Hello and welcome to NewsClick. Today we have with us NewsClick chief editor, Prabir Prakaistha. And we will be talking about Turkey's military intervention in Syria that goes by the name of Operation Olive Branch. So Prabir, what triggered this operation? Well, the immediate trigger seems to have been the American statement that they are going to raise a Syrian border force, which will be constituted of what is being called the SDF, which is really allied to the Kurdish YPG, which is the political military arm of the Kurds at the moment in Syria. So this seems to have been the initial trigger. If it is the Syrian border guard, it is not necessary it will be only in the border between Iraq and Syria, which is east of Euphrates River, which has recently been captured by the SDF and the Kurds. But it could also conceivably extend the border with Turkey, because as you know, the Kurdish regions, which are under YPG control, is also Kobanei, what they are calling the Kobalei Canton, what they are calling as Afrin Canton, and they are really referring to this as Rojava or western Kurdistan. Now this also is the border of Syria, but in this case the border is with Turkey, not with Iraq. So this seems to have been the immediate trigger that what the United States is planning is to use Kurdistan, what the Kurds have been asking for quite some time, and created on the border of Turkey and on the border between Syria and Iraq, that seems to have been the American plan. So Turkey saw that as a threat, because they have been battling what they have argued as a situation is threat of the PKK, which is again the good, it is really the main Kurdish party, which YPG is also aligned to. So let us not think the YPG is independent of the PKK. PKK is the mother party, it is in south of Turkey, and they have been in battle for what they would call as self-determination, what the Turk would call, Turks would call as secession for quite, for a number of decades now, and it under Erdogan it is broken out again. So given this situation, I think Turkey saw it as a threat that this could become a long-term threat to south of Turkey, and therefore they decided to teach quote-unquote Kurds a lesson by intervening militarily in Afrin. Afrin is really on the southern border of Turkey, and it is also isolated from the rest of the occupied, the rest of the Kurdish occupied territory. Therefore it is relatively militarily easier to control and probably intervening. Do you think it was an amateurish move by the US by ignoring its NATO allies' security needs for an exchange for illegally, as you said, illegal and unsustainable occupation of the northeast Syria? Well, northeastern, now eastern Syria. Yes, I think, you know, when we talk about United States to talk about amateur is difficult to understand, because they have been bumbling, they have done things in West Asia, which have been opposite to what their supposed aims were. So it doesn't seem to make any sense that they would do something where Turkey's direct interest would be harmed, and Turkey would take perhaps immediate action. Military action of course is something we should really oppose, because yes, Turkey might claim that they would like to stop the Kurds from doing something, but that is really the Syrian government's task. It is not Turkey's task to intervene in Afrin, but yes, I would say that Americans don't seem to know whether they are coming or going. What their policies have been in the region is a mystery to all of us. The only two things that we can see is there's a continuity in the policy. One is its allegiance to its alliance with Israel and to Saudi Arabia. On that, they don't seem to have wavered. It's hatred to Iran, which I think is a part of all of it. And the second part of it is continuation of wars in the region, which break up the countries. Though it has an adverse impact on the region, it has an adverse impact on the globe as a whole. But it seems to be the belief, probably of the Israelis as well as the Americans, that a smaller set of states which are broken up would be much easier to control. So even if they have a 10, 15, 20-year decades of battles over there and wars over there, in the end of it, if they get a fragmented West Asia with small Arab state lads, state lads broken into different identities, then what would happen is the area may be much easier for the United States to control. Of course, it forgets that the world is not going to stand still. So there are other countries who are going to come in. Iranians have a stake in the region. Russians have a stake in the region because the Satoos was the only naval base in Mediterranean. So with all of this, I don't think the American policy is going anywhere. And as you rightly said, Turkey is a NATO ally. In fact, it is the largest military force in NATO after the United States. So to get into a situation where the military intervening against a set of forces which is supposed to be under U.S. umbrella really speaks very poorly of, shall we say, American foreign policy or American interventions in the region. So what do you think will be Syria's and Russia's response to this, also putting in context the Sochi talks that will happen at the end of this month? The Sochi talks were supposed to go further than what were called the de-escalation zones. In certain areas, the forces which at the moment were fighting would pull back and each of the de-escalation zones would have some guarantor as it were that the peace would be maintained in that region. So in Idlib, which is where Afrid also is, Idlib is the place where Turkey was supposed to be a guarantor of a part of it. A part of it the Russians were giving such guarantees. Now what seems to have happened over here is that before the Turkey's military intervention took place, it seems there were talks between the Kurds and the Syrian government as well as the Russians. Russians have maintained that equation both with the Kurdish forces for a long time. Kurds have, YPG has an official embassy in Moscow. They have also maintained the relations with the even the Syrian government forces. They haven't fought till now when the Aleppo war was going on, battle was going on. The YPG was tacitly in support of the Syrian government forces. So this they have really not come as it were to major blows. They might have had skirmishes here and there but they generally tried to mitigate their battles and to see that it doesn't go out of hand. They have had some clashes but really it has not gone out of hand. This time when they asked the Kurds whether they would agree that while they could discuss what would be the autonomous autonomy they would have under the Syrian control but would they accede to be under Syrian authority and would they let the border be controlled by Syrian forces with Russian help. The Kurds seem to have turned that down. They said no. They seem to have refused it. They seem to have said no. We will look after and defence ourselves. We want an essentially independent and autonomous Kurdistan and therefore this was not welcome to them after which it's the Russia and Syrian government tacitly seems to have allowed the Turks to attack Afrin. Now we don't know what will happen if it comes to the United Nations Security Council, what position the Russians would take. The Syrian government has publicly said that the Turkish intervention is illegal and that it shouldn't it has really violated its territorial integrity. This the rates said earlier also when they marched into when Manbij was taken over by the Kurds and that then there was a possibility of linking up the Kovane Canton with the Afrin Canton. The Turks had come in over there. They are still there. The Syrian government had protested but they had tacitly allowed it to happen. Will they do the same in Afrin? Will they actually intervene at some point? Will Russians and Turkey, Russia, Turkey and Iran and Syria sit down and try and resolve the Afrin question by which Syria regains its sovereignty over or control over Afrin? All these things we'll have to do but it is true in this for the forthcoming Sochi talks. It does appear that Turkey is now more likely to align with Syria and in Idlib they are going to give the Syrian government forces a free hand except in the areas like Afrin or in the near the southern border with Turkey but they already have certain degree of control. They might allow the Syrian government force to take over the rest and withdraw the support from certain sections of the rebels who were tacitly being still supported by Turkey. So there is a real alignment going. It does appear that Turkey is shifting away from the United States as a consequence of the declaration of the United States to support militarily the Kurds in this fashion and there is a possibility that this is going to go against the Kurds in the long run. We have already seen in Iraq the Kurds lost heavily when the Barzani declared autonomous region to independence and the Iraqi forces, the government forces intervened, took over and a lot of the area which Barzani were controlling in the name of the Kurdish autonomous region. Whether similar thing would happen here we have to see because we cannot forget while Afrin is really Kurdish, while Kobane is really Kurdish there are lots of areas which the Kurds today control including southern, southeast of Syria which is really Arab. It is not really Kurdish. At the moment the Kurds are very unhappy with what has happened and we will have to see how it develops before Sochi and after. Kurds seem to have said they will not participate in the Sochi talks anymore which itself would be a disturbing development. As these issues progress will be coming back to you to discuss it and thank you for talking to us. This is all the time we have for NewsClick. Thank you for watching.


