So yeah, it's a great honor to have Dave Renzen here with us. Dave is a technical advisor to the Alphabet CFO which is the Google, you know, he works for Google. Dave happened to be the original creator of Customer Reliability Engineering and also he's been deeply involved, you know, with basically leading Google's global network capacity planning as well as serving in a variety of strategic roles for Google's Site Reliability Engineering and we thought who better than Dave to kick off today's session which is kind of a new theme as you all know. We've started the, you know, this year we introduced the Chaos Engineering or as some people prefer calling it a liability engineering theme and having Dave with us to kick start the day with this really fascinating topic and I'm very intrigued by your topic.  Thank you very much for coming today. Over to you. Thank you. Thank you very much, Nuresh. Can folks just give me a thumbs up and make sure they can see my slides. Fantastic. Thank you very much. All right. Thank you for the introduction. Good evening from California and the United States, everyone. I am Dave Rensen. I'm a senior engineering director at Google and today I want to talk to you about one of my favorite topics which is how to completely ruin the things you care about in your life by making them perfect. That may seem a little counterintuitive. So we'll go through this. I'm going to move through these slides kind of quickly. There's a lot of content here and I want to make sure I leave enough time for questions at the end. Here's the basic outline of what I want to talk about. Perfection is unattainable. It just is. And we'll go through demonstrating that. But we live in a world where we are encouraged to compare ourselves, frankly, and our work to sort of this impossible standard. So today I want to talk about why chasing that goal in particular will destroy the systems, the companies, the relationships, the lives, frankly, of the people you are supposed to be helping or making better. And why learning to live with mistakes and in fact injecting some imperfection into your day-to-day life actually makes things better in the end. So if there's any takeaway here, it's this. Perfection is your enemy and you should fight it every chance you get. And the good news is, I promise, and this is not link bait, that my last slide in this presentation I will tell you the two-sentence secret to happiness and success in your life professionally and personally. And that's not a click bait headline. I mean that for real. So let's start. I was born and raised here in the United States. And so I have a, you know, let's call it a traditional Western education when I was, I don't know, 12 or 13 years old in school. I was asked to read this poem. We were all assigned this poem. It's called An Essay and Criticism by a British author's named Alexander Pope. And it's a really interesting poem because it's one of these pieces, one of these pieces of art or work that has this unique property that no one's ever heard of it, but everybody knows it. Because a number of very famous expressions, at least famous expressions in English come from this poem. And so some of you will have heard these before, like a little knowledge is a dangerous thing. The idea that we are most dangerous when we start to learn about a topic. It's a well-known phenomenon in the world that first-year medical students, for example, all think they have all the worst possible diseases because they've learned a little about how they work and not enough to experience to understand when they really do or don't have it. Another expression that comes from this poem, which is fairly common in English, is Fool's Russian where angels dare to tread. The idea here is that the older you get, the better your judgment and wisdom. And you're a little more careful about the things you do because you can evaluate the risks a little better. But probably the most famous saying to come from this poem is to err as human but to forgive divine. This is a really important statement, particularly the first part. It is a central feature of being a human being to make mistakes. Well-intentioned errors. This is what Pope was saying. And by the way, if you've ever heard that expression before, usually if I'm talking to a large audience and I'll ask them who's heard of this expression and then you talk to them a little bit more, most people think like maybe William Shakespeare or somebody wrote it. But it's not. In fact, now you have a probably useless piece of trivia that that expression tears human to forgive divine actually comes from Alexander Pope. Now that sentiment about humans, a basic facet of humanity is imperfection, is making well-intentioned errors, doesn't begin anywhere near Pope. In the Western tradition, it goes back at least 2,000 years. When I got a little older in high school and college and had to read some of the, you know, more famous Western philosophers, there was Seneca and he said Erari Humana mess said in Erari Perseverari diabolicum. Lucy translated means to err is human Erari Humana mess. But to persist in error said in Erari Perseverari is diabolical or inhumane would be the right way to think of it. And what he meant by that is it is the most natural thing in the world as a human being to make a mistake. But after you've discovered you are making the mistake to knowingly persist in a condition of error because of say pride or ego. How many of us have sort of had the sense that we're doing the wrong thing but decided not to change what we're doing because we were just sure that if we banged our head against it a little longer would get better or maybe laziness, maybe we don't want to do the work to figure out what the right thing is to do or sometimes just because we're stuck we're like a deer in the headlights. We're overwhelmed by how wrong it is and we're just not, we're very uncertain with what to do next. But anytime you knowingly persist in a condition of error you are doing something wrong, something unethical rather something frankly inhumane. So we might say this that in fact we embrace our humanity by embracing the fact that we are fallible, that we will make mistakes. In fact it is maybe the central most characteristic of what it means to be human being is to make a well-intentioned mistake. Now with that in mind we're going to talk about what that means for reliability and what it means for running systems and in the end why understanding that how it leads to sort of the secrets of life.  So I apologize if some of this is new or rather excuse me some of this is a repeat for some of you. I'll go through quickly. I'm going to use the word reliability a lot in this conversation and I understand that that term encompasses a lot of aspects. When we talk about a system whether it's a computer system or an economic system being reliable we might mean availability meaning is the system there when I need it does it answer or correctness when I ask it a question does it give me the right answer or latency you know that it responds in a reasonable amount of time or error rate you know how often when I go to use a thing does it give me an error rather than respond with some response. All of those are aspects of reliability and we might mash some of them together as we talked with each other but for the purposes of this conversation let's use a more intuitive definition and just say a system is reliable when it works in the way that our users expect and need. So our computing systems are reliable when it works when they work roughly as our users expect. Oh and also I'm going to use terms like users and customers and stakeholders and I'm going to use those roughly interchangeably in this conversation don't don't read too much into that. All right so I just spent a few minutes telling you that it is the most human thing in the world to make mistakes right that is a defining characteristic of what it needs to be human to make a well-intentioned error. Well I'm also going to I'm also a Google SRE like I really care about reliability a lot. So I'm also going to say this that the reliability of the system is its most important feature period more important than any other feature you can think of my logic goes like this if a system is not reliable any system meaning it does not work the way our users expect to need then they will not trust it and if they do not trust it they will not use it where there are alternatives or they'll invent alternatives and eventually there will be no users of the system so you will have some system with no users which means it has a value of zero and you can see it in all and I'm just mean computing systems it's election season here in the United States so we might even say a system of government works that way too and by the way the way you get feedback in a system of government is with a vote and we see this throughout history so again not just in mechanical systems or computer systems but economic systems and whatever alright so reliability is the most important feature I don't think I'll get a lot of disagreement in this crowd some of the things I think you won't disagree with but we can save it for the Q&A I'm an SRE which means I am very concerned in a good way like it's really important to me to automate away all the things that can be automated but the question is what are the things that can be automated what are the things we can give to computers well to answer that question we have to realize two important things one humans are terrible machines we are terrible computers never mind the fact that the term computer was actually a job title for people who actually calculated figures by hand computers as we understand them little boxes that do things repeatedly humans are terrible computers when I'm in front of a crowd sometimes if it's large enough I'll do this experiment like three slides ago well I'll ask you all to raise so your left hand and then I'll take a picture and roughly at this point in the presentation I will show you a picture the picture that I took and you will see that in a crowd of a hundred or a thousand people about 20% of the people will have raised the wrong hand even though everyone in the crowd knows they're left from right and everyone heard the very simple instruction raise your left hand this is because humans are terrible computers if you ask a human being to do the same task over and over again about 20% of the time they're just going to do the wrong thing that's a little depressing but it's also a little hopeful because computers are terrible humans as it turns out and will always be terrible humans I think and it comes down to this the difference between intuition and judgment these are two terms that people confuse all the time or they mix up all the time so for our conversation today I want to define this judgment is what we as people use to make a decision when there is no more useful data to get or no more time to get it judgment gets better with experience our judgment is adults it's better than it was when we were children and the older we get the better our judgment will become intuition is different intuition or gut let's say is what we use where we don't feel like getting more facts or spending more time we tend to use intuition when we're being a little bit lazy let's just say and they are not the same things we can prove that humans are uniquely good at judgment and that human judgment gets better over time with experience we can also by the way experimentally prove that humans the human intuition in particular is not much better than chance well if human intuition is not much better than chance and I can point you to some studies about how you can prove this yourself then I can program a computer to use intuition right because I can program a computer to flip a coin so the answer to the question is what do we automate what do we give to the computers boils down to this give to the machines everything that doesn't require human judgment and take from the machines everything that does so maybe the machine is very good at telling me say we were you know in a court of law maybe a machine would be very good at telling me if a photograph has been doctored has been altered in some way that doesn't mean that we should allow the machine to tell us what to think of that information what verdict say to have in a trial that is an issue of judgment okay I don't think any of that's going to be super controversial sometimes when I talk to people however the next couple things can you know it can be a little iffy and this might be an area where we talk in questions I will say to you that based on what we have just said before that a goal of perfection at zero errors 100% success is not only unrealistic it is counterproductive it is damaging so my argument goes like this there is no system ever created by human beings no mechanical system no computing system no economic system no political system ever created in the history of human beings that has been perfect that has been 100% successful or had no errors that is not terribly unusual to think of because I don't think anyone could name me a system in nature that is the same too human beings are the product of you know hundreds and hundreds of thousands of years millennia of evolution but still when our genes copy they copy with mistakes so even that process which has been refined over millennia makes mistakes all the time well if nature doesn't build perfect systems and it does not and if we have time later I can explain to you why the sun is not an example of a perfect system then it is not reasonable to think that humans will in fact we might say that it is inhumane because it denies the essence of humanities what Seneca and Pope and others have said but it is worse than that your users do not need perfection in fact they will not notice perfection so when you pursue perfection to the extent that you are