Hi everyone, we'll play a video first, yeah, hit it, sure SeaTrip, a travel industry leader and pioneer 1999 the SeaTrip dream team, James Liang, Jiqi, Shang Nampang and Fan Min founded SeaTrip Forging ahead on a 10-year journey bearing great dreams and hopes Any talented people joined them and the team quickly grew in its first two years SeaTrip quickly fulfilled its great early promise Incorporating biz express and acquiring modern express business travel services and Beijing high-end air ticketing company a wide range of teams contributed laying solid foundation for the company's continued growth in 2002 SeaTrip exceeded R&B 100 million in monthly transaction value In 2003, SeaTrip made its IPO on Nasdaq and set a three-year record for a one-day share price gain for an IPO After this successful listing, SeaTrip took a leading role in the market and began to revolutionize the industry In 2004, SeaTrip first introduced its vacation packages supermarket Establishing a new travel trend in the market Customer-centered service philosophy has been the key factor that helped SeaTrip excel In 2007, SeaTrip moved its headquarters to 99 Fujian Road, Shanghai 2008 Prime Minister Wen Jiabao visited SeaTrip headquarters in Shanghai Expressing hopes the company would continue creating a brighter future In 2010, SeaTrip established a major new call center in Nantong Jiangsu province To enhance its presence in the leisure tourism market Continuing to set trends in China's online travel service market and laying the foundation for future growth Travel with SeaTrip in the decade to come A little bit of highlight promotion for SeaTrip When we play the video, we have a meaning for it, not just promoting And the team will talk about that during the presentation, why we play the video Relating to the cloud technology, today everything about clouds Let me hit my thing, this is a agenda, but we will go through it First one, come back Problems we are facing, I just want to talk about that one As head of technology for SeaTrip in the last 10 years has a tremendous growth In the next 7 years I mentioned in the morning and business growth 10 times So right now we are adding new business lines In the last 6 months we are adding 5, 6 new business lines, side doubles every year And business wants something yesterday as a technologist you guys are doing And that you heard in the past many times Then side availability must go up Infrastructure costs must go down So those are the KPIs, I have to keep high, it's the high level When I say side availability goes up is that 10 times more reliable than today Infrastructure goes down per order Today we have a dollar a month per order cost And I want to drive that down aggressively Cloud going to help at 2000 degree, we will share some showcase with you guys Yeah, call center I mentioned Next one, the V desktop I touched in the morning So the reason is that why we do this, one is cost reason And I mentioned that and there is a bunch of other reasons I didn't go in detail in the morning Here you see fast deployment of the physical box deployment versus virtual box deployment Which will be much faster with the V desktop Massive configuration changes What I mean is that if you know managing the data center and you will see Some OS upgrades, browser upgrades from IE to Chrome Now if you're dealing with 100 desktops it's not issue If you're dealing with Southern become headache Now if you're dealing with 10,000, how would you be able to do it? Take a year to upgrade OS because business you continue, right? You guys stop answering and let me finish the OS upgrade, you cannot do that So that's the with the VM on the back I just swap the VM like one second You got everything new, right? So that's physical versus virtual, that's the flexibility we're talking about All right, so the another thing challenges that I mentioned Three call centers physically distributed to different regions Now we're creating more call centers Something we already working right now is called SOHO Remote call centers, some people can call answering call from home That requires technology to do that And now we have many agents working from home And if we have technology ready for them, that's increased call age workforce Also lower the cost because they don't have to physically come to the call center answering the phone Some calls can take from at home is okay So those are the reasons Then the private clouds today we're going to get more details in why we're doing that One thing I mentioned 10 times growth in the quite large hardware Computing power, computing capacity And more machines when you manage that too And today if you look at the data center, it's a typical, okay I look at a couple data centers across industry, e-commerce side Server hardware utilizations at call center, any given call center Without cloud technology is very low And that's the number I can call it and you can use it for your reference And some majority of call centers, server utilization is only about 25% So in other words 70% the power, the capacity was not being used And because the technology used to build the call center and how you manage it So there's quite a depth details on that But I don't point is that is if you don't have a technology to manage the call center Cost going to the cost increase like this And so there's a call elastic computing If we can shrink and expand as needed as the traffic grows as business grows That would be best utilize the resources in the IDC environment So the businesses that you are talking about If you talk about new businesses they don't have much traffic But it's a new business you have to work on Also you have to do active-active because achieve high availability So two servers minimum however the business Today's server is like a four core ACPUs and it's kind of a commodity already And they don't, new business doesn't consume lots of computing powers But you need to give a server to them So put new business stack together as into one server That would be reduce the hardware footprint dramatically So that's the thing we have to use in some virtualization cloud technology First deployment I don't, you guys know already So it's provisioning VM stuff Right now we're adding servers every week like hundreds servers Then how you can quickly, I don't want to add servers like take weeks to add I want to take a minute to add servers into the data center You have to have some technology to do that Otherwise manually do installation it's not possible at that speed Alright, so those are some reasons we like using cloud technology Especially is open stack is we using in many areas, both areas And now I introduce my team, they working on this technology for a couple years now And have, they want to share with you technical know how they did it Let me introduce Simon and Yiting at the same time So both of them, in my team is system engineering And they have creating a group working on the cloud technology in those two areas They're getting into technical details, okay, have a phone Okay, thanks Thank you very much Okay, thanks Eric to highlight the problem we are facing And also the approach we are going to take My name is Simon Chen, I just mentioned And I'm the director for system engineers Our department is responsible for carry out or implement the strategy For Ctrip's cloud computing So once the direction is set, the strategy is set The next thing is some technical decisions So we have to, from my department we have to make some tough decisions First of all, why open stack? Why open stack? We take, we took quite a long time to evaluate a couple of options A cloud stack is one, and also whereas others And when you went to make us, made our mind, is the IBM joined the open stack camp That's the beginning of the year, so make this decision a little bit easier Before that we do the both tracking and try to see which one is easier And also if the data set is more or richer So after IBM joined the open stack camp We designed the open stack technology As a very big active community And meanwhile it has a relatively richer module set And of course it has the heavy endorsers like IBM And also we noticed it has some mission critical deployment already Like people have already done that So we think it's sort of a proven technology So we adapted the open stack as our technical, the cloud computing platform And then we have to decide, okay first of all we have to go to the cloud cloud Why we go to a private cloud Actually this is the relatively easy decision In China as you guys already know, there's no alternative The Amazon is sort of too far away They talk about coming to China and keep talking, keep talking And we don't see them arrive And Microsoft set up something, but to us it's less proven So we think oh bad, bad, we just go with the private one That's why we decided to go to the private cloud And the third one is why we build a in-house cloud management platform Instead of buy one or use some ready technology or product The reason is we try a couple of combinations But we all run into the problem with the integration With our existing tools and platforms So we decided we'd rather just go from the ground up Build the one ourselves It will manage both bare metal and VM provisioning And also other management tasks The third one, strategy has been set And the technology decision has been made Now it's execution time So all of the execution depends on the team We have to make a decision If we want to take time to build a full staff team Or we rely heavily on the community And we have a small team So we designed a small team Because one thing is the open stack engineers are very hard to come by We are still hiring, but it's not easy, you guys know So we decided to have a small team but rely on the community We have right now 15 engineers, very talented Very dedicated, highly dedicated And as you can see we have within a year Delivered three product lines The bare metal provisioning management The VM provisioning management And also VDI management platform So our team is very dedicated If you want to put a face to the team Here's the guy And Yi Ting Wu is the senior manager for our cloud platform He will go through the details Tell you guys what we have done And what lesson we have learned So here's Yi Ting Another tip, here's the mouse Later on if you have a wireless keyboard and mouse You can use it as a click, very handy You can try at home Thank you Simon and Eric Eric and Simon introduced a lot of business requirements And also our cloud strategies Now I'm going to give you more detail from technical perspective So I think Eric during the keynote We introduced a lot of numbers about our core centers And right now we have more than 10,000 core center agents Besides those later How was it? Sorry Hello? Hello? Besides those later I'm going to tell you a story It was beginning in the early this year And one day Eric and Simon will meet together And Eric asks, hey Yi Ting Is it possible to build a VDI solution for our core center? And I say, yeah, we can try