atmosphere, channels 8 to 15. Window size, a control for zooming in and out of the EEG data, with a default window size of 10 seconds. To trend, a toggle to enable or disable trend removal from the EEG data. Filter order, a parameter to set the order of the filter, typically ranging from 1 to 5. Hall spectrum, a toggle to overlay the non-filtered spectrum, frequencies below 0.5 Hz and above 30 Hz. Frequency scale, an option to choose between linear and logarithmic frequency scales. Noise removal performance, displayed as signal-to-noise ratio, SNR, and mean square error, MSE, of the EEG signal. Raw signal, visualization of the EEG signal in the time domain. Spectrum, representation of the EEG signal in the frequency domain with the ability to view specific frequency bands, delta-theta-alpha-beta-hole spectrum, if selected, additional information. Users can observe changes within different frequency bands by selecting specific frequency ranges or by nebling the whole spectrum option. The application uses a wavelet-based noise removal algorithm, with options to customize its behavior. Users can interact with the EEG data by playing, scrolling through the file, and adjusting various parameters. This flask application would serve as a user-friendly interface for analyzing EEG data, allowing researchers and practitioners to explore and manipulate the data in both the time and frequency domains while evaluating the effectiveness of noise removal techniques. The application would provide valuable insights into EEG signal processing and analysis. Welcome to the EEG spectrum visualization application. This specialized tool offers an intuitive interface to visualize intracranial EEG data, both in the time and frequency domains. File scroll bar, a horizontal scroll bar, allowing users to traverse through the EEG data files seamlessly. Play functionality, users can hit the play button to auto scroll through the EEG data. The rate can be adjusted using a play speed control. Yeah, we'll start developing in a second. We're currently on this EEG noise removal thing. Just in the project itself, move it up, the top, and just so it's clear what was updated last. This changes obviously not in production yet. So yes, that's the old thing that we had. We had some videos on it, but unfortunately it doesn't work anymore. That's why we are re-doing this. It used to be LabVIEW, now it's Python, NumBuy, SciBuy, Pandas, and it kind of, yeah, looks same, same, but different. This is what it currently, this is what it's gonna look like. Hopefully we can publish it today so we can have something to play with. Yeah, just quickly checking. This is a bit weird, but yeah, the toggle button is nice. Levels, right, you can barely see. It's a fact, so yeah, if it's level, we'll be able to go all the way down to zero. Yeah, so that's the original signal, the noisy one next to it. There's a kind of natural DC in it, so when you hit the training, the almost overlaid one top on the other, yeah, you kind of see the noisy there at the back. Some of these wavelets will be doing more than others. Yeah, there's not really much difference between them. These two things don't work. I get a 400 error at the server. We'll have to fix that. We'll probably have to describe properly, make a prompt, proper prompt, so we can actually fix it. Let's start populating, yeah, let's start with the HTML file. It's the same file, right, that's the, no, that's okay, close that. So we have the HTML popped into chpd, and we have that line that was working quite successful. This, no, where's it? Yeah, confirm reading the code, not generate anything else. And this bit is handy, because normally it should respond with... I've read the HTML code you provided. It appears to be a web page for visualizing EEG, electroencephogram, data with various controls for data processing and visualization. That's right. So thanks for confirming that. And then next, let's pop in the JavaScript and the same prompt, but just reading, not generating anything else. I've read the code, that's great. It's kind of, it feels like this is important in what the order, this is done. Let's do CSS as well. Confirm reading, don't generate anything else. I've read and understood, okay. It's giving itself a human-like properties, which is a bit odd, but whatever works. And the last snippet of code is the Python Flask application. So let's switch now. And the switch does work. It's the number of levels. It's going all the way to zero. Voltage 5k, something simple. Change the trend to a toggle switch. It's 24 years. Yes. See if it understands the context. It does. That's not very helpful, because you can copy the code when you have before and after. We have the trend and HTML. Do we? It's actually called the trend toggle. It's already a toggle. We're replacing the whole thing. Yes. So now it doesn't have the label. And what else? In the Flask code you provided, you're already reading that the trend primes are from the request, which will be true if the checkbox, now double switch, is checked and will be missing if it's not checked. So actually checking for the ID for it is the trend toggle. I wasn't even working at all if the KIT was the trend. Yes, that's the same. This means no changes required in the backend code since you're already handling it correctly. Note, ensure that the CSS code for the toggle switch, which you previously provided, is included in the index. HTML file or any link star sheets so the toggle switch will be styled correctly. Okay, now the only problem. It's not actually working. I guess because of the name, because this name used in JavaScript, it should be just the trend, is it? Right, now it's working. Yeah, just leave it as is. We just need the label. Checkbox, the trend switch. I have another switch, span, where the noise, that goes on top there, trend as name. Right, that's working fine. Right, we wanted to later we'll rearrange the whole thing. Let's see, log. Log can be changed to a button. I don't like the drop-downs. That can stay as is. Okay, we're ready. If everything is working, we can publish this. But show how we let know what you think already to publish. I would say, yes, if everything is functioning, there's still a problem that you replace the chair. Let's go through this for a sec. The effect of the nosing is not very clear, but it's there, but not very clear. Okay, that's just the way it is. The effect of the word transfer is not clear. It can all spin buttons be replaced by spin buttons, replace by sliders, checkboxes, replaced toggle switches. All right, we have about this one. Let's pop this in. So we want to make sure signal to noise ratio SNR and mean square error showing actual values. To show the signal to noise ratio SNR and mean square error MSE, for the EEG data, you need to calculate and return these values from the back end and then display them in the front end. Here's how we do it. Modify that, get underscore data root to calculate and return the SNR and MSE values. Okay, we're using from sklearn metrics import mean underscore squared underscore error. Okay, it's fine. I'm GitHub cobalt works. It's really imported mean square error. And from math it's trying to import square root. Well, we don't really need it, do we? Okay, now get data. What's this thing called? This input will be called from the client side to get the data. Okay, rest of the code is like, why should we have anything SNR? At the moment, no. So at the end of this function, which getting ridiculously long, so suspect later will be really hard to troubleshoot if there are any problems with doing this bit. How shall SNR calculation signal underscore power equals MP mean original underscore EG underscore data asterisk asterisk to noise underscore power equals MP mean original underscore EG underscore data EG underscore data asterisk asterisk to SNR equals 10 asterisk n hash MSE calculation MSE equals mean underscore squared underscore error original underscore EG underscore data EG underscore data hash prepare the response response underscore data equals more underscore signals original underscore signal the noise underscore signal Spectrums, Spectrums, SNR, SNR, MSC, MSC. Right, so we have to... Didn't tell us, but we need to replace it. Okay, we don't need this one anymore. SNR square root, is it? Okay. And EG data. It's the filtered EG. It should be legit. Oh yes, that's mean square error. Let's see here. It's not getting much information. Canascope pilot explained this using sklearn metrics to input arrays, the original and the filtered one. The mean square error is a measure of the average of the squares, of the errors. That is the average square difference between the estimate and the actual values. It could be a better explanation. We might later get the chgpt to explain it in a better way. So let's see if it actually works or not. We need also to change the front end. Well, in the front end, we already have the metric there. We need to make sure it's actually taking SNRSC, SNR... Yes, MSC, yes. The capital... Wait, it's not called SNR output, is it? The different way can HTML span ideas in our value? Why is it different to that? ID MSV. Okay, in JavaScript, make sure we have it. We have some SNR... Yeah, they called SNR output. Why not just call it SNR everywhere? Get determined by the... This will so not work. Getting an error. Script not found, something not found. Yeah, it's not even showing the error. It's the name of this thing, isn't it? Okay, so in Python, it's called SNR response data. Just fire response data. Where do we get the raw signals? Okay, so it should be just data, raw signals. Spectrum should be the same data spectrum. SNR output section will be used. Okay, what am I doing wrong? Two decimal places... JavaScript is using... I'm not using jQuery. Why are you saying I'm using jQuery? Using element by ID. Yes, that's right. You are correct. I'm not using jQuery, I'm just using Vanilla. Whatever you call it. Vanilla JavaScript. Why do I need to do this anyway? It's inside when data... I think it's happening after when data... You know, confused. It's showing me the values. Okay, HTML, 7IT. Let me see. Apple there, that's the same. Why am I not seeing the text? Let's get rid of that for a sec. Comment it all out. Right, we have the numbers. Our main thing is how to make sure everything fits the one green. Noise is on by default. Yeah, that's the color bit. Yeah, we haven't broken the inputs into three parts yet. Okay, we did all this. Make sure it fits into the screen. Actually, do it all. It's too much to ask. Yes, I already have that. Okay, the other thing to make sure... I need to give me like specific suggestions. Yeah, I don't know what's the regular screen size, but still it doesn't fit. So it's okay for the text to be below, but everything