and welcome to the session. This is Professor Farhad in which we'd look at a CPA simulation. A CPA simulation is no more than a multiple choice presented in a different format. So do not be intimidated by a CPA simulation. For example, this question is about the fair value in the versus the equity method. It doesn't mean it's something different than a multiple choice because I can ask you about the fair value in the equity method in a multiple choice setting. All what the examiner does is they will take the information, break it into an exercise. Now this exercise, now how can you prepare for an exercise like this? Well, you use your CPA material, obviously whether you are taken back or Roger Gleim, you would use something similar to college exercises. So the simulation is basically an exercise that you see at the end of the chapter of your intermediate accounting, advanced accounting or governmental accounting. So this is what it is. So you have to be familiar with this information. Now how can I help you as Farhad lecturers? On my website you will find additional information for your CPA exam as well as your accounting courses for that matter to help you solve these questions.  You do need those courses, you may need additional explanation, the theory behind the material to help you understand the material because memorizing or only answering multiple choice will not help. So a strongly suggestive, you check out my website, at least check out how well is your university doing on your CPA exam. This will give you an idea about your performance. Please make sure to connect with me on LinkedIn and on YouTube. Let's go ahead and get started.  So a lot of information, once again they could be a multiple choice, they could be a simulation. Let's work with them in a form of a simulation. So on January 4th we have Adam Bakery paid 340 million for 10 million shares of Maggie's common stock. So Adam bought Maggie, 30% of Maggie's investment represent 30% in the net asset and gave Adam, Maggie gave Adam the ability to exercise significant influence. This means if they want to use the equity method they have significant influence. Adam chooses the fair value method option for this investment. So notice Adam chooses the fair value option. That's important. Adam received dividend of $3 per share on December 15th toward the end of the year. Maggie reported net income of 230 million. The market value of Maggie's common stock on December 31st is $32 per share. On the purchase date the book value of Maggie's identifiable assets were 880 million and the fair value of Maggie's depreciable asset with an average remaining life of four years exceeded the book value by 120. Yes, usually the depreciable asset, the increase in value. Therefore we have a book value of 880 plus 120 an additional fair value. So the fair value of Maggie's company is 1 million. The remainder of the excess of the cost of the investment over the book value of the net asset purchase was a terrible to good will. We don't have to worry about this. So we're going to prepare the journal entries first using the fair value option show the effect on that income show the effect on the balance sheet. Then we're going to prepare the entries under the equity method. But again, once we're done with the equity method, we're going to go back and report everything at fair value. So I will show you both the equity and the fair value and what's the effect on that income? What's the effect on the balance sheet? Let's go ahead and get started. Starting with the first transaction when we purchased the company, January 4th. We're going to hear you're going to be using the fair value method. Here we're using the fair value. But Adam purchased the 10 million shares for 340 million. Simply put, you bought an asset buying a company. You are buying making an investment. That's simply an asset. So we debit an asset investment and equity securities 340 million credit cash 340 million. Simply put, you purchased an asset. So basically we're done with the first line. We purchased an asset. Done. The investment represents 30%. That's good information, but it's not really relevant for the fair value. That's done. Adam chooses the fair value. We already know this for this example. Adam received dividend of $3 per share on December 31st, on December 15, 2021. And Maggie's reported net income of 230 million. Now that's good. Maggie reported net income of 230 million. That's not really relevant for us nor it's not really relevant for us. Why not? Because since we are accounting for the investment at fair value, we don't care what they reported of net income. Therefore, we don't really do anything for net income. Now, we get paid $3 per share. Well, we have to account for this. We have to account for this. Why? Because we actually receive dividend. Well, if we receive dividend, we're going to have to debit cash because dividend is in cash. We're going to debit cash 26 million. And we are going to credit, we are going to credit dividend revenue 26 million. So simply put, I'm sorry, receive dividend rather than $3, $2.60. I apologize. So receive dividend of $2.60. Therefore, debit cash 26 credit dividend revenue 26. Now, at the end of the year, so basically we're done with the dividend. So I just made an error. It was $2.60. For this example, at the end of the year, the fair market value of the stock is $32. Now what happened is this, if you purchased a few purchase 10 million shares for $340 million, it means the cost per share, the cost is $34. What happened at the end of the year? The cost went down to $32. So therefore, you have a loss of $2. What does that mean? It means you have to record a loss, a fair value adjustment, a fair value adjustment. It's a loss. So when you have a fair value adjustment, what you do is you should have a credit and fair value adjustment. So I'm going to show you how to use the fair value adjustment, but this session is not about this account. If you want to learn about fair value adjustment, please go to my website. So here what we're doing is we are adjusting the portfolio or the investment to market. So simply put, the fair value adjustment is zero because this is the first year we purchase this investment. Therefore, the fair value because we have a loss, we should have a balance. We should have a balance because we have a loss. We should have a balance of 20. Well, guess what? If the balance should be 20 because our losses are 20 because $2 times 10 million equal to 20 million. Therefore, we have to credit fair value adjustment. Well, when we credit fair value adjustments, what do we debit? We debit a loss. We debit a loss of 20 million. Therefore, we debit a loss on investment and that loss goes into net income and we credit fair value adjustment of 20 million. Now, if you don't know how to adjust your portfolio to market or your security to market, please go to my website. I do extensive explanation and extensive review of this topic. Okay. Now, going through all these transactions using the fair value method, the question is, what's the effect on income? Simply put, how much goes on the income statement? Well, the dividend revenue goes on the income statement, 26 million right here, then minus the loss. So we have a plus 6 million on income. What is the value of your investment? Well, the value of my investment, I purchased it at 340. Then I am going to have a fair value adjustment credit of 20, which is a contra asset, my fair value adjustment equal to 320. And this is the fair value, pretty straight forward concept accounting for these transactions. So basically what we did is we purchased the stock, no entry when they reported net income, recorded the revenue, the cash dividend revenue, and we adjusted the investment at the end of the year. We're going to do the same thing, but now we're going to be using the equity method that after we use the equity method, we go back and we adjust everything to market. Let's start with the equity method. We purchased 310 million worth of 340 million debit, investment and equity affiliate, which is an asset credit cash. So rather than call it just investment and equity securities, we call it an equity affiliate, but it's the same concept. It doesn't really matter, they're both assets, you made an investment. Now, so we're done with this, purchase the investment, we're done with this. Now, this is important. We have significant influence. So the company received, not company, our investee, which is Maggie's, reported 230 million. That's good. Now, if Maggie reported 230 million, we are going to get 30% of that. We're going to get 30% of that. Therefore, what we do is we'll take 230 million, 230 million and multiply it by 30%. And that is our share of income. So 230 times 0.3, we're going to record 69 as an increase in the investment. So this is important. So I'm going to have this investment account here, investment and equity affiliate. It's this account here, investment and equity affiliate. I started with 340. Now, how do you do investment under the equity method? Under the equity method, you will increase your investment by the proportionate income that the investor report, the investor report, the 230, you qualify. 69 million is kind of yours from the income. It belongs to you.  So we're done with the income. Now, also, they, again, not two, it's 260, they paid you 10 million times $2.60. You're going to debit cash 26 million. Now, under the equity method, what do you credit when you receive dividend? Now, think about it. Where is dividend coming from? Where dividend is coming from income? Dividend is coming from income. So you remember this income here, the 230 million? 230 million. When the investor, when Maggie pays the dividend, it's going to come out of this income. You already accounted for all this income. That's yours. So simply put, your net income is here. Now, the dividend comes out of this income.  Therefore, you debit cash 26 million. You credit investment by 20, 26 million, 26 million. And this is for the dividend. This is for net income. So net income increase your investment, dividend reduce your investment. Now, what else are we told in this problem since we're using the equity method? We are told when we purchase this company, the book value were 880 million. There was an additional fair value to the depreciable asset. In total, we had, the company was worth a million. What happened is this. So you did buy, in a sense, you did buy those 100, that extra fair value. Now, what happened is this, you cannot count this extra fair value because kind of, you over, not you overpaid, you paid for the fair value. But you have to make an adjustment. So simply put, of this 120 million, you got 40% of it. I'm sorry, 30%. So you got 40 million of this. You got 40 million of the 120 million. Simply put, you paid, you did not buy the whole company. You bought only 40%, which is 30%, 30%, not 40, which is 40 million. Now, what's going to happen is this, that 40 million that you have, you're going to have to reduce your income by because it's going to, it's basically, in a sense, it's an increase in depreciation. You're going to take this 100, the 40 million and divide it by four, 120 million times, let's do it again, 100 and pull the calculator here. This way we don't make any mathematical mistake. We have 120 times 0.3, which should be, oh, should be, sorry, should be, that's why that's the math, 36 divided by four, nine. Therefore, it's 36. That's what I thought. This is 36 million. So you purchased this 36 million. 36 million will have divided by four equal to nine. So what happened is this, you're going to have to reduce your income by nine, nine million because this is extra depreciation. Therefore, you debit investment revenue, credit investment and affiliates. What you do is you reduce your investment account by an additional nine million and this is for the depreciation adjustment. Again, if you have any questions about how we did this, this is a whole topic by itself. This is a whole topic by itself. Go to fourhatlectures.com, but this is what we do since there was extra fair value that we purchased, we reduce revenue by that extra fair value of depreciation. Now, at the end of the year, we are told the stock price. Now, let's first find the investment account. Well, if we take, let's do it on a calculator, this way we don't make any math errors. So we take 314 million, we started with plus 69 in revenue minus 26 in dividend minus nine as depreciation adjustment. So right now the balance is 374. Well, is this the true balance in investment? No, they tell us at the end of the day, we chose to use the fair value. Although we account for it using the equity method at the end of the year, we adjust. Well, how much do we have to adjust the investment to? We have to adjust the investment to fair value. What is the fair value adjustment? 320 million, which is 10 million shares times 32. What does that mean? It means I have to reduce my investment by an additional 54 million. So I have to make an adjustment, reduce my investment by 54 million. Therefore, I debit a loss 54 million. I'm reducing my investment and it goes into net income because I choose to report this investment at fair value and I credit my fair value, which is right here at 54 million. Now my balance is correct 320 million. So let's look at the effect of net income overall for this method. We're starting with investment revenue of 69, investment revenue of nine. So what else? So we have investment revenue. This goes on net income. This is no net income. We have no debit, sorry, credit revenue, which is 69 minus 9 equal to 60 minus 54, which is a loss. It goes on the income statement. So the net effect is 6 million. That's net in effect. And what's the investment balance? The investment balance is 320 million. And hopefully you knew they should be the same as the prior method because in the prior method, in the prior method, we use the fair value method, 6 million and 320 million. So they both they should equal to each other because after we after we accounted for it during the year using the equity method at the end of the year, we adjusted to fair value and we are allowed to do so. Once again, I'm going to invite you to check out my website forhatlectures.com. Here's my proposal to you. You might be studying for the CPA exam. My subscription per month is $29. You can cancel. So all your risks $29, $29.99 to be more specific to maybe have a chance of increasing your score by 10 to 15 points. So $30 to check out your see if it works for you. It worked for many other students. Check out my LinkedIn recommendation or check out the recommendation the student reviews on my website. That's my offer to you. Are you willing to risk that to see if you could improve your score substantially? That's your call. Don't shortchange yourself. The CPA is a lifetime investment. Study hard. Good luck. And most importantly, stay safe.