And so we get started and do a prototype And at that time we also heavily developed Based on OpenStack And finally we find that OpenStack is actually a very good back end for VDI And we start hiring and then we build a small team And today we just load all the 1,100 things In our core center and both for some interns And the goal is to deploy 1,000 with desktop instance in our core center And also we start a new satellite office Which will have 300 agents So we will also deploy the desktop layer And there will be several hundred miles away But we will have dedicated network to connect our headquarters and the satellite office So I'm going to give you a demo To help you understand how we set up this demo And also thank you to the staff They have a lot I have a small sync line here And then you will connect to a switch And that switch connect to our laptop On the laptop here The sync pad and actually he run it open to end With DevStack and also KBM And that Windows 7 is actually We just started playing the slides and also the videos So that sounds fantastic, right? We already being in our VDesktop Now I'm going to give you some demos here This is our login window It's pretty simple Our core center agent just enter their username and password That password actually is our core authentication Which is use Windows ADS backend And then you just login Let me type in the password Here it goes, it's connecting Now you see the video, right? I'm going to play the video again Now just for a second Okay, I'm not going to play it again Now We also have This is the dashboard based on horizon And we added dashboard layer for the VDI And actually you can see a VM pool Which is over here I will give back bigger Yeah And you can see some cool information here The MAC address about the sync line And also the IP address And we use the demo user to login this VDesktop And we also have some very cool dashboard here This, those data are not accurate Because we don't actually have so many VMs running But to give you an idea We will use this dashboard in our production And also I would like to Level something really interesting Now you see that we use the VMC And you see yourself, right? It's recursive, okay? This is really cool And I think when our customers Or any call center agents They have problems And our help desk We will just login and see the scenarios What else happens, right? As soon as possible Now I'm going to go back to the slides So let's give you an overview How we put the desktop on top of OpenStack And we simply add sync line And for Keystone we use the Windows ADS backend And actually there are a lot of development based on Keystone So that it can support some group policy And domain, OU, something like that To support the VDI So for the sync client It will first try to authenticate with Keystone And then when it is passed authentication and authorization It will go back and say hello to the Nova API Give me a VM I need this template and this spec And the Nova API will try to Talk to the scheduler And the scheduler will find the best match VM, either it will be in our existing VM pools Or it will schedule a new VM Across our computer nodes And also we have NAS storage I will give more detail later And this is to give you an overview about our stack And in the computer node We are actually running In local storage with two 200GB SSD And also 160GB memory Two sockets, 8 cores CPU And for networking we have that And also internet network And we use NAS And it actually mix with Sata disk Which can have good performance And also cost balance For the ARM sync client This is the very important thing We have different spec for different scenarios Or different requirements It can have one CPU or two CPU Or one gig memory or less As for the computer nodes We are actually running on Ubuntu And yesterday I actually See the CEO of Ubuntu We have a small chat It's based on KBM And normal on top of it And in the network side We are using Neutron We open VStorage and VMode And currently it's quite enough for us For the VMode So for Swift We will use it to backup For the instance backup Or snapshot Or templates in glance For the Windows roaming profile And some folder redirection We will put it into our storage In the backend And for the ARM sync client We are actually running also on Ubuntu And we use stored for the large scale Deployment and management And obviously it's using the spies So you know our core center Is actually running 24 by 7 And on shifting All right During the shifting period There will be Thousands of users Trying to boot their VM And log in and log out So one challenge we have Is how to deal with boot storm We try many solutions like Fiber channel over Fiber channel And it turns out that It can have good performance Really expensive for a large scale deployment And easy to become a bottleneck And we also try local storage It simply doesn't have Enough IOPS for us Finally we will find the best match Which is the commodity SSD And our performance Testing data show that You can link Chrome 1 VM In one second And we put 50 VM At the same time It takes 30 seconds Really fast And good enough for us And we also have a program To simulate the workload Of our core center agents And the testing results show that We can actually log in 60 Windows 7 VM In single computer node So what about logging in and log out storm We try to put our roaming profile And folder To redirect into the NAS And the reason why We don't put the user data And profile in local SSD Is that it's quite expensive Some users will have several Personal data And also it will become A single point of failure Once the computer is down There's no way Or you need to take a long time To recover the user's data So for user profile Actually it's quite small For our scenario Like 10 MB But for user data It will be several hundred MB Or several gigabytes And for the first login It generally takes 20 to 30 seconds That means Windows also Need to prepare for desktop And for the second run It will be less than 10 seconds So what about live migration I think live migration is really important For maintenance and also for Prior really good User experience to our customers Thanks to KBM And actually with golden image Both on the same On different computer nodes With link clone You can just migrate incremental To another computer node And our testing data shows that With one gig memory Two gig memory And one gig incremental It actually takes 30 seconds And since we have the same client In the user side In the user side We can make our live migration Transparent to our customer Because when we do live migration And the connection between the user And our computer node Will be disconnect And we have reconnect A mechanism in the same client In the same client we will try to Talk with Nova API Hey, what's happening Where's my VM Okay, you migrate to another host And I will connect to that host For access And that generally will take Like 3 to 5 seconds And user will just see the brand And see our logo reconnecting And it will recover So this is also really cool And good enough for our scenario So what about user experience You know we already deployed 100 same client In our core center And some user sometimes They will complain Okay, the VM is not as fast As my desktop So how will you measure Your performance As our core center is Actually our agents get a call And then they will Talk with our customer And finally we will put the order Or change the order in our offline system So the page speed Of offline Is really important To measure our user experience So we have a real time monitoring And you will see How many times we spend On the dome landing And how many for DNS do cut So I'm going to end Here for the V desktop Because we also have Another cool demo to show you Okay, it's about bare metal provisioning So generally when Side ops put the bare metal On top of the rack It will take several weeks Before it really takes Transaction in production You will see that Side ops will power On the bare metal And you will be able to Provide provisioning with them And then waiting for networking Engineer to allocate IP address And the assets Will need to do the baking And also hardware configuration Such as ILO configuration Rate Upgrade the firmware And also configure the BIOS After that you can have OS installed But especially for Windows Even you have OS installed You still need to do a lot Of post install steps Such as patching Activating licensing And Configuring IP address Or even a nick bond And finally you can switch it To the production video And you can start all your applications On that bare metal So at the time When we tried to build The provisioning system We take We look into the bare metal provisioning There is a driver existing in the code But it does It has very limited feature It only takes the PXE And there is no way to do Post install Maybe you can do it With puppet or a shot And at that time We have already been working on laser Which is a Public labs sub project And with laser you can do a lot more Also we also customize laser I will give you more detail later And with our Normal laser driver We actually can take over this auto discovery Baking Hardware configuration OS installation And also the post install And right now we are also working with our Networking engineering team to Do the video switching API And actually they already have the API ready And we are working on testing Now I'm going to Switch the demo to my Mac Okay I think you can read it on the screen Okay So this is our customized portal And This is actually a very cute logo So auto discovery Now you can see several parameters In this table And this one I think we can click and see This is From Huawei You see a lot of details here A lot of details Memory, nick, mac address CPU And also we have several HP parameters here Like 360 and you can do search 360 And here you go Now I'm going to provision one parameter here Let's say we want to play with this one 360G7 Okay And then click the provision button Here is the UI and then you can select Baking How many hours you want And what's the rate you want to do Okay, rate one And what's the OS? Currently we support ESSI 5.1 And also Ubuntu SandOS Including Windows 2K8 Let's try Ubuntu Oh sorry, we forget to select The networking configuration So for the ILO we have several Network here And we choose the BMC Which is our provisioning Our ILO Oh sorry, it's our ILO video And then we also have a Production video here Let's go down And then just click Now you can see the Job is doing Here, right? It's launched And you also see several You cannot see the IP address Because It haven't go to the Sorry, go to the neutron And allocate for IP address So that's for the ILO IP Now you can see many jobs Already being done here We can click and see What we have And here is Where you submit And what's the IP address Being allocate to BMC or the NIC For your production video And also here is the detail log Baking firmware Sorry The screen just log And also for For the BIOS And pre-seed This is