else in here will fit into one screen. And if it's not big enough, it should adjust the size of the charts. You do that. It also can be displayed side by side. I didn't try this before, but let's do it again. Just grab the whole panel. But still how to improve A into the... Yeah, hopefully it understands that the text can be below. Reduce padding, margins, the space between the UI elements, especially at the top, can be reduced to provide more room for content. I don't know, I think that's okay. Group related controls together in distinct sections. For instance, wavered denoise, gorbages, and levels can be grouped under waver settings, paneled. That's interesting, but that's only grouping the controls. Similarly, channel number, window size, the trend, filter order, whole spectrum, frequency scale, and scroll can be grouped under another panel like signal settings. We actually had a prompt for that. It should be into three parts. To make that clearer, let's go with this. Drop down menus. If the number of options within a category grows, consider using drop down menus. This is especially useful for selections like gorbages one, if more wavered types are added. Yes, we're really doing that. Vertical alignment, instead of a horizontal layout for controls, instead of stacking some controls vertically to use vertical space more efficiently, especially if the screens width is limited. Yeah, we generally want to have like an adaptive layout. Use adaptive. Yeah, that's right. Adjust the height of the graphs based on their importance. If the frequency spectrum graph is secondary to the waveform graph, it can be displayed with a smaller height. If this ring is wide enough, the EG signal in the time domain and the frequency domain should be side by side. Alright, how did you know? That's what I'm going to say there. Responsive design. Let's continue with this response from GPT-4. Responsive design. Ensure that the UI adjusts itself based on the screen size. This is crucial for users with smaller screens or when the application is accessed on tablets or mobile devices. I don't understand, do I have to do it manually or can I just tell everything to be adaptive? That's it. Check the size of the screen or device tabs. If certain settings or views are not always needed, consider using tabs to switch between different views or settings panels. This can help de-clutter the main view. Yes, but also... Minimize use of borders. While borders help define boundaries, overusing them can create a cluttered look. Consider using subtle shadows or changes in background color to differentiate sections. Ensure that primary controls or information, like the graphs, are more prominent while secondary controls can be less emphasized. This can be achieved through size, color, or typography. Opt for modern UI components, modern sliders, toggles, and buttons can be more compact and visually appealing, helping to enhance the overall aesthetics while saving space. That could be interesting. This one is for you. Feedback and testing. Lastly, collect feedback from actual users. They can provide insights on which controls they use most often. What information they need to see most prominently and which elements can be minimized or brewed together. This is exactly where I need your help. Remember, while optimizing space, it's essential to maintain clarity and ease of use. It's a balance between aesthetics, functionality, and user experience. It feels like it forgot the actual code. Is it not generating any code? I told it not to. Can we do all specific code suggestions? Yay. Signal to noise ratio, SNR, and mean swear error, MSE, outputs, designate specific areas on the UI where these values will be displayed prominently. You might consider using read-only input boxes or labels with a different background color and different output values. Yeah. Nothing has changed. Output box. I think I have an output box, do I? Yeah, I'll change the colors later. Alright, we don't see the difference because SNR output is suggesting that there are no values in it. Let's do F12 here, CTRL-SHIFT-C. I'll show that straight from the browser. Okay, what is the text? Is the text color coming from? Okay, I don't know what's going on. I don't see the numbers. Check the HTML. Read only. The view is okay, but the whole view can be an output box. Does look better, but unfortunately, I do not see the numbers. Okay. Let's move on. Use CSS flexbox or read to create flexible layouts to adapt to screen size. Right, it's container. It's CSS. Do we have it? No. Now, the other question is, we have a secondary CSS, and this one does have a container, this black flex, black rub rub. Yeah, so we can't really do it because we already have container features in the styling of the whole project, so we want to change it here. You want the EEG signal in the time domain and the frequency domain to appear side-by-side on wider screens. You can use a media query to achieve this. Right, so by default, stack the charts vertically for mobile devices. Yeah, let's try now. Oops. This should be definitely wider. That didn't work. To do the whole... No, the chart is there. Okay, let's do the whole thing. Just in case that's the problem. Okay, that doesn't work obviously. What am I doing wrong?  No, we do not. A co-plot container. Right, because we need this. Assuming you're using a charting library, you would add the chart containers in your HTML and use the above CSS to style them.  No, that's not what I use. I use this. I don't have all the previous code, so why giving general responses? Given the provided HTML snippet and the requirements you mentioned earlier, you can modify and style the plots to fit responsibly side-by-side on wider screens and stack on top of each other on narrow screens. Here's a suggested approach. Right, so instead of... Yeah, we use a plot container. Asterisk, by default, set the plots to take the full width of Asterisk. Asterisk defined a media query to style for wider screens, Asterisk. Right, we're sure we're doing it. Okay, so in smaller screens, it will be like the larger screens. It will be plotted side-by-side. Which is nice, because we have everything fitting in. And nicely, let's see. It looks like on a mobile iPhone 12 Pro. Oh, no. Don't have money for that. So we won't be testing on it as well. So that's Samsung Galaxy. Not sure which size I have. I can just check on multiple ones. No, I'm just trying to make sure it's not a tablet or anything. But yes, doop. Yeah, what just happened? Yeah, mobile doesn't fit really well. On larger screens it will appear like this side-by-side, so it should fit a fit-in touchscreen. Yeah, this takes too much space, doesn't it? Let's restart. Right, so it does look much better. The errors we're having, yeah, they will be sorted in production. And the problem with smaller screens is that it takes too much space. You can remove eG here. Just so it takes a bit less space. Yeah, those still don't really look like outputs. The provided HTML is well-structured, but ensure our CSS works correctly. Ensure the plots are within a parent container, which will help in managing the layout more effectively. Yeah, okay, do I have this already? HTML, Rubber, no, not being used. So why do I need it? The clear, both style in the last div ensures that any content below the plots will not wrap around them. This is a traditional method used after floating elements. What's that? The clear, both style in the last div ensures that any content below the plots will not wrap around them. This is a traditional method used after floating elements. I don't know what does it mean, but I can try it out. When the browser screen width is greater than 1024px, as specified in the media query, the two plots will be displayed side by side. For narrow screens, the plots will stack on top of each other. Adjust the 1024px threshold as needed based on your design requirements. Okay, if you say so, I'll keep that number. I don't have any better ideas. Okay, but what's up if we do have a plot container? Yeah, we don't have those status, MIT, so that's the same. We don't have any clearing anywhere, do we? No. Okay, so we have those two plot containers. We replace the whole thing. This is like that as well. Yeah, this one's still not working. So yes, there's always more room for improvement. Yeah, let's assume the window size is not changing. Yeah, I don't know what to do on a smaller window size. It just doesn't fit. Yeah, something will have to go. Labels could go. Yeah, that will be hard. Leave it for now. It's working okay. Yeah, just last thing, those things still. Let's try this again. Hopefully it doesn't get upset with me because I'm asking the same thing over and over again. I'm trying those two outputs to actually look like outputs. Let's see. Yeah, so they change okay. Wait, the trending should be on by default. Trend, on to fix the previous thing. Yeah, one of those two outputs actually look like outputs. HTML, just two outputs as now, blah, blah, blah. And CSS, let's check SNR. Right, so it just looked like that. Let's keep them off for now. Let's see if actually. Yeah, so those ones, where are the values? The values, the values are not there. Anyway, hopefully it will appear on bodykears.com later today. We have an extra tool to play with. Provide feedback if you have time. That would be awesome. Change the output container, no. Oops, I didn't copy paste properly. We have a plot container. We don't have a label, no. Output, no. The problem is that this SNR is not showing any values. It's the old one. Yeah, the new one looks more like an output than where are the values. Yeah, just not there. That's it. The less than output greater than tag represents the result of a calculation and is semantically appropriate here. Pairing each with a less than label greater than ensures clarity. What span ID? So we have the text output ID. It's not being shown yet. Don't like all this fluff. Okay, maybe I did it. Numbers, it is to the actual values. The values are there. What's going on? To ensure that the signal to noise ratio SNR and mean square error MSE outputs do not show their actual values by default or until a particular action is performed. You can set the default values to a placeholder like or any other string indicating that the value is not available or not calculated yet. This way, users know the values are pending or not available. The values are there. Sure, the problem is... When you fetch or calculate the actual values for SNR and MSE, you can update the outputs. However, until then, they'll show. Until the updates run show function is called with actual values, the outputs will continue to display. This ensures that the placeholders are shown until you're ready to display the real values. Yeah, I'm happy to do that, but that's not