Kiitos. TH0. I'm Mikael, founder and CEO of Supermatrix. And today I'm going to tell about our story. And there are a lot of very successful growth companies here at Schlage. But I think in some ways our story is rather rare. So I think not a lot of these companies have been started out as a Toyminimi. In Finnish sole proprietor. So one-man businesses operated from once bedroom for five years. until suddenly going on a huge growth trajectory. So let's go to our story, but first just briefly on what we do. So you have the context. So we help companies measure all of their online marketing activities. So if you do a lot of advertising in different platforms, so you advertise in Google, Facebook, Twitter and so on. You may have a web analytics tool, you have a CRM. You have all your marketing data split between different systems and it's really difficult to get the big picture on how your investments in marketing are paying off. So what we do is we collect all of this data together and we make that data available to you in the tools you can use for analyzing and reporting that data. So it can be a spreadsheet, it can be a BI tool or we can also push that data into your data warehouse if you have that in use. And basically our clients, we have 17,000 clients and they range from one-man SEO and practitioners all the way into huge companies like Google and Warner Bros, Zalando and so on. And now to the story. So the company has very humble beginnings. So I started the company alone in 2010. My first client was a web store that sold front pocket wallets like this. And as a reward for my work, they sent me one wallet. Not even the bills does the wallet. But from that pitiful start, somehow over the years we got to where we are now. So 55 million euros in annual recurring revenue. Growing fast, we've always been profitable. And what I'm really proud to say is that we've used zero euros of investor money to build the company. So we've raised two rounds, but we've always been profitable so we didn't really need the money. And most of that money, most of that was secondary. So setting shares and even the money we raised we still haven't used. So it's in the bank or even invested back into the stock market. So how did we do this? So I'm going to go through some of the points that I think were really important along this journey. So first how I got started with the company. So this was me 13 years ago before I started the company. So I was working as an analyst in another company here. My task there was to do reporting for various parts of that organization. And I loved analyzing data, allowed making conclusions based on that data. But a lot of my time actually went into gathering that data. And that was really tedious. So basically copy-pasting numbers from various platforms into Excel where I was making these reports. And I really hated that. I wanted to get rid of that part. And especially I was struggling with Google Analytics because we had a lot of websites that we were tracking with Google Analytics. So I went online looking for a solution to this problem. I didn't find a solution. But instead I found this web forum where this guy called Nick working at Google had posted a question if there were solutions for that. Because a lot of Google customers were asking Google if there was a way to do this. And people had suggested solutions to Nick, but none of them worked. So what he did was he posted that the first person to actually make a working solution gets a Google t-shirt as a reward for that. And I thought that's perfect. I get to try to solve my own problem and maybe I can earn a free t-shirt as well. And I'm not a developer by background, but this seemed like an interesting problem and not too complicated. So I went into work, created a small script to get that data from Google Analytics into Excel. It worked really well for me at that job. And then I also posted in that forum and said, you know, here's this thing you can take into use for free. And people loved it. And also this Google guy, he liked it a lot and he sent me the t-shirt. But what was actually more important was that then he blocked about it in the Google Analytics blog and that generated a lot of interest toward what I was doing. And I think that proved that there was a lot of demand. There was a big problem that no one was really addressing in any good way. So a lot of people were struggling with the same issue that I was struggling with. So collecting all of this data together, spending a lot of time on really boring stuff. So I didn't need to invent a problem. The problem was clearly there. And, you know, I started getting requests from all over the world. Can you do something more than the simple script? And, you know, I had never thought about becoming an entrepreneur, but the opportunity seemed really good.  And like I said, I didn't have a developer background. So I didn't have the excuse to really build anything, anything very great. And I also didn't have a business background. So I never, you know, had run a business. I didn't have any understanding of how to run a business. I was just an analyst. So basically I was lacking the tech skills and the business skills. So basically I didn't have any of the relevant skills you need as a tech entrepreneur. But nonetheless, I decided to try my luck on this and found my own business. And what I think was key in eventually making success was that even though I didn't really have the skills in the beginning, I did very concretely understand the problem. And the problem was huge and unaddressed. And that in the long term then was enough to get the company going. And so I started the company early 2010. And of course I didn't have any capital to invest into the company. And because I didn't have any of the skills and I didn't have a background as an entrepreneur, no sane investor would have invested in what I was doing. So I didn't even consider that option. There was zero sense that any investor would believe in me. So I didn't even try to get any capital for the company. So what I did from the get go was being just really stingy, just keeping the expenses super low. And you can see my expenses for the first couple of years were really, really low. And actually when I started the company, the biggest purchase I made was this IKEA chair. So I could sit in my bedroom and work on this. And I think like for a lot of non-technical software company founders, the first thing you think you need is you need capital so you can hire developers to build a product for you. And I claim based on my experience at least that that is false. So programming is actually not that difficult that you think. Even as a non-technical person you can learn enough to build an MVP that can already create value. And then you can prove you have something. You can get some traction and maybe later on you can then hire the developers who will fix all of your messy amateur code. But you can get started on your own as well if you just put time on it. So I didn't hire developers. And also on the go-to-market side, I didn't have the capital to hire any salespeople. Of course I didn't even know I have a product that can be sold successfully. I didn't want to do sales myself. I'm not a sales person. That's not really my passion at all. So what I did is I just made the product such that it can sell itself. And I think PLG product-led growth wasn't really a term back then yet. But that's what I did. And that just worked very well for us for many, many years. And I could also say that in the beginning of the company I didn't really have the ambition to build a huge company. So I was really happy making just a living for myself. And what that meant was that I didn't put in place any of the structures that you would if you were aiming to build a big company. So I didn't really hire any people, expensive people to assist on that and didn't take any services that would cost me money. We kept the expenses really low. And that meant that the whole company was built on a foundation of efficiency. And that's been crucial for our success and continues to this day. So I didn't have the ambition to build a great company. But I did have ambition to build a great product. And I really love building product even today and getting satisfaction from people using that product. And that's where I put all of my effort just on the product. And there's a lot of stuff, a lot of distractions, I would say, for founders of startups. And you can put a lot of time onto this stuff. And I'm not saying all of this is a waste of time necessarily. But I think in the early days nothing can be a higher return on investment than putting time on building the product. So working with the clients, getting their feedback and building the product. While some of this stuff can be useful, I think in my case I didn't really put any time on any of this. And I think still I managed to get the company going. So I don't think any of this is really necessary and can be a distraction as well. Of course funny saying this in a startup event, but still. One key thing to our success has been the distribution of the product. So I would say actually the distribution in some ways has been a more critical differentiator for us than the product itself. And what that means is that we've always had the strategy of embedding the product into existing products that are already in use in these companies. So the first product I sold was actually this Excel file. So I was sending an Excel file to my clients. And that was really handy. Some of the clients really appreciated that because then you could bypass their internally IT security processes so it's not an installable software. It's just a file you're taking to use. But then going forward like the strategy has kept out. So we expanded into Google Seeds, into Looker Studio, Google BigQuery and so on. And always the strategy has been to embed tightly into these products. And why that has been good? Well the problem that we are solving is the proliferation of marketing platforms. So the problem is that there are already different platforms in use in these companies. So the last thing they are looking for is to add yet another platform. So no one is looking to add a super metrics platform on top of all the other tools they have in use. So what we do instead, we allow them to keep using the tools they have, like Google Seeds or Excel or Looker or whatever. And then we just make it easy to get the data into these tools. So it's really handy for the user point of view. They don't need to onboard their organization into yet another tool. But it's also been a good business strategy for us in other ways. So of course this has meant that we can partner with companies like Google or Microsoft. And we can make their product more valuable to their users. So we are complementary offering to theirs. And that's meant that we can, you know, we've been able to have like co-marketing, co-selling with these companies instead of competing against them. So many of the existing solutions, they were end-to-end solutions. And that means that you are directly competing against companies like Google, which I wouldn't recommend to do. So making it easy to get started. So what this PLG motion in our case, how it concretely worked is that from the very good go, we kept like removed all the hurdles from getting started. So you can get the free trial without giving any information to us. You certainly don't need to give a credit card to get the free trial into use. You definitely don't need to have a call with our salespeople to see the product like many of our competitors required to do. Instead you can get the free trial into use. You can see the value. And if you like it, then you can, you know, buy the product on our website. And again, you don't need to talk to anyone to do that. So everything is very streamlined, very straightforward. Of course this came out of necessity. As I was working alone in the beginning, I wouldn't have time to talk with all of the clients. But it's also been really great for the efficiency of the company. The monetization. So from the get-go, the product did provide a lot of value to the clients. That was clear. So it was a critical part of the marketing team's workflow to be able to analyze what they were doing and being able to report on that. You know, the clients if they were a marketing agency or to higher ups in their organization. But I really kept the monetization really low. The pricing was really low for many, many years. And I think that was a good strategy. How it was possible, because the expenses were so low. So I didn't really need to maximize the monetization in the early years at all. So I could leave a lot of money on the table. And of course that meant that like, I had a decent product, super low price, I really could capture the market. And there were competitors that were VC funded starting around the same time. And you know, they had raised millions in funding. But that also meant that they had put in place all of these expensive structures and they were burning money. And of course they couldn't afford to do what I did.  