actually in the IPXC And finally you will see Something that APD gets And also Configure the networking I will also show you a Windows Let me find the Windows Here Sorry, I think the Screen doesn't work It turned on and my Mac Actually auto battery Sorry But I think we We almost have them all down Okay This is my Think client So it doesn't I think it's really cool And it's really fantastic And I really want to take this Opportunity to say Thank you to my team They might not be able To see the live videos But I also want to Say big thank you to them They are very tenant And also Working really hard To make this happen So again, thank you I think we We haven't ended yet Sorry We haven't ended yet And I see the slides Thank you Is it possible to Okay We also have one more So it's a lot of stuff We've been doing I'm going to bypass this one And those are A lot of details here I'm not I don't have time to cover And yesterday actually I'm talking with The public lab team And they Actually working on I think it's almost the same thing They want to bring Nova inside OpenStack Sorry Bring laser inside OpenStack And I think we can We maybe can Collaborate together And bring Maybe sometimes we can Become open source And that's the one thing We really want to do So some challenge here And now for VM provisioning We have lots of VMs And the QV farm, Dell farm have more than 1000 VMs And also 1500 VMs in production And I think OpenStack Have a lot to help us To Speed up the This cycle Because The developers may need Dell VM And they also need Some feature testing Lot of performance testing And also the final staging Before you really go to production We have all those environment Based on OpenStack And it really helps a lot And also for the The builder Actually it's a Distributed build system It's on top of OpenStack And actually we are Doing that based on Jenkins And Jenkins has Actually has a cloud plugin Which could do auto rescale So If we have too many build requests And we will just scale out And when the queue Becomes smaller And we probably just Decum some VMs And return it back to our pool So for VMware support You know we are already Using VMware in our production So We need to have a feature To help our Operation to import Those existing VMs And ESSI As for the VMware driver We add some enhancements And also we also talk to IBM developers And maybe there will be some Collaborate For networking We use in Neutron And also we support DB switch And distributed virtual switch And also we add Customization specs support The template measurement Will be Help operation to Import the existing template In the data store So for scheduler filters We have a very unique Requirements Which is that we have some ESS hypervisors Which they use in local storage So That doesn't make much sense To create a cluster And it will block you Because once you need to move One hypervisor in or out It takes a long time So we just create A hypervisor folder And put those ESSI Under this folder So we add a schedule filter There And also for data store For different Tenants We can have different data Store Regular especially So that can be used to Find the right one Data store for you And we also integrate With DNS service Windows AD as a service Including the Zavix API I want to take some seconds Here to mention about our DNS We actually For the internal DNS We are using Windows DNS For a historical reason And our Internet DNS will be combined And we also know that There is a project called Desanet So we will We will work Working on And bring our DNS API And add that driver Into Desanet So One more thing is That we are Working on Bringing our Or migrate our Excessive VMware infrastructure Into KBM Some reason Like cost saving Lots of license saving Actually And also we can As a website You can do HGA inside application layer Instead of in the OS layer Or in the infrastructure layer And we also Want to Have more open source And a pan This month We just start Building our New staging environment 100 VMs Could be more On KBM And we are Just starting Migrating our QV farm There farm And also our Open-style We will add features So that we can Support both KBM and VMware together And we will see More migration In production Next year So I think That's all Thank you Thank you for joining This session And thank you for Your support


Welcome to Spotlight Advanced. I'm Roger Basick. And I'm Alice Irizari. Spotlight uses a special English method of broadcasting. It is easier for people to understand, no matter where in the world they live. Ito wakes up at six in the evening. He wears the same clothes that he did yesterday. His crowded room is filled with empty drink bottles and food wrappers. A huge desk takes up one side of the room. When Ito wakes, he does not get ready for class. He does not get ready for work. Instead, he goes to his computer. And he plays video games for the entire day. Sometimes his mother comes to bring him food. He thanks her, but says nothing else. Ito's life is the same the next day and the next. He has lived without seeing other people for the past three years. Ito is one of Japan's Heikikomori. A Heikikomori is someone in Japan who withdraws from society. Usually, these are young men. They isolate themselves from other people. And they depend on their parents to survive. Today, there may be a million people living like Ito. But why? Today's spotlight is on the Heikikomori. Japan has a very unique society. Traditionally, parents and children live together for many years. Several generations often live together at a time. And each family member supports the other. Younger people take care of the older. And everyone takes care of the children. This can be very positive for family members, knowing they will always have help. But it also lets people become Heikikomori. A Heikikomori knows someone will take care of them. So they do not need to find a job or go to school. But people do not become Heikikomori because they do not want to work. It is usually because of a great pain. A Heikikomori might have lost a job, or they might be bullied in school. They see these events as failures. And once they have failed, it is difficult to try again. Masaki Kegami is a journalist who writes about the Heikikomori. He told the Japan Times. The Japanese society makes it difficult for people to succeed once they have made a mistake. I think most Heikikomori have had difficulty with their working life. They have been hurt by their human relationships there. Other cases might be people who have had bad experiences at school. Or they have been through troubles, accidents or illnesses. Some have stopped working to stay at home and look after their aging parents. There are many different reasons. It can happen at any age. Many Heikikomori isolate for economic reasons. After the Second World War, Japan's economy grew quickly. College graduates got jobs easily. A person could get a job in a company. And that company would employ them for the rest of their lives. But in 1991, the economy crashed. Companies stopped hiring. Or they continued to employ older workers. It became harder and harder to get a good job. Many people had to find lower paying work. Or they did not find work at all. This economic time period is known as the lost decades. The lost decades hurt a lot of people. In Japan, much of a person's self-worth is based on their job. Not having a job or not doing well in a job causes a lot of shame. And some people feel so worthless that they stop trying at all. Now Hiro Kimura was a Heikikomori for ten years. He told the Japan Times... If you finish university in Japan but do not get a job, people look at you strangely. People have a strong sense that you should be working. I was embarrassed. I did not want anyone to see me. When I saw someone wearing nice clothing, I would feel like I had caused trouble. I hated seeing working people. I would compare myself to them. It would make me feel very bad. I felt a strong sense of shame. Many Hikikomori also have depression. A kind of extreme sadness. Often, a depressed person will want to move. They will want to work on things. But they feel that they have no worth. They believe their efforts will not change anything. So they feel even worse and become more depressed. It is difficult to recover from this way of thinking. Fusat Ikida is a Hikikomori in his 50s. He became a Hikikomori when he returned to Japan after living in a different country. He felt that he could not fit into Japanese society, so he became very depressed. He told the Japan Times, I closed all the curtains, but the light outside still reflected on the curtains. I could see it from the back of the room. It felt like society was continuing on without me. That feeling made me lonely and insecure. Curtains were not enough. So I blocked more light from my windows. I made it like a cave. It made no difference whether I slept in the daytime or nighttime. Having a Hikikomori child is also very difficult for parents. Many feel that they must continue to support their children. But they also feel responsibility. Most do not know how to help. And some fear talking about their children, because they also feel shame. Maika Ilan is a photojournalist. She helped document the lives of many Hikikomori. She told Muse Magazine. Parents know that people will see them differently if they talk about their children's lifestyle. They expect them to return to normal for months or years before seeking help. Most parents feel that a Hikikomori child shows they have failed. Having someone help with it is like getting rid of your responsibility as a parent. It is like getting rid of your child. Once a person becomes a Hikikomori, it is very difficult to go back to normal life. Most do not know how to live by themselves. And they do not have the skills to find a new job. The Japanese government has opened up some jobs for Hikikomori. And there are special programs to teach them skills, like cooking. But the most helpful programs for the Hikikomori help them find friends. After all, a person who is lonely or depressed does not only need a doctor. They do not only need help with getting a job. They need someone who will tell them the truth. They need someone who will try and understand them. To make them feel like they are not alone. Do you know anyone who is lonely? How could you reach out to them? Tell us about it on our website at www.spotlightenglish.com. You can also find us on YouTube, Facebook, Instagram, and Twitter. The writer of this program was Dan Crisman. The producer was Michio Ozaki. The voices you heard were from the United States and the United Kingdom. All quotes were adapted for this program and voiced by Spotlight. You can listen to this program again and read it on the Internet at www.radioenglish.net. This program is called Always Alone, the story of Japan's Hikikomori. You can also get our programs delivered directly to your Android or Apple device through our free official Spotlight English app. We hope you can join us again for the next Spotlight program. Goodbye!