pursuing it past the point at which your users will notice it you are wasting time and effort and each marginal unit of improvement by the way is exponentially more expensive than the previous one so you're wasting opportunity just to stand still for a thing your users won't care about and not only that because it's not achievable eventually if you grade people on a scale of perfection you will create dishonesty so my first job at Google was to build Google Cloud support and I never built a support team before it was fine and one of our original goals because I didn't know any better is we had a goal of 100% customer satisfaction and in the beginning it was fine because as we got better customers got more satisfied yay but then we got to an asymptote over which we could not reasonably improve and what we started to notice which thankfully we noticed early and corrected quickly is because people couldn't get couldn't really achieve better than let's say 95% customer satisfaction they would start unintentionally doing things like only sending customer surveys to customers they thought would be happy which is a lie even if it's an unconscious lie you will turn your employees and the people you work with and people in your life into liars as honest people if you expect perfection from them so the good news is is no user of any system demands perfection no computing system you build needs to be perfect period end of story because you don't have users using it all the time or the path between you and your user is imperfect the phone they are using is not perfect the networks they are on is not perfect etc etc you only need to be as good as the least perfect thing that sits between you and your user and in fact there is a magic line and it is truly magic because it has this wonderful property when you are under the magic line your customers will be unhappy and they will tell you but the minute you get over the magic line they will become indifferent so we did this exercise as we were building cloud support as our systems got more reliable our customer satisfaction improved no big surprise but then at a certain level of improvement customer satisfaction did not increase there was a very steep knee at the top of that curve the reason is because our customers couldn't perceive the difference even though each marginal increase was very much more expensive and so it turned out to be a waste of time to chase anything better than that and the good news is is there is hardly any system on the planet where you can't get user feedback and use data to judge where this line is so a well run business a well run system people who are doing their job will aim to run just a little bit above this magic line not a lot above it and certainly not below it just a teeny tiny bit excuse me above it so okay we have perfection which we know we are not supposed to chase we have this magic line above which our users are indifferent we need to name this space between how good you really need to be and perfect so we have a name for this in SRE it's called the error budget it's a name I love because the principle of the error budget says we should treat that level of acceptable imperfection as a budget we should spend it so obviously if you consistently overspend your error budget it means you are consistently under your magic line which means you have consistently unhappy users that's bad you don't want that because again it's not reliable they won't trust it they won't use it you'll soon have no users but if you consistently underspend your error budget if you are consistently above your magic line that you are wasting opportunities to learn to experiment to invest in other areas you are standing still you will be paralyzed and not be able to innovate and then guess what your users won't use your systems anymore either and so the magic of running a system is finding the magic line and managing your error budget we're going to dig into this but I need to define just a couple more terms because we use them all the time in SRE the first term is called a service level indicator an SLI a service level indicator is a thing that we measure that tells us how our users are experiencing our system so we might measure say the latency how long a request a popular request takes in our system and we'd usually be more formal we would say something like the latency of this request and its 95th percentile measured over 5 minute buckets it's usually like some value at a percentile over an interval but the point is though it's from the user's perspective it's a metric the user cares about that's a good SLI latency, request latency is a good SLI a bad SLI would be something like CPU load 0% of your users care about CPU load what they care about is effective high CPU load like what does it do to latency that's an SLI a service level indicator the next term is an SLO a service level objective basically is that's the value we want our SLI to be when we measure it so if we're measuring latency so we say we're measuring the latency of a certain popular request in our system at the 95th percentile over 5 minute intervals that's our SLI now we give it a target and when we measure it we want it to be less than or equal to 100 milliseconds 100 milliseconds now is our SLO it is our objective and now we have a formal name for our magic line the magic line is the SLO the service level objective it is the bar of reliability keeping in mind reliability encompasses a bunch of terms where if you're under it you are underperforming for your users and if you are over it you are underperforming for your company because you're wasting resources I should say very quickly SLIs and SLOs have the unfortunate property of sharing two letters with another term called an SLA a service level agreement they mostly don't relate to one another an SLA a service level agreement is a contract it's a promise you make to your customer that says if you don't meet certain conditions in the contract they have some kind of financial remediation it's done by lawyers and sometimes sales and marketing people if we live in a perfect world an SLA would be your SLO plus a small buffer and plus some consequences but an SLA is almost never that so for the purposes of this conversation and most of the conversations you'll have around reliability other than just making sure your SLAs aren't insane like they're really out of line with what your SLOs are you won't really look at your SLAs they are external your SLOs and your SLIs are internal okay so we know we can't be perfect we can talk about how much imperfection is permissible the error budget we can use some terms to define those magic lines what do we do when we have too much perfection when we blow our error budget okay well we've talked about a little bit why it's a bad idea to underspend your error budget because you're wasting time and money and resources but what do you do when you overspend it? keep in mind reliability is your most important feature so if you have become less reliable than your customers need or expect it becomes your most important thing period more important than everything else to fix that and get back in there good graces let's say get back on path so the easiest thing to do when you blow an error budget is a feature freeze and it's the thing I always recommend people start with let's work a concrete example here suppose we have a budget of 10 errors per every 30 days and we push a change that accidentally causes 20 errors okay we've now spent two months of budget or 60 days of budget you know for what was a 30 day budget we've overspent by 30 days so what are we going to do for 30 days while we recover budget obviously we're going to fix whatever mistake it was that caused us to overspend our error budget so that we can not spend anymore we're going to freeze new features I mean we're going to stop developing code for features excuse me and we're going to spend our time working on things that improve the reliability of our system so for example it might be we pushed a change and the reason we blew our error budget is we didn't notice in time we didn't notice this was a problem until our users started complaining to us well then maybe we'll spend a good chunk of those 30 days building tighter monitoring like watching our systems more closely building automation to do that it might be we noticed but we didn't have a good way to roll back the change we rolled out or actually let's try an easier one that we noticed but we rolled out changes too quickly so that the blast radius the impact radius of the change got too wide too fast so now maybe we'll spend our time building progressive rollouts or maybe we noticed it but we didn't have a good way to roll back the change that's true in a lot of systems so maybe we need to spend that 30 days building automated rollbacks the point is is that if you get into this mode where when you blow your error budget when you overspend your error budget you freeze for the amount of time it takes for you to recover the error budget which is just arithmetic and focus all of your time on the reliability aspects of your system you are less likely to blow your error budget next time because the mistakes that you make will have a smaller blast radius the important thing about this is you need to have this policy whatever it's going to be in place before the bad thing happens because then people aren't arguing in the middle of an emergency about what to do it's just math we know what to do we fix it and then we focus on how we improve it so that's all fine but it's not by itself good enough if you really want to embrace your humanity by embracing your imperfection it's not enough to have an error budget you also have to think about how you talk about these things internally the first thing to understand is it is basically never true that it's not your fault let's take a real example today Twitter had a large global outage lots of very smart people at Twitter who work really hard let's take a hypothetical example suppose the problem and I don't know what the problem is but suppose the problem was they had a big problem with their database I don't know but let's pretend and you're a front-end developer like you're a web developer you don't work on the database you might be tempted to say I don't work on the database it's not my fault it's the fault of some dependency that I have well I'd like to point out two things number one your customer, your user does not care nobody who uses Twitter today cares whose fault it is which part of the stack failed it doesn't matter to them the second thing to point out is it's almost never true that there's nothing you can do so in our hypothetical let's say you were the front-end web developer and your stuff's all working fine we're gonna put that in finger quotes but you depend on the database that just wasn't there for you at some point you need to ask yourself a question could I insert a caching layer in front of that database so that I could serve old data could I fail gracefully in some way could I put some caching into the front-end UI layer for my user like there's always something you can do so the first cultural sort of thing corporate cultural thing to get over is it's everyone's fault like there's something everybody in the entire serving path can do to make their bit less dependent on whatever broke that having been said it's very important that we do not blame humans ever this is an important cultural thing we have at Google whenever bad things happens we don't blame humans we just don't alright so let me give you a hypothetical let's suppose we have a poor unlucky human his name is Adam and he's walking in a data center and he trips over a power cable and he trips over the cable and the server that's sitting in the rack comes flying out of the rack you know smashes into a million pieces against the wall and unfortunately that server was running some user facing service so now that user facing service is down so we've got Adam who tripped over the cable so we might say oh it's Adam's fault he should have watched where he was going or we might say you know it turns out the server was installed by Betty and she didn't screw in all the mounting screws and if she had screwed in all the mounting screws you know that wouldn't happen that's Betty's fault or we might say hey there's Charlie and his service was only running on that one machine his user facing service like Charlie should have been running on more than one machine so it's his fault or maybe I don't know maybe we can say Danielle is the front end web developer and her UI was relying on Charlie's service to talk to customers and maybe it didn't cache data or fail gracefully so maybe it's her fault like whose fault is it there is it's the system's fault that's the most important thing it's the system's fault when you blame humans they do not honestly share what went wrong and what say mistakes that they made and therefore you do not learn and when you have a customer facing error that's like an investment you have just lost some user trust it's a sunk cost it is gone so now you have to get as much value from that investment as you possibly can and the way to get value from that investment is to learn as much as you can and the way you do that is that people are honest when you blame systems people will make the system better humans will make mistakes you should not say Adam made a mistake Betty made a mistake it's Charlie's fault it's Danielle's fault whoever no no we say Adam was the unlucky human who happened to get caught this time because he happened to trip over the power cable we should ask why did the system allow a power cable to be on the data center floor why did the racks that we buy allow someone require screws why not snaps why does the software system allow someone to singly home a back end service on one machine why does the UI framework that we use not