So they had the sales model that meant that they couldn't be selling the product with 50 euros per month like I was doing. They needed to charge much more for that motion to make sense. So that meant that even though they were well funded and they put in place all of this structure, we came out as winners from this competition because we were more efficient and we had the model where users could acquire the product on their own. And of course then later on when you already have the market then you can start capturing more of that value that you generate and that's what we are doing now. But for a long time, as you can see from this graph that shows our ARR development, we didn't really monetize pretty much at all. And then hiring sales last. So we had a really good PLG motion going on. And that was super successful for us. So until for the first eight years, we didn't have any people working on sales. And everything was going very well with that. But now we do have a big sales team. So what did change? What did we start to change that starting from 2018? So basically the answer is here. So we were seeing a lot of big companies becoming our clients. So there were like individual marketers and analysts from say Disney were using the product. Of course it wasn't like the whole marketing team in Disney would be using it. But like individual people in these companies and then they were paying us like 500 a year for that. And we just saw that because these companies has huge online marketing budgets that there must be a lot of value that we could be generating there and could be capturing if we were a bit more active with our sales process. And we just started with the PLG motion with the sales service. We could never really drive that to the full extent. So we started building sales team. And what the sales team does is they talk with the most potential trial users when we see a big company coming as a trial user and trying to convert that to a bigger deal size. But mostly they work with our existing client base so we have 17,000 sales service clients and they try to find the opportunities there. So companies like Disney or Dyson and so on that are using the product to a little extent with individual people and then we try to expand that usage across their marketing teams. The PLG motion is still working so it's still generating half of our revenue but during the recent years the fact that we built the sales team on top of that has accounted for more and more of our growth. I have to say like there are also challenges in merging these two models together so PLG and then layering the sales team on top of that. I didn't really anticipate the challenges when I started on that journey. So of course when you have a PLG model per PLG then everyone's interests in the company are pretty well aligned. But then when you start hiring sales there starts to come these conflicts of interest as well so maybe something that is good for PLG good for the self-service motion maybe good for the company as a whole may actually be really detrimental to the sales team and as their compensation is tied to their own performance it's really difficult to start making any changes that affect that balance. So looking back maybe we should have planned that better beforehand like what is available self-service what is the sales team exactly targeting and so on. I think we've been very successful with what we did but it's not been without these hiccups. So here you see the timeline of the company some key events. So I see it took a long time to get from starting the company alone in 2010 to hiring the first employee then even more years to hire the first sales person and starting building that sales team and maybe I could highlight the two investments here so as the company was built on this efficient foundation so always being profitable we didn't really need the investments and when I then eventually got into some conversations with investors it was a really good negotiation position when you're profitable you don't need the money and that has meant that well first of all I can still control the maturity of the company even at this scale because I could negotiate good deal terms but also we could do a lot of secondary so myself and other early employees selling their shares to investors and getting some money out of the company and of course that's you know good for your piece of mind at least you don't need to worry about money that much but all the other things in the company and to conclude I think some of the things in our story I think are a bit counterintuitive and probably different than with a lot of other growth companies so taking it slow in the beginning being patient maybe not having that great ambition in the beginning can actually be a good thing if that means that you focus on the product build on an efficient efficient foundation efficient go-to-market model at least in our case that has led to a lot of success for us you know studied statistic in the university so I can say a sample size of one is not significant but I can say in our case this has worked I give no guarantees to anyone else though and I wanted to end presentation here by inviting all of you to our beautiful office in the center of Helsinki for a party tonight unfortunately the party has been way over booked so I will not be able to do that for all of you but we have our own guys here somewhere so if you can find a super matrix person they can if you are very nice give you an invitation to the party so that's it, thank you everyone


Have you ever heard of the term road rage? Oh, yes. Have you ever seen road rage here in Hawaii? Yes. You have? Do you want to talk about it for a minute or two with us? Oh, it wasn't personal. I mean, I've kind of seen it. But you've seen it on the roadway. What kind of examples of road rage have you seen? Ooh. Bad stuff or kind of just rude behavior? Impatient. Impatient behavior. Impatient behavior. Where people are just kind of trying to get to the place and just really irritate it because somebody's, you know... But what happened? Hawaii used to be a land of people who just... All people still do. They still take their time. Yes. And people always wave, and whenever you let them go or something. So back in the day before H1 and, you know, before statehood, people, the fastest they ever drove was probably around 40 miles an hour. Maybe the maximum, huh? Well, the roads weren't that crazy. They were pretty bumpy. Maybe it was 30 miles an hour, right? No, I think there's still a lot of bullpaw around. What do you think is happening, though? You know, did you know that... Did you know... It's bad. It's bad. But do you know that Hawaii is the number one ranked state for road rage? Would you have ever guessed that? Really? Yes. I think it's because people are so disgusted with the fact that it takes them forever to get anywhere when it's really the early morning hours and the late afternoon hours. The rest of the time, I think, people are pretty good. Do you ever see road rage in the parking lots of shopping centers and things like that? Oh, yeah. Where two people are trying to get a parking space and they're both edging over. Yeah, I've seen it. Have you seen them honk or just yell at each other or all the above? Sometimes they'll yell. Yeah. If you had any advice for someone before they get on the road, what would you tell them? Start early. Plan a little bit earlier. Yeah. And if you had any advice for someone who was in the middle of road rage, not that you're sitting in their car or anything, but what would you say to them? Just be calm because you never know what kind of person is in that other car. And it could be somebody who is really upset about something else and it just spills over. So you don't want to get into a tangle with them. We have an expert, a national expert here that actually is a professor at the University of Hawaii, Manoa, and he's an expert about road rage. And you know what he said? He said, don't think about just you and your family if you're in a road rage situation, but think about the driver's family and friends. And I thought that was really interesting, that you can help diffuse a whole situation by the other person thinking about the family and friends of someone who's having a bad day. What do you think about that? Well, fair enough, sure. We all have bad days. Unfortunately, some people take it with them behind the steering wheel. That's not unusual. I think when they're in their cars, they are sort of isolating them from other people. They're in their own bubble. That's a very good point. They're not being responsible on a social level. Right. And my last question is, have you ever been the recipient of someone's rage on the road? No. No, not really. But I've been the one who's been angry at somebody. What kind of thing made you angry? Just out of curiosity. There was a line of cars waiting for a turning light.  This car comes out, the young man in it, you know, and he's coming from the side road, entering the lane that we're in. He came up very close to me. He figures, oh, little old lady, I'm going to go right in. And he was just a few feet away. And actually, he wasn't in a position to really get in. He would have to cut in front of me to get in. And I thought, you know, we've been waiting here for two or three lights, the third light coming up. And I said, and this young guy thinks that he can intimidate us. So I... Boy, was he mistaken? Yeah, I moved up a little bit and he moved up a little bit. And I said, you tried, buddy. You know, I just thought to myself, this guy has no business trying to cut in when everybody is waiting.  But, you know, it wasn't a dangerous situation because I knew what I was doing.   And he thought he was dealing with a little old lady. Was he mistaken or what? What's your name? Oh, geez. Never mind. I just want to thank you for spending the time to share your story and wishing you much aloha today. Oh, thank you.


Biden is a bigger danger to U.S. democracy than Trump. Robert Kennedy Jr. President Joe Biden could be a bigger danger to U.S. democracy than former President and presumptive 2024 election rival Donald Trump. Another presidential candidate, Robert F. Kennedy Jr., has said, Biden is the first president in U.S. history to mobilize federal agencies to silence his political opponents the politicians stated. Speaking on CNN, Kennedy Jr. pointed to his being blocked on various social media platforms during Biden's term in office and branded this as an effort to censor political speech that violated the First Amendment of America's Constitution. He also attributed the censorship to pressure from the White House. Kennedy stated that he still views Biden as a bigger danger even though Trump played a role in the January 6th riot at the U.S. Capitol. The question was, who is a worse threat to democracy? And what I would say is, I'm not going to answer the question. But I can argue that President Biden is, because of the First Amendment, Erin is the most important. The candidate told presenter Erin Burnett, I can make the argument that President Biden is the much worse threat to democracy. And the reason for that is President Biden is the first candidate in history, the first president in history that has used the federal agencies to censor political speech so to censor his opponent. The politicians Instagram page was suspended in 2021 over what its parent company Metta described as repeatedly sharing debunked claims about the coronavirus or vaccines. The account was reinstated in 2022 shortly after Kennedy announced his election campaign. Kennedy, 70, is the son of former U.S. Attorney General Robert Kennedy and the nephew of former U.S. President John F. Kennedy, each of whom were assassinated during the 1960s. The U.S. warns China against any attempts to help Russia in its war against Ukraine. The United States remains concerned about the strengthening of cooperation between Moscow and Beijing and is against any steps by China that could help support Russia's aggression in Ukraine. U.S. State Department spokesperson Matthew Miller said this at a briefing in Washington. We have made it very clear that we don't want to see China do anything to help support Russia's aggression in Ukraine, and we will continue to make that clear, he said, commenting on a planned summit meeting between the Russian and Chinese leaders. Miller also added that the United States remains concerned about the full-scale partnership between Moscow and Beijing. Kremlin leader Vladimir Putin plans to visit China in May to hold talks with Chinese President Xi Jinping. China should reason with Russia about the war in Ukraine, said French Foreign Minister Stéphane Sejeunet in Beijing. The Chinese government plays a key role in the respect of international law, including on Ukraine's sovereignty, and therefore we are clearly expecting that China will send very clear messages to Russia. Stéphane Sejeunet said during a press conference in the Chinese capital speaking alongside his Chinese counterpart Wang Yi. China presents itself as neutral on the Ukraine war and hasn't formally condemned Russia for the invasion. Instead, Beijing has deepened ties with Moscow, even deciding to ditch the United States dollar for bilateral trade. Last month, China's Special Envoy for Eurasian Affairs warned about the war escalating and said both Ukraine and Russia agree that negotiations rather than guns will ultimately end this war. China wants Europe to let Russia sit at the table for future peace talks, or Beijing will boycott such discussions.


