My name is Abby Kearns and I am the executive director of Cloud Foundry Foundation and for those of you that aren't familiar, it is a sister open source project to a cloud native computing foundation underneath the Linux foundation and we house Cloud Foundry, the open source platform as a service. So I am responsible for Cloud Foundry, the open service broker API projects, and all of the extension projects that go around that. And you're probably wondering why I'm here. So today I'm going to talk a little bit about what we're doing in Cloud Foundry, some announcements we had a couple of months ago around Kubernetes, but I'll say what does collaboration look like for all of us collectively in the future when we think about cloud native? But first I want to build up towards that because I think it's important to talk a little bit about where we're going technologically wise and why it's important and more important now than ever before. Back in the 1800s, some rich parents had the idea to really fundamentally change supply chain logistics and actually even management of that supply chain across the United States and they had the idea for the first transcontinental railroad, but that idea wasn't enough to make it a reality. It was the thousands of engineers and builders and creators that over decades transformed the both the idea of what supply chain could look like, but also the movement of people across the United States or in the early 20th century when an architect had the idea of bringing together the Marin County and San Francisco which to date had been really they've been transversing those two areas via boat and it was very unpredictable, very unstable and he had the idea of bridging those two communities together, but it was the builders, the creators that turned it into one of the icons that it is today and and by the way in order to achieve that it required a great deal of collaboration beyond just the builders and the architect. It required collaboration on the different communities and counties on funding and enablement and how to bring two communities together and then a little bit more recent. How many of you here have a smartphone? How many have two? That's right, we all have it. You know today for me it is an invaluable tool, but in 2007 when the iPhone first launched it was interesting, it was nice, it was had really changed the user experience of a phone, but it was still a phone that texted and took photos, but then something happened in 2008 in the app store launched and that fundamentally changed the phone. Those thousands of apps created by the thousands of developers collaborated together to make that platform an invaluable technology that it is today that powers most of our lives, so I would like to posit that right now the developers are the hero of the story, developers are the heroes that are guiding where we're going in technology. I'll give an example of this and I know that I use digital transformation a lot and I'm actually frankly looking for a new word so anybody got a new word to replace digital transformation I'm all ears, but I think about banking and I think about the fact that I now use my phone to do 100% of my banking. Five years ago that was not the case, but today I'm able to do that because of an application that sits on my phone and that may not seem like a lot, a mobile app, but think about what that means for my bank. My bank which is a Fortune 100 enterprise company, what that mobile app means to them because a mobile app isn't just updated once a year, it's updated over and over again and so that mobile app represents to my bank the fact that they have a mobile application, they have continuous delivery somewhere because they're constantly iterating on that application and they have a quick feedback loop between me, the user, the engineering team, the developer as well as the product team and so you start to see this mobile app as a representation of what's beginning to be agile practices and you see a mobile app as a representation of what cloud native collaboration, but a cloud native application architecture starts to look like and so for me that banking app and the combination of the developers that have created that on a platform represent where I think cloud native technology is going and the need for that collaboration so we're seeing organizations like my bank that are trying to figure out how to change the way they think about code, not just how they think about it but how they write it, deploy it and then iterate on that code because you can't just push it out one time and be done with it and that then really helps them rethink how systems are managed, secured because everyone needs security and then finally scaled is these applications take off and they get momentum and people get excited about them. So what we're starting to see is these organizations are taking these apps and taking these steps and using that as an opportunity to rethink fundamentally how their business thinks about technology but more importantly how it leverages that technology to change their business. A couple of months ago we at our Cloud Foundry Summit in Basel talked a little bit about Kubernetes, actually we talked a lot about Kubernetes. For those of you that are here at this event and have been following Kubernetes it has fundamentally changed the way we think about containers container orchestration and I'd say that we all agree that the momentum it has in the marketplace is fundamentally changing how we think about cloud native around that. We were also looking at our users that are trying to figure out how to run these cloud native apps alongside more containerized workloads, large applications that don't just necessarily fit neatly in that 12-factor app platform and so we combined Kubernetes with Bosch, our open source tool chain and together created Container