have data caching or graceful degradation built right into it we don't blame the people we blame the system and in fact if you really want to do this you go one step further you celebrate your biggest failures your biggest mistakes and most critically your biggest near misses so this is the thing we happen to do with Google twice here we have our performance review season and we we look for people who pushed out say a bad config that caused an outage they raised their hand they helped fix it they wrote a good detailed post mortem they did a good design doc for how to make the system less likely to have this problem in the future for the next unlucky person we go we find those people twice a year during performance season and we promote them whether they ask for it or not and we bring them on stage at our weekly all hands whenever we're allowed to do that again called TGIF and they talk about this big thing they went through it's a really important cultural thing for us and I think it should be for you too to reinforce to people people are just the unlucky victims by standards of these mistakes that the system should not have allowed it last thing I want to say in this topic really quickly is that reliability and speed are not natural enemies of one another if you do these things if you operate with an error budget with really well defined SLOs that are connected to business values if you have a good plan for what to do when you blow the error budget if you have a good plan for recognizing when you're under spending your error budget what you will find is that as you adopt these things your reliability and your speed your velocity increase at the same time because error budgets align the people who are concerned with risk with the people who are concerned with speed so maybe in your company the feature developers want to go really fast and the operations people who have the pagers want to go really slow because they care about the reliability an error budget puts everyone on the same page and by the way you could use an error budget to align any two groups that seem misaligned against speed versus risk like I have personally used error budgets to get lawyers and marketing people to align on a path forward which means you have happier engineers happier customers and higher customer satisfaction and by the way you will not be able to escape these principles like this isn't just some sort of lofty religion I'm coming from a mountaintop to preach this is all hard-learn lessons as your systems grow you will have to adopt things like this otherwise you won't be prepared as you grow I think many of you are probably familiar with the expression good luck is what happens when opportunity meets preparation Eliah who Goldrat had a great expression which I was loved where he said good luck is when opportunity meets preparation but bad luck is when lack of preparation meets reality and here's the interesting thing our systems have global users and they have a lot of moving parts which means they have emergent behavior which means it's not enough to have a good rollout process it's not enough to have a good rollback process and tight monitoring that's not enough our system is going to evolve behaviors we did not specifically design into it and we will find out all things being equal we will find out about those behaviors badly when our users tell us usually in the form of support tickets or you know whatever blog posts that's bad we don't want that this is where a system like chaos engineering comes in because we can use things like fault injection or artificial resource constriction or fuzz testing or randomized load swings laser beaming to carefully but consciously probe the edges of our systems to see which emergent properties they may have created for themselves before we learned I like to look at it this way this is my unbelievably succinct and probably incorrect definition of what chaos engineering is since we use the principles of chaos engineering to discover emergent properties because the presence of emergent properties creates lack of preparation or bad luck for using Goldrod's definition to me chaos engineering my unnecessarily compact definition is it is a discipline for systematically minimizing bad luck like making sure we are actually as prepared as we think we are in the systems that we are running and by the way I don't just mean our computing systems I also mean our people systems companies, collections of humans are distributed systems so I'm an engineer I like analogies but I hate to tell this to you this is not an analogy this is a truth it's a fact companies are actually distributed systems where humans are the nodes and almost all the complexity in any company of greater than say three people comes from the human beings that you design the real complexity is with the people and the reason is and it gets back to our first slide humans make well-intentioned mistakes even when they're not trying to humans look a lot like a fairly opaque microservice that is only partially reliable this by the way is an analogy but I think a pretty useful one we are the semi-autonomous units of execution we have inconsistent inputs and outputs opaque system internals we're basically buggy biological microservices ask a person to do the simplest thing like raise their left hand and you can be sure that like 20% of them are going to do it wrong consistently as a distribution and you shouldn't feel bad about it because it is the definition of who we are alright so I know I want to keep this to about 45 minutes and I'm looking at like 34 or 35 minutes so and I did promise you that by the end of this sort of whatever Diatribe presentation that I would tell you the secret universal secret to happiness and success in life in two sentences and so I want to be honest and I want to do that gentle people of Agilindia here is the secret to happiness and success in life in two easy to follow sentences you ready number one it is no sin to fail do not live your life worrying about whether the thing you are going to do is going to fail the sin is failing to notice rather than spending your time trying to do meticulous planning to make sure the thing you are trying to do does not fail you're much better off spending your time contingency planning how quickly can I notice that this thing that I want to do is not behaving the way I want it to it's failing not meeting my goal how can I adjust how can I limit the blast radius how can I roll it back how can I mitigate it if you spend all of your planning on that rather than being paralyzed with this notion that you are going to fail what it means is you can fail a lot more when the blast radius of your mistakes goes asymptotically to zero the amount of risk you can take goes asymptotically towards infinity I guess I should say asymptotically towards infinity it's not strictly mathematically correct but how about really really high it's essentially one over zero and that's the secret the secret is not paralyzing yourself with a fear of failure the secret is contingency planning so that you notice quickly learn quickly and adjust and adapt so we're at like 36 minutes of a 45 minute presentation and I wanted to leave roughly 10 minutes for Q&A so we're kind of on time now is your opportunity for pitchforks and bullhorns and torches and all the things people like to do during Q&A and I think Nuresh is going to maybe read the Q&A to me so I don't have to go find it in the UI thank you for being by the way a very attentive audience wow what an amazing start of the day thank you Dave this is just mind-blowing love the perspective on error budget I think that's such a powerful thing of course your last slide on the two sentences that will make you infinitely happy or successful of course is mind-blowing but I think the error budget I think is something I'm going to steal from your talk very very powerful way to you know get two groups to align I think that was really powerful so thanks a lot and you can see the light cloud over here and it's just raining likes I'm glad the first question is from Sunil and Sunil is asking is artificial intelligence likely to take over human judgment by your definition no but I do think it is likely to thank you for the question Sunil no I think it is going to narrow the range of things we think are judgment but no fundamentally computers can't do judgment they're deterministic even if the contents of the black box are unknown to us they're still deterministic systems and judgment is non-deterministic that's why it is judgment this is a lot like the argument about we thought a machine was intelligent if it could play chess and beat a grandmaster and it did but we still don't think machines are truly intelligent we thought a machine is intelligent perhaps if it can create art like music and machines are beginning to do that but we still don't really think of machines as intelligent what it's really doing is just sort of asymptotically refining the edges of the places where we were previously using judgment perfect the next question is kind of piggybacking on that from Ashok where he says when you say prefer human beings over computers for judgment work I understand that fully autonomous machines is not a good idea is my understanding right yes that is yes that is you have a correct understanding the next question is from Ruchi she's asking sometimes while building a system requirements go goes missing or not captured correctly for this scenario would it be incorrect to question a human involved in capturing requirement how can the system be held responsible for this well you should ask the question this way Ruchi let's assume that Dave that I'm the product manager and I did a poor job of defining the requirements the question you should ask is why do we have a system a product development system or process at the company that allows a person like Dave to accidentally miss an important set of requirements like is there something we can do so that the next unlucky Dave doesn't miss an important requirement in the definition process right you don't want to say Dave is bad at his job I mean it might be true that I'm bad at my job and that's a different kind of a question but overall unless we are terrible at hiring at our company I or the other product managers aren't likely to be bad at our job but we are definitely likely to be human and miss important things and so rather than rather than go to the product manager and say come to me and say Dave there's this important thing that you just missed you should come to me and say I notice that this requirement that seems important is not in the definition I'm curious for your opinion what is it about the requirements definition system that permitted this gap to exist and is that working as intended that asks me that encourages me to be introspective without being accusatory I hope that makes sense yeah makes total sense thanks for that the next question again is from Sunil he's asking let me quickly scroll here so he's asking is there a need for perfection that is zero defect for places like medical software where lives are at stake no in fact if you go look I did research on this three or four years ago because when I was hired at google one of the people who interviewed me is a guy named Ben trainer Sloss he invented SRE he's the subject matter expert on the topic in the world and Ben like to say you don't need perfection unless you're doing something like a pacemaker for a heart which I think we'd agree is a fairly vital system and I thought well I believe for real that humans can't be perfect and therefore no system of human designs can be perfect our pacemaker is perfect like am I wrong so I went and looked the average pacemaker has a reliability of about four nines it's much less than 100% it's about four nines meaning 99.99% of the time when your heart has an arrhythmia the pacemaker will detect it and correct it correctly but that other time it will not but you know what that turns out to be acceptable because even a very fragile human heart can tolerate that level of error and that's good because if a pacemaker or some other piece of medical equipment had to be perfect there would never be new medical equipment because we'd never be done testing it so the answer is no medical software or medical equipment has to be perfect because A. it never is but B. even the most life sensitive stuff is nowhere near perfect great the next question is from Sakshi organizations these days continuously focus on target zero defect delivery this has a great impact on sales as well as customer finding it fancy what would you suggest as an alternative marketing strategy or market strategy sorry and how could the mindset of such organizations be changed sure so let me do that in reverse order the way to change the mindset of an organization like that is to point out to them that never in the history of the organization have they actually delivered zero defects and if they think they have delivered zero defects then they're lying to themselves usually by let's say unintentionally mismeasuring but whatever that's the first thing they have to kind of grips with is they're making a promise they cannot keep and an honest person will hold them into account for it and like they'll get sued it's terrible you don't want to do that I find it useful in life to think about the things as a collection of optimizations and constraints so any system can only be optimized for one thing at a time but it can be subject to a bunch of constraints so what I would say is in that case the number of permissible defects or the reliability of the system is a constraint because it has to meet a certain bar but not be above it and what you optimize for what you market against are the shiny new awesome features of the system it does new awesome thing X it does new great thing Y it gives you new capability Z and when you need it