Well, hello, and welcome to the Newton Design Series. I'm Heidi Joy-Trathaway with the OpenStack Foundation, and I'm here with Sean McGinnis. Would you please introduce yourself and tell us a little bit about you? Sure, I'm Sean McGinnis. I'm the current project team lead for the Cinder Projects for Newton. I've been working on OpenStack now since Ice House, and I work for Dell in our Minneapolis office as a senior principal software engineer. Great. Well, tell us a little bit more about Cinder. What does it do? Cinder is the OpenStack project for managing block storage devices. It provides an abstract interface over the top of various traditional block storage backends to be able to use things like LVM or a vendor separate physical array to provide block storage to virtual machines and containers. And Cinder is one of our most adopted projects in OpenStack, one of the top six most adopted projects. Tell us about some of the hot topics that your team discussed in Austin related to Cinder. Sure. Well, a lot of the discussion was actually probably not too interesting for end users directly. We had a lot of changes in Mataka that effect. Different things that the developers working on the code need to be aware of. Things like now implementing microversions in the API, doing things like rolling upgrades. So there's a lot that we need to be aware of and then hopefully these features as a matured will actually be useful to the end user. Things like the microversions will allow a consumer of the OpenStack cloud to request a specific version of the API. So we're able to keep going and making changes but they're able to get the behavior out of the API that they expect. Other things for replication. Again, that's been an ongoing theme. We are continuing to evolve that and then just things like user messaging on errors and various issues. Great. Well, then tell us more about your Newton planning and specifically the kinds of user needs that your team identified that you're working to solve. Sure. Since I brought it up, I guess I'll talk about replication first. We've had some development over the last couple of releases for replication. We have replication support. They're not adopted by all vendors that are able to do replication. So one of our priorities is to expand that coverage. Hopefully we'll get, I think we have five or six back-end vendors right now that support it and we'd like to expand that to have it be a more common functionality, not just a niche thing. There's a lot of talk about expanding on replication and really until we get that good adoption, I'm a little hesitant to add more functionality on top of it. That said, a lot of this is really driven by our own conversations. I'd love to hear any feedback from end users up there, what they're looking for with replication, what their needs are so that we can make sure that we meet their use cases. The other, another top priority for me, at least what I'd really like to push in the team, we have a few people working on user messaging. That's the idea that being able to return internal error reasons better back to the end user than what we have right now. Unfortunately, a lot of times the experience isn't so good when things go wrong. If you're using Horizon and you create a volume, you might just get back a really generic message saying that it failed. And then it's up to digging through log files and repping messages and things to find out. We'd like to make that experience better for end users, be able to actually tell them this failed and here's why. And then another hot topic and priority was kind of a general theme, I think, for all the projects right now is moving over to using the OpenStack client. As it has been so far, we have a Cinder client, that command line interface, and it works great, it has all the functionality, but it's specific for Cinder and there's one specific for Nova and there's one specific for all the other ones. So in case anyone hasn't heard, OpenStack client is the one CLI to rule them all. It's pulling in all that functionality from all the different clients. So we want to move to that. We do want a one consistent good user experience, but there's some functionality that's in the Cinder client that we haven't gotten into OpenStack yet. So there's been talk about deprecating the individual projects CLIs. And I can't go forward with that until I'm sure that we're not going to lose any functionality by directing everyone to the OpenStack client. So we do have a couple of people looking at that, seeing what's missing and getting those two up to par so that really if we do get rid of the CLI from Cinder, that end user can still do everything that they need to do through the OpenStack client. I guess one tricky part of that we needed to figure out was that we just recently added a plugin for our CLI for our Brick client. So OS Brick is a library that handles all of the storage interaction on the operating system for being able to do things like re-scan and discover new SCSI devices when we expose a new volume to a host like a Nova compute host. So we added a plugin to Cinder CLI to be able to do that outside of Nova for being able to do things like ironic or really just storage management directed by the end user just on their own. So we just had to make sure that that functionality can still be met through OpenStack client. Looks like we might have a path forward there. So hopefully we should be able to move on to OpenStack client and have that one common interface for everything. Sounds like a lot of new great features and enhancements. As you're looking ahead toward Newton's release in October, what would you say are the top three new features or enhancements that your team plans to deliver? Well, I think another recurring theme, active-active high availability, that's some progress was done in Metacor for that and there's still some pieces that need to get put in place, but I'm hoping it looks like we might be able to get that in Newton. That'll be great for making just the services more robust. We're looking at replication, as I mentioned, really would like to see more support there. Test coverage is actually a priority for us. There's good coverage of the core functionality of Cinder, but there definitely are areas that have very light or really no test coverage. And we wanna make sure all of that is being covered through CI tests, functional tests, unit tests, just to make sure that whatever we're delivering when end users pick it up, there's no surprises. Great, so one more question for you. As you know, the product work group has established some themes that we use to connect the dots between four projects, and those themes are scalability, resiliency, manageability, modularity, and interoperability. So I wanted to know which of those themes do you intend to address most heavily in Newton? Of course, all of them to some degree, but I guess I would say the top two, if I picked two out of there would be resiliency with things like high availability. We want Cinder to be solid and manageability with things like the error reporting back to the end user and making things like open-side clients accessible, usable by everybody. Great, is there anything else you wanna add to the book swatching? I guess just always asking anyone interested in getting involved, we can always use help, code reviews, documentation, really anything. You don't need to be a programmer.  You can stop in on the IRC channel, open-stack Cinder, and just ask if anyone needs any help, and I'm sure some people would have some suggestions. Thanks, Sean, for telling us about Cinder. Thank you.