kind of a solved problem. You know, like literally the numbers. Okay, can you regenerate it? Don't like this response. Why would you do that? Why would I do that? What did I say? Made a mistake. Make sure are showing the actual values. Sort of that. Yeah, now the values. Let's try again. So this one is working okay. However, is not showing the actual values. It's locked down in these little things. The less than output greater than tag is designed to display the result of a calculation, but you may need to set its value using JavaScript. If you have previously been using inner HTML or text content with a less than span greater than tag, you'll have to adjust the method to work with the less than output greater than tag. When you update the values of SNR and MSE in your JavaScript code, you should be doing something similar too. With the less than output greater than tag, you need to adjust this too. The difference is in using value instead of text content or inner HTML. Isn't just output okay, get element by a T. If you prefer using a less than span greater than tag as it was working before, but want to keep the styling of the less than output greater than element. You can use a different tag, like a less than div greater than or less than span greater than, and style it to it like an output. Okay, so this worked. And no, does not style the output. It's still not showing the value. Safe thing. Works. Okay. This one doesn't work. Doesn't work as well. Troubleshooting. Check if those any CSS supplied to output container less than label greater than less than output greater than or styled output that could be hiding almost placing the content. You can do this using browser developer tools, e.g. inspect element in Chrome. And now there's nothing affecting this being a Cromot. Ensure that there's no JavaScript that's inadvertently targeting and changing these elements. You can add a temporary inline event listener to log any changes. Be sure it's not changing. Should have saved span, styled output, and it's all dead out. Right. So maybe it was changing or something. Okay. Just getting rid of all of that. So I think we're ready to publish. What? Do it a bit later. Yeah, this should be an easy quick, easy fix working. Now it ran on by default. It's suggesting to change HTML which is easy. Yeah, we will continue this later on today. See you in a bit.


rolling toward the California shore, the mighty Pacific Ocean helped man check a wild fire. How all this happened is a story. The story of a watershed fire took place near the city of Santa Barbara. To the northwest are the foothills of the Santa Anais mountain divide, whose brush-covered canyons unfold like a blanket of green. Now, fly through historic San Marcos pass in Los Padres, and you are in the heart of the Santa Anais watershed. This watershed feeds the Great Kachuma Reservoir, and through irrigation supplies water for the heavily farmed coastal terrace and canyon floor. The region produces truck crops, drain and pasture, nuts, lemons and avocados. Farmers and ranchers depend on a constant supply of life-giving water from these mountains. Los Padres national forest lands make up 70% of the Santa Anais drainage. The natural growth of sage and chaparral has little commercial value, but grass and the roots of scrubby brush help the land store the precious winter water. Every year, thousands of summer residents, campers and picnickers move into the cool green hills. Water keeps their paradise alive. Water held in the hills by nature's green blankets. Burn this blanket, and you can wreck the watershed. Many service agencies, including the local fire departments, help the Forest Service protect these watersheds. Closer to the hills and Forest Service headquarters, fire-controlled crews are constantly trained. During the fire season, both national forest and private land are patrolled by forest rain. Late one summer in one of the worst extended periods of fire danger in years, the rangers closed the forest. Still, they had a fire. At a ranch in the foothills, a generator house caught fire. The water shed with its precious plant cover was aflame. The distant smoke signaled disaster. Additional firefighters were rushed to Refugio Pass where the fire had started. The forest fire is from the air. A helicopter gave the fire boss a movable platform, a vantage point from which he could direct the attack. Smoke from the blaze billowed for miles. And there it was, licking the ridge. Ironically, they named the fire Refugio, which in Spanish means refuge. It looked like a big one. The fire had picked a bad time to happen. With more than 50 other fires going in California, federal and state forestry officials were being hard-pressed. A manpower crisis and equipment shortage were shaping up. On the fire line, the first crews never dreamed it would take 2,500 people nine days of continuous work to control the fire. A terrible price to pay for man's carelessness. But they did see that the fire's early rate of spread defied control efforts. On the ridge trails, men stretched hoses down the rugged terrain. Additional bulldozers supplied by the Navy and surrounding counties were brought in to help the forest service cut fire lines. They worked in pairs in threes, building defenses, and connecting the roads. One vital fire line was cut by eight bulldozers working side by side. Only by removing the fuel from the advancing fire could the hungry monster be stopped. One's plotted strategy checked shifting winds and worried about equipment and manpower. Their crews of regulars and volunteers needed rest. They were reinforced by firefighting Indians, flown from New Mexico and Arizona. About to be sent to various camps along the growing fire perimeter, the men moved as a relaxed team, saving their strength for the battle ahead. In steep, rugged country, it takes trained men to fight wildfires. They moved canyon growth until it frequently seemed to explode. By 30 mile an hour winds, the fire jumped three quarters of a mile and started spot fires. Not all of them were caught.   Sea bees and marines were ordered into battle. Men and equipment had to be rushed to the fire lines, which now stretched for 70 miles around the fire. Wind-driven flames became a real threat to cities in the valley below.  Nights and days, weather refused to relinquish control of the rampaging and erratic mountain fires. Veteran firefighters, both state and federal, agreed that weather held the whip hand, as wind shifted the flames in one direction, then another, burning 85,000 acres of watershed. The firefighters' arsenal was equipped with the latest weapons. Nevertheless, the fiery necklace embraced the hills. When the blaze jumped the roads along the south coastal slope, traffic had to be stopped by the highway patrol and the county sheriff's office. No matter how much the drivers complained that their perishables would spoil, Highway 101 and old Marcos pass were closed. Highway 150 was partially closed. Only emergency traffic could get through. Roads also served as airstrips for helicopters.  Even the Great Southern Pacific Railroad had its schedules of passengers, mail and freight, interrupted when it was signaled to a stop. The railroad heated the warnings issued by the fire headquarters. The fire has jumped the tracks, ties, power lines and telephone poles are ablaze. All traffic is stopped. People who lived in the hills had to be evacuated as fire threatened their homes. About a hundred Marines were ordered to bear Creek tanks. Directed by forest rangers, they helped state and county fire crews in a bold backfiring tribe. The Marines used regulation flame throwers.  The tactic is to burn the fuel ahead of the advancing fire. Selecting the time when wind and weather were most suitable was a job for experts, for men with knowledge born of long years of research and experience. A desperate touch and run job, but a sure way to stop the fire from spreading if it worked. Was a success. The weary men had earned their rest. To supply, service and feed the army of 2,500 firefighters, three camps were set up. Men and equipment were mobilized from a hundred mile area to take part in the fight. Firemen came all the way from Los Angeles. Even before the fire was out, water and land management experts met with local authorities at the federal building to plan emergency measures to reduce erosion and flood damage hazards. They surveyed fire damage from the ground and from the air. By helicopter they set out to figure which creeks might flood now that the slopes will burn bare, which areas had to be receded for temporary cover to help check and hold the flow of heavy rain. Which stream channels had to be cleared? Four million dollar destruction and the eight hundred thousand dollar battle presented a grim spectacle as the Kachuma watershed was rolled up into a cloud of smoke. Now, some of the same firemen and equipment were used in mop-up work. At long last the fire was surrounding. Some fire lanes held, some roads held. Some didn't. From Santa Barbara to the north was a near solid mass of charred blackened brush. Some properties were safe. Heavy bulldozing kept the wildfire from this residence. A new house on the hill caught fire. A few burned to the ground. Many citrus orchards were scorched. Even a wooden tank full of water burned. Guard rails on Highway 101 hardly slowed down the great destroyer. A shock to the whole was the blackened California landscape. Here the Southern Pacific right away had been crossed by fire. Communications were interrupted when telephone poles burned like matchsticks. From the mountaintops to the canyon floors the land was ravaged. It is here that the mighty Pacific Ocean helped man check the wildfire. For several miles the ocean served as the barrier on one side of the fire. Nature's blanket was gone. The black mountains sprinkled with white ash looked unreaved. Pushed was the battleground. The only noises were the rumblings of rocks loosened by the fire. The severe burn almost enveloped the watershed. Before the fire an acute water shortage already existed in the south coastal area. Now the supply would be even more limited. Because the fire burned all the way to the water's edge, ashes and soap would collect in the reservoir, cutting down its capacity. Under the onslaught of rain, stark and naked peaks would soon shed their thin skins of soil, unless covered by another green blanket. Flood was the new worry. Nothing was left on the land to hold or to soak up or to slow down the water. In the rainy season, four inches of rain in one day is common in the mountains. Water would wash the black hills and dump ashes and mud on the civilization below. Dried stream beds were a concern. Could they carry the expected flood waters? Investigators found downstream channels choked