In the name of Allah, the Gracious, the Merciful. All praise is due to Allah, Lord of all the worlds. Then, peace and blessings be upon all the Prophets, messengers and messengers. Beloved of Allah, the Merciful, in the name of Allah, the Merciful. Mustafa, Muhammad, and his family, the good, the pure. May Allah bless Muhammad and his family, Muhammad. Dear viewers, peace and blessings of Allah be upon you. We thank Allah, Subhanahu wa Ta'ala, that has blessed us to be discussing the Holy Verses of Quran and the Nations of Ahl al Bayt, peace be upon them. We have reached Surat al-Baqarah and the verse that we're Allah, Subhanahu wa Ta'ala, states, alladina, talking about the pious people, taqeen, alladina, yuuminuna, bilgayb, those who believe in the unseen, wa yuqimuna salah, and they maintain the prayer. Wa min ma'razaqnahum yunfikum, insha'Allah, one or two hadith. Wa ba'ad yuqimuna salah, and an explanation, insha'Allah, we continue. Wa min ma'razaqnahum yunfikum. Imam Sadaq, peace be upon him, says, this is in also Qafi Al-Sharif, Noble Qafi Volume 3, page 343 where Imam Sadaq, peace be upon him, says, tasbih al-Fatima, aleyha salam fi kul, yawmin, fi dobwere kul, salatin, ahabbu ilayy min salat alfraqah, fi kul, yawmin. If we want to add more and not to be happy and content with the bare minimum, you know, some people, when it comes to the religiosity, they do the bare minimum, but when it comes to the worldly affairs, they go for the highest and highest and they are not satisfied and happy with the bare minimum and they're not content. Well, we want to shift and change and change our perspective differently. When it comes to religiosity, we shouldn't be satisfied with the bare minimum. Too little bit extra, a little bit more mustahabbat, add on to it. When it comes to the worldly affairs, bring the expectation a little bit lower.  Imam SAW says, after Salah, insha'Allah, our Salah, we have done it. We have given the importance of Salah. As we mentioned in the previous episode, we sit on our prayer mat, we wait for the Salah, we pray our prayer in the same spot in our house, so our mind becomes in habit of when we come, we get Salah, knows okay, this is the time for communicating with the Creator, and we have organized place after Salah, Tespihat Fatima al-Zahra SAW, very, very, very recommended. This will be our first action plan in this episode, insha'Allah. Thirty-four after, we say Allahu Akbar, Allahu Akbar, Allahu Akbar. Thirty-four times, Allahu Akbar. Thirty-three times, Alhamdulillah. And thirty-three times, Subhan Allah. Imam SAW says, do for me doing this Tespihat Fatima al-Zahra after Salah, it's more loved to me, more loved, I love it more than praying one thousand rak'a every day. How much more thawab it has, Tespihat Fatima al-Zahra, very, doesn't take maybe more than, less than, it takes less than a minute, more, a probability. So, our Salah, it finishes, when the time finishes, we have to start seeing the effect and the influence of our prayer on our demeanor. Number one, being thankful because we read two times, if it's Salat-ul-Subh-Tur times, we read Surat-ul-Hamd, thankful, Alhamdulillah, Rab-ul-Alamin. So that should always remind us, after our morning prayer, until Salat-ul-Zahr, we have to keep being thankful to Allah SWT, as much as we can, as much as we can, saying verbally, thankful in our heart, being grateful to Allah SWT, in our action, showing thankfulness to Allah SWT. And then always seeking aid from Allah SWT, because in our Salah, again, we said, so we always seek aid from Allah SWT. Again, these are the effects of Salah that should come into our lives. So this is the perspective that Salah has given us. And also, being asking Allah SWT for us to be on the right path within every decision that we make, that needs constant reminder because we keep forgetting. That's why we need to pray Salat-ul-Subh. Start our day with a meditation with remembering to be thankful to Allah SWT for what he has given us, and asking Allah SWT to make our day and to aid us in our challenges, what we discussed about it in the previous episode, and then asking Allah SWT, Ehden al-Saraat al-Mustaqeem, O Allah, keep me firm and steadfast on your path.   We go to work. We go to school at home with kids, with parents, with husband, with wife. Whatever we do, we keep these three elements in mind. Probably, by the time of Salat, the Duhur time comes. Same way that our physical body needs, again, boost of energy, boost of protein, or our soul also needs another boost. Salat al-Duhur. Again, we gain that spirituality. Our soul is revived. Our soul is cleansed from the sense that we might have committed from being negligent toward Allah, other human being, the creator, and everything that is surrounding us. Again, that boost again. Until Maghla, again, we might go through things that we suppose not to go through, again, that cleansing before we go back to sleep. So this is what we need to get out of Salah and making sure that God forbid, God forbid, we are not from those people who don't pray. Insha'Allah, none of us are. Insha'Allah, we believe in the importance of Salah. But chapter 74, verse 43, we see Allah, subhanahu wa ta'ala, brings within the holy verses of Quran, people ask, people haven't asked from people of Hellfire, what made you into Hellfire? What happened to you? How come you end up in Hellfire? Qalu lam nakumina al-Mussaldeen. They will answer. People of Hellfire will tell other people that we were not among those people who prayed. So God forbid, God forbid, insha'Allah, we are not from those people. And then again, advice to the parents where they have said, what can we do for our kids to be praying and loving prayer? Allah says, chapter 20, verse 132, wa amur ahlaka bussalat wastaber alaiha and enjoin prayer on your family. And your followers and steadily adhere to it. Don't give up. Keep reminding them in the nice of manner, with gifts, with appreciation, with telling people, Masha'Allah, he's praying, see how he likes to be appreciated and how he likes to be thanked by him, a gift slowly, slowly, slowly, and keep wastaber alaiha and steadily adhere to it. Continue to remind them and remind them and remind them and don't give up and you cannot force. Allah Subhanahu wa ta'ala hasn't forced us. So we have to not give up and keep, that becomes an action plan, and keep telling them, keep reminding them in different ways, different times. We do whatever it takes to, to remind. Let's go to the next phrase within this verse. Insha'Allah, we finish this verse tonight. Time allows us, wa mimmar razaqnahum yunfiqoon. In Quran, 27 times giving charity has been mentioned with establishing salah. If you remember, within the previous episode, we said that if we summarize the mission of the Prophet to two objectives they had, two missions that they had. One, to construct and rectify the relationship between people and Allah Subhanahu wa ta'ala, which that, the epitome of that is salah, and the second mission was to establish that relationship between people and other people amongst themselves. So that is zakat. Wa mimmar razaqnahum yunfiqoon. We discussed about it within the previous episode. It's about giving and giving everything that we can from everything. Not only some people might think of zakat, wa mimmar razaqnahum yunfiqoon. They think of zakat, they think of wajibat act. Wajibat, it's not only wa mimmar razaqnahum yunfiqoon. It's not only wajib money that we must pay, which is Khoms and zakat, no other than that. Because zakat, it's only applied to nine things. The wajib zakat applies to nine things. To cows, if a person has cows, or has camels, or has sheeps, or has gold, which is the coin money, not the normal gold, the jewelry that they will know. The coin, if it's a golden coin, that people buy and sell with it, or silver. And from commodities, raisin, date, barley, and wheat. Well, this day and age, typically people don't have it, especially people in the West. We don't have cows, we don't have camels, we don't have sheeps, and we don't have golden coin or silver. And we don't have raisin, and date, and barley, and wheat. So we say, okay, we pray, and this doesn't, wa mimmar razaqnahum yunfiqoon. If we want to translate it, which none of the interpreters have said, this applies to zakat wajib. No, this is general, to all sort of giving, and it's not only to Khoms, anything that we can give. As we mentioned, for example, zakat ul alemin nashrokh, that if a person has knowledge, must educate other people and spread it out, where Imam Al-Sadr, alaihi s-salam, in Tafsir Nur al-Thaqalain, Volume 1, Page 26, says, under this verse, wa mimmar razaqnahum yunfiqoon, Imam says, anna ma'nahu, the meaning of this verse is, anna ma'nahu wa mimmar al-lamnahum yabuthoon. From what we have taught them, us Ahlul Bayt, alaihi s-salam, we have taught them, they spread this knowledge that we have taught them. It's very important. This is a duty upon each and every one of us, the knowledge that we have gained from Ahlul Bayt, alaihi s-salam, to spread to other people. That is a kind of zakat, especially after the tragedy of New Zealand, New Zealand, and 50 Muslim brothers who were killed. People are learning about Islam while it is obligatory upon us, the followers of Ahlul Bayt, alaihi s-salam, to teach them and educate them about the knowledge of Quran and from the perspective of Ahlul Bayt, alaihi s-salam. So, Salah is something, one thing, zakat next to it, again 27 times where Salah has been mentioned in Quran, zakat has been next to it. Salah, zakat, Salah, zakat, Salah, zakat. Salah without zakat is not accepted, and zakat without Salah is the same thing. We have, we've seen within the story of the life of the prophet, according to the historians he came and he kicked some people out of the Masjid. Why? He said they do Salah, but they don't pay zakat. Zakat is any charitable thing that we can give to other people. This is, again, zakat specific zakat to nine things. For example, if a person has 40 sheeps, somebody has a form, has 40 sheeps, every year that comes to it, he has to give one sheep to the needy people, or if he has five camels, he gives one sheep. So, that is zakat wajib, and also zakat al-Fitrah. It's also zakat wajib. No. Wa-min ma-ra-zakh-na-hum-mi-un-fiqoon. From whatever we have, the transition goes, and spend, spend out of what we have provided for them. Whatever it is, again, as we mentioned within the previous episode, if it's a reputation, if it's a knowledge, if it's a connection, he knows someone that can solve someone's problem. He's a doctor, he sees needy people, well, he helps them, he visits them freely. If he can't pay from his own pocket for the medicine, well, if he can't do it, he must do it. If I know how to, for example, fix a car, I see one of my community members, his car broke down, and he has nobody to fix it for him. Well, I do it for him for free. Whatever speciality that we have, whatever talent that we have, whatever potentials that we have within us, wa-min ma-ra-zakh-na-hum-mi-un-fiqoon, Allah that has given us, we must provide to other people. And we must be very, very careful not to neglect this part. So the action plan will be, let me diagnose myself, let me go through my lifestyle. Have I had this kind of personality of giving to the Islamic centers they need, to the needy people, orphans, sick people, people who want to get married? For example, right now in Iraq, I have people contacting me, because I came from a state they think, okay, I have connection. Alhamdulillah, I try to call other people, collect some money, help people who want to get married, but they don't have the means to get married.  