Hi, welcome everybody to Analyzing Software Using Deep Learning. What we'll do in this module of the course is to look at one of the cross-cutting or underlying problems of this whole field of Analyzing Software Using Deep Learning. And that's the problem of how to actually represent the source code of programs. In particular in this module we will look into how to represent tokens, so the basic building blocks of source code. And then in a later module we'll see how to use these representations to find representations for larger snippets of code. So here's an overview of what we'll cover in this module of the course. So at first I will explain the token vocabulary problem. So the problem that we're trying to address here, which is essentially about the many tokens that exist in source code. Then we look into a couple of approaches for addressing this problem. And one of them, which we'll spend some more time on, is to use pre-trend token embeddings. That's an approach that has been very popular in natural language processing, but also shows to be pretty effective in modeling source code. And then in the third part of this module we look into learning embeddings that cover both natural language information and programming language information, which is pretty useful to not just reason about source code, but also natural language artifacts associated with source code, such as comments or queries that someone might type into a search engine in order to find some source code snippets. As usual, this work that we're covering here is based on a couple of recent papers. Today there are more than three papers, so I'm just highlighting three here on the slides that are recommended reading for this module, because a lot of the content that you see here is covered in these papers. And as usual, of course, these papers provide many more details. All right, so let's start by looking at what we're actually trying to model here. So one way of looking at source code, and that's the way we will mostly focus on here in this module of the course, is to consider source code as a sequence of tokens. So we can think of tokens as the basic building blocks of code, because basically every piece of code, no matter in what programming language is composed of tokens. So in order to reason about source code and larger code snippets and maybe entire programs, the first thing you always need to do is to reason about individual tokens and have some way to represent these individual tokens in a way that is suitable for neural networks or for deep learning. To illustrate this problem a little bit, let's look at this piece of JavaScript code that we are seeing here. So this basically consists of a sequence of tokens. So there, first of all, is this comment, which you can think of as just one common token, or which you could actually split into multiple tokens if you actually look into the words in this comments. Either way, it's fine, but no matter which way you take, you'll get some tokens out of this. And then we have some more tokens here. For example, this identifier followed by the dot, which is also a token. And then yet another identifier followed by the open parenthesis, which is yet another token. Then we have this literal value. So this constant 100 is called the literal and then a comma and many, many more tokens. One more kind of token that I'd like to highlight here is this function keyword because that's another kind of token that frequently appears in source code. So talking about the kinds of tokens that you can find in source code, there are essentially two categories of tokens. One is all the tokens that are fixed by the programming language. So there are some tokens that just inherently belong to the language in which the code is written. And these are the operators, things like parenthesis and curly braces and so on, and of course, the keywords of the language. Typically, this is a relatively small set, depending a bit on the language, but usually it's a couple of dozens, maybe 100 or 200 tokens, but usually not more than that. In addition, you have a second category, which typically provides many, many more tokens. And those are the tokens that are chosen by the software developer. In particular, these are two kinds, namely identifiers, so variable names, function names, names of properties and so on, basically everything you can name in the program and literal. So all these constants like string constants or numbers that also appear in the source code. And in the second category, you usually have a lot of different tokens simply because developers are pretty creative in creating new variable names or coming up with specific constants that matter for their particular program. Now, this huge size in particular of the second category of tokens that we find in source code brings us to the problem that we're actually talking about here, and this is the vocabulary problem. So in essence, this problem is about this huge number of tokens that you'll get as soon as you look at not just a tiny program, but at a larger program or what you typically do in the context of analyzing software using deep learning when you look at a large code corpus. So the problem here is that if you have a large, very, very large vocabulary, it is usually difficult to represent and also difficult to reason about. It's difficult to represent because there are just many things you need to represent, so any trivial approach for doing this will end up with very, very large vectors and those are typically not very efficient if you want to train a model. And the huge vocabulary is also difficult to reason about simply because there are so many different kinds of tokens that it may be difficult for the model to actually generalize and understand the more general patterns that you can see in source code, but that may not be apparent if you see so many different tokens. This vocabulary problem is relevant for two kinds of models, and these are both models that take code as an input and models that produce code as an output. So if you think of a model that takes code as an input, let's say a model that reasons about a piece of code and tries to determine whether the code is buggy or not, then somehow this model needs to take all this code in. So if you have a lot of different tokens, then the problems that we just talked about, representation and reasoning are a problem on the input side. On the output side, we also have a problem because if the model tries to produce code, for example, by providing code completion, then it must predict one out of many, many, many different tokens each time, and that's an inherently challenging problem because if you have many options to choose from, it's much more difficult to choose than if you had just a few options. So let's look at some concrete data to illustrate this vocabulary problem a little more. So what you see here is a plot taken from this paper here where the authors of the paper have looked at 14,000 projects and basically plotted how many different tokens you find if you take either all of these 14,000 projects, so this is the 100% here, or some subset of them, for example, just half of them. And what you see on the vertical axis is the number of different tokens that you find when you consider all these projects. And what you can see here is pretty surprising. So one thing you can see is that if you consider all these 14,000 projects, then there are almost 12 million different tokens. So this is not counting the number of tokens you have in total in this code, but it's the number of unique tokens. So there are 12 million unique tokens in these 14,000 projects. Now, 14,000 projects may sound like a lot to you, but actually this is a reasonable corpus size for realistic learning on software. Now, one thing you could do, of course, is to ignore some of these tokens. So for example, you could ignore all comments because maybe in comments you have a lot of diversity and maybe let's ignore also all strengths that occur in the code because maybe there are a lot of strength constants and they are very specific to the program. But even if you ignore all of those, so this is the gray line that you see here, then the number of tokens is still very large. So it's still above 9 million, so it's still a lot and more than you can reasonably model. Another possible approach is to actually split tokens. So instead of considering all these identifier names that developers come up with, let's just split them based on the usual conventions that are used to compose identifier names which are camel case and snake case. If you do this, as you can see, the total size of the vocabulary reduces quite a bit. So this is something that definitely does help, but what you should also see in this plot is no matter how you model the vocabulary, whether you omit some of these tokens or split them into sub tokens, there seems to be a linear growth in the number of tokens in the vocabulary when you add more projects and this linear growth doesn't really seem to saturate anywhere, even if you consider up to 14,000 different projects and this is the essence of the vocabulary problem. Now there are different ways how you can handle this vocabulary problem and we will talk about three of them which happen to be the most popular ones in this course. So one way which we'll cover in some more detail in just a minute is to abstract the tokens. So the idea is to not consider the tokens as they are but to abstract them in some way so that you get a much smaller vocabulary, but the disadvantage is that you're also losing a lot of valuable information because you're essentially abstracting away the details. For example, if you abstract away the details of identifier names, then you're losing all this implicit knowledge that developers put into meaningful names and the model just cannot learn from that anymore. Another possible approach is to consider just the end most frequent tokens that occur across your code corpus which turns out to cover a pretty large fraction of all tokens. So this is great by just looking at some number N of most frequent tokens. You can cover quite a lot of all the occurrences of tokens in normal source code, but there's a disadvantage still and that is the auto vocabulary problem which basically means that if you focus on the top N tokens then there will be some tokens that you do not cover and if you add more code, there will be more and more of those tokens that you do not cover and that is not really good because then you're missing parts of your input or cannot predict some of the outputs that your model should predict. And then there's a third option which we'll also look at and this is to embed the tokens into a vector space such that you get a vector embedding or a vector representation of every token that has constant size even if the code corpus is growing. The main challenge here is that it's non-trivial to obtain an effective embedding and we will look into some approaches for doing this in a few minutes. So let's have a more detailed look at the first of these three approaches and this was to abstract tokens so that we have a smaller number of tokens all. So let me start by abstractly showing what is actually meant by this idea. So let's say this is your source code and each of these little boxes is one of the tokens that you have in the code. So for now it doesn't really matter what these tokens are but let's just call them token one, token two, token three and so on. So if you have this code there will be many of these tokens and some of them may actually be the same. So maybe this is actually not token five but maybe this is token two again because some of the tokens are of course occurring again but even if they occur again there will still be a very large set of unique tokens. So now what abstraction will do is to abstract these tokens into a smaller set of abstract tokens. So let's say that maybe T one and T two are abstracted into the same kind of abstract token and then this instance of T two will of course also be abstracted into this abstract token A one and let's say that A two is an abstraction that will cover both T three and T four. And now what you, so this is basically the abstraction and then the result of this will be that our token sequence now looks differently because now we can represent the sequence from above as A one, another occurrence of, oops, another occurrence of A one and then an occurrence of A two followed by another occurrence of A two followed by yet another occurrence of A one and so on. So after this abstraction, the code will look differently and we are as with any abstraction we will lose information but the benefit is that we have a much smaller set of unique tokens in this example only two instead of four. Now this general idea of abstracting the tokens into some abstract classes of tokens can be instantiated in many ways and one of them is to do this abstraction based on the kind of token. So let me illustrate what this means through an example. So let's say we have some code that maybe checks if some variable file is unequal to null and if this is the case then we are writing into a variable called line what we get if we call file dot read. Now if you look at these tokens then you can basically assign a kind to each of these tokens. So this could be a keyword. This could be an operator or a parenthesis. So depending on how exactly you group these kinds this is different. This is an identifier and here we have another operator and here we have for example, a literal. And now what we could do is to basically rewrite the sequence of concrete tokens into a sequence of abstract tokens by just saying there's a keyword followed by an operator followed by an identifier followed by another operator followed by a literal and so on. Okay, so basically every token would just be represented by its kind or what you could also do you could keep the programming language specific tokens as they are. So instead of doing what I just did you could also keep for example the keyword if and keep this open parenthesis