to work it's going to be there and work for you which is not a specific promise around defects you're promising zero mistakes that's a lie it's an actual lie you're being dishonest with customers and no company that does that will have customers for very long great I know we are over shooting a little bit with the time but there are just so many interesting questions and I'm thinking we'll just spend another five minutes if that's okay with you Dave totally fine with me I leave it entirely to you awesome so the next question is from Ankit and his question is 7 out of 10 is probably what is acceptable or he's using a number like 7 out of 10 is probably acceptable or what is required to reach from 7 to what is required to reach from 7 to 10 to 9 to 10 would need probably some effort that was needed to reach at the you know much more effort to reach 7 to 10 so then what derives what drives people or company to make that extra effort that won't even matter at the end and the cost that costs them huge costs slash effort alright well first that's an excellent observation let me yeah it's super linear say it's exponential let me give you a good rule of thumb every 9 you want to add to a system say we're talking about availability but it doesn't matter so if I want to go from 3 9s 99.9% available if I want to add that 4th 9 go to 99.99% available will cost me 10x not just 10x development cost 10x ongoing operations cost therefore adding 2 more 9s will be 100x 3 9s 1000x etc etc it's roughly 10x super linear that's a good rule of thumb the answer is companies say they want to pursue it because it seems ideal to them and like it should be attractive and they don't really do the math about the cost I would argue that very very very few companies who say they want to make that incremental improvement you know get asymptotically close to 10 over time actually legitimately invest the effort to do it they just say they're going to and and either fudge the metrics or don't really hold people accountable or whatever and that by the way that's not okay any of those outcomes are not okay it's not okay to not deliver the promise you're making to your customer it's not okay to fudge the metrics because now you're flying blind in your business it's not okay to tell people you're going to hold them accountable and then not hold them accountable that's a bad precedent to set in the business like other bad behaviors happen because of it so I think a lot of companies pay lip service to that sort of improvement and then don't actually really invest in it in fact I think most companies don't even really do the math to ask the hard question what would it take to really make that progress and so it's like an empty promise that they think people aren't going to hold them to and maybe that's true because after all humans don't have expectations of perfection so maybe they know it's you know kind of a kind of a lie in a sense but I still think it's a terrible way to run a business because it just creates awful inefficiencies at a minimum very well said the next question kind of is similar from Ujwal does that mean if we need to design an application with an expectation of 99.9% availability or handling X number of concurrent users per day we have been drawing the wrong expectations right from the onset well it's hard to say for a new system it's hard to know what line you need to draw but remember these two you can design these things in two directions so here's the good news no matter how long it takes you to design a system if the system is at all successful the amount of effort and time you spend operating the system will easily overwhelm the design and implementation time right and if it doesn't if that's not true then you have a shortlist system and it's not even worth discussing because it's been an unsuccessful system so if it takes you a year and a hundred people to design a system for four nines and you're going to operate it for ten years let's say I'm making these numbers up of course you can begin to measure so let's say you really do design it to be I forget if you said three or four nines let's say four nines just for math for four nines and you deploy it and it really is four nines cool first of all are you stably there okay you are great and are users complaining to you no they're not okay it's time to experiment it's time to test is this exactly the right error budget so let's start taking a little more risk in the system and see if we go from four nines to maybe three and a half nines and see what happens by the way that will happen anyway you'll have outages so whether you do it as a natural experiment or a conscious experiment you should proactively introduce error into the system a little bit not a lot of it but just a little bit to start to ratchet it down a little bit and start to collect some samples and you might find that you get all the way to three nines or three nines before you start to see a lot of customer complaints okay now you're starting to circle around where your SLO should be we didn't talk about this in presentation but for a new system you should be evaluating your error budget in SLOs like do I have the right SLO at least once a quarter because you just you don't know and so it might be okay to over engineer in the beginning because yes well that have been wasted effort from the engineering it will but remember all the costs over the long tail are going to be in the operation of the system not in the engineering of it so you want to play during the operations to find the right line right great the last question we'll take and then we'll try and wrap up here I know it's late for you Dave so thanks and thanks again for hanging in there so this question is from Pallavi she's asking understand understand contingency planning is important but however I've seen examples of contingency planning beaten to death how to balance this or is it required to stop somewhere like where do you draw the line yeah so I use a rule of N plus two which is to say I have a plan okay now I stack rank all the ways I think you can fail and I take say the first you know five of them and I plan for those and then in that part of the tree the next layer down right so I've ignored let's say I find a hundred ways it could fail I take the top five so I'm ignoring the other 95 and then in the next layer of the tree where I'm underneath those five I ask well given that failure in this mitigation how could that fail and I do that you know for another say five things so now I've planned two layers deep on my top five things that's my rule of thumb that's enough contingency planning most contingency planning shouldn't take more than about a month in most systems if it takes a lot longer than that you're either doing too much or you don't really understand how you expect your system to work but I agree you can absolutely over engineer your contingency planning so my rule of thumb is two layers deep on your top five things and that is in my experience almost always enough I'll sneak in one last question sorry I think this is a good one this is from Metswin to determine the magic line we should need to get customer feedback we would need to get customer feedback however minority of customers may be asking for a perfect system so we to determine the magic line we need to get customer feedback however minority of customers may be asking for a perfect system is there a rule we can apply to determine which customer feedback gets ignored so two things it's actually well it's not my experience that customers expect perfection they expect the system to meet their well their expectation and they almost never expect perfection but let's assume for the sake of the question that your hypothesis whatever your thesis is we'll just accept that so question is minority of the customers are actually asking so your goal is not to have zero complaints that isn't the goal the goal is to find the tipping point between a consistent trickle of complaints say one percent of complaints or something and where the step function is where the one percent becomes ten percent that's where the magic line is you will never have zero complaints just doesn't matter even people who don't demand perfection will misunderstand how the system is supposed to work and therefore we'll complain right or just misread everything like all of those things were going to happen so you're never going to have zero complaints so you can treat the people who want perfection as kind of the background noise of complaint you'll have no matter what that's irrelevant to your business because it's not practical anyway they're asking for a thing you can't deliver what you want to find is where does it spike that's where the magic line is I hope that answers the question yeah I see the thumbs up so I think people are happy with that answer alright fantastic I think Dave this was a really really brilliant way to start the day thank you so much for sharing your insights with us and thank you very much Dave again for joining us greatly appreciate it thank you everyone


Hey everybody, welcome back to my Star Wars channel. My name is David and today is episode 11 of my Diagostini Build Your Own X-Wing. We would be honored if you would join us. Hey everybody, welcome back to the channel. Thanks for being here. That's right. We're doing another installment of the Build Your Own X-Wing. Now in the past, I've said that lately those videos have been kind of boring because it's just a long, sped up, quiet video of me working and building. And so not exactly entertaining to watch. And so I thought what I would do is edit it, make it a little bit more succinct, right? Concise, small, short, and just give you the highlights, right? Just give you the highlights. Lately, I've been having a little issue with some of the parts staying together, been dabbling with super glue more than normal. You're not supposed to use super glue on this. Everything's supposed to snap together or screw together. But some of the snap together parts fall later on. And so anything that's fallen off, I have super glued back on. This last week, the engine, one engine would broke off twice. So I had to do a little bit of fixing on that. But hey, if you're not into the X-Wing Build, I understand. I also do vintage book reviews. I do vintage toy reviews. I've done Star Wars game tutorials, anything that has to do with Star Wars. That's what my channel is all about. There's no hate here. I love everything that prequels the Han Solo movie, all of it. If it says Star Wars on it, I'm a fan. So let me show you the latest updates with the X-Wing Build. So, case in point, for the next two things I'm going to show you, it took five volumes just to build these next two things.  And so it's kind of a slow process. Every single one of these volumes has just only a handful of pages of instructions, which I think for me as a model builder, it's a little too slow because some of the things, they're like, put this big piece in and then you have to wait for another episode. And then it says, now put the small thing on the big piece. And I'm like, wait. I wish you had told me that here because then I would have done this part differently. So not knowing the next step, you know, having to wait for the next step. It's a little frustrating, but I'll show you the two pieces that we got to build. The first one is the Proton torpedoes. They're kind of cool. I don't know why these don't have ends tips yet, but I haven't gotten to that part yet. And I have no idea where they're going to go. Like, I have no clue. I don't see anywhere on the X-Wing that this giant thing will fit. So I'm really looking forward to finding out where this goes. But yeah, this was two or three magazines just to build this one little thing. And then there was the power generator. So again, more of these fun little 2B things, those are always fun to build. And not everything lined up perfectly on this one. But I don't care because this is going to go under the body. So you'll never see this, you know, unless you open the body up. But I mean, it's a cool little addition. And I mean, it's fun to know that it's under there. So this is going to go like in the long part between R2 and the engines at the top. And this kind of just nests right there. But then, you know, the outer shell will click over the top. So yeah, you will never see this. I guess you can imagine that if there was a problem with the X-Wing, you know, the technicians would lift that shell off and they would have access to it. But it's only just going to make this massive X-Wing even heavier. So yeah, these two things, that was five volumes of tutorial to build these two things. But I've got some really cool footage to show you of the electronics and the lights and sound. So we'll cut to that now. Okay, so the lights and everything, motherboard, wires, none of it works until you get to issue 74. And then after you get to issue 74 and you plug in wire number 20, your engine lights work with the remote. I mean, you can't understand how happy I am to know that these work. I actually did the wiring correct and that it actually works. I am so, I am so stoked. And yes, that means my laser cannons work as well. I got my lights turned down low. So you can actually see them. They're not as bright, but they are working. And I don't have them mounted down, but this is just a sample. R2's head spins around. I don't, it works. Let's just say it works. All right. And I never thought the X-wing wings would open. I'm positive I did it wrong. So look at that. They open. They're loud, but they work. It works. And that's everything, right? That's everything. Like I said, I wanted to keep these short. Keep these short so that they're easier to watch, easier to digest, makes you come back. So thanks for hanging out. I'll answer any comments down below. May the force be with you guys. I'll see you next time. Bye.