We had a case, for example, a family had a cancer, and they had to go to chemotherapy, but they don't have money to provide. We talk with some people, they provide it, and we, Alhamdulillah, were blessed to be the means to this. So do whatever you can. So wa-min ma-ra-zakh-na-hum-mi-un-fiqoon, whatever that you have, you don't have to be a millionaire to pay. No, you have a ten dollars, you can pay a dollar, pay that dollar. I conclude with this story. Rasulullah was sitting within the masjid. A person came and he gave him one dinar as a charity, and he left. Another person came and gave him ten dinars as a charity. He left. The third person came and he paid him hundred dinars, and he left. Rasulullah told his companions that these three people, in the eyes of Allah, they are equal. The companions were surprised. How come the first person paid one dinar? The second person paid ten dinars. The third person paid hundred dinars. How come they are the same? Rasulullah said the first person, all of his belonging was ten dinars. He paid one tenth. The second person, all of his belonging was hundred dinars. He paid ten, that was one tenth. The third person had one thousand dinars. He paid hundred, that was one tenth. That's the ratio that Allah SWT sees. So, if we don't have it yet, let us have that characteristic. Let's ask Allah SWT to give us that blessing to be able to give for the sake of Allah SWT and Ahlul Bayt Aalim, salam. We will conclude with the most important du'a, and that is du'a Faraj, insha'Allah, asking Allah SWT to hasten the appearance of our beloved Imam Mahdi Aajir Allah Faraj Al-Sharif. Bismillah ar-Rahman ar-Rahim. Allahumma kulli waliyyika al-Hujja min al-Hasan Salawat uka aleyhi wa ala aba'ih fi haadah sa'atuhi kulli sa'a'ah waliyyan wa khafradah wa qa'idah wa nasirah wa dalilan wa aina hatta atuskanu wa ardakatua wa tumatta'u fiha taweela barahmatika ya arhamar rahameen.


We can quantify how fast gas molecules move by their root mean square or RMS velocity. The internal energy for one mole of gas molecules, roughly 22 liters of gas at room temperature, is u equals Avogadro's number times the average kinetic energy of a molecule, one-half m times the mean square velocity. This equals the molar heat capacity, three-halves Avogadro's number times Boltzmann's constant, times temperature T. Avogadro's number times molecular mass is the molar mass, M, and Avogadro's number times Boltzmann's constant is the gas constant R. Substituting these and multiplying the previous equation by two, we get Big M times mean square velocity equals 3RT. Solving for the square root of the mean square velocity and calling this the root mean square velocity, we obtain VRMS equals square root 3RT over Big M. The molar mass of argon is about 40 grams or .04 kilograms. Let's take a temperature of 300 Kelvin, about 27 degrees Celsius or about 80 degrees Fahrenheit, and a nice warm day. Then the RMS velocity of atmospheric argon is 433 meters per second. For comparison, the speed of sound and air at this temperature is 347 meters per second. Air molecules are moving very fast. This presents us with a fascinating picture. When exposed to warm air, our skin is being bombarded with countless molecules traveling at supersonic speeds, yet we don't perceive this bombardment. If the molecules are equally likely to be moving in any direction, our experience will be of nice, warm, still air, and our local wind turbine will sit motionless. Yet if the molecules have a common macroscopic velocity that is only, say, about 1% of their microscopic velocity, we will feel a breeze and our local wind turbine will turn and generate electricity. Kinetic energy varies as the square of velocity, so the microscopic kinetic energy due to the thermal motion of the molecules is on the order of 10,000 times the macroscopic kinetic energy of the wind. Yet our wind turbine converts only the tiny macroscopic component into electricity, and the macroscopic component is only there on breezy days, while the microscopic component is always there. So why don't we build a machine that extracts the vastly greater and always present microscopic kinetic energy of the molecules? The answer is because it's not possible, and the reason why has to do with the second law of thermodynamics, which we'll consider in a future video. Another important characteristic of the microscopic motion is how far, on average, molecules travel between collisions. We call this the mean-free path. Here's how we can get a rough estimate of this. Assume molecules have diameter D. In fact, molecules are not hard spheres with a well-defined surface, but we can take D to be an effective diameter. As a molecule travels through space, it sweeps out a cylinder of diameter 2D and length L. The diameter is 2D because if the center of another molecule lies on this cylinder, the two will just collide. From the gas law we can solve for V over N, the volume per molecule, as kT over P. We set this equal to the volume of the cylinder, L pi D squared, so that there is, crudely, a 50-50 chance of the molecule encountering a second molecule as it sweeps out this volume. Solving for L, we get kT over pi D squared P. Plugging in values, T equals 273.15 Kelvin, which is zero degrees Celsius, standard atmospheric pressure, and an effective diameter of 3 times 10 to the minus 10 meters, or 3 angstroms. We find a mean-free path of a bit more than 10 to the minus 7 meters, or 1 tenth of a micron. Having characterized some of the average microscopic properties of the gas molecules, let's consider the state of the gas system as a whole. We can view this at the molecular level, in which case we talk about the microstate of the system, or at a larger-scale level, in which case we talk about the macrostate of the system. The dynamical state of a single particle is characterized by its position and velocity at a given time. For a system of N particles, we need to specify the position and velocity of each particle. This provides a complete description of the microstate of the system. Then, assuming we have a model for how the particles interact with each other and the walls of the gas container, we can calculate the force on the Ith particle as a function of the positions and velocities of all the particles. By Newton's second law of motion, this gives us the Ith particle's mass times acceleration, denoted by ui dot. Acceleration is the rate of change of velocity, so multiplying it by a small time increment dt gives us the change of velocity, dui. And velocity is the rate of change of position, so multiplying it by dt gives us the change of position, dxi. We can use these increments to update all particle positions and velocities and then repeat the process indefinitely to predict the system's microstate at any time in the future or past. This is the basic recipe of Newtonian mechanics, and it works, in theory. In Newtonian practice, the obvious practical problem is that a macroscopic volume of gas can contain millions of billions of molecules. The bookkeeping and computations required to describe the evolution of so many particles is simply intractable. And, as we'll see shortly, there's a more fundamental problem when trying to track the microstate of a gas through time. Yet, the macrostate of a gas, characterized by quantities such as pressure, volume, and temperature, is describable, even though it arises from the indescribable microstate of its vast number of molecules. Now we come to the more fundamental problem with describing the microstate of a gas, chaotic motion. The motion of colliding balls is chaotic. Specifically, a small change in the initial state grows exponentially in time. Let's explore this with a simple model. Two blue balls are fixed in place. A red ball moves between them, bouncing back and forth. Can we predict the motion of this ball arbitrarily far into the future? To represent gas molecules, we'll use some of the order of magnitude values we've developed so far. The balls are about three angstroms in diameter, about 1,000 angstroms apart, and the red ball travels at about 400 meters per second. The time between collisions is the distance between the fixed balls divided by the speed of the red ball, about 2.5 times 10 to the minus 10 seconds. The inverse of this, about 4 billion, is the number of collisions per second. An atmospheric gas molecule, under typical conditions, undergoes billions of collisions per second. Here is a numerical solution of the dynamics of this system. If the centers of the three balls fall exactly on a common line, and if the velocity of the moving ball is exactly parallel to this line, it will bounce back and forth forever. Now, let's consider what happens if the red ball's velocity is tilted by an angle theta zero. As it travels a distance L, it will drift vertically, so its next collision with a blue ball will not be head-on, but will have a vertical offset. For theta zero very small, the ball's centers will be offset horizontally by the diameter D, and vertically by H equals L times theta zero. The red ball's direction of motion will make an oblique angle, phi, with the line through the ball's centers. When it bounces off the blue ball, it will travel at that same angle, but in the opposite direction relative to the center line. We can determine the angle phi from the top figure as approximately H over D, which is approximately L over D times theta zero. After one collision, the red ball will be traveling at an angle theta one, which is approximately two phi, or two L over D times theta zero. The collision has amplified the initial angle theta zero by a factor two L over D. The distance L is much larger than the diameter D, so two L over D is a larger number. The red ball will now travel at an angle theta one. The next collision will amplify that by a factor of two L over D. After n collisions, it will travel at an angle theta n equals two L over D to the nth power times the initial angle theta zero. The angle grows exponentially with the number of collisions. Eventually, the red ball will fly off into space and no longer collide with the blue balls. This will happen if the vertical offset H is equal to or greater than the diameter D. Let's write D equals the distance L times the angle theta n, which is two L over D to the nth power, times theta zero. We can solve for theta zero as D over L times D over two L to the nth power. This is the initial angle that will cause the red ball to undergo n collisions before flying off into space.