but then abstract away the identifier by just saying there's some identifier then again we keep this not equals operator because this is part of the language and we would also keep null because that's also part of the language same for the closing parenthesis but down here we would again abstract things into identifier and another identifier dot yet another identifier and then keep the parenthesis and also the closing curly price. Now one big disadvantage of this abstraction that I've just shown is that we're losing a lot of information and in particular where we are losing the information that some of these identifiers actually refer to the same variable. For example the fact that this occurrence here of an identifier and that occurrence of an identifier actually both refer to the file is probably interesting because you typically check if something is not null before you do reference it and if you just abstract everything into identifier then we're losing this bit of information. So a different approach that does not have this disadvantage is to do this abstraction by consistently renaming identifiers. So let's look at the same example that we've just seen again. So we would again have this code that checks if file not equal to null and then reads a line of this file using this read method. And now if you would want to consistently rename this file or this code so that every identifier is replaced by some abstraction of it but in a way where we can still see that two occurrences of an identifier refer to the same identifier then what we could do here is to do the following. So we would keep all the programming language specific tokens but instead of having file which is a programmer chosen token we will now say this is ID one or some other abstract name of an identifier and now for line we would pick a different name ID two but now here file occurs again so we again say hey this is ID one so we see that this is the same name and then read is yet another name so this would for example become ID three. So essentially we would build a map from the original names that we see in the code to some abstraction of names but in a way where we consistently rename occurrences of the same identifier everywhere so that we do not lose that much information. Now of course if you would do this across the entire code corpus you wouldn't really reduce the size of your vocabulary because you would basically just have another ID one, two, three and so on for every identifier name but you can only do this on a much smaller scope. For example you could do this for every file or maybe for every method or function that is analyzed. All right so now abstraction is one way to deal with the token vocabulary problem. The second approach that I'll briefly talk about here is to just keep the top end tokens of the code vocabulary. This idea is based on the observation that this vocabulary typically follows a so-called long tail distribution. So essentially what long tail distribution means is that there are some tokens that occur pretty frequently but it's only a few tokens actually. And then there are many, many other tokens that occur infrequently and that give you a long tail of infrequently occurring tokens. Now based on this observation if you just keep the end most frequent tokens and abstract or maybe throw away all the others then we still will cover a large percentage of all occurrences of tokens without having a huge vocabulary. For those tokens that are not among these top and most frequent tokens, what we'll do here is to represent them as some special unknown token which basically says that we don't know what it is but there is another token which is not part of our top end. Let's have a look at some concrete data to see how well this top end idea could work. So what you see here is some data taken from a paper that has looked at roughly 100,000 JavaScript files so a pretty large corpus of source code. What you see in this table, so in the first column what you see is the size of the vocabulary that one could set. So for example, in the first row you see a scenario where we would say, okay, we only focus on the top 1000 tokens in this whole corpus. Then here you see what percentage of all the unique names this set of top end is covering. So for example, by just looking at the top 1000 tokens we would only cover 0.4% of all the unique occurrences or all the unique names in this corpus. But then the nice thing is, and that's what's shown in the third column is that this will cover a relatively large percentage of all the names that are occurring in the code. So with just 1000 unique tokens we will for example cover 63% of all occurrences of tokens. And the reason is that there's some tokens that occur pretty often. So there's some names like I and J and maybe X and Y and also some longer names like list or set that occur pretty often. And this is why a small percentage of all unique names will cover a relatively large percentage of all the names that you find in the code. So another line that is highlighted here is to pick a vocabulary size of 60,000 which only covers 24% of all the unique names but happens to cover 90% or 91% of all the occurrences of names. So this covers almost all the tokens that you have in the code while having a much smaller vocabulary size than you would have if you just naively consider all tokens that ever occur somewhere in this code base. Good, so now we've seen the first two of these approaches that you see here. We've seen how you can abstract tokens and reduce the vocabulary size that way. We have seen what happens if you just consider the top and most frequent tokens. And then in the next part of this module we'll actually look into the third approach which is to embed tokens into a vector space. So this is all for this first of three parts in this module. Thank you very much for listening and see you next time.


So, earlier this year, we actually received some positive news from the Biden administration as it relates to foreign policy, and it has to do with one of the most egregious human rights violations that our government is propagating abroad. Drone strikes. So, the New York Times reported, Biden secretly limits counterterrorism drone strikes away from war zones, requiring higher level approval is a stopgap measure as officials review whether to tighten Trump-era targeting rules and civilian safeguards. Now, this is a bit vague and we're not going to get into that article. But essentially, at that time, if you read that article, I think the reasonable response was to kind of wait and see what happens, right? The implication is that he'd be scaling back drone strikes. But by how much that was not necessarily to be determined. But if he does less drone strikes than Donald Trump, who ramped up drone strikes almost exponentially after he took office, then that in and of itself would be a victory. But now we're getting a little bit more information about how much Biden has scaled back his administration's use of drones and the preliminary information that we have suggests that he's scaled it back significantly. And to me, as someone who, I mean, my political development centered on drones in 2015, when I had to write a graduate paper on the destruction caused by drones, this is a really, really positive development. So as Michael Hirsch of Foreign Policy explains, in August of 2020, the man who is now U.S. President Joe Biden's Deputy National Security Advisor, Jonathan Finer co-wrote a privately circulated memo titled Ending the Forever Wars, written with two others who have since joined the Biden administration, Christine Abizayid and Brett Rosenberg. The memo laid out a detailed program for extricating the United States from the two decades long campaign dubbed the War on Terror that began on 9-11. Six months into Biden's presidency, the administration has said little about its longer term plans in dealing with Islamist terrorist groups around the world, apart from announcing the withdrawal of troops from Afghanistan and yet airstrikes by drones and other U.S. kinetic operations in trouble spots around the world outside conventional battlefields have dramatically dropped since Biden took office. The president imposed the partial moratorium as his team conducts an intensive review of every aspect of America's global counterterrorism efforts, which have spread over two decades from Afghanistan post 9-11 to Iraq, Yemen, Syria, Libya, Somalia and parts of the Maghreb, Southeast Asia and West and Central Africa, as the finer memo notes. One of the recommendations enacted from the memo is to raise the threshold for use of force. This includes eliminating strikes against individuals whose specific identities are not known. These are signature strikes and who are not identified as tied to immediate U.S. force protection concerns or otherwise posing as imminent threat to United States as the memo puts it. No truly reliable figures exist, especially for drones, since for most of its existence, the drone program has been shrouded in secrecy. In his final year in office, President Barack Obama briefly opened the window revealing that during his term, drone strikes, conventional airstrikes or cruise missiles used outside conventional war zones like Afghanistan had killed as many as 116 civilians, but other independent monitors put the figures much higher. Obama's successor, Donald Trump, made the numbers classified again. The cutback in drone strikes is one element of a much bigger rethink. One Biden administration official who has been involved in discussions said that while a policy isn't yet set and it's not clear when it will be the upcoming 20th anniversary of 9-11 is an important target date and what is amounting to an exhaustive evaluation of the overseas terrorist threat. So keeping in mind that we don't have exact numbers and figures at this point, this is really, really good news. The article is reporting that drone strikes are at an all time low since the drone wars have been initiated. And I would like for them to stop entirely. But to bring them at an all time low, which obviously suggests lower than when Obama left office, that is a really good sign. And Biden absolutely gets credit for this. Countless lives are being saved because he's scaling back drones, at least for now. Now, we still kind of have to wait to see about the outcome of their assessment. Right now, it's a temporary partial moratorium. But still, this is a really, really good sign. And that's not to say that I agree with Biden on his foreign policy. Overall, I have huge disagreements with Joe Biden. I mean, this individual started the war on terror. He voted for the Iraq war. And on top of that, as president, he's bombed Syria twice. He's basically doing a proxy war with Iran in Iraq and Syria. And, you know, on top of that, he, as I record this video right now, his press secretary isn't ruling out sending troops to Haiti as a result of the assassination of their president. So there's a lot to critique with Biden when it comes to foreign policy. But here, when it comes to drones, one of the most egregious human rights violations propagated by the U.S. government. That's this is great news. Objectively speaking, this is really good news that Biden deserves a lot of credit for. This is excellent news. Now, to me, I essentially lost faith in Obama. Like the wake up call that I got was when I learned how extensive his use of drones and signature strikes were. And, you know, I read articles about the victims of drones in Pakistan, Yemen and Somalia that haunt me till this day. You know, it's not just that there are victims that are killed by these drones, but children in other countries like Pakistan. They have PTSD because of the buzzing that they hear from drones. They feel as if they're being hunted. They feel as if, you know, whenever it's a sunny day, they don't want to go outside because the drones will be patrolling. People there know of other individuals who have been killed by U.S. drones. We've drone strike, struck grandmas in their gardens. We've, you know, struck weddings. It's an egregious human rights violation and not only should that end, but I think that is incumbent on the U.S. government to pay reparations to anyone haunted by these drones. Anyone who lost a family member should be given reparations. It's such an egregious human rights violation. And so with Obama, he got an office and once he took over immediately, he did what Trump did. He ramped up drones. And then when there were reports that they were killing a lot of innocent civilians, he scaled it back. And then right before he left office, you know, he tried to put in some some mechanisms that would lead to greater transparency by the U.S. government about who we are killing and not killing. But Trump. Undid that like that, right? He he undid all of that easily and he scaled up drones by 400 percent, more than 400 percent. So to see that Biden has brought it down to pre-Trump levels, but, you know, even lower than the time when Obama scaled them down, presumably excellent, excellent news. This is a victory. And I think that Biden deserves a lot of credit. And sure, maybe it's temporary. The partial moratorium is only a partial moratorium, and they're conducting this comprehensive review. But for them to not come in guns blazing, that is actually different in comparison with his two predecessors. And it's a really good thing that I want to applaud credit where it's due. I have a lot of criticisms of Joe Biden, but when it comes to drones, I think this is a phenomenal development to know that he has drastically scaled back the use of drones. These are killers. They're illegal. They violate the territorial sovereignty of these countries, who we are not at war with and haven't declared war with. And so not only are they illegal under US law, but they're illegal under international law as well. So they have to stop. And the fact that he scaled them back dramatically, that's a really, really positive development. And I think it's important that he gets credit for this.


