how big is that group of people. If you have a great solution that is only going to work for ten people, that’s interesting obviously. It has some interest, but then maybe it’s not interesting from the commercial side. It may have value to have a very simple solution that solves a piece of the problem for a lot of people but then it’s not relevant enough or not magnitudes of order greater than whatever is out there, so that people say, “Yeah, it’s nice.”, but it definitely doesn’t solve the key problem in a way that they would drop everything that they’re using or that they’re doing right now and move to your solution.
As we went through these iterations, we found a middle ground. I think one of the best things that we did is that process of, again, mapping out these very different groups of people, niches, groupations of people with a similar problem and then try to figure out who has the biggest problem with less solutions out there and how could we help them. It’s interesting what we found out it our case – we found out that diabetes can be very managed and even reverted in some cases, as research suggests, with nutrition. There’s obviously a big need, a market with many solutions around other stuff, around logging, or around a reminder to take pills, or about devices to measure your glucose. But almost nothing around actually giving suggestions of what people should eat.
And, at the same time, there is so much information out there, so confusing, and so much very amazing content, and so much shallow and researched suggestions. That’s where we started. We started building a tool for people with diabetes. What happened is after we started building the product and talking to more people, and now specifically focused on that, we started to fill in this problem/solution picture. It’s a natural pull from the market.
I’m curious, where did you get the people you spoke to? You said you came completely outside of the industry, so I guess you didn’t have any kind of previous lists or anything like that. How did you go about acquiring these early adopters?
Yeah, that’s a good questions. Basically doing a little bit of everything. It’s the digital version of knocking on doors. Engaging with people on different social networks. There’s groups of people from all kind of diets and interests on Facebook, on Twitter, Reddit, different communities. There are obviously communities and websites that are specializing in different things. There are meet-ups like Bay Area Type 2 Diabetic group that’s also interested in the wearable technology. There’s all these groups of people and obviously it’s a lot of work. I can say – and I’ve seen in our case and I’ve seen it in other people – that it gets … we get lazy about it, or it gets annoying to keep on talking to people about the same problem over and over, but then obviously it’s amazingly rewarding.
How did you narrow down on the Diabetes Type 2 people? Did you have some kind of market segmentation? Did people voluntarily share this information about themselves? How did you come up with the profiles, so to speak, with the persona for your first version?
Yeah, so there’s two things. What happens first is that in particular type 2 diabetes is a problem we know very well. It happens that both Victor, my co-founder’s father, and my father are both type 2 diabetics, well were, they both passed away relatively recently. Victor, my co-founder, was diagnosed at some point with pre-diabetes. We understood how it works in general. Obviously through them, through the doctors and by living with them for so many years gave us … – obviously, this is very different than living it, I can’t argue with that – but it gave us a general idea of how to talk to people that are going through that process. That exactly helped us to build, not actually one persona but a few personas. That obviously we started talking to people and to friends.
What happens, what I’ve seen a lot is when you start a new company you have these ideas, you feel that your idea is a trillion dollar idea. You don’t want to share it with anyone unless it’s a very advanced technology or something. But at that stage the best return on investment is actually start talking about it and that’s how you get feedback. What happened to us a lot in the beginning is that we were talking about it and people would say, “Oh yeah, I have a friend who has type 2 diabetes. You know, you should talk to her. You should talk to him.” That’s what we did. We did that a lot – directly talking to people and finding people through connections or through networks or groups of people. We did a lot of testing value propositions via ads. We would run ads on Facebook for people with a Type 2 Diabetes interest, and then we would present maybe 20 different value propositions and figure out what people click on.
Could you give an example of what were the value propositions? Just show us how that could be formulated.
Yeah, so for example one of the early things I remember we were interested in knowing is if people, for example, cared more about … Again, there’s two things here. Even though the underlying technology and the underlying solution don’t change much, there’s a lot about how you present it to the user. Obviously you go back and forth. You have an idea. You have a general idea for a solution that you’ve presented and then you presented many different ways. That particular way that seems to catch better in the market, so then you take then and then you back and do the product with that in mind.
In our case, for example, we had, for example, a big initial question of are people interested in the technology itself – because, you know, we are from the technical background so it was super exciting to say, “Yeah, it’s powered with artificial intelligence!”, and all those things. We were curious, do people see more value when they see the words “artificial intelligence”, or not? Are people more interested in personalization? Are people interested in genetics maybe? If you basically narrowed it down to personalization, then you have many different ways of addressing personalization. You can address just in general, say personalized diet. You can address it from, say, for some people it’s just important that they can say it’s gluten-free or vegan, and it matches my personal preferences. For some people personalization means that it uses my genetic information or my blood lab test information, my pulse and so forth. In the technology side exactly the same thing. Again, so we start building this matrix of different value propositions and then you test it out with different languages, different images, trying to figure out what draws attention.