Well, thank you. Thank you everybody for joining in for this talk. So I'm going to be speaking about some work which I've been doing with my collaborator, Owen Maiden, who is another open force field researcher, and we've been really trying to do a data-driven study to try and elucidate which types of physical properties should we be optimizing the non-bonded van der Waals interaction parameters of our force fields against to yield the largest improvements in terms of force field performance. So to begin, I just wanted to give an update on the infrastructure, which is not only going to allow us to do this study, but to do many studies like it, because we're a really quite exciting point where we can now almost routinely refit the non-bonded interaction parameters of our force fields against even larger data sets of physical property data. And this is really made possible by this seamless integration between force balance, which is our fantastic optimization engine created by leaping Wang and his group at UC Davis, and the open-fifth evaluator framework, which I've built as an automatic scalable framework for estimating data sets of physical properties from molecular simulation, as well as their gradients, which force balance can then consume to take informed optimization steps, leading to, with almost no human intervention, these nice refit force fields. Additionally, in addition to just having this refitting pipeline, we can now routinely take our refit force fields and then use the evaluator framework to benchmark them against to gain large test sets of physical property data, gain insights into where the limitations in our force fields may be, and then take those insights and learn and make data-driven decisions based off them to decide what changes might be needed to make to our force field functional model. Maybe we don't have enough chemical environment types in the right area. Maybe our training data set is deficient in certain areas, but we can now do these kind of cyclical self-consistent optimization epochs with our almost no human intervention, with the only human intervention really being required to use the training and the test sets, but even that we're working to automate. And so the study that I'm going to talk about now is really the first major study that we've done using this automated fitting infrastructure. And so we've been trying to determine which types of physical properties are most informative to train the van der Waals non-bonded parameters of our force fields against. So historically, most force fields have taken the approach to train their non-bonded van der Waals parameters against only pure properties. So pure liquid densities to try and get information about the relative sizes of the molecules, pure entities of vaporization to try and get some information about the cohesiveness between energies. But while pure densities are kind of routines trained against, and until these vaporization are challenging for two really main reasons. For one, it is really, really difficult to find diverse and especially accurate data sets of entropy of vaporization measurements, especially data sets which are permissively licensed and open for us to use. So there's not much data to train against. And actually in terms of calculating these properties, we know that our fixed for our fixed charge force fields can't actually or can't at all capture the change in polarization when a molecule goals goes from the liquid to the gas phase. So if we're training against this, we have to either hope that this this contribution from the change in polarization is small. But if it's not worth essentially building electrostatic contributions into our van der Waals parameters, which we don't want, all we have to try and correct for this. But this is just another potential source of error into our calculations. So we as a consortium have been really interested in maybe moving away from pure properties, or maybe at least finding properties which are complemented to them, especially a mixture of properties, properties of systems of mixtures, especially enthalpies of mixing, binary enthalpies of mixing as a potentially better replacement for entropy of vaporization as it doesn't have these limitations, and also binary mass densities of liquids, either to complement or possibly replace these pure pure liquid densities. And we're excited about these properties for a couple of a couple of key reasons. For one, there is a significant amount of mixture data to available to us. There's hundreds and thousands of these mixed properties available in the NIST thermal archive of of thermal physical properties, which we can automatically pull down and pure rate in machine readable formats. We think machine mixture properties with mixed properties, it should also be easier to build training sets which are targeted towards reproducing well specific interactions in an almost modular way. Consider if you want to capture, say, alcohol ketone interactions with mixed properties, all you'd have to do is find a mixture where one component is an alcohol, the other component is a ketone, and on mixing use and capture this interaction. Whereas with pure properties, you're going to have to find these weird molecules which can take more and more different functionalities. So it's a lot more difficult there. And I think one of the big potential benefits, and this is mainly just a hypothesis at the moment, but we think possibly improving on these mixture properties, especially entropy of mixing, may correlate with also improving our performance at predicting things like binding for energies, salvation for energies, partition coefficients, things which rely on a lot of different interactions between components which which entropy of mixing will inform us about. So to kind of investigate this problem, the approach we took is to take the open force field 1.0.0, essentially the first parsley force field, and then refit a select number of its van der Waals parameters against different, against essentially four different training sets, where each training set contains a different set of combinations of the properties that I just mentioned. So one containing only those pure properties is kind of a historical baseline. One containing only those mixed properties, which I spoke about of being of interest. But also then combinations of these pure and mixed properties, with the idea that we're not quite sure if mixed properties are enough to constrain things sufficiently. i.e maybe we train on mixed properties and improve mixed properties, but at the expense of making pure properties worse. So we want to also see, you know, do we need to include pure properties to make sure things are kind of consistently constrained. For each of these training sets, we just focused on a limited set of functionalities for which we had the most diverse data available to us. So sets of alcohols, acid, zesthes, ethers, ketones and alkanes. But with the hope that any results gained from this study on this functionality set should continue to generalize as we move to broader, more diverse training and test sets, like what we required for when we try and refit for sage later in the year. So in order to do essentially these four unique optimizations, we need to build both a pure training set and a mixture training set. And then for those training sets, which contain a combination of both types of properties, we could just combine these two distinct sets in different ways. Essentially the pure training set that we constructed had a total of about 56 pure data points, and even a mixture of pure liquid densities and pure entities of vaporization, all the kind of ambient conditions, but for a diverse range of different components exhibiting the functionalities of interest. For the mixture sets, this contained about 200 data points total, so about an even split of entropy of mixing data points and binary mass density measurements, where each of these components was the mixtures in the set. And for each of these mixtures, we tried to choose data points for three different compositions, three different more fractions for each mixture at 25%, 50%, and 75% composition, again at ambient conditions. I think one of the main goals in trying to build this mixture training set is we're not only wanting to get a diverse set of molecules, but we really wanted to select mixtures which exhibited behavior all the way down from almost no deviation from ideality, i.e. where these mixing properties are mainly determined by the properties of the pure components themselves, all the way up to large deviations from ideality where in the mixture there's new interactions which weren't necessarily present in the pure components, such would be the case in say alcohol and esters, where esters themselves can't form hydrogen bonds, but they can with alcohols. And it's this region of large deviations from ideality that we think training against these mixed properties are potentially going to be the most informative. So then for each of these four training sets that we built containing these four different combinations of properties, we did our optimizations using our automated fitting infrastructure, and here I'm showing the objective function of the fit as a function of the optimization iteration, and one can see that all four of these, the objective function decreased almost immediately by a function at a factor of about two to three-fold. So it does definitely seem like our optimization is pushing things in the right direction. One can also look at the RMSEs of the different properties at the beginning in the end of the optimization and almost universally across the board, things improved, with the exception of maybe one outlier which I don't necessarily want to go into in this talk. So the question then is how well do these results generalize to broader test sets of molecules? So to kind of test the generalization, we built a significantly larger benchmarking set than the training set. It contained about a thousand total data points. It contained again pure liquid densities, and the piece of vaporization for all of those different functionalities that we were training against contained a significant amount of mixed data points, so entities of mixing, binary mass densities again, but also we included binary access and all the volumes just to make sure we're getting the difference between the pure and binary densities, hopefully correct. And again for each mixture included, we looked at three different compositions and kind of ambient conditions. I think the main deviation here between the test and the training set for the mixed properties, as well as for the mixture training set, we only included say alcohol, alkane, alcohol ester, ketone ether, alkane, ether and alcohol acid mixtures, but in the test set we tried to contain all different permutation pairs of these different functionalities, including things like alcohol, alcohol, ether, ester, even though we didn't necessarily train against them just to ensure things were generalizing. So we did the benchmarking, we spun up our automated benchmarking infrastructure. Here is the results of that. So here I'm showing the root mean squared error of each of the different properties that we were benchmarking against, where each of these colored bars represents the results of benchmarking this property against this force field, which was optimized against this kind of unique combination of properties, where this red bar was a benchmark done upon the initial open FF 1.0.0 force field as kind of a benchmark baseline. So any improvements relative to this red bar means things got better essentially. So what we can see from this is that almost universally across the board, things did improve regardless of the types of properties that we were optimizing against, but looking at this a bit more particular. So this far right bar, which I believe is light blue, was a benchmark done against the force field optimized against the only WRO properties. So one did see some improvement in the pure properties. These are 95% confidence intervals, but so we can't really say improve too much because there's no statistical significant improvement here when training against only WRO properties. We did see improvement in the binary mass density, but we saw almost no significant improvement in the entries of mixing and actually a significant degradation in the excess molar volumes. And again, you know, going back to what I was saying at the start, we were hoping and we kind of wanted to see improvements in entropy of mixing because we think this is going to be correlated with those properties more of interest. So compare that to these first three bars, which