Okay, very good morning to everyone and happy Valentine's Day so Just a quick heads up for the fellas out there. Don't forget to To stop off at the petrol garage on the way home pick up some cheap cheap bottle of plonk and some flowers for your missus You can thank me later for the reminder But just having a quick look back to markets then what have we got on the agenda for the briefings So a bit of an update on the virus We're going to talk about German GDP that's just come out and that's the precursor then for the eurozone number And then we've got what happened surprisingly yesterday, which was The stepping down of Suji Javid the UK Chancellor So we'll have a little talk about what does that mean of potentially for UK assets and the UK government and Brexit and Then there's quite a busy calendar for today So just to run through some of the data points and so on Don't to look out for and then I'll hand you over to Sam to look at the charts from a technical perspective. So Starting off then just quickly talking about the virus Quite interestingly Who Bay so the main kind of epicenter if you like of the the red blob so if we were to zoom in here You probably know the lay of the land quite well now having looked at this number of times So there's Wuhan just on the right hand side And Reported cases were up about almost five thousand remember it was just around 60k yesterday, but the actual death toll has been revised down It's not if the people have been magically revived back to life Actually, I just lowered the number. So still obviously quite questionable. These numbers aren't really accurate We all know that they're kind of rough guidelines to go with But the idea here being that if you're actually looking at the sensitivity of markets as of today to this It's still relatively benign. I would say yesterday a little bit of a sell-off But if you look at those US equity charts from really what was the pattern of price activity from yesterday Other than the morning sell-off that we had soon as the Americans came in we just started rallying back higher again And I do think that there are You know from my experience having gone through say the sovereign crisis does Kind of reminiscent of this similar type of price action where different geographic regions have different interpretations and Certainly for America where there's these primaries going on there's Trump obviously lots of activity as per usual There's economic data that's been pretty strong as corporate earnings coming out You know unless the number in America of these cases, which you know if I just flip over my screen Remember USA had quite a few cases initially right when the outbreak was beginning and that number really has not changed one bit And so for the American market I think the psyche there as per Generally and the reason why I mentioned the sovereign crisis a few years back was that you'd get this big kind of maybe European reaction to a subject matter the Americans would come in and the less really it was getting to the point of mass Escalation of an issue they were just so much more focused on their domestic Kind of information and I do think that's a little bit the case at the moment. It's kind of this the situation in China obviously is Meaningful but as far as the Americans are concerned and I think the price action yesterday kind of reflects that it's just They're focused on other things and on the pecking order of that hierarchy of the most Market-moving factors. It's clear and present a danger But it's not the biggest one and these other poor positive things are beating it out at the moment So still to be monitored the corona virus But again as I said looking at global stocks as a whole summary as a kind of a collection of indices Generally wrap about a percent overall. We're heading for the second weekly game So despite the kind of usual regular intensity on the subjects global markets Barring the exception of some of the mainland perhaps But even that the Shanghai and Hangsang up again overnight only the Japanese market was a little lower in the Asia session so Moving on what have we had this morning? There's been a couple things if I just quickly flip over my chart We actually have had already the German GDP figure and came in at zero But if you remember the chart that I was showing you from the graphic that I had in the Macro menu Report that I issue on a Monday at that point in time the analyst expectation was for minus 0.2 remember we was talking about in previous briefings this idea of the second Kind of period of contraction in three. Well actually then zero is pretty good Not not so much of a bad thing. I mean obviously logically speaking in Germany stagnating is not good But comparative to how downbeat and bearish generally people have been on Germany It could have been a lot worse So actually when you look at the the euro dollar chart if I quickly just flip over here to the euro dollar currency pair And if you go to 7 a.m. When this data came out there was close to zero Reaction in if anything if you look at it on a shorter time frame the euro went up And I think that's fairly reasonable in the short term overall I don't think it really detracts from the trend that euro dollar is presently in and if I put euro dollar back on a daily Continuation chart remember what we were looking at yesterday We were basically had confirmed the break of that summer low in euro and we were talking about again This euro dollar chart is completely unaltered So the the the rectangle boxes that we've got there are the same We've had for a number of days and you can see how technical the market is, you know You have these little blips Let's say of isolated information, but the overall narrative hasn't changed at this point in time The fundamentals are still that the dollar strength and euro weakness And so then it's just about being patient and as per that line at the 108 52 here in euro dollar Which was those previous highs that we had I think this is what Sam often refers to as the macron gap up on that first round of the French elections back in April of 2017 we've hit that to the tick and and so again Just patience if you are holding that medium term position all things being equal on the top level those dynamics for this currency pair Have not changed and and then here we are down at that point So a good hold to these levels if you were if you were in that trade over a slightly longer time frame This does lead us up then to the eurozone number that's going to come out a little bit later on this morning I don't think there's going to be too much Emphasis on this to be honest having had Germany at flat I'd be expecting this broadly to be in line then with expectations to remain probably around point one From a range Perspective on that piece of data the flash GP coming out the eurozone the range is flat to point two So if we did get a flat reading what if we've got a surprise and a negative reading not sure statistically if that is even Possible given the German sitting right at zero But if we did then you know a retest down at the lower bound of the range that's been in place it's the overnight Asia-Pacific session on the euro could lead to a bit of a rundown an extension down to the S1 Okay, moving on quick look at the UK Because I remember I was delivered delivering a lecture yesterday when this actually came out And this was a bit of a bit of a shock really it was talking this time yesterday about how Suji Javid Dominic Rab these types of characters who sit in the most influential seats in in the cabinet in the UK government were pretty safe It was supposed to be a bit more of a formality for them Absolutely wasn't the case for Suji Javid apparently kind of cornered by the PM and and Dominic Cummings to fire some of his staff in order to more align Basically, Suji Javid's own personal treasury team with that of the Prime Minister's economic advisers to bring them more aligned so I Would imagine then so Dominic Cummings can have more direct control over exactly what it is that they're doing and the Treasury is a lot more better equipped in terms of the size and depth Let's say of the department in that sense. So what you would want then is Suji Javid on board but obviously he was reluctant to fire some of his staff stuck by his guns and so he resigned So what does this mean then going forward? Well a couple of different things his replacement is quite key And the impact so let's talk about the replacement first which is this chap here You probably would recognize him if you were if you were semi plugged in to the Political debates that were happening on TV. He deputized if you like for Boris Johnson in those leadership debates You remember the first couple of rounds. We had an ITV and BBC, you know Boris didn't even turn up That was how confident he was he sent in his kind of His next in command on the on the televised stage and I'd say Rishi Sunak did a pretty decent job I don't think many people were actually that aware of who he was He's only 39 years old and he's risen through the ranks pretty quickly only coming to kind of front bench positions over the last couple of years Who is he? Well, he's a he's a former Goldman Sachs employee Haven't actually managed to see what exactly his role was so I don't know whether He was an intern or whether he actually had a bona fide role of seniority or not I can't really tell you at this point But the key differences here is his Javid was seen as a fiscal hawk Whereas Sunak is seen much more aligned with Boris and Cummings giving his stance on Brexit So gee Javid was a remainder Whereas this chap here were voted to leave His voting history in terms of all the different parts of legislation that we've had to do with Brexit another Debates in in the lower house. He basically has been an ardent Brexiteer Supports reductions in corporation tax cuts to capital gains tax He's gone on the record in favor of infrastructure investment All of these things then would be much more aligned with the government that would mimic then a little bit more of an approach of Fiscal stimulus in a Trump-esque style in order to see you know the economy flourish The opposite then of a fiscal hawk, which would be tightening the belt to make sure that you can afford these changes. So As per usual with this type of chap I mean obviously people who voted for Brexite and Middle England were voting for you know Parliament to represent the normal man in the street So just like Boris and all the others this chap went to one of the most expensive prestigious private schools in Britain studied at Oxford went to Stanford University And he's married the billionaire daughter of the Indian oligarch of emphasis So yeah, just like you and me a nice