to the bridge heights. An immediate task was to clear these channels. The Secretary of Agriculture had promptly allocated emergency funds. Bulldozers went to work. U.S. Department of Agriculture agencies, cooperating with the local soil conservation district and the county, tackled the big job. The task called for clearing 47 miles of channels to reduce flood damage hazards. The land was being prepared for the aftermath of fire by some of the same bulldozers which cut fire lines just a few days before. Soilsmen and engineers hoped to make the water flow, not flood. Technicians found that the burn had extended into the soil. The roots of the grass and brush carried the fire down two and three inches. One rain and the ash would form a hard crust. While the ash was soft and fluffy, was the time to seed the watershed. A fleet of airplanes took on the tough job. Each strips, each plane flying a predetermined flight plan, dropped its cargo of seeds and hope. Constant communications between flag men and the director at the airport was maintained to assure complete coverage. Sampling boards were spotted throughout the rough terrain to check the distribution of seeds. Flagmen reported directly to the pilots with flag signals for each successive run. By sowing grass seed immediately after the burn, these teams tried to take advantage of the first rain so as to get the best results. When the weather was good, they seeded as long as there was light. Then, with all his science and ingenuity, is but a speck when he tackles the gigantic job of reseeding a watershed. Then, 21 days after authorization, more than 65,000 acres of national forest and privately owned watershed lands were sown with grass seeding. The weather held. Grown-up lands were sown with grass seeding. The weather held. Growing conditions were right. With man's help, a temporary blanket of green returned to the land. And so, a miracle. The miracle of new plant cover. Given this protection, the old growth may eventually return and find the soil together again. The Refugio Canyon fire is now history, but its grim reminder of man's carelessness will be around a long time. It takes years to grow a brush cover as effective as the one that wildfire destroyed. On steep slopes in Southern California, grass is only an emergency stopgap. Reseding can never restore the homes, the recreation areas, and the wildlife that were destroyed. You must be ever alert to the threat of fire. Here, scorched earth was the price of human carelessness.  When you safeguard earth's green blankets, you protect yourself and your land forever.


From around the globe, it's theCUBE with digital coverage of Dell Technologies World, digital experience brought to you by Dell Technologies. Everyone, welcome back to theCUBE's coverage of Dell Technologies World, digital experience. I'm John Furrier, host of theCUBE, Cube Virtual. We're not in person this year, we're remote, we're doing the interviews, we're not face-to-face. So thanks for watching. Two great guests to talk about Dell Technology Storage and Data Protection for the VMware environment. It's got Caitlin Gordon, Vice President of Product Manager at Dell Technologies and Lee Caswell, Vice President of Cloud Platform Business Unit, also known as CPBU for VMware. Lee and Caitlin, great to see you both. Thanks for coming on. Thanks for having me. Yeah, thanks, John. So what a crazy year, we're not in person. Usually the events are awesome. VMworld recently went on and then you guys have the same situation. We're all online now and it's really kind of highlighted the customer environments of cloud native and I've been saying this on all my reports and all the CUBE interviews that the executives who are in charge are now saying, look at our modern apps have to be cloud native because the obvious benefits are there and containerization has become mainstream but yet IDC still forecast, about 15% of enterprises are still fully containerized with a huge amount of growth coming around the corner. So you're seeing this mature market where containers are validated, they're being put into production. People are now moving hardcore with containers and you have the Kubernetes. I got to ask you Lee and Caitlin, what does this mean for the customers? Are they getting harder pressure points to do things faster? What does it all mean for the customer? Yeah, I'll start and Lee, you can add to it. I mean, I think what we see is the trends that were already happening have now accelerated and modern apps were kind of the top of the priority list but now it has really expedited but at the same time, traditional applications haven't gone anywhere. So there's this dichotomy that a lot of IT is dealing with of how do I accelerate those modern apps while also streamlining and simplifying my environment for my traditional apps and not only do I need to the right infrastructure to have that for production workloads modern traditional but also form a data protection standpoint how to ensure that those are all secure and do all of that in a way that simplifies life for whether it's the data protection admin, the BM admin or even the developer, right? All of the different folks involved and needing to make all of their lives simpler has just really exacerbated a challenge and really given us a lot of opportunity to try to solve that for customers together. Lee, what's your take on the landscape out there? Yeah, I'd emphasize that speed really matters today, right? That we're really looking at how do you go and deploy new applications faster, right? New ways to get engaged with customers. I mean, it's not happening physically anymore. So how is it happening? Well, it's happening largely through applications. And so as you now basically develop new applications more quickly, containers are a way to speed the pace of applications. And the theme that we continue to drive home is that that means infrastructure has to respond more quickly. And it means that for the teams that are managing infrastructure, it really helps if you have a consistent model where you can get more done with the same teams and leverage all the experience you have as well as the security and infrastructure resiliency model that we are bringing together to our customers. This brings up the real question. And if this comes up, kind of, you see more at the executive level, like we need to have a modern application direction and they'll go, everyone goes, yeah, of course thumbs up, then they go, try to make that a reality. Because even though DevOps and infrastructure's code is still the viable old path, it's hard. It's a Gatelin. We're talking about edge to core data center, hybrid to multi-cloud. There's a lot going on under the hood there. So you guys are doing a lot of stuff together, VMware and Dell Technologies. What's the solution for customers? They got to move faster, as Lee pointed out, Gatelin. How are you guys working together to make that infrastructure more modern, faster, programmable and reliable? And make it simpler for the customers, right? I think it really comes down to one of the most powerful things about the partnership is that from the Dell Technologies standpoint, we have really a plethora of different solutions to support your VMware environment, whether it's a three tier architecture with PowerEdge, PowerStore or leveraging VxRail are very commonly going to be both of those. You have the right infrastructure to support the production workloads and have a consistent operating model between them, leveraging VVols and primary storage side and all the integrations we have with the XRail. And then we have with PowerProtect Data Manager great integrations and some recent enhancements that make that even better and are now able to protect Tanzu, protect the VCF management domain and not only have the storage but also the protection for that environment but do it in a way that supports what the VM admin needs and also gives that consistent protection, consistent storage, consistent operating model for the rest of IT. And at the same time, you're enabling the developers to move faster. Lee, you guys have been doing a lot of joint development and we've been covering a lot of the news of VMworld, a lot of joint engineering, a lot of joint integrations. You guys have been collaborating with Dell Technologies for a long time, also the relationship. Where is that today? Can you expand on that a little bit and take a minute to explain the joint collaboration? John. I'll start with the fact that good marketing is really easy when you have great engineering. And so the work that we're doing together between our companies now, we have a lot to talk about, right? I mean, the work as Gailen mentioned right around V-Balls integration, for example, on PowerMax, right? And on PowerStore, right? I mean, you start looking at the integration work that we're doing together. It means that customers are getting the benefits of the joint integration work and testing, right? That comes, and so you're guaranteed out of the box to work. Also, don't forget that containers and all of the things we're doing around containers, it's basically designed to accommodate the fact that containers are spun up more quickly or destroyed more quickly. They're shared across the hybrid cloud more frequently. And without an inherent security model and built-in data protection, it's really hard to go and see how you can deploy these with the enterprise resilience that's demanded at enterprise scale. And so that's what we're doing together, right? And, you know, we build great software, but without great hardware partnerships, it's one-hand clapping, right? It's about getting our teams together, right? That really makes it sing at the customer level. You know, I think that's a really example of the business performance results have come in. VM, where you guys have been doing a great job. I go way back to the years ago when Pat and Raghu were talking from Amazon and all, since then it's been joint development, joint integrations, and that's a great business model for you. And so, Caitlin, I want to get back to you because at VMworld, we covered Project Monterey, the new initiative for VMware, but a year before they had Project Pacific, that came to life with product results. Tanzu specifically, you guys had the PowerProtect data manager that we talked about in the summer, but now for Tanzu support and Tanzu environments, that's super relevant. Can you share any updates on your end on the PowerProtect data manager and Tanzu? Yeah, I couldn't agree more that great engineering makes our jobs a lot more fun and a whole lot easier. So we've been really lucky and the partnership we've had has really never been stronger. So yeah, the most recent release of PowerProtect data manager introduces the support for