In the burb we need to give this thing a nickname better than the burb We're headed to the 8% monster retreat to make people like you freaking monsters of the insurance industry Everyone give this actually the 92% that fell want to be monsters Hey, welcome to my lodge. I wish it was mine, right not yet coach birds place Coach Bert's Lodge We're here for the 8% monster retreat super excited about it. You should have been here We're going to take your you and your business to the next level. Welcome to apricot monster retreat Let me show you around real quick. We're here at the 8% monster retreat And we're just here connecting with other top producers other monsters in this in this area of the business You have to be open to learning constantly. You're not open to learning You can't possibly improve and learning doesn't just come from books through schooling It's also learning from other people that have been there where you have been or where you're at right now And you need to hear from them what steps they took to get to the next level We're gonna spend a lot of time with our attendees today coach Michael Burt Landon McCarter and myself talking about how to blow up your brand how to build amazing website How to use YouTube to generate passive leads forever how to drain immediate leads how to recruit how to attract how to coach How to train how to retain we're gonna talk through all of that I need to surround myself with people who are having the success that I want and looking at people like a Cody asking The success he's had in this industry not only for himself But by looking at the people he's helped work and develop and train to have the same success Following the same system that he teaches. I look to go. I got to be around that guy We're spending a couple days With a lot of power players in our industry Not only networking getting to know one another really spending some quality time building some relationships But also sharing all the things that we've done You're gonna be thinking at 10x levels and that's it's just gonna get you to the next level that you need to get in your business What will you be known on planet earth for? If you're gonna become known for the number one person in the world at one thing What would that one thing be? At any level where you're at if you're just starting in the business Or if you are successful in the business and just want to take your your ideas to the next level You have to learn from other people and this is the perfect program to do that I always say I feel like a lion trapped in a cage and I want to be around people that go Hey, you know what? Here's the key that cage and we can let you out We can help you to succeed in many other aspects of your life besides just what you're succeeding in now It's great event where Cody and and coach bird have brought all the experts to actually answer those questions and engage in a Very great brainstorming setting the events. I've always said this events are freakin powerful You start spending time with one another you start sharing you start learning There's something about the power of this so where you and Cody ask is we're doing something right like like you need to Be thinking that does that build your personal adventure story up to know from the tricks behind the scenes Into how well are you educating my users like these boys coming from Missouri to Tennessee to get in front of people and put On a person That's the concept right when I saw this ticket for the monster retreat I just jumped on it because I know how powerful it is to you know be around people who are thinking massive


It's a very good thing that they called it the God particle.  So they just put God particle and that's a very clever ploy, marketing ploy. You must be glad that scientists are becoming market savvy. They've always been in a subtle way, now they're getting little overt. So, you know, for most people, their idea of science is just the new telephone model that's coming up. The next model of iPhone is their idea of science. All these people who never ever were interested in any kind of physics, now talking about particle physics, world over, everybody's talking about particle physics, that's an achievement. Breaking a proton and coming up with a boson, is that an achievement? That needs to be seen. But it's definitely an achievement that suddenly the whole world, at least the educated part of the world, is thinking particle physics. That's an achievement. That's a good achievement. At least people are thinking of science as not a way of milking their existence, not entirely, at least a little bit. They're willing to spend ten billion dollars just to know something. I like that. There is right now no technological use, but they're willing to spend ten billion dollars and thirty years just to know something. That sounds very spiritual to me. It usually doesn't cost that much, but so having achieved one thing, having made a social goal, scientifically what does it mean? In yoga, we see it this way. There is... Existence is like this. There is something called as Stoola, Sukshma, Shunya, Shiva. Stoola means the gross existence. Physicality is Stoola. Everything that you can see, everything that you can sense through five sense organs, everything that you can see, smell, taste, hear and touch is considered Stoola. This can be analyzed with your intellect and understood and grasped. And this is always made of, as we know today, of Anu or atoms. Because it's physical in nature. It's a complete block of... building block of existence is an atom. And if it's made of an atom, once enough number of atoms are there, we can touch it. We can smell it. We can taste it. We can see it if it becomes big enough. Initially in a microscope, after that with our bare eyes, we could see it. But if it goes beyond the perception of the five senses, but still it is physical in nature, we call it Sukshma. So when you enter into Sukshma, it is still physical, but you are not able to grasp it.  That dimension, if you approach it, we call it Vishesh Gnan or Vishesh Gnana. That means an extraordinary knowledge. Gyan or Gnana means knowledge. Vishesh Gnan is extraordinary or significant knowledge. So Vishesh Gnan is referred to as Vijnan. The word Vijnan is being very loosely used today. But essentially it means this, those dimensions which cannot be perceived through five senses, if you perceive them, that is Vijnan. So today science is entering those spaces because never ever is anybody going to see a Higgs boson. They are only going to see its footprint. Even now they only saw the footprint, they did not see a Higgs boson. But because they see the footprint, they believe he is there. Somebody is gone by, they left the footprints, we have not seen the man, but we know it's gone by. We go into the forest, we see the pug marks and we say, okay, there is a tiger. We haven't seen the tiger. In fact in a tropical forest it's very difficult to see a tiger. But we will see pug marks here and there, just like that, they saw the footprint. So they're entering Vishesh Gnan.  There, your intellect will be completely useless. Your senses will be completely useless because there is no physicality. Where there is no physicality, your sense organs and intellect will become absolutely redundant. If you go beyond Shonya, there is something that we refer to as Shiva. When I say Shiva, don't imagine that calendar man from Shivakashi. Shiva means that which is not. If you touch that which is not, we have always seen it is not physical in nature. It is not physical in nature means it does not exist, but it is opaque. How can that be? It is not in the realm of your logical mind. Today modern science believes the whole existence has to oblige to human logic, which is a very limited way of approaching life. The whole existence will not oblige to human logic.   The existence will not fit into your head. Your logic can analyze the physicality of the existence. Once you cross the physical dimension, your logic is completely out of its realm. So there are… because this is a dialectical culture, the same science has been expressed in story forms. I cannot go into the whole story, it's a long story. But when I told the story to one of the top scientists in the world, when I was speaking to him and I explained this, see this is how it is, this is the nature of the existence. This is from the yogic lore, but we are always told not to believe the lore till it becomes a reality in your experience. And this is the reality in my experience. If you go like this, this is what will happen. So when I spoke to this very top level scientist who's a Nobel laureate and I was telling him that this is what it is. There were a group of… Hello, there was a group of them and when I explained this is how it is within me, what do you think? They said, Sadhguru, if you can give a mathematical backbone to what you're talking, this is Nobel Prize stuff.  