representative of the man on street But I'll leave my political persuasions aside The main thing that this does mean though is what does it mean for markets? Well This is the yield on the UK 10-year government bond And actually if you see a bit of an uptick here, it's quite interesting because ultimately Yields rising to their higher since January after jab Javid left You know, this is that idea this notion then that Boris can start kind of changing the game a little bit under the parent direction of Cummings and look to tie everything together and again having everyone on board with that same View where they can pull the strings from one direction and one place. I think is is quite strategic in that way okay Going to have a quick overview of the calendar and a few things to cover off We've already talked about the data forthcoming in a short while at 10 o'clock Going into the US session though. It is pretty busy today. You do have US retail sales coming out there your major 130 data release You've then got industrial manufacturing Data from the US business inventories. You've got the preliminary University of Michigan sentiment number then for any oil Traders, they've got the Baker Hughes Rick count as well Pretty light on the ground in terms of speakers We do have a B. O. J. member speaking London time at 10 a.m. You got feds Mester a voter leaning Hawke speaking late afternoon 445 p.m. London time Terms of some of the equity movers for this morning We've had AstraZeneca and RBS out of the UK just having a look at some of the actual movers here at the open RBS is seen down about 2.7 AstraZeneca down about 3.4 Renault is down about 2.6 the outperformer this morning EDF is up about 7% One final interesting comment as well that was just made a short while ago Bank of England Governor Mark Carney, even though he's outgoing. I do think it was quite interesting in the sense that he said Central banks may have to look through one or two quarters of data due to coronavirus impact is seeing UK business Confidence rebound some firming up of consumer confidence the thing that I thought was quite interesting about the Carney comment Where was that even though he's outgoing? I think that that's pretty much a tactic that will be deployed by most of the central bankers So the ECB the Federal Reserve and so on this idea that these central banks have to look through One or two quarters of data meaning then that the likelihood of any near-term policy changes or tweaks I would say is probably minimal because it's only then that they've got to see them Well, what is the underlying performance of the economy once you kind of X out the impact and the repricing in of the? coronavirus So yeah, just thought I'd add that point in okay, I'm gonna hand you over to Sam Just bear with me one moment Okay, how'd you ever Sam? I wish you a good weekend. I'll see you in the chat room. Thanks so much guys Good morning, and happy Valentine's Day to everyone. Let's have a quick look over How things are going to get stuck with that with stocks overnight Asia drifted higher China finishing up for the week just Let's have a quick look at How we've we've traded you can see late last night and this has actually been the Way these markets traded in some there has been some decent moves into into the close And yes, they can see after we did break higher We came back to test what was the previous higher the day and that's acted as a flaw To a move to the upside so keep a watch on that around 33 72 It was what we were saying Yes, there's a decent line in the sand for balls and bears and you can see once we go Above it the bulls took over so for me That's again the line in the sand today hold that up We're just trending lower from the the high that we've got so keep that on Let's get a little trend line on there although not quite yet the the free test that you would like to see Just keep a watch to see how how that holds out, but it's onto a 15 minute chart to get a couple of lows in the mix as well 3380 you could argue that could be maybe a short-term flaw or at least a level of interest So to have marked up and then you're really looking down towards 33 60 if that pivot was to go I'd say that would be another area to consider the low that we got to yesterday just about Reaching the the high that we had in the 10th will be it not quite And those really be the levels that I'd focus on same situation for now on the next thing as well Gold yesterday as we said started well pushed higher and then was really chopping in a range and This would be still the the lower part of that that I'd focus on 15 75 and a half Keep that marked on and then the the highs that we just couldn't really get through at all I was doing a trade review yesterday around Half three four o'clock and we were saying that it was looking like this level was going to hold and it held again at 115 so definitely had that marked up to the upside 15 81 on that worth noting Fridays this year Have been the worst day of the week for stocks and that could be Helpful for gold to push to the upside and just bringing this into the bigger picture It is quite a key resistance zone going back to here to levels not traded since the well other than yesterday Of course the the fourth of the month and then above that 15 85 could be the level to target As well, I know we're not near the low at the moment But if we were to push down against stocks pushing hard could be the reason for that Probably worth is getting on this trend line again You can see here on the 12th the both the morning and the afternoon before hitting it again Not too long ago around half seven is guiding price price quite nicely So if we were to break that to it downside, you can see a quicker move lower the currencies Well, as we know euro has been pushing lower Again today you have the pivot with an area of Resistance from a previous day i.e. yesterday. That's actually just so well really hasn't I mean look at you know If you go back to yesterday fantastic you then break the pivot in the the afternoon on the What day would that be Wednesday acts as good resistance level Tuesday? We don't quite make it but give or take a one tick or two and and that holds up fantastically well and previous days you can just see what What a point to basically go short if it hits the pivot you're underneath go short or if it's above and you and you break below it And you go short as well, and you'd be very very happy Today or well every day really ruin a bit of a range So you got our you know think about that that pivot level is a place to go short to continue the trend If we break out the little range that we're into the downside of course start thinking about bigger moves and mark up the level from Macron's gap As you know, I quite like to keep saying that There as well. So, you know not too much going on now And I got the data coming out in not too long That could really be the main driver here So that's something I would keep an eye on but pivot for me looks pretty good if that was to fail Which it hasn't really in a while then you could get a quicker rally. I know it's a relatively small range still but 108 86 could be the level that that looks to come into As well the pound. What are they you say for the balls? Is that going to continue or not? I think moves like this we're getting the fib on since there's any potential levels to get in lowered down If there was one that comes in around the pivot 134 that should certainly be of interest. We went through a couple levels just below that yesterday Around 130 and that would be ultimately where we close the week. This is what I want to see Can we confirm that we're going to push or you know break above here? and if not And I think we do push lower ultimately, but if we can drift lower down to 130 I know it's 61 ticks away. I think from a medium term or you know couple day trade Perspective, that's a lovely area to consider to get in trend line from the lows as well We would match up quite nicely with that and of course if we close the week below Not only is the trend line broken that key support as that would then potentially terms resistance again We can push low to the upside the high of This on almost reached yesterday, but the higher the fifth that's somewhere to have marked up as well 130 84 Finishing up on oil before quick-looking that that's what it's doing on the open 50 50 is my line in the sand Didn't quite make it yesterday morning Finish the week above there, you know You'd be very relatively happy to still be long whether you want to hold that risk over the weekend or not There's another question What you'd be really happy with is if we can get above 52 bucks That would be really nice for any oil balls If you're you know bearish if we can get back below that 50 50 fantastic opportunity Maybe to get short to target some of these lows that we've already had this year Looking intraday at the little range that we're in. It's this, you know markets open up relatively quiet It's almost waiting for Sorry something to to happen price is getting squeezed in here from those loads of breaker. That is potentially an opportunity Or a break of the upside as well But range band trade this may not happen really until the afternoon quick. Look at the Dax to wrap it Quiet trending lower from those highs much like the US expertise as well See if we can get a bit of a trend on there from that high It's respected enough little false break on the open understandably to keep a watch on that a break above there And it could lead to another another person Unbelievably still that would be another all-time high for that's what can stop You know this market and let alone the US equities as well But a couple levels below to see how marked up as well the lines in the sand for the facts I mean, I would argue this is as good as any here 13,712 be looking at that as well any questions as usual Please do let us know good luck to the guys and saves to today trading competition. This is where heroes are made There's no prizes coming second apart. Well, there is a prize coming last my last wooden spoon But we all know it's all about getting that first prize 20 contracts limit on each product for futures Likely gonna be a bloodbath, but I hope everyone has a good day and I look forward to catching you all next week You