that Tanzu protection. It also introduces really important things like storage-based policy management. So in vSphere, when you set up a storage policy, you have data protection as part of that and you have the integration with PowerProtect data manager. So you're able to automatically protect new VMs that are created by that storage policy, it being applied, but at the same time, it's also being tracked in PowerProtect data manager. So you have that consistency across enabling your vAdmins and enabling your data protection, your IT team to keep track of that. We also have a tech preview that we did at VMworld about how we're working as from Dell technology standpoint to innovate around how do you protect some of these VMs that are so large and so mission critical that you need to be able to protect them in a new and innovative way that doesn't disrupt the business. And we did a tech preview of that and it's something you'll hear more about from us too, but it's VMs that traditionally would be in this category of unprotectable because of the impact it could have on the environment and how we're really looking to do that in a more efficient and intelligent way. So we can actually protect those VMs and there's really a whole lot more when you talk about object scale and everything else that we've done. It's really exciting and I think Lee and I have ever talked as much as we do now and it's been a lot of fun. It's been great following both of you guys on theCUBE interviews over the years, the success and the vision. We had early conversations about what the plans were, it's kind of all playing out. So I want to congratulate both of you at VMworld Dell technology. So good job. Going forward, the collaboration, I want to get to that in a second and drill into it, but Caitlin Lee, I want to get your thoughts because one of the big themes this year, besides COVID and all the issues that that's highlighting, but in the cloud world, automation has been the number one conversation we've been hearing and with that you've got machine learning, all the tech around that. As you abstract away the complexity of the infrastructure to make the modern apps, automation has been great. The business cross-connect is everything as a service. We're seeing this is the big wave coming. Could you guys share your vision on how all this submission VVOLs and all objects scale, all these things. There's a lot of plumbing underneath and a lot of tooling, a lot of part piece parts. If that gets programmable, automation kicks in, which then enables everything as a service. Could you guys both share your vision on what that means in terms of what's going to change and what would impact the customer? Yeah, and it's very relevant for this week, right? Dell Technologies World, that's a big part of what we've announced this week and our commitment to really bringing our portfolio as a service. And it's really interesting, especially for folks like Lee and I who have been doing kind of more product marking and talking about speeds and feeds and thinking about how you make the product life simpler and how do you automate that, have the intelligence built in things like VRO have been such an important part of that, especially with PowerStore coming to market. But if you think about where that leads us, it actually changes everything, which is when you have everything as a service and we're really delivering outcomes to our customers and no longer products, that automation is actually just as important and maybe even more important, but it's not the end user that cares about it directly, it's actually us, because as Dell Technologies, we become the ones managing that infrastructure, owning that infrastructure. And the more automation we can bring in, the more intelligence we can build in for ourselves, the more insights we can give to our customers, the better that service can become. And it's really a flip from how we've always been thinking about and really rolling out automation. It's not actually about enabling our end users to do anything, it's actually about enabling them to not worry about any of it, but enable our own organization to support their outcomes better. So it really changes everything. Lee, what's your thoughts on this? Everything is so good.   When you think about the VMware Cloud Foundation, right? Which is the integration of all of our vSphere with vSAN, with v, you know, our NSX products, the vRealized Management Suite, Tanzu now, right? All of this pulled together. One of the things that's interesting is when you go to the public cloud, we have some experience now where we always deliver that full stack together. And what that does is it frees up customers to go and focus on the applications, and they're thinking, stop looking down at the infrastructure, start looking up at the apps. And so we're offering and bringing that same level of experience to the on-premises data centers and now bridging that across the hybrid cloud. That all of a sudden gives you this sense that, hey, I'm future ready. No matter where I am today, if I'm thinking about the hybrid cloud, I can go and move there, right? And with our partnership with Dell Technologies, there's such a great opportunity to bridge that uniquely, by the way, across all of my on-premises infrastructure, including common policy-based management back into storage through our VVOLs efforts, right? And then back in through object scale, right? Into object-based applications and through our DP efforts, data protection efforts, then back into like full data protection. And so what you get now is we're helping customers realize that I got this. I can take new Kubernetes orchestrated applications and I can make them work and do it with the same operational model that I have today, start spending more time on the applications less time, basically configuring and managing underlying infrastructure. Yeah, Caitlin, you had mentioned that I only have the top of the segment, ease of use, making it easier, simpler. Great stuff. On the future, Leigh, I got to ask you about Project Monterey. We did a lot of coverage on it at VMworld on SiliconANGLE and theCUBE. I love how this comes out. It's always, you know, the brain trust at VMware lays out the future. They fill it in throughout the year. Expect to see some meat on the bone there. But what is that going to do for new capabilities and with Dell Technologies? Because it's end-to-end, right? This, Michael Dell and I talked, I think two years ago at Dell Tech Worlds and then last year, he hit the point home hard end-to-end with Dell Technologies. It kind of feels like it's going to be a good fit. Could you share how that Monterey project fits in with Dell Technologies? Yeah, we were so pleased to be showing this together with Dell Technologies at VMworld to showcase this new idea that you could basically go and start offloading CPUs and using smart NICs as a way to basically now provide a more, let's call it a, you know, an architecture that allows you to be responsive to new application needs. So let me talk a little bit about that. So when we opened up Tanzu, right, we got this out complete inflow pouring of new container-based Kubernetes orchestrated apps. So what we found was, hey, they're driving a lot of CPU needs. They're driving a lot of scale-out security needs for things like distributed firewalls. And so we started looking at this and what's clear is we need to basically use the CPU very judiciously. So it's basically reserved for the apps. And so what we're doing now is we're basically saying, there's an opportunity for us to go and offload the CPU for things that look more like infrastructure, including ESXi and other things. And at the same time, then we can go and work together with Dell Technologies to be the deployment vehicle. And so just like project Pacific, which was going broad, if you will, this project moderate, which is going deep, like the Canyon, John, not far from here, is, you know, a source of all new discovery, right? Where we'll be working together and over time, just like the project Pacific name faded to black and became products, Tanzu, VCF with Tanzu, vSphere with Tanzu. We'll see that project moderate will evolve into new products coming together with Dell Technologies. Kather, can you elaborate on, take a minute, explain the product, how this renders into products because I can also imagine just the benefits just from a security standpoint, efficiency at the platform, let me see the range of things. Could you take a minute to explain the impact on product? Yeah, and I think you'll hear a lot more about it, but we're obviously excited to be partners on this as well. And I think it's just another example of the more intelligent the infrastructure can become than the rest of the entire IT organization can run more efficiently. And that can come in the form of the AI built into PowerMax. That can come in the form of the VVOLs that we have both in PowerMax and PowerStore. They can come in the form of it, even just the fact that we have now built a fully containerized S3 compatible object store platform called Object Scale, which we have now in Early Access that can run on the vSAN data persistence platform. And it just gives you the ability to leverage this, all of the right technology and we can continue to really partner on that. And I think project moderate really opens up even more opportunities to do that. And you'll certainly hear more from us on that in the future.  