@alexanderptitsyn Thank you so much! Glad you liked it

@codewithimanshu Great to hear from you! 1) The product is set up in a way that minimizes the need to interact with it. We don't want people to waste any time so all of the data is gathered automatically and is then presented to users in two forms: easy to understand graphs and intelligent AI suggestions. So I'd say it's pretty user-friendly and should require minimal effort to use and understand it :) 2) So far over 95% of our customers reported that it helped their companies improve performance and help understand their teams better. We are currently working on combining all of that information in more detailed case studies that will be then published on our website! Thank you!

@alexanderptitsyn Thank you, Alexander! Good luck on your launch as well!

@codewithimanshu @dfeles Thank you Daniel! Good luck on your launch!

@alex_ciubuc_bezos Alex, free unlimited product access if you are related to Jeff Bezos and get him to try the product, hah! 😅 Thank you for your support!

@maria_anosova Thank youuu

@erkin_bek @maria_anosova Thank you, Maria! And good luck on your launch!

@rihab_zd Rihab, thank you for your question and love for the video is always appreciated :) Most of the features are available to both: employers and employees. Our goal is not to create a tool that will help not just employers and managers, but also help employees better understand their work and what they can do to improve. And as such most of the info is available to both parties!

@vladimir_zivkovic Thanks for the feedback! Looking forward to it

@vladimir_zivkovic Thank you!

@erkin_bek @atsuhiro_teshima Atsuhiro, we have two sources of data: the majority of the data will indeed come from our desktop app, and then we also have some secondary data that can be obtained from third party sources, for example your team task managers. But the core of the functionality can work with just the desktop app!

@alex_egorov Thank you so much!

@alex_egorov Thank you, Alex!

@radu_tudosescu Thank you so much!

@rimash Thank you!

@dilan_lacin Thank you!

@dilan_lacin Thank you!

@moon10 Thank you so much! You can test our product for your team and increase productivity

@moon10 Thank you for your feedback! There are actually quite a few features that look to improve happiness and wellbeing of teams. From burnout prevention to the ability of auto scheduling onboarding calls for new team members. Feel free to sign up for the demo and we'll show you everything in detail!

@orysia_khimiak Thank you so much for the review!

@orysia_khimiak Thank you for the support!

@ujwalkr Thank you, Ujwal!

@sentry_co Andre, great question! Most companies, including Spotify are using some form of performance management. The problem is the majority of companies use fairly useless questionnaires as the basis for their data. Employees and managers receive regular questions and are responding about how they think they are doing and how they think others are doing. And this is not about just performance. But culture too. Unfortunately, subjectivity and bias is what ruins great cultures. And our goal is to provide objective data to help you improve performance and culture. Imagine if when you want to a doctor they would just look at you and say they think you have cancer. You'd probably think they are crazy. Cause at the very least you would want them to do some bloodwork. Or if you fell and now your hand is hurting really bad, you'd probably want them to do an X-Ray. Or imagine if during Olympics the gold medal would go not to the person who is objectively better based on the number of seconds it took them, but rather who some people *think* should win. Analytics are all over us and only when you truly understand what is happening you can make it better. So in my humble opinion anyone who wants to improve wants to know exactly what is happening. Be it with performance to understand your strength and weaknesses, or be it by identifying burnout to help your employees not get overworked. When you merge automatic analytics and AI great things happen :)

@stoicbasil That last sentence is interesting. Work breadcrumbs + AI. It has to be objective and AI is more objective than people. But the data, is still going to be the challenge. Esp within innovative work. The minute you start to measure, the cobra effect kicks in. An no, apple, Spotify etc. Dont measure. because the figure out it doesn't work. They just let amazing people do their thing. I speak with these people all the time and I have worked at places like this. At least not in the departments where they work.

@sentry_co Hey Andre, just double checked and it seems like Spotify does have a fairly standard performance management and measurement with employee and "squad" reviews, 1 on 1 reviews with the manager, etc. I'd go on a limb and say that the vast majority of strong teams have at least some sort of performance management that based either on subjective or objective data, or the mix of the two. And that makes sense - you want your teams to grow and in order to help teams grow you need to understand what's going on. And though I haven't spoken to anyone at Spotify, we had beta users and user interviews with people from Microsoft, Facebook, and Amazon. Here are two examples that show the problem: - A senior engineer of 7 years at Amazon reported that every time he opens the computer he gets asked a performance question. I'm guessing Amazon (at least in his department) decided to break down long lists of questions into smaller daily ones thinking this way they'll make employees more inclined to answer them. The person we interviewed said many people, including said engineer, routinely provide random answers to those questions. - Similarly, lead engineer at Meta, reported that to collect all data he needs for a single review of one person he has to spend at least a whole day digging tasks, and other data to review how that person worked. Imagine you manage a team of 15 and then half of the time you are spending just collecting data for reviews instead of building great products. If only there was a better way... :) We hope to become that better way that will help all of those larger companies stop wasting time and instead focus on building amazing products :)

@stoicbasil Spotify dismantled the squad concept a long time ago: https://www.chameleon.io/blog/sp... I think these teams are much more progressive now. Ditching the old corporate ways in favour of less complexity and more output. Im sure many corporations will stick to the old ways for a long time tho. Just not the most progressive teams. That's my "biased" observation at least. In anywise. A thought experiment: take any majorly successful opensource project and try to backtrace who had the most impact on the project. Take swift for instance. The first years it was only one person. Then 10 years later one person adds something very small but profoundly useful for devs all over the world. Yet it counts for 0.000000001 of committed code. Now the first person who started to make swift. Obviously added profound value. And so did that last person too. And in-between there has been 1000s of people on the project. But trying to backtrack or score their contribs Is impossible imo. As quality is harder to measure than quantity. Now I could be wrong. Im just making arguments today as I find this field very interesting. But if there was a model that could measure impact and score performance. We could actually apply the model on all opensource projects out there as they contain full history of contributions. And if it worked. Wow. that would be something. End thought experiment 😸

@sentry_co Andre, love our discussion! For your thought experiment, although this is not what our software is meant to do, in your own words you are saying that the person added "profound value". So you are yourself giving a measurement to the value of their contribution. And what I'm saying is that the way your brain comes to this conclusion is probably not that difficult and likely involves considerations such as who started it first, who create the initial architecture, who contributed to the main frameworks, etc, and then also who actually contributed how much code. So if you, looking from afar, can estimate the value that was provided then I promise you actual analytics with AI can estimate it much more accurate and faster if properly setup :) So if anything, you are talking about a progress limitation. Something that maybe is not done "yet". But something that definitely seems possible in the near future :)