And now the Mole Mystery Theater, presented by M-O-L-L-E. Mole, the heavier, brushless shaving cream for tender skins. Good evening. This is Jeffrey Barnes, welcoming you to the program that presents the best in mystery and detective fiction. Tonight we bring you a rollicking comedy mystery written by Sid and Larry Sloan and entitled, Follow That Cat. In it you're going to meet a couple of very strange detectives. Moe and Julius, taxi drivers who spend all their time between calls reading detective stories. This, in their own opinion, qualifies them as expert detectors. And when they attempt to apply their knowledge to the solution of a murder, they get some rather startling results. You mean they catch the murderer red-handed, Mr. Barnes? Well, I can't tell you that then. Okay, but I can tell you, as well as our men listeners, when it gets caught red-faced and sore right after shaving, it's probably cause he's got wiry whiskers or a tender skin. So he needs Mole, the heavier, brushless shaving cream. Yes, sir, with Mole, it's smooth, so smooth. It's slick, so slick. It's a smooth, smooth, slick, slick shave you get with M-O-L-L-E. Mole, the heavier, brushless shaving cream for tender skins. That's right, Mole is the shaving cream that's heavier, the cream that's right for a wiry, hard to cut beard or tender skin.  So you shave faster, closer, easier, and you shave painlessly with Mole. The heavier, brushless shaving cream for tender skins. Mole. And now for tonight's Mole mystery, follow that cad. Maybe you were reading in the papers about them two hat drivers, Julius and Moe, which is solving this here lock and murder. Well, I am personally acquainted with these two characters very well. As a matter of fact, I am one of them, Moe. You see, Julius is all the time reading a magazine called Absolutely Authentic True Crime Fiction, in which is run in a series by the name Daniel Daremore Detective, which is our ideal. Now, on the day in question, Julius and Moe is parked in a hack stand at 55th and 6th, waiting for a fare. And I am standing with my foot on his running board while he is reading A-A-T-C-N. Then a dark, pointy man climbs into the camp. Yeah, yeah, go on, Julius. Driver, he whispers hoarsely, oigently. Driver, follow that cab. Look, what's the matter, what's the matter, Julius?  Let me see. My wife's appendix, 11 years. May I have been pushing one for nine? So? So what? It says here in black and white that the dark, pointy man is saying, driver, follow that cab. Did a guy ever say that to you, Moe? Gee, you're right, Julius.   Yeah, I bet you could, Julius. You're smart. Smarter than Daniel Daremore. I would not go so far as to say that. I would.  Maybe, but you know, Moe, I could make with the clues and the fingerprints. Yeah, fingerprints. Just give me a chance, Moe. Just give me a chance. I'd show them. Can I go with you on your case, Julius? Sure, Moe. All good dicks has got stooges. I'd see it, Julius.  Follow that cab. Oh, it just ain't done, Moe. It never happens. Driver, follow that cab. Huh? I said follow that cab. Hurry. Yes, sir. Why, Julius? Well, what do you know? That cab we're following is pulling into the curb in front of that apartment building. Stop. Stop right behind him. Yes, sir. Hey, look, the guy in the cab is running into the building. He's not going to get away from me. Hey, wait a minute. Wait a minute. What about my cab? What happens? What's the deal? It's Moida. That's what it is, Moida. Moida? Yeah, that big so-and-so is running off into that building without paying the tab. And I'm going in there and collect. Come on. It's Julius. Moe. That was a shot ringer now. A real shot ringer? Yeah. Come on, something is going on in there. There's the guy who was in my cab. Come on, Moe. Yeah, right where it's at. Come on, Moe. Come on, please. Hey, you. Someone's been shot in this apartment. Shot? How do you know? I heard it, Julius, didn't you?  I ain't asking you. I'm asking this gent here. Oh. Okay, mister. I was trying to get into this apartment when I heard a shot fired inside. Oh, you was trying to get in, hey? Besides jumping taxi fares, you're also up by glass. No, no, you don't understand. Hey, Julius, the door is open. What? It was closed just a minute ago. I tried it. A likely story, a likely story. Julius, if the door's open, why don't we go in and see what goes? Don't rush me. Don't rush me. Come on. Everything looks okay in here. Holy mackerel. A corpse, Moe. A corpse. Yeah, and dead too. It's locked. Jay, how come you know his name? He... I was associated with him. And he was the guy who was fallen in my camp. Hey, Julius, look. He's got something in his hand, a dead corpse. Get it, Moe. No, you get it, Julius. I don't want to touch those thefts. A fine assistant you were going to make, afraid as stiffs. Get it. Okay. It's a piece of paper. Give me it. Yeah, sure, Julius. Here. I've been trying to see you for weeks. You can't brush me off any longer. You stole Joan away from me. Signed Boyton. What's your name? Boyton. I wrote that note. Ah, this is very serious.  This guy... I told you before, Moe, I am running this here investigation. Now, the way I see it, you two guys is in love with a lady by the name Joan. But Joan isn't a lady. Now, look here, buddy. Leave her mortality out of this. Please. Joan is the name of the song, a song I wrote which Larkin stole from me. I tried to see him to get what belonged to me, but he wouldn't let me in his office.  So, you knocked them off to get even, huh? Julius, leave us called the cops. You're not going to turn me in. I didn't do it. Wait a minute. Wait a minute. Maybe you didn't. Suppose we was to let you go, Boyton. Then... Then you believe? You can't do this, Julius. I'm doing it. Thank you. Thank you. I didn't kill Larkin, but I'll have a terrible time trying to prove I did it. Just a minute, Boyton. Before you go, just for the record. Where do you live? Who? Yeah, we gotta know. Just for the record, you know. Oh, uh... The 709, East 78th Street. Take that down in the notes, Mo. Notes? What notes? Take it down. Oh, okay, Julius. I got it. All right, Boyton, go on. Beat it. Thanks. But, Julius... It's outside. Julius, we're gonna get into all kinds of trouble. Let that guy go. He done it. I know, Mo. I know he is knocking off this body here. But if we turn him into the cops, what's in it for us? Nothing. If, on the other hand, this case is a mystery, no one knows who done it, the cops is baffled, and then we are solving it from the clues. What clues? Think of the tabloids with my picture on the front page. Heroic hacky solves case which is baffling entire police force. Think of it. Go on. Think. I'm thinking, I'm thinking, but I'm worried. Don't worry, we got this address, ain't we? I think of them things. Now we gotta find some clues. Clues? What for? We know Boyton. You done it. You said so yourself. Of course I said so, but that ain't the way a good detective works. It'll be like reading a back of the book first. We gotta show the cops we solved this here case legitimate. We gotta start at the beginning. With fingerprints? Correct. Now, I just happened to have here in my pocket my little nifty, knick-knack fingerprint detective kit. Oh, did you write away for one of them? I seen the ad too in the magazine. 49 cents covers postage and hand lint. Yeah, just the same as the G-man uses. For 49 cents? Well, nearly as good. Now, let me see, that bottle there on the table. This one with the liquor in it? Don't touch it, don't touch it. I don't want you to smudge the prints, understand? I will take care of that bottle. Oh, yeah, sure, Joyce. Now, I just blow a little of this powder on the bottle to make the fingerprint show up. Hey, look, what did I tell you? Fingerprints. Gee, real ones. Now, I need a clean white handkerchief to wrap around the bottle right there. Then, now... Julius, you're drinking the evidence.  The evidence is on the outside of this here bottle. Oh, that's not bad stuff. Now, look, wipe the rest of the things off in this room, door knobs and other things. We got the evidence now. We don't want the cop should get it too. This'll make it baffling. Yes. Now, let me see. I wonder... Holy mackerel, what goes, Julius? My meter and my cab, I forgot to pull a flag. The meter's still running. Wait a minute, Julius. Where you going? I got to turn off the meter. Stay down. I'll be right back. Holy mackerel, look at that meter. A buck, 65. Is this your cab? Oh, yeah, lady, but... I want to go to Grand Central Station. Hurry. Yeah, but lady, I'm busy. Didn't I see you cancel the meter just now? Yeah, but... I'm not busy anymore. Come on, I'm in a hurry. No, lady, you'll have to get out of the cab. I ain't driving you no place. Look, how would you like to lose your license? I wouldn't. Listen, I've hired you to drive me. If you refuse, you can lose your license. No, okay, okay. Don't get yourself in an uproar, lady. I'll take you. Okay, Grand Central Station. Come on, this is the place. Now, I think maybe it was a false alarm sign. Some crank hearing shots. Well, we'll soon find out. Here, this is the apartment. Is that your choice? Stay right where you are. What? Cops? Yeah, and what are you doing here? Me? Yeah, stay right where you are. Don't make no false moves. Come into your apartment, Mike.  Okay, Bill. Hey, take a look at that. That's Locke, and he's dead. Yes, I see he is. Brother, you've got plenty of explainer to do. Start talking. As the curtain falls on Act One of tonight's play, Mo finds that being a detective has its ups and downs. And he's really got himself... Excuse me, Mr. Barnes, did I hear you say ups and downs? Yes, I guess you did, Dan. You know, I have a secret solution for some of the ups and downs that men have. Come on over here and listen closely. Men, if getting up in the morning gets you down because shaving is torture, chances are you have wiry whiskers or a tender skin. So try Molle, the heavier brushless shaving cream for tender skins. Why, Molle gives you a shave as smooth as an apple.  With Molle, you shave faster, closer, easier, and you shave painlessly. Try it. See if you don't say, it's smooth, so smooth. It's slick, so slick. It's a smooth, smooth, slick, slick shave you get with M-O-L-L-E. Molle, the heavier brushless shaving cream for tender skins. Molle. Now back to Jeffrey Barnes and act to follow that cab. Molle and Julius, taxi drivers and would-be detectives, having apparently caught a murderer red-handed, let him go so they can solve the case by the clues. Having wiped away all the fingerprints to make the case more baffling, Molle is left alone with the corpse, all unaware that Julius is not coming back. And now the police have arrived on the scene and confront Molle. Well, buddy, what have you got to say for yourself? Myself? Nothing? Don't look at me. I didn't do it. Yeah, that's what they all say. Frisk and Bill, see if he's got the murder weapon on him. He ain't got nothing on him. I'm being framed. What does he do with a gun, buddy? It ain't here. I've just been looking for it. It ain't here. Oh, it ain't. Hey, Bill, careful how you touch those things. Might be fingerprints, you know. Oh, there ain't no fingerprints either. Oh, no. And why do you say that? Because I wiped... Because you wiped?  