What was the winner in your case? What value proposition resonated the most?
Not that surprisingly, the underlying technology was not particularly interesting. Obviously, if you send them by location, say, if you go to the Bay Area, then all those people are more interested in technology side, but again, in most cases it never happened that technology was more of interest, or more actionable, or more relevant for people versus, for example, personalization. Personalization was a very big issue. For example, in May we saw a lot of interest in people when we mentioned genetic testing or genetic-based nutrition. What we did was we build this list of people and we actually talked to them, and the thing that we realized is that when we say genetics it’s just a proxy for personal. It’s as personal as it gets, it’s your DNA. It’s not that people, I do but most people don’t, have their genetic sequencing and probably don’t even know much about it but the idea of DNA, of genetics, makes sense as well. “This is me. This is going to be 100% about me.” That was very relevant for one side. Definitely that was very deep.
The second thing had to do with the contextual availability of information. It’s interesting that one of the things that happened, and I’ve seen in happen a lot in Suggestic and previous companies and with other companies, that people sometimes are very excited about a concept and then when you talk to them you realize that even though they say one thing, the real need is different. It goes back to this old sentence from Henry Ford that said “if I went and asked people what they want they wanted a faster horses”, right? You see it happening. It’s quite funny. It’s very interesting. People are very interested in the personalization side and when we started talking to them they really didn’t know exactly what it meant. They knew it was important. What we realized is that it needs to feel, obviously it needs to be personalized to create good results, but also it needs to feel very personalized. That was, by the way, a big motivation for why to build the chat bots.
At the other side is this idea I was mentioning of whatever information we’re giving people needs to be relevant in their context. I don’t know if you’ve ever been with a nutrition, a dietitian or coach or something like that, but what happens, for example, and I tried it many times in different types of people in different parts of the world, and the experience is relatively similar.
What usually happens is you have an initial conversation. You define your goals. I want to lose weight. I want to gain muscle mass. I want to be healthier. Or my doctor told me I need to eat better. That happens a lot. I have no idea. I don’t want to do it. My doctor told me I should come. You get weighed and you get your height and they measure you waist or whatever it is, and then they say, “Okay, now we’re going to build a program for you and you’ll get this.” I’m exaggerating obviously a little bit. It’s not every case, but you get this copy they take out in the drawer of the desk. It’s a photocopy that’s been copied 25 times over and over. “Yeah, here’s your plan.” If you get some personalization usually it’s in the form of “Oh, you say you don’t like onions so let me scratch that down from the paper. You can replace that with, I don’t know, potatoes or whatever.” Whatever, or here it says one portion, you can have two because you are a big guy.
Then you take this paper and you say, “Okay, now what do I do with this? Half of the things that are here I don’t like.” I go to a supermarket and say, “Half of the things here they don’t sell it in my local grocery store. There’s no kale or quinoa here.” That’s a lot of cases, and then you have all these other cases. Lunch I usually eat at the work cafeteria or I eat in the restaurant next door, so what’ll I do then? I work late or work nights. What do I do now? I don’t live in California. There’s not all these healthy restaurants around me. I live in the middle of whatever. There’s only McDonald’s, so what do I do then? And so on. You start realizing that version of personalization in terms of it’s not about the program itself, it’s about telling you in this situation this is what you should do now.
For us, the best example of that is Waze, the GPS app, the navigation app. Think about it from this perspective. It’s something that I personally love this concept and when, at least in my case and I’ve seen it over and over, but when you start driving for the first time with a GPS, especially with Waze, that gets better at handling the traffic and all these things. So, you start using the Waze, and sometimes you say, “Okay, this is interesting, ” but then you get all these weird routes that you’ve never seen before in your lives and it seems like so far and then it says, “Yeah, you’re going to get there in 15 minutes and that doesn’t make any sense to you so you go against it. You say, “No, it doesn’t know anything. It’s totally wrong. I’m going to go my way.” Then it takes you half an hour and you say, “Okay, maybe I should’ve listened to it.”