I said I'll never bother about the mathematics. It's true for me and it's… it has transformed everything that I am because it's true for me. Everything that I ever was changed simply because I touched this dimension within me. I don't wish to work equations for that. And anyway, Nobel Prize wouldn't mean anything to me. I would be too embarrassed by such things. So this story, I will… because I will make it so brief, there could be holes in it. If I make it elaborate enough, there will be no holes in the story. It's a perfect theory. And we have proved it within ourselves that it is true. But you want to build a ten billion dollar instrument under the ground to prove the same thing, it's up to you. This can be experientially proved within yourself if you are willing to go into the depths of what this is because this is made exactly the same way the whole universe is made.  And even now, science is also only inferring. Today modern science has admitted that it's an ever-expanding universe or an endless universe. Rather, ever-expanding is a yogic term, they are calling it an endless universe. If it's an endless universe, trying to travel across the universe and find out the nature of the universe is untenable, isn't it? Simply out of question. The only way you could know the nature of the universe, nature of the creation and the source of creation is by going inward because whatever you ate in the morning, whether it's a idli or a dosa or a banana, has been transformed into a human being in the last few hours. Nobody else can do this except the source of creation. So if the source of creation is right here, if you want to know anything about creation, isn't it the best place to consult? If you want to know anything about creation, isn't the source of creation the best place to consult? And if you had to go to heaven for this, you could give it up. If it is right here, why don't you consult? Simply because you too enamored by your own thought. You think you are going to capture the whole universe with your thoughts. It is a foolish way to approach. The only reason why science has survived is because of technology. It keeps throwing out technologies. If no technologies were coming out of science, they were just talking about all the things that they have been talking. People would have beaten them down for the money that they spent. And it's happened in the past when there were no technologies and people just spoke science, they were beaten down, isn't it? So science has its value in terms of utility, but science cannot open up the existence for human experience. It will not, it can never do it because they are going with intellect. Intellect as an instrument works only to dissect. The only way intellect can approach anything is to break it up and see. If you ask a scientist to find out something about this flower, first thing is he will break it up into pieces. I think I should ask a scientist to make me understand one of you. He will talk about dissecting you then. If you break this up, you may know many parts of this. You may know the structure of it, you may know the chemistry of it, but you will not know the beauty of it. You will not know the completeness of it because flower is an expression of a plant finding its fulfillment. It is the highest thing for the plant. For its life, this is the highest happening. It's the flowering of that life. You will not know that. You will not see the hand of the creator in this if you break it up. But as a whole, if you are willing to pay attention, absolute attention, if in your approach you make this flower more important than yourself and keep your focus on it, you will see the whole universe in this. If you break it up, you will have petals, you will have other parts of the flower and you will come to vulgar conclusions and then you will learn how to make use of it. So right now, unfortunately our approach, what we call as science has become like this, how to use everything in the universe for our benefit. If you see a tree, in America they call it wood. I was… this happened when I… when I was in college. This happened about four years ago when I was in Mysore.  They wanted to see. What has happened to me? So after I spoke there and we had two-day event and then my English teacher came and hugged me and she said, now I know why you wouldn't let me teach Robert Frost. I said, why ma'am, why would I not let you teach Robert Frost? I like Frost. She said, no, do you remember you wouldn't let me teach Frost? Then I remembered. One day she came and she opened the book and she introduced Robert Frost as a glorious poet and then she started off. Woods are lovely, dark and deep. I said, stop. I said, a man who calls a tree a wood. I don't want to listen to him. She said, no, no, Robert Frost is a great poet. I said, I don't care how great he is. A man who calls a tree a wood, I will not listen to him. I didn't let her teach. I didn't let her teach Frost. So after many years she comes up to me and she says, now I know why you wouldn't let. So if a tiger comes here now from the mountains, I mean if you come from Sri Lanka, that's different. If a tiger comes down from the mountain, looks at you, he will think, wow, dinner. So you look at a tree and you think, wood. It's all right for a tiger to do that because that's all he knows. But it's not all right for you to do that, isn't it? But right now that is all science has become. Anything we see, how to make use of it. Anything we see, how to make use of it. Even an invisible atom, we won't leave. Even the goddamn boson. Already people are talking about in how many ways it could be used. We could make a boson bomb.  If we make a boson bomb, all of you will just vanish.  Yes? Because you'll cease to exist just like the boson.  You want the technology? But they will tell you, no, no, no, there could be medical uses to it. We could do this, we could do that. Yes, I know all that. But just the idea, it doesn't matter what, you cannot look at anything without thinking in what way can I make use of it is a very cross way to exist. With this level of existence, you will have everything and you will have nothing. This is modern life. People have everything like never before and they got nothing in their lives. Nothing that you can call of any worth. People who have everything and cannot feel life in any significant way, when they die or when the moment of death comes, they will see that they spent this whatever number of years without living a moment. Because what can I get, what can I get is a sure way not to live because from what you get you can only make a living.  So giving does not mean money or something else or something else. It is just that this moment if you look at this tree, how totally you can give yourself to this tree, that is how intensely you know life.  Completely. Life will evade you. So this kind of science which denies you life should be restricted if you ask me. I know I'm going to be hugely unpopular because somebody is going to Twitter and says, Sadguru says science should be curtailed. It's all right. I'm telling you, a science which is driven just by what can I get out of it needs to be controlled a bit. Otherwise human beings will have everything and they'll have nothing. When that is your experience, then you will destroy everything. Today the planet is not being destroyed because of something else. It is just unbridled use of technology, isn't it? It could have been used for our well-being, but it's working against us because we have not worked other dimensions of life. We're just going with this, what can I get, what can I get? If you go with this, there will be no planet left after some time. But even if the planet is left, suppose we rope in another ten planets, still it will not be enough. We will still have nothing, we'll have everything. This is my experience. Science should have been just a quest to know not to exploit the creation. It is just a longing to know. Human longing to know wants to find expression in every possible way. Physical sciences is one of the ways perfectly fine. But once it starts serving the masters who sponsor it, then if businesses sponsor it, they're looking for profit from it. If nations sponsor it, they're looking how to make more powerful weapons out of it all the time. You must understand this, the cutting-edge science always first becomes cutting-edge military technology. Only after that it comes down to other users. How many lives has it taken? How many more do we want to take? That's a question. So this goddamn particle, one good thing it's done is the whole world is thinking particle physics. I like that. That's nice. But do you know the Boson is named after Satyendra Nath Bose? You know this? No? It's an Indian mathematician, a self-taught mathematician. As Albert Einstein acknowledged, he said, the western sciences couldn't take a single step without the Indian mathematicians. The fundamentals of mathematics came from the east. But always here, the other ethos of the culture said, you should not do anything that's exploitative to nature because nature was seen as mother nature.  You take only what you need, nothing more. Because of that attitude, this science was not converted into technology and that is the wisest way to handle science. Just as a quest, a tool, a way to know and nothing beyond that and technology must be really controlled what is absolutely needed. That's all that should be done. This unbridled usage already people are talking how to use the Boson that they have not seen. When we can use the atom that we have not seen. So it doesn't matter what you see, you are thinking of how to use it. This attitude, unfortunately, has been further fuelled by the attitude of science. This need to be checked. Otherwise, it will be our nemesis.