Great to have you guys both on. And congratulations on the great event. Final question for both of you. Talk about this has been a crazy year. We're not face to face, so everything's gonna be online. What should customers and partners and people watching know about the relationship between VMware and Dell Technologies this year? What's the big message to take away? What should people walk away with and think about? I think it's never been stronger than ever than it is right now. We have never had more breadth and more depth of integration. I think that the partnership on the engineering level, on the product management level, on the marketing level, we have really never been in a better place. And what my team has really enjoyed with VM World Season and you coming up on Dell Tech World Season is we've really enjoyed the fact that we've had so much richness of that integration to talk about. And we also know there's even more coming. So from my standpoint, we really feel it and probably the best and most rewarding time we hear about that is when we bring new things into market and we hear that back. And when Power Store came into the market and over the past few kind of first months in market, one of the most resounding feedback that has come out as one of the most differentiated parts is that it is so incredibly integrated with VMware. But we've even gotten questions from analysts asking, did you purposely make it feel like you were really working similarly to a VMware environment? And you know what? It just shows how closely we've been working as organizations is that it comes a very seamless experience for our customers. Lee, final word. What should people walk away with this year and the relationship between VMware and Dell Technologies? Well, I think the best partnerships, right, are ones that are customer driven. And what you're finding here is customers are actually encouraging us, right? We're doing a lot of three-way meetings now, right? Where customers are like, hey, tell me how you're going to go and evolve this. How do I basically modernize, right? And preserve my existing investment perhaps or update here or how do I grow? Like customers have really complex individual situations. And what you can find is that we're helping jointly not just simply with the engineering side, which is awesome, but also with the idea that we're helping customers go and deploy responsibly in a time where it's very difficult to plan. And so if you come to us, we can help you jointly plan for the future in uncertain times and make sure that you're going to be successful. And that's just a great feeling when you're a customer looking at how do you deploy going forward in this, you know, with the amount of pace of change that we've got. Paul, I want to congratulate both of you. I've been following you guys. Success has been proven out on the business results and also the products and the enablement that you guys are providing customers has been great. Thanks for coming on. Great to see both of you have a great event. Thanks for coming on. Thanks. It's always a pleasure. Okay, I'm John Furrier here with theCUBE, covering Dell Technology World's Digital Experience, 2020 is theCUBE virtual. Thanks for watching.


Hello, ladies and gentlemen, product managers and product enthusiasts all. My name's Will Perkins and I'm a product manager at Deliveroo. I'm delighted to be here with you today. Alas, I speak to you over the airwaves, but I hope one day to meet you in person at a product school event or something similar. I'm truly humbled to be able to address you today and steal, I hope, just 20 minutes of your time to talk to you a little bit about how I think about effectively using data, particularly when it comes to you in a form where you may not necessarily be familiar with it and is perhaps presented to you by stakeholders or a third party. So, without further ado, let me take you back to the 1970s Britain where the radical fringe of left-wing political thinking was dominated by the Labour firebrand MP and pipe-smoking aficionado, Tony Ben. Skeptical of those in power, he formulated his famous five questions to ask those with it in order to get to the truth about how they intended to wield that power. What power have you got? Where did you get it from? And whose interest do you use it? To whom are you accountable? And how do we get rid of you? This, he believed, would tear away the veils of obscuration and get down to the truth of the matter. Now, as product managers, we are also interested in the truth. In order to make good, well-reasoned, rational decisions, we need to have a reliable understanding of what is going on the real world and how our products interact with it. And excellent, and some might say, the best reflection of that real world is data. But data is merely a tool, and like all tools, must be wielded skillfully and to a purpose. We must understand its abilities and limitations. It's also not a Swiss army knife. To achieve our goals, it needs to be used in conjunction with other tools in our toolbox. So like an operatic conductor, product managers must pool together the various sections of the orchestra to create that rich soundscape and create that picture. Data may be playing first violin in our orchestra, but it needs the support of the others to really hit those virtuoso high notes. So as Tony Ben interrogated political power to find truth, so can we with our data. Why don't we take Ben's five question framework for defining truth and create our own that we can use to make the most out of our data and ensure we are making the right decisions for our products? I want to know what motivates the collection, processing and presentation of a dataset. I want to know where the metrics I'm looking at are actually the ones I care about and are the right thing to be measuring. I want to know how the data's been generated. Is it high or low integrity? And once I'm happy with that, I want to know how I can validate that data. Ask whether it's really telling me what I think it is. And finally, I must say I'm not quite as much of a revolutionary as I would tell me. So I'll take his final question and turn it on its head. If somebody's interested in using data to inform their decision-making, then how do we get them on board as product managers? Now, I'm not sure you often hear is that the data doesn't lie, but think about that for a minute. Think about the data you were asking me to buy on a daily basis outside of your work. Generally, I find it's an advertising, either trying to sell you a product or a political idea, perhaps. In this context, one could be forgiven for thinking it does nothing but lie. The data doesn't lie, it's a nice sentiment. And I get it, it's inanimate and human interaction is required before it means anything. But frankly, to take this phrase at face value is a little naive. And the one thing that product managers cannot be is naive. We've got to be sharp. We've got to understand what we're talking about. And so we've got to be skeptical about any information we're used to make those decisions. Because ultimately we and we alone are accountable for those decisions. No one else, and we certainly cannot blame the data we're using. Data can be wrong, it can be misinterpreted, and yet on occasion it can be used to lie. Now please don't see this as cynical or dismissive of it. I think skepticism is healthy. Questioning our assumptions will help us to better understand what is true and what isn't. It will help us avoid blunder, improve the quality of our decision-making and ultimately will make us better product managers. So then let's get stuck in with question one. What is the motivation? Let's pick up that thread again of where we see data in our day-to-day lives outside the world of product management if such a thing does exist. We mentioned that people use data to try and sell us things. And often we won't even notice or recognize that data is being used. Now apparently it's one of those rules of the internet that one must have cats in their presentation. Now I don't usually go into these sort of things but rules is rules. So here we are. I don't know if any of you remember this classic, I guess 80s or 90s whiskers advert campaign for cat food. Eight out of 10 owners who expressed a preference said their cat preferred whiskers. I know it's a bit of a mouthful, isn't it? But this advert has some fairly compelling data in it. So it must be true. And therefore we should all go out and buy this product. Well, I don't know if you know anything like me but when I see things like this I have to go have a bit of a lie down in a dark room because my product manager brain just goes off the wall with it a little bit. What does this mean? You ask 10 people and eight of them said so or 80% of 10,000 people said it. And who are these people? Do they often present their cat with a selection of dishes for their supper? And then what do they prefer it to? Do they prefer it to a succulent roast chicken or perhaps going hungry? What about the owners who didn't express a preference? What do they think? In this case, I'm skeptical. This is obviously about selling as a product and we have no way of knowing whether it's true or not. And yet, and yet there's a bit of me that still just nods along and says, okay, next time the cat's hungry, I'll go and buy the whiskers. You know, it's effective. It taps into our psyche somehow and advertiser wouldn't do it otherwise. We face this type of thing as product managers in our jobs. People will regularly approach us with ideas for things they want us to build and a great way to sell that idea is supporting it with data, which frankly is brilliant, far better than doing it out and we should wholeheartedly encourage it. But it's totally natural for them to want to make that data as compelling as possible. It's their job. But it's also our job as product managers to understand why they are showing us this data. Unlike in an advert, we have the opportunity to put our skepticism into practice. We can question what their motivations are for presenting their data. Through this, we can understand why that data has been presented in the way it has. What is present in it? What is missing? What assumptions have we made? We could even do another whole video on this, a subsection of 20 questions to ask when you're thinking about the motivation for things. But that's for another time and another place. This is also our first opportunity to understand whether we're looking at the right data and the right metrics, both in order to understand the problem and also to measure the impact, which provides with quite a nice segue into question two. Are these the right metrics? So join me as we venture further back in time from the 1970s to 1914, World War I and Europe as a flame as the major European powers confront each other on the fields of northern France. In the decades since the last major European war, military technology had taken great strides forwards, but the tactics and use would be recognized by a soldier fighting in those same fields a century earlier at the Battle of Waterloo. Officers wore white gloves and ostrich plumes in their hats whilst leading troops. The mounted cavalry charge was still a common sight in the battlefields. Soldiers of all nations went into battle and were in cloth headwear. Even the rather satisfyingly named German Picklehalber with its distinctive brass spike and crest on the front were just made of a rather thin leather and provide little ballistic protection. As the machine gun made surface war for impossible, trenches were dug deep into the ground to provide cover. Artillery bombardment then became the modus operandi for all armies. Now, when artillery shell bursts, it would blow up into the air huge amounts of earth, rubble and rocks that would then rain down on those sheltering in the trenches. Military commanders noticed that this was resulting in a number of soldiers being taken back to aid stations with head injuries increasing rather dramatically. Now, British commandant in particular had picked up on this as a good metric to try