You keep on testing the GPS. You keep on testing the app and then at some point you realize it’s actually much better than you at doing that job of suggesting the best route. When you reach that point what you actually do, and what most people do, kind of let go of that mental process and just completely endorse it and give it to the app. Say, “Okay, you know what Waze? I’m sitting in my car. I do this ride from the home to the office every day twice a day, sometimes more, and I know the way be heart. I don’t want to even think about it. I just click on the app, you tell me what to do.” Now we’re like monkeys now. We just drive and turn right, turn left and keep on moving forward. I think that’s in many ways what we need or what we want to liberate some of the mental process of choosing, of understanding. If you’re an expert driver, if you love to drive, if you love the city and you’re looking around obviously you want to do your own route and do whatever you like to do, but for most people we just want to give that responsibility of making those choices to someone that knows better about the traffic, knows better about the city and it knows you better.
We expect that Suggestic and other solutions in other areas get there at some point. At the point that we can really, really, really give you a good suggestion, not only something that’s healthy for you but something you’re going to like, something that’s available, something that really reduces your mental effort, gives you more energy, makes you feel better and impacts your long-term health for the better.
Does the app allow you to provide user feedback? Say you suggest some kind of meal and say the user accepts this option or rejects it. I’m curious how the feedback loop is organized? How exactly do you learn about this particular person?
The feedback loop that powers our technology is actually one of the most powerful things I believe we have. It’s been filed for patenting, by the way – in case anyone’s listening. Joking. Not only that, but it’s one of the first things we thought about, the importance of the feedback loop. Basically yeah, so what we do is the process, imagine it like a big circle. The process is we start by taking existing nutritional programs or diets, science-based or validated. For example, we have taken currently into the platform over 25 programs. Everything from something simple as a vegan diet to something more complex like the American Diabetes Association Nutrition Guidelines for people with type 2 diabetes or the DASH diet, which is recommended by the American Heart Association. Then what we do, we take that information that exist and has been scientifically validated, it’s already recommended by doctors for example, or dietitians. We take that and we encode it as a base in the system. You, as a user, you can say, “Okay, so I want to start with that as a base, a paleo diet or a ADA Nutrition Guidelines.” On top of that you add whatever additional personalization that you for sure know you need. For example, I hate onions. I don’t do dairy and …
I don’t like cilantro. Allergies or just preferences in general. There’s a lot of those. That’s the base. That’s a very early version of a personalized program. Then what we do is we start giving you suggestions based on that through the app, through the chat bot interface. As we give you those suggestion you give us feedback on those. You’re able to say, “You know, this is wrong.” Wrong can be this doesn’t match my diet. “No, you told me this was vegetarian but it has meat.” That happens.
That’s a particular problem with anything that has to do with artificial intelligence. Stupid in the beginning and it gets smarter very fast, but your early users need to be very understanding, very passionate about it to really give the feedback to say no, this is not vegetarian, this has meat and it has this type of meat. You can say to him it’s wrong or you can say I don’t like it. You can say I do like it. You can save it as a favorite. You can add it to your log if you have it, maybe you’re sitting in a restaurant. Maybe you’re sitting at the Cheesecake Factory and it’s 40 pages of mostly very highly caloric, full of fat food, but there’s some very actually good options in there. We try to match those. Maybe you have a salad and maybe you have a, I don’t know, chicken with salad, whatever it is. I don’t know.
Then you say, “Oh yeah I had that and I need to add it to my log.” We take that information. All that information goes back to the log and we try to figure out. Obviously that helps us in a different way. It helps us clean and improve the data in the system and it helps improve the recommendations particular for you and for people like you. We can say, Misha in particular likes these and then we can say but he’s following a vegetarian diet, so if many people that are vegetarians like this then it makes sense that more people that are vegetarian like this. You can figure there’s a lot of stuff happens in between, just this simplification of it.
Then there’s the active feedback which is basically looks like clicking “I like this”. Then there’s the passive feedback. The passive feedback is the sensor data, your location for example. Right now, are you at a restaurant or you at home? When you open the app we try to figure out person spends around this time of day he’s at home, it’s around breakfast time so we shouldn’t suggest, I don’t know, a grocery list. We should suggest maybe something that we know you have at home and that you can cook, maybe do eggs this way or cook this thing that way, whatever it is. That’s passive feedback. That’s data that we gather even from these things that we recommend and you never clicked on, you never looked at. That’s also feedback that we use.
Do you have to do much of an explanation of the choices you present to the user? Does it play any role or it always ends up, boils down to the “try it, say if you like it or not”. How do you measure the motivation mechanism?
Yeah, so as I mentioned in the beginning, one of the most important things that it underlies the way we relate to the user is we think of it as user success. What we think when we start everything is how can we help the user be successful? In this case success means whatever they have as a goal, but it’s health related. How can we help them be healthier? Since we’re talking of nutrition because they’re in this case, and in most cases in that specific context means how do we help the people be successful in terms of adhering to their diet more times a day in average? Basically it’s about sticking to your diet, which people find most difficult.