were benchmarks done against force fields optimized against mixed properties. We do see significant improvements to the mixed properties, even though we're benchmarking on different type of interactions that we didn't necessarily train against, we do see significant improvements to the binary mass densities and we don't necessarily see at least improvements, but we don't see degradation at least of the binary excess molar volumes. But I think one of the key things that we do see is actually if we look at their performance of predicting the pure properties, at least, again, not too much significance here probably due to the small size of the pure property benchmark set, but we don't degrade by only training on mixed properties. We don't degrade their ability to predict the pure properties. So it does seem like we can get away with only optimizing against mixed properties without necessarily having to worry too much about things being being under constrained. And again, just highlight the reason why we think we should be quite excited about these improvements in entropy of mixing is because and we're testing this hypothesis now, but we hope improvements here should correlate well onto some of those other mixed alike properties which are the most of interest to us. So one can also look at the r squared, so here exactly the same kind of plot, but now on the y axis we've got the r squared and not too much extra information here. And theory main takeaway is actually when we benchmark the entropy of mixing against a force field optimized against the mixed properties, we did see significant improvement, a significant improvement in the correlation of these properties. And we can dig into that just a little bit deeper. So here on the x axis, I'm showing the estimated values of the entropy at the binary entropy of mixing for each of the different force fields that we're benchmarked against on the y axis, the experimental value for the binary entropy of mixing. And if we look at this left most plot, which was the benchmark done upon the open ff 1.0.0 force field, we see these large clusters where there's a significant systematic error in certain mixtures of functional groups, where each of these colored groups, colored dots, represents a different mixture of different functional groups. So especially alcohol and esters, alcohol and ketones, and possibly even alcohol and alcohols were poorly represented in the initial starting point. And if we look at this end plot, which was the benchmark done when we'd only retrained on pure properties, and we really don't see any help in improving this systematic error, or at least not much. But compared against when we optimized against the mixed properties, perhaps not too surprisingly, the systematic error almost completely went away. And the reason why I'm highlighting these orange green and red dots in particular is because these are mixtures which show significant deviation from ideality, i.e. there is interactions present in the mixture, not there in the pure. So including these mixed properties optimizing against these mixed properties do seem really important for capturing those deviations from ideality, and we do think this should help improve our force fields significantly. So just to wrap up quickly then, I really want to emphasize again, we've got down with this fantastic automated britting infrastructure, both on the QM side, which I've shown here, but also on the non-bonded side. These kind of studies would have been so laborious and human intensive previously, but now we can do them almost effortlessly, when most of the human time just goes into building the training and the testing sets. So this infrastructure is allowing us to do data-driven studies into force field science. We're now seeing from these studies that actually mixed properties do seem to be a good target to head towards for future force field optimizations, especially it seems like we can replace this problematic enthalpy of vaporization with our enthalpy of mixing measurements, and not degrade, but actually hopefully gain performance. And the last thing, and the kind of missing piece of the puzzle, which unfortunately the calculations haven't finished in time, but we're now doing these benchmarks against expanding the benchmarks to include a host guest binding affinity to really try and test this hypothesis that improving these mixed properties in particular enthalpy of mixing do lead to improvements in these host guest binding affinity and other mixed alike properties of interest. But we should be sharing those results in the next couple of weeks. So as everything that we do in the consortium, all of the input analysis scripts for the study is on GitHub, the frameworks that were used are available on GitHub. And just to finish off, I'd again like to thank my collaborator Owen Maiden for his his help on this project, Philippine Wang for his substantial efforts in helping to create integrated evaluator into force balance, and also Christopher Bailey from OpenEye who has given such a significant insight into helping to design design and analyze this study. And then of course everyone for their attention. So thank you so much and if there's any questions, I'd be happy to kind of answer those now. Hi Simon, this is Jamshad. Hi Jamshad. Really good stuff. Nice to see you again. I was just thinking out of this, there's a really good scientific question that could come out nosefully easily. And that is that, you know, the potential functions particularly with van der Waals parameters, we did the parameters, I guess originally were optimized on pure systems. And then we have these built-in Lorentz pathological mixing rules. Now, you know, I'm not quite certain what the history is to the mixing rules. I've intended to look into them at some stage, but I've never really done that. But there is a good rationale for them or whether they are empirical. Now, what this could in fact is to actually determine what the limits of those mixing rules are. So if you did actually focus on the pure properties and then see how well we are able to reproduce the mixing data. So my point is that one, there's two roots here. One is to, of course, to optimize the van der Waals parameters themselves. And the other thing is, or maybe one could do both. And the other thing is to actually focus on the mixing rules too, that one could perhaps either derive a better mixing rule. Because at the moment, I think those two are also parameters. Yeah, so I think they're absolutely spot on with this. And this is an absolutely fundamental issue that we want to look into the future. Like you say, we know these mixing rules have been chosen historically. I think the mixing rule for the sigma parameter was chosen based on hard spheres. And I think the epsilon mixing rule parameter was somewhat chosen to reproduce effects of electronegativity. But as a consortium, we definitely want to look into this and explore what mixing rules we should be using and what impact they have. So like you say, we could potentially do a study such as this to explore the question. And that is definitely an avenue. But we're also looking at more quantitatively rigorous approaches to also study this. So on Friday, my colleague Owen Maiden is going to be speaking about Bayesian methods to try and quantify, to kind of put a number on, you know, what's the performance of these of these different mixing rules and other various aspects of the force field for a given data set. And so that's also a potential study and route that we're looking into, although that one's a bit further off. Thank you. So have you, do you have thoughts yet on places where it looks like you especially will need to couple charge fitting into this? I guess as BCC fitting comes online or is that a little too far ahead to see yet? Yeah, I mentioned when we did the optimization and actually we saw this in the benchmarking study, when we kind of split up the performance into, you know, what kind of environments were there, almost universally across the board, we saw that the performance of predicting ketones density, either binary density where ketones were in excess of pure density where ketones were just on themselves, almost universally degraded. And it was just ketones. So we think this may be linked to maybe the van der Waals having to accommodate and kind of correct for deficiencies in the charge schemes there. So I think this is one of the avenues where we think this might be might be important. But we're still in the early stages of looking into that. Great, thanks.


Hey, kids! Today we have a very special guest, Officer Dan! Hi, kids! We're going to learn everything you kids need to know about the police! Haha! Yeah! Number one, police officers have cool lights on top of their cars! Super cool! Number two, police officers wear neat badges! Really neat! Number three, police officers have no legal obligation to protect you from imminent harm! It's true! I can sit back and watch as someone stabs you repeatedly! Yeah, that really happened! A subway train in New York! The victim himself had to disarm the serial killer who the cops were there to find!  It's settled law! Yeah! It's kind of amazing!  Score too high on an entry to ZAM, and some places won't let you into the police academy! They think you'll get bored and quit! Number five, the more arrests an officer makes, the more court appearances are required! Which means more overtime and larger retirement benefits! Oh yeah, we call that pension spiking! I can retire at 45 with more income than the mayor! It's a pepper-sensitive structure! And I can keep that pension, even if I commit murder! Haha! Number six, some police officers get to work with doggies! The only dogs we don't shoot on sight! Number seven, police officers can violate your rights and get away with it! That's called qualified immunity! Number eight, becoming a police officer takes a lot of training! It took me three and a half months to become a police officer! It took Mr. Barber two years and three exams to become a licensed hair stylist! Yeah well, scissors are sharp! Number nine, cops aren't required to know the law even as they arrest people for breaking it! I can pull you over and search your vehicle without any legal justification! As long as I think I have justification, anything I find is admissible in court!  Is that real? We have very good unions! Holy s***! Number ten, police officers get paid with our taxes! Police misconduct is also paid through your taxes! So, we pay you to protect us if you don't legally have to do? Yup, that's civil law! And then, when you accidentally or intentionally violate us, you pay for all that too! Wow, that's really s*** dude! Number eleven, the police car has a cool siren! Haha! Haha!


When people hear ergonomics, they often think of this, but in truth, ergonomics is simply fitting the workplace to the worker, rather than the other way around. And that includes lots of workplaces, construction, manufacturing, transportation, health care, you get the idea. So today, we're going to cover some ergonomic basics that apply to virtually any workplace. These include proper posture, managing forces, and excessive repetitive motion. Ready? Let's start with posture. Sitting, standing, walking, it really doesn't matter. Proper bone and joint alignment is key. So straighten up that back, keeping ears, shoulders, and hips in a straight line. Good. Now squat by hinging at the hip. Uh-uh, keep everything aligned. Now sit. Perfect. Maintaining that posture through your day and work, any work will reduce fatigue, eliminate strains, decrease stress on joints and ligaments, and have you feeling great. If posture is good, forces are evil. Why? Because they assault your posture constantly. There are lift and carry forces, grip forces, push and pull forces, and just plain old gravity. Hey, let's help you out with this. To avoid injury, don't lift, pull, or push too much. Let your posture guide you. And if need be, get help or use a hand truck or other tool. Also, move that body. If you stand up a lot for work, sit.  Every change is an opportunity to realign and think about your posture. Our last Ergo 101 topic is repetitive motion. Doing the same task over and over and over and over and over and over and over and over, you get the point. Repetitive motions from jobs like these can cause carpal tunnel syndrome, joint problems, poor circulation, and more. Fortunately, these injuries are easy to avoid. First, by arranging your work area to fit you and support good posture, and second, by stretching. Stretches like this, or this, or this, well, maybe not that. Every 30 to 60 minutes can make a world of difference and prevent injury.