and address. Field Marshal, the Earl Hague then will play the role of our product manager here. He took measures to reduce the number of head injuries sustained by his troops by introducing the steel helmet smartly modeled by the chap here on the left, the Mark I Brody helmet that's called. This is a very good example of product design. It's protective, has a nice rim to shield the eye, to shield the eye from the sun and on the back of the neck from anything falling behind you. It's also got a sort of rather jaunty aesthetic which soldiers of all times rather appreciate. But interestingly and rather alarmingly, once these helmets were introduced, the rate of head injuries actually increased and increased dramatically. Now, why was this? One theory was that the added feeling of safety that the helmet provides led to soldiers acting more recklessly, taking more risks, sticking their head above the parapet where before they might not have. Yes, I suppose this is a viable hypothesis but it's based on an incomplete understanding of the data set. Head injuries per thousand is not the only metric that is important here. The most important metric, particularly for those actually having to wear the helmets is the rate of death. Yes, the head wounds increased but only because soldiers who would otherwise have been killed by shrapnel were instead only sustaining injuries. So here our product manager I found a good metric that got him onto the target but he needed to go one level deeper in his understanding to get to where he actually needed to be. I even think about in terms of prospecting for gold. You find traces of the precious metal on the surface and in streams but one needs to dig deep underground to find that main gold steam. So we think we have our metrics we want to measure and move. Well, before we get too excited we need to think about how reliable this data is. And one way to do that is to think about how it's been generated. We must remember that data doesn't emerge out of the ether, pristine and incorruptible. Like I can think of it like the body of one of those preserved saints in a Venetian church that has said sort of waff the scent of flowers even after centuries in the tomb. Instead we must recognize that at some point somewhere somebody has written a bit of code that creates this data based on a set of assumptions valid at the time and to the best of their knowledge available. Knowledge which may have since developed and changed. Thinking of this data, think of data in this way I find helpful. It is the work of human hands and as such is susceptible to all the failings of human endeavor. Continuous iteration and development can improve the integrity of data but it can also result in errors and misunderstandings get an institutionalized. We should be mindful of this and if we aren't familiar with the data we should check it. Honestly, I cannot tell you the number of times I have been confused by naming conventions thinking that one metric was one thing when in fact it was measuring something completely different. That data was generally created by your user's interaction with a product. When someone presses a button on your app or on your website, it fires an event and then builds up a data set. So again, you need to understand the motivations of your users when they're engaging with your product in order to gauge the integrity of that data set. Let's look at two examples. Web traffic is fairly reliable. You know when someone has landed on your webpage and there's generally little room for error or motivation for misuse. But even here, you may find a deliberate denial of service attack on your website can make it look like your site is extremely popular when in fact it's a malign actor trying to bring it down. And at the other end of the spectrum, a common source of data for us PNs comes from customer support. Now I think this is generally an invaluable way of understanding the pain points of our products. But just remember that often this data is generated through the manual tagging of issues by agents who have to interpret the reasons for the contact. Generally this is fine, but sometimes it could create distortions as agents try to squeeze issues that aren't catered for into the existing categories. My top tip here is that if your most common category is other, then you need to be very, very careful with how you use that data. Also, if you have anything gamified in your product, you've got to be careful. Somebody very worldly wise once said to me, if you create a game and players are gonna play it and people play to win, even if that means cheating, users will do what they need to do in order to achieve their goals. I think a great example of this, and I think a truly awesome way of addressing it is through the dating app Tinder where one arranges romantic liaisons with other like-minded people in their local area. Now, Tinder saw that people didn't necessarily want to be restricted to their local area. They wanted to search far and wide for their sweetheart. And so people began to mock their location data in order to check out potential dates in other areas. Here, users were purposefully creating incorrect and misleading data in order to achieve their goals. Now, if anyone's ever engaged with this sort of thing, location mocking is pretty difficult to stop. And so rather than trying to stop this outright, I think whoever the PM was at the time had a brilliant idea to not just embrace it but make it a premium feature, giving people the ability to search anywhere they wanted. It was understanding how the data was generated, recognizing the anomalies in it and pairing that with the knowledge of the motivations of the user that they found a really, really innovative way to monetize their product. So to our next question then, how can it be validated? For those non-plane spotters and people listening on audio, we're looking at the cockpit of a 787 Dreamliner, one of the most advanced aircraft in the world. Now, it's very, very possible to fly this plane using the data from purely the instruments. And in fact, much of the time, the aircraft can fly itself on nothing but the data. But not everyone is flying a Dreamliner. Most of us product managers are working startups or scale-ups where the cockpit perhaps looks something more akin to this. Now, one could possibly fly this just by looking at the dials with a map encompassed on your lap. But I think it's a good idea here to actually take a look out the window and see what's going on around you. To perhaps push this analogy one step beyond its limit, a lot of the time in product development, we don't even get the plane to start flying. We have to throw ourselves off the cliff and build the plane on the way down. This includes conceptualizing and discovering the metrics that we're going to use before we even build the instrumentation to measure them. Sometimes we have no idea what we should be measuring. And there are infinite number of things that we could measure out there. I think it's here that user research or customer insights or just your knowledge of the user really comes into play. Where you don't know what you should be measuring, user research can provide you those insights and tell you what is important. If you find yourself lacking data, it can provide you with a good read on what you should do next. Even if you're flying that Dreamliner, it's a good idea to have a look out the window every now and again and just validate the data. Make sure it's really telling you what you think it is. So always look out the window, ask yourself, how can you validate data with user insights? And on the flip side, how can you quantify user insights with data? Now, if you're perhaps indulging for a moment longer on the subject, I might take a bit further. I've spoken a bit about how data is a reflection of the real world, but this is really sloppy use of my language because data is not a reflection. It's not a true if reversed image of the world. It's more of a technical drawing that can have an incredible amount of detail, but lacks the color and life of the real world. Counter to this though, user insights is more in the style of an impressionist painting, a Monet or a Cezanne, creating a vivid image of the world. But again, it's very difficult to measure anything on one of these paintings. One wouldn't necessarily want to build a structure from them, but nor would anyone want to live in a schematic. The two complement each other very well because they opposite sides of the same coin. And when you combine them both, you can create a thing that is beautiful, but also practical. I see product user research and data analysis as the Holy Trinity, or for the more ancient Roman mind of you, the triumvirate of product discovery. Everything you need is in that golden triangle. My top tip here is to try and sit between your user researcher and your product analyst and keep up a conversation between you. I think that in the insights you gain from that conversation will really drive your product to new heights. And don't just take my word for it. This guy used to work in Amazon and he says a similar thing. He says the thing I've noticed is when the anecdotes and the data disagree, the anecdotes are usually right. There's something wrong with the way you're measuring it. Now, big Jeff isn't saying dismiss the data for anecdotes. He's literally saying be skeptical. If your gut is telling you something isn't right and as a PM you'll develop a great instinct for these things, then it probably isn't and you should check it. And so finally, where Ben wants to kick you out in the product management world, we're a bit more accommodating and friendly and we're gonna ask, how do we get you on board? I just think if somebody has shown the interest and the spark to take, identify a problem, take a bit of data, put it around it, maybe perhaps come outside hypotheses, we want to think about how we can get them into the community and encourage them to become product managers themselves. I think organizations like the product school do a brilliant job in training up people who want to be PMs. But I know that when I was coming into the industry, I found the first hurdle was even knowing what a PM was and it was a thing that I could do. So I would encourage you to take these people under your wing, teaching them about product management and product thinking. And if nothing else, you have a group of people around you in your business who you can rely on to probably do with excellent material for your product roadmap. So then to finish, love your data, but be skeptical of it, validate it, understand it, use it to its full potential and you will build awesome products. And finally, get out there and spread the good news of product management. Thank you very much.