Runtime. A Container Runtime alongside the Application Runtime both which can be managed by Bosch are what we think of when we now think about Cloud Foundry and the representation that that brings is the opportunity to give developers and builders and operators an opportunity to run the right tool for the right job. One of the things that is really interesting about Cloud Foundry is that it's open source the same with Kubernetes. We're at an open source conference and for me open source represents opportunity. Opportunity to bring together diverse people from diverse backgrounds across a multitude of companies and organizations to solve really hard problems and so we think about the power of open source for not just Cloud Foundry Foundation but also the cloud native computing foundation and the communities that straddle both of those the opportunity exists to bring people together to collaborate to solve really hard problems in a space that's changing pretty quickly. Diversity is a part in the proverb but a little bit of a soapbox I carry around with me as a woman in tech and I think for me it's more important than just saying diversity it's saying inclusivity and making everyone feel welcome because one of the things that I'm most passionate about is that beyond bringing more people to the table I think that's the only way we solve really interesting hard problems is getting more people to the table with different perspectives and so my theme today is I really thought about what am I going to talk about at Cloud Native Computing Foundation and Cloud Native Con is really what is the power when you bring together such a broad group of talented people to solve really hard problems and how do we get more of them in and get more voices to the table because I think that's where the power of this collaboration really breaks free but it also I think that's where we solve the really interesting problems for end users that are trying to develop applications but more importantly are changing their business so we often talk about collaboration alongside Cloud Native and open source within Cloud Foundry because for me it's important to give developers the best experience but allow that technology to continue to evolve and take advantage of really new and interesting technologies that are are coming into the market like Kubernetes but something you might not know is we collaborate across a lot of different open source technologies today container runtime takes advantage of Kubernetes but one of the things we launched in 2016 was the open service broker API and that is where we took the Cloud Foundry service broker API and extended that beyond Cloud Foundry Kubernetes was the first to take advantage of that followed by OpenShift and we're seeing more and more companies take advantage of the simple and elegant way of binding a service to an application but more importantly we've taken advantage of quite eagerly the OCI initiative and bringing that run C capabilities inside the platform but then we've also been heavily involved in CNI which runs in Cloud Foundry, CSI as we're really figuring out ways to integrate and attach storage solutions to the container but then also Envoy and open tracing so each of these technologies are areas where we are their collaborator and they're leveraging today but it also for me represents what the power of this collaboration is I think CNCF highlights the power of bringing together a multitude of technologies that are making cloud native possible because it isn't just about creating an app it's about managing that app scaling that app managing the services around that app being able to address your concerns and your organizations and making that possible for you to do at scale so one of the things that I'm most excited about is the fact that all of this is available in open source and so that each and every one of you can not only take advantage of the technology and pulling it into your existing solutions but more importantly that you can participate in these technologies so going back to bringing more voices to the table when we think about the power of open source and the power of cloud native it's getting more voices to the table if you're building agile teams what are you doing you're asking more voices to be heard the voice of the user the voice of your customers the voice of your operations team your DBA security compliance network you know the list goes on for people that you want to hear and have a voice and say and how applications are created managed and scaled so when I think about cloud foundry and we talk a lot about where cloud foundry is going and how it's evolving we're combining these best of breed solutions either through the open source so getting more voices to the table and building that bridge for other people to participate and bring their ideas but also the collaboration on the technology aspect we've started with container runtime and application runtime but there's so much more potential to bring these technologies together to build on and innovate to allow these technologies to get to scale and for us what we've seen an interesting phenomenon and I've been in enterprise tech a really really really long time and I'm not going to give a timeline because that dates me but I've seen containers and so I come through once or twice so what's interesting to me though is seeing how a lot of these organizations are starting to catch on in new ways beyond just paying lip service to the new technology and figuring out ways to integrate it they're using it to fundamentally alter their business and I think as we think about what our our job is here it's the realization that technology that we're building today is going to fundamentally change the landscape of many enterprise organizations and not just how they use technology but it's going to fundamentally change