People start new things and try new things, but the long-term problem is sticking to it. By the way, one of the interesting things is that there are not so many things in nutrition that have been scientifically proved. The reason behind that is that … As a group we have a Chief Medical Officer and everything I’m saying related to that. I’m not quoting Suneil but it basically comes from him. One of the problems with nutritional studies is that it requires really big, big groups of people and very long time. In the research world usually those things happen less. It’s easier to get, if you’re a pharmaceutical company and you have your budget and all the tests and studies to validate your drug, but if you’re a researcher at a university it’s harder to get a budget to a 20 year, 20,000 peoples research about the powers of broccoli. Less interesting.
There’s not much that we do know for a fact in long-term. The only thing that we do know is that if you adhere to a healthy diet in the long-term, that has positive effects. That’s very clear for everyone. Nobody can argue with that. What we did is try to figure out how do we help you adhere to that diet in most of your meals most of your time. What we try to do with this concept of the adherence score, what we do is, as you try out the app obviously, you will see actually every time we give you a recommendation we actually give you a score. Maybe you’re following a Paleo diet and you have salmon with some veggies and something like that then you’ll get this has a very high adherence score in terms of how that much is a Paleo diet. If you have a cheesecake and that has a very low adherence score.
There’s obviously additional gamification process where we’re trying to show very clearly to people, know what’s healthier for them. We really don’t know that much, but what really, really, really matches their diet that they chose, for whatever reason, that this is the best for them. Does that make sense?
Yeah, yeah. Perfect. You mentioned you had data but you tried to use some kind of visuals in your chat bot or did I misunderstood that?
You saw that on the demo, yeah.
That could be it.
Yeah, it’s a new interface that we’re working on using augmented reality. Again, if the underlying intelligence, or let’s call it Suggestic brain, doesn’t really change. What changes is the interface. One way of interacting is through the chat bot. We’re working with a number of companies that are really in touch with our “nutritional brain” via APIs and they connect directly and they use that intelligence in their own applications, in their own platforms. Then we’re working on this additional user interface or user experience that relates to augmented reality, which basically you put your phone on top, for example, of a restaurant menu. We’ll do real-time scoring, adherence scoring and nutritional knowledge as to what’s there in the menu.
Yeah. I was about to ask how you handle situations when something, for example, is not covered, not in your data base. If user even can to report what he had for lunch if it was not in one of the options you provided. It looks like you’re making advances in this area as well.
Yeah, it’s a work in progress obviously. There’s so much things that we want to do, that’s for sure. Hopefully we’ll get there as we move forward.
That’s awesome. Let me clarify. You don’t have to be diabetic type 2 or whatever to use the app, right?
I guess these are the people who would be the most motivated to use it, right? Because I guess diet is probably one of the key recommendations for them, but anybody can use it, right?
Like you said, you enter your preferences and it goes by those.
Mm-hmm (affirmative). It happened because of this. We started, as I said, with focus on diabetes and then as we started building and had a very early version of product and starting showing it to people they would ask us, “Hey, but what about me? You know, I’m not diabetic, I just want to eat healthier”, or I want to eat this way and that way. We said, “Let’s just …” Given the way it was built we could encode and build all this additional interactions with different programs to make it available for everyone.
Yeah, that’s awesome because when I first saw it I thought, “Well, that narrows it down, it’s for fighting diabetes,” and I felt left out because I don’t have the condition. Yeah, now I understand that this is something anybody can use.
Shai, this is totally awesome. Thank you so much for sharing this stuff. Where people can find you to learn more about work you do?
My pleasure. A quick way is Suggestic.com. Obviously we’re in Twitter, Facebook and most social networks. iOS users can download directly Suggestic on the app store – just type Suggestic, you’ll find the app. Sign up, enjoy. Please do send me every feedback that you can find. If you’re not iOS user – it happens, I’m an Android user first…
Yeah, me too.
Do sign up, do follow us. We’re working on an Android version and hopefully sometime next year it will be available.
Okay, so you are already working on the Android version?
We’re beginning to.
You have a plan for it. Okay, okay, good. Because that’s another thing… Usually when people go mobile, they go iOS first, and as an Android user you also feel a bit left out. But I know. You’ve got to start somewhere.
Exactly. I had to buy iPhone just for this.
All right man, thank you so much. It’s been a great pleasure, and talk to you soon!
Thank you Misha so much, it’s my pleasure too.