On day 18, we have a new teaser to get you excited about what's coming soon. It starts with 95% and we have a new winner in this $100,000 sponsorship for small channels powered by freedom. Let's get started. A few hours ago today, we launched the 95% teaser. Click here in case you missed it. That will open in a new window so you can see what it is that's coming very soon. I'll tell you one more thing and that is you can all obtain this thing, whatever it is, and we will help you get there. Hope that helps. If it doesn't, tell me in the comments below your guesses and we'll give you another hint tomorrow. Today's winner is behind me. Look at that amazing beast gamer. He is the winner of today's $100,000 sponsorship because he has been helpful for every single day of this sponsorship. He's created so many comments, so many tips to help you grow that I noticed you today. Amazing beast gamer and you win one of these five prizes. To tell us which one you want, click here. This video will tell you exactly the four steps you need to do to claim your prize. How do you win? You help people down there in the comments below and on the freedom forums and if you keep helping people consistently, we will notice you and you will win. This is only day 18 of 365 days. My promise to you is to make a video for the next year every single day where we celebrate one winner, but we all win because we help each other grow in order to pick that one winner. You see how that works? I hope you're excited for our teaser. Tomorrow will be a new hint and we'll see you then. My name is George, founder of Freedom and you've been watching Let's Discover Freedom. Click the first video to learn what is freedom and how it helps you grow. Click the second video to learn about sponsorships for all Freedom Partners, even if you have just 10 subscribers and click the other videos like How Do I Leave Freedom to learn more. Click Get More Views to learn how to build your audience faster on YouTube. Click Get More CPM to learn how to make more money from your channel. And click Get More Music to learn where to get more music to spice up your videos. Click the first playlist to watch The George Show on Freedom. That's my show. Or watch The Anthony Show or The Alex Show also on Freedom. And we will grow together as a family because this is the Freedom family. You are part of it. We are all part of it and we're all growing together. So grow with us and partner with Freedom. That play now button right there on the video bar is all you need to click to get started.


We're on a mission, oh my god. Hey yo, chillin' at the pool. Did you boys miss us when we were gone for 36 hours? We went on our wedding anniversary celebration. You miss Michael. I know, Michael miss you. Yeah, that's fair, that's fair. We have returned from our 7 year wedding anniversary celebration. Rested and we were gone, I was calculating 36 hours. Is that right? That's it. And we just feel rested and renewed. We were both like, should we go home? We should go home, right? We were ready. And now we're just on the patio with the boys. We're enjoying dinner. I know, crazy. During jalapenos. We're having a little party tomorrow. The mailman, aka Brandon the man, brought over some apple pies.  If you didn't see yesterday's vlog, you gotta go watch it. It explains why we love the four seasons and how we are able to go there. Because guess what? They don't give the rooms away. And I explain why they don't give them away in yesterday's vlog. Go watch it, go watch it. We love you guys. And YouTube is interesting. Two years ago, when I started making the daily video, I didn't frankly think that we'd be like building relationships with people. On the interwebs, I was maybe a little naive in that, not having that foresight. But sure enough, through the comments below, through the question of the day, through other social media, through various ways, we have built up relationships with some of you guys. Especially those that comment a lot, so we appreciate that. And so by now, after two years, you know that some days we don't hit that record button as often as other days. And that was the case today. A little hodgepodgey, kind of a smorgasbord of video clips for you on the VLOG. If the story wasn't clear, it's because, well, that was just how the day unfolded. So, but guess what? That doesn't matter. Because by now, you have come to understand, and I have come to understand that the sun will rise tomorrow. The camera hopefully will turn on tomorrow. YouTube will turn on tomorrow. And can you imagine if they shut down YouTube at night or like for an hour every day? Oh my gosh, whoa, like an on-off switch? That would be hilarious. Anyway, everything will be here tomorrow. I'm confident in that, and we will be here. And I'm guessing you will be here. And that's cool. And so, even though today was smorgasbordy, hodgepodgey, we're gonna, we're making up words now, we're gonna keep plugging for you. See, beauty, work card, love each other. You know the rest. You know the rest.


myself other town officials then the first pitch will be thrown out by two fifth generation members of the herd family which i am fourth generation so you can do the math there and then they'll be followed by the first game at the new field will be the two triple a powerhouses the giants versus the dodgers um so if you're in the area you should definitely come and watch that game as well when again oh sorry it is thursday may 25th the ceremony starts the dedication starts at 5 30 i'm going to be in an apple sorry i would come you know to see billon and besley's new field uh mr de coursey thank thank you mr chairman a couple of things first i i uh want to acknowledge and thank our fire department for the work that they had done on that that the fire in dodley street since our last meeting and that was potential very dangerous situation with the materials that were inside that building and it did a great job and nobody was injured there was a moment there to scare but um happy that that that it was extinguishing with a lot of other businesses around there and the materials is potentially very dangerous situation and i came to mind in terms of the great work our fire department's doing just this afternoon i was working from home and there was a appeared to be a fire it was a fire i think at a neighbor's house um and the fire department came and quickly extinguished it um and it is a homeowner there was living alone who had come out and uh talked to the firefighters they did their work and spent some time with him um he felt good about the work there was some um an issue behind his house that they extinguished and uh also in in the last month since our last meeting my mother-in-law um had a health issue up the heights and uh the shoes brought to to mon harbor nothing serious but um my wife drove in the rescue truck with to the to the hospital and amiri uh and my mother-in-law and um were very relieved that that that great attention they received and uh just when you see these examples in your neighborhood and um we saw with dudley street when the bell rings you never know what you're going to and uh they came and today was a situation where it was uh easy to put down relatively speaking right there's nothing that's really easy but uh you just want to thank them for the great work that they do thank you mr coursey well said mr diggins i have no announcements or your business very well i don't either everything that i was going to say has been said and that is just great motion to adjourn second and a motion to adjourn by vice chair heard seconded by mrs mahan all in favor i think i posed your adjourn