the way they offer that technology and engage with their customers an example of that is Home Depot how many of you are familiar with Home Depot you've probably been there once or twice right when you think about Home Depot do you think about cutting-edge technology cloud native applications no you think about DIY big box retail but Home Depot is actually for me one of the most innovative groups out there right now and the last two years they've gone from zero to 2,500 developers they have 2,500 cloud native developers writing cloud native applications right now in that two years they've gotten over 1,700 applications in production alone so they are revamping their back end they're revamping their point of sale but also the e-commerce tool rental and many other technologies they are bleeding edge for a lot of these new technologies in the retail space they're getting currently over 2 billion requests a month across their platform and for them the fundamental change didn't come just from being able to develop the applications but it also came from them being able to do one deployment every six months for a single app so now they're doing deployments every half hour think about the change that gives to you as an organization when you can deploy a new application to production during business hours every half hour that changes your teams that changes the way you think about technology but it changes what you think your potential is as a company and by the way their their stock price is showing that their stock price continues to go up it's the highest it's ever been and when I think about the role cloud native plays in companies like that we'll see a lot more of that we'll see a lot more of companies like Home Depot and Comcast and Ford Motor that are fundamentally changing the way they think about these technologies and what they're doing with them and by the way one of the most amazing things about Home Depot is they took this idea of building developers and creating developers and created boot camps so now that they take team members and train them on how to be a developer as we think about the shortage we have today in cloud native skills cloud native developers a company that can really think about agile think about cloud native and then turn that into an opportunity for their entire organization is a really powerful statement on the role technology continues to play in the evolution now it couldn't be up here and not brag a little bit about our community so I have to give the the plug one of which is sitting here Suze hi Suze we have nearly 70 members in Cloud Foundry Foundation one thing I'm most proud of though is over 40 of our member base are our end users so companies like Home Depot and American Airlines are leveraging this momentum to not only use open source but give back to open source over the last two years we've had over 110,000 commits nearly 7300 pull requests and that's across 513 repos Cloud Foundry is very big and very complex and we continue to be excited about the momentum the velocity but the involvement by a variety of companies that continue to make it the best technology possible earlier this year we also launched a Cloud Foundry certified developer training and certification program to date we've had over 10,000 people sign up for the training and certification and continue to see organizations like Volkswagen and Home Depot and many others take advantage of that to build their cloud native teams so as I leave you here today one of the things that I think a lot about is how do we take what is so special and amazing about open source and carry that forward beyond just our technology companies but to the variety of companies that are just starting to think about technology in new ways and so I think a lot about building community getting more voices to the table in our community and getting more people excited about open source and cloud native and how do we continue to build those bridges with other communities that are doing really cool stuff like the Kubernetes community, Envoy, Open Tracing, GRPC and many other projects so I would invite you that are here to join our community or come to one of our summits our next one will be in Boston in April you can also run the Boston Marathon at the exact same time so you could do two things at one time but I want to leave you with that I think cloud native in the momentum around open source are just going to continue and I'm super excited to see that growth here at cloud native con and I'm excited to see so many faces here that want to learn more about our collaboration efforts so thank you by the way any questions yes ma'am that's a great question so Kubo and the question was explaining Bosch and Kubernetes. Kubo as it was fondly named is Kubernetes on Bosch and that was formally donated it was originally created by Google and Pivotal in late 2016 early 2017 and it was formally donated to the Cloud Foundry Foundation in June of this year and Kubo was a really confusing concept and so and having that part of Cloud Foundry as a whole was really confusing and so what we aim to do was build a bigger story so we renamed it in October container runtime so that you have the opportunity to run containerized workloads within Kubernetes alongside your cloud native apps that are running on top of Cloud Foundry's elastic runtime in the same environment orchestrated by the same tool chain and you have that opportunity to choose the right tool for the right job and make it easier for your operators make it easier for your operators to spin up the VMs to scale the VMs to patch it to handle security and identity management and all of those feature functionalities you can now wrap into a single platform because we've always advocated that is the right tool for the right job and elastic runtime isn't the right platform for every single