I wasn't saying that. Where's Julius? Where is he? He was gonna say you wiped him off, right? I... Where's Julius? He said he's coming right back. One false move out of you and I'll let you have it. You really think I've done it? Who else? You look mighty suspicious. I didn't do it. But he's done it, but I tell you. Yeah, I will tell that to O'Brien. O'Brien? O'Brien? Yes, buddy, he's chief of the homicide bureau. And you're under arrest for murder. So, they are taking me to see Inspector O'Brien. At first, I won't talk because I remember what Julius says about how we are gonna solve the case our own selves and get famous. But finally, I am realizing that instead of getting famous, I'm getting a hot seat if they can pin the murder on me. So, they throw me in the cell and the next morning, about 10 a.m. Julius! Hello, Mom. You know what day this is? Huh? This is visitor's day. Oh. You got just five minutes, mister. Mo, why did you do it? Do what, Julius? You don't have to pretend now, Mo. You can tell me I'm your friend. I don't get it, Julius. What? Mo, crime does not pay. Why did you do it? Do what? We know you've done it, Mo. Confess and things will go easier with you. Confess? Calm, calm. My patience is getting exhausting, my friend. You know you're moited, Larkin. Moited, Larkin. Julius. Don't make me put you through the toy degree, my friend.   What are you trying to do with me? You know what happened. That's all right. All right, Mo. Settle down. Settle down. I was just practicing. Practicing? For what? You know the way Daniel dare more once he gets a crook in a corner how he starts to make with a toy degree and busts him down? Oh, that way. You didn't have to do that to me, Julius. I didn't do it. At least scared for a minute. Did I? Julius, you've got to get me out of here. Don't worry, my friend. That little matter has just been taken care of. I turn any evidence to Chief O'Brien. You mean the bottle of whiskey with the fingerprints? The empty bottle with the fingerprints. Yeah, the empty bottle. Oh, I give O'Brien Boynton's address. Then you were in the clean. The cops will pick up Boynton and I will be in a tab lodge as the guy which cracked this case open. Here comes Chief O'Brien to let you out now. Hi, Obey. You say a man named Boynton gave you this address, 798th Street? Yeah, officer, that's right. That's where he lives. Did you make the pinch, Chief? You got him, huh? We checked that number. It's the middle of the East River. Huh? It may be moved. Oh, Mo, the rat has given you the double cross. Julius, ain't they going to let me out of here? Just a minute, Mo. I'll do my best for you. Look, Chief, this man is not guilty. What about the fingerprints on the empty bottle I turned into you? Oh, yes. That's another thing. We've checked that too. It's Bytons. No. It ain't Mo's. No. It belongs to the man who's going to burn for Larkin's murder. Whose prince is it? Yours. Mine? What do you know? I took me on Prince. Come in, you two. Sit down. Yes, yes. I suppose you are bringing me in here to your office because the entire police force is baffled, huh, Chief? I knew you would have to come to me.  Sure, sure. Don't get excited. Say, what's all this here stuff on a table, Chief? Oh, that's some odds and ends. We picked up in the Larkin apartment. Keep your hands off that stuff. All right, all right. Clues, huh? They might be. Stay away from it. You've done enough damage already. Yeah, enough damage already.  Very lucky. Yeah, lucky. How do you mean, Chief? Boynton has just given himself up. He saw in the evening papers that we were looking for him, so you're in the clear. Boynton, give himself up. Julius, we are free. Yeah, very interesting, extremely. By the way, Obi, is Boynton confession? No, but he will. I am sorry I cannot see I die, would you Obi? No. No? No. Now, I have a theory about who is knocking off who. Oh, look, you. One more theory out of you, and I'll send you up for 10 years.  We're going, we're going. Are we, Julius? Yeah. He certainly is lucky for us that Boynton gave himself up. Moe. Boynton didn't do it. Didn't do it. But we've seen him. You said he done it your own self. It's a dame which done it, a red-head dame. She's the dame which got in my cab yesterday and makes me drive it at Grand Central Station. Why, huh, Julius? See this picture? Yeah. That's her. I took it off the table in O'Brien's office. It was found in Larkin's apartment. Julius, you should have done it. I remember she was very suspicious, nervous. Yeah, but what about Boynton? Moe. All good dicks are getting off on a red hair and now an end. Man, remember what happens to Daniel D'Amore in the case of the Black Nighty? Oh, but that wasn't a red-hairy. He got off on that was...  Now, look, this red-head dame, which is in such a stu to get away, is the real killer. Yeah. Yeah, Julius, a red-head-hairy. Moe, we know who done it. We got a picture. Now all we got to do is get her. Yeah, all we got to do. Wait a minute, Julius. Remember what O'Brien said about being accessories? Accessories, shmasseries. We got the real clue now. Let's go find her. Julius, turn off the radio. You'll run down the battery. Look, Julius, we've been waiting part-time in front of Larkin's building for eight days and I'm waiting for that dame to show up. Would you matter getting discouraged a look? Julius, maybe she won't never come back here. She's got it. She says so in the book. A criminal always returns to the scene of the crime. I know. Yeah, and that song which Boyton wrote, it's driving me nuts. All the radio programs are playing a thine at all. I like it, but okay. Look, let's quit this detective business. Julius, I get back into the hat business. That dame won't never show up. Moe, look. That dame, the one we're looking for. The red-head-hairy? Yeah. Hey, she's going into the building. Come on, we're going after her. She's Larkin's apartment. There she is. Okay, lady. You are under arrest for the murder of Larkin. I don't know who you are, what you're talking about. Look how Moe, she's reaching for a gun. Yeah, you hear me? I got it, Julius. Nice work, my friend.  Well, I, uh, okay. Okay, you got me.  Hey, Julius, the dame's born. I can't help it. Crime does not pay. Yeah, you're right. I killed him. I killed him because he mistreated me. He beat me. Me and my four children. Four kids?  Well, I'm sure. It was bad enough when we were poor, but when he started making money, he just threw us out. Out into the cold, cold night. We were starving.  Yeah, I had to sell my new mink coat to get food for my little children. A guy like that didn't deserve to live. That's what I kept telling myself, so I bumped him. I killed him in self-defense.  I didn't know what I was doing. Naturally. Hey, Mo. Yeah, Julius. Got any money on you? About a buck seventy-five. Give it to me. For her? Yeah. Here. I couldn't accept it. I insist.  No, don't mention it. Here, lady, take it. Oh, gee, thanks. Thanks a lot. You're going to let me go now? What else? Oh, you're so kind. You're so sweet. Oh, you're just saying that. Could I have my gun back now? You want it back? What for?  Yeah, yeah, sure.  Give it to her, Mo. Here. Here, lady. Open up, lady. Open up. The car. Goodness. Oh, look. I'll hide behind the door. You open it.  I'll handle him. Oh, so it's you two again. O'Brien. I thought I told you the next time I caught you monkeying it.  He's out cold. Oh, lady. You shouldn't have done that. OK, OK. Let's cut the comedy. What comedy? Come on. We're getting out of here. You care about time? Oh, yeah, but...  I'm giving the orders. Let's go. I've killed one man with his gun. Yes.  This is Jeffrey Barnes again. In just a moment, we'll bring you Act 3 of Follow That Cab.  Now, plain water does that. So if you want to fight a common type of dandruff effectively, the thing to do is use double dandereen. Double dandereen is a scientific product that goes to work on your scalp and actually combats the dandruff by killing the germs that many outstanding authorities contend are a cause, and it kills them on contact. Now, the reason why double dandereen is so amazingly effective is that it contains an active antiseptic called alzan. This is a special ingredient used by many hospitals because of its remarkable efficiency, and no other hair preparation contains alzan. You get it only in double dandereen. So try double dandereen and see why most ordinary hair preparations can't compare with its dandruff-combatting effectiveness. If you're not completely satisfied, return the empty bottle and get your money back. Buy double dandereen at your drugists. Step on it. Step on it. The cops are gaining on it. I can't go no faster, lady. I've got it down to the floorboards now. Just take it easy with my cattle.  I'll tell them what to do. Yes, ma'am. Julius, they're shooting at us. Oh, Lord, don't let them hit the tires, please. Two can play at that game. Lady, you could have rolled that window down. Don't bother me. Did you get them? No, but I'm afraid what's going to happen to us... No! Hey, what's the matter? You hit Julius? No, worse. They hit the meter. It's 75 cents already. Well, turn it off. Turn it off. I can't. It's jammed. I wouldn't should have stopped it. It didn't. It's going twice as fast. $3.94, 25... 5.40 or less. What, lady? Turn left, mother.  No street? Capturing the culprit, like Julius says. You see, she is the dead guy's ex-missus, and she don't like him. The reason she is coming back to the apartment is that she knows there's some cash hid there. Well, Barton is let go by the cops, and he gets his song back. And that is why the papers are calling it murder in A-flat. You see, A-flat is an apartment. But the best thing about the whole deal is Julius and me is becoming real famous. No kidding. You get the magazine, absolutely authentic, true crime fiction. Our true-to-life stories are appearing in this week's issue. And now this is Jeffrey Barnes again, inviting you to be with us next week when we present Richard Connell's The Doctor and the Lunatic. Here is a story that is as tense and suspenseful as follow that cab was comic. A mysterious madman is on the loose.  Plenty of excitement, action and chills and a real bang-up climax. So don't miss Richard Connell's The Doctor and the Lunatic. The original music for the Molle Mystery Theater is composed and conducted by Alexander Semmler. Follow That Cab was written by Larry and Sidney Sloan. Johnny Gibson and Larry Haynes were featured in tonight's program. This is Dan Seymour saying goodnight until next Friday at the same time when the Mystery Theater presents The Doctor and the Lunatic with the National Broadcasting Company.