application and more often than not enterprises need that path forward to say how do I make big baby steps to get to 12 factor apps microservices even serverless at some point just how to giving people that path any other questions yes don't have visual aids to do a deep dive comparison but I will say that one of the biggest comparisons and I feel like Brian Graceley at Red Hat has done the best description so I will steal from him is it structured versus unstructured and having the piece components in one platform versus a highly opinionated structured components in another platform there are some similar technology technologies like the open service broker API and now kubernetes but there's a lot of differences in terms of other technologies that are used within cloud boundary that are not necessarily a one for one match and open shift any other questions all right well thank you all for your time


pick number one Josh Allen for most regular season passing touchdowns as of this recording Josh Allen is a plus 700 and no surprise there there's tons of value also keep in mind that the Buffalo Bills are the favorite to win it this year you take a look at Josh Allen's target Stefan Diggs Gabriel Davis Isaiah McKenzie they also added tight end OJ Howard so another target for Josh Allen you take a look at his season last year very solid he threw for over 4400 yards had 36 touchdowns only 15 interceptions I also think Josh Allen in the Bills are playing with a bit of a chip on their shoulder when you factor in the fact they lost to the Kansas City Chiefs in the playoffs and all it really took for the Chiefs was 13 seconds to close out that game so I'm gonna take Josh Allen to ball out this year and I'm gonna take him to have the most regular season passing touchdowns and I'm taking that plus 700


I've been asked to give a brief overview of the current European regime for intermediary liability, as it stands at the moment, with a view to identifying the gaps in the current framework and the controversy that those gaps have given rise to, and moving on perhaps a bit to a discussion y cyfnod o'r ddweud ymddangos sydd wedi'u bod yn cael y dyfodol. Ac y fydda'r cyfnod o'r ddweud ymddangos y cymysgau sydd wedi'u gwneud o'r ddweud o'r cyfnod y dyfodol. Felly, fe oedd y cyfnod ymddangos yn y regime neu arlaedd ymddangos? Yn yr hyn, mae'n ystafell y ffordd, y cyfnod ymdangos ar y regime sydd yn y dyfodol yn y dyfodol yn y dyfodol. Mae'r dyfodol yn yn 231 E.C. Felly am y dylau phobl, yr hoffa hwn yn cyflawnio mwylog a'r hoffa hwn yn cyflawnio'r cofodaf meydych chi'n pwylo hwn yn cofod ddebygu. A'n cyflawnio hefyd, ein suffegau mae'r hoffa hwn yn ddiwedd. Dydyn ni'n mynd i'ch gwahod maes arddangos, fe hwn i'ch gwahod maes ardechau. Mae'r amlwgau, a'r hoffa hwn yn ganlu i ech ddeddiw nawr, byddoch ei hyn o'r hoffa hwn. Mae hyn yn cael dechrau at y gofodau pan yw'r hoffa hwn. gael ei dweud i'w tahacaeth Cymru. Dyna os yw i'w ddweud y ddweud ffresfydiau Cymru,MA, o'i cael ei ddweud i pethau mynd dros bendeidlenol a ddweud i bryf, i ddwaith i'w ddweud y ddweud. Mae'r ddweud eisiau ddweud yn dros ddweud i ddweud yn y gwasanaeth yn ei ddweud i ddweud, ychwaneg o dechrau, y ddweud, y ddweud i ddweud, mae hwn hi'n olwfodeg o'r ffordd tŵr o ddweud i'w ddweud. those more attention to this one, the hosting sure harbour. What are the conditions for the enjoyment of hosting safe harbour. I In order to enjoy the protection of the hosting safe harbour an intermediary must not have had knowledge of the fact that it is hosting illegal information, and if it does have such knowledge than it must act expeditiously to take down or block that information now a little bit more attention is necessary here because that the knowledge threshold changes depending on the type of liability from which the intermediary is seeking protection. So if the intermediary is seeking protection from criminal liability, then the knowledge threshold is one of actual knowledge that it is hosting illegal information or content. If on the other hand the intermediary is seeking protection for civil liability than the threshold is a bit lower, in that case the intermediary must not have had awareness of fact or circumstances illnesses," from which it becomes apparent that it is hosting illegal information. So, essentially, what Article 14 of the Eiftschamunieth Directive does, is it hints towards a so-called notice and takedown regime. However, a little bit of attention is necessary here because the Article 14 notice and takedown regime is not as detailed and elaborated as equivalent regimes that we find in national jurisdictions, in Europe, and also other i amlwg presbytynrif ynictwys ar y bydd hyn sy'n eitemall a'r rhywunol yn y pryd y bydd ybarth i'ch anethau i amlwg sydd y dyma wedi yma yn yr uwch, ac mae'r DMA-wyllwer y byddai a'u cyffredinol yn Royall Ysgwrs, which offer quite a detail iddyn nhw i sgolaeth yr yrwntgen i'r rhywunol. Ond ac mae'r rhai a fydd ymlaen chi'n mewn rai ysgwrs, ac wedi ar worni'r rydw i'r oed yn y Rhyw ond wrth y byddwyr yn ymhwyffyr ac yn sydd yn gwaith i fynd i'r cwmysigol yw ymgyrch yn ei ddweud'r rydym yn fwylliantodol ar yr ymdyn nhw i am rydym yn ddiffusio'r cyffredinol ar y mynd arweithaddol yn cyflawni'r gŷnol. Rydym yn cyflawni'n cyffredinol yn ymgyrch â'r ffeyddfyrdd ac yn mynd arweithedd y llyfrfyn ymddangos dim yn eich fod yn ei ffawr. Byddwn yn ychydig ar roedd ddwyng â'r cyllid yn llwyddiadau o'r cyllid yn ymdillodau, ond mae'n gydig i'w ddechrau i'w ei hosedol. Ers y bydd ymlaen nhw'n ffordd o'r cyllid yn ymlaen nhw, mae'r cyllid yn ymlaen nhw yn ymdillodau. Yn ymlaen nhw'n ddweud, y cyllid yn ddweud ymlaen nhw'n ddechrau i'r cyllid yn ymlaen nhw'n ddechrau, ymlaen nhw'n ddweud. Mae'r cyllid yn ymlaen nhw ymlaen nhw'n ddweud. Felly, rydyn ni'n dweud y gweithio'r cwysynt yma ar gyfer y cyfnod Gwgolfrans a'r cyfnod Llorielfyrs, y cyfnod Ebay. What of those cases said? Well, those cases remain controversial to some extent. In those cases, essentially what the CJAEU did is it took the opportunity of the questions presented before it to, depending on your point of view, clarify the current intermediary liability framework or add an additional condition for the enjoyment of that framework by intermediaries. What does that mean? Essentially what the CJAEU did is it relied on the title of Section 4 of the Commerce Directive, which, as you might recall, reads the liability of intermediary service providers, to declare that in order to enjoy the protection of the safe harbors, service providers must be intermediary. It then defined the notion of an intermediary by reference to Recital 42 of the Commerce Directive, and it declared that in order to be intermediary, a service provider has to be sufficiently neutral. And this means that the services that it provides must be of a mere technical and automatic and passive nature, such that they do not give rise to knowledge or control over the information which is transmitted or stored. Now, this has been a bit controversial with a number of commentators arguing that Recital 42 of the Commerce Directive was never intended by the legislator to be applied to the hosting safe harbour, but only to the mere conduit and caching safe harbors. And this is particularly important for the hosting safe harbour, giving the evolving nature of the notion of hosting over the decade and a half since the adoption of the Commerce Directive. So, when the Commerce Directive was first adopted, the notion of hosting seemed to be pretty straightforward. It involves the provision of space on servers for the hosting of webpages and so forth. In the meantime, however, with the evolution, the emergence of modern Web 2.0, which rests on user-generated content, the notion of hosting has evolved towards intermediaries that perhaps have a bit more of an active role. So then the question emerges, does the hosting safe harbour protect those intermediaries? What does the CJU said in this regard? Well, that's not entirely clear. It has given some indications in Google, France and eBay, L'Oreal versus eBay. What it has said is that the mere provision of hosting services, the provision of general information on those services, the setting of terms for the enjoyment of those services and the receiving remuneration are not in and of themselves enough to deprive an intermediary of the protection of the safe harbours. However, if the intermediary is involved in drafting the content, in promoting that content or presenting that content, then it can no longer be considered sufficiently neutral in order to enjoy the protection of the safe harbours. So that's what we know on that front so far. Now, what's important to understand with regard to the safe harbour regime of the Commerce Directive is that it only protects intermediaries from liability in the very strict sense of the word. That is to say liability for monetary compensation. Each of the safe harbours contain a final provision in their final paragraphs, which make it clear that the safe harbours are not able to protect intermediaries from liability for injunctive relief. And in particular in the area of copyright, this possibility of injunctive relief against intermediaries has been given great effect by the European legislator. So in 2001, in the Copyright Directive, in 2001 the Copyright Directive was adopted. Article 83 of the Copyright Directive instructs member states to offer right holders, the holders of copyright, the possibility of implying for an injunction. Member states must offer right holders the possibility of implying for an injunction against intermediaries whose services are used by third parties for the infringement of their copyrights. And in 2004, with the Enforcement Directive, Article 11 of the Enforcement Directive expanded that obligation to all intellectual property rights holders. So all intellectual property rights holders must be given the possibility of applying for injunctive relief before a court for the protection of their copyright. Now might any kind of obligation be imposed on intermediaries in this way through injunctive relief? The answer to that is no, limitations do exist. The most important of those limitations, or at least the most straightforward of those limitations is again found in the Commerce Directive and specifically in Article 15 of the Commerce Directive. Now according to Article 15 of the Commerce Directive, member states may not impose on intermediaries general obligations to monitor the information which they transmit or store, or general obligations to actively seek out indications of illegality. So while injunctions may be imposed for the enforcement of copyright and other intellectual property rights on intermediaries, those are through injunctive relief, those obligations might not involve general monitoring. Is that the only limitation possible on the possible injunctions that can be imposed on intermediaries? No, further limitations have also been introduced by the caseler of the CJU. Of particular importance in this regard are the Sabam cases, so Sabam versus Scarlett and Sabam versus Netlog, as well as the more recent Telecable Veen case from 2014. Now what the court did in those cases was in addition to relying on Article 15 of the Commerce Directive of course, it also interpreted intermediary liability as a question that involves clashes between fundamental rights. So it raised the question of intermediary liability in a way to sort of higher constitutional claims. It reverted to the very basic principles contained within the Charter of Fundamental Rights. The relevant rights that were identified from the court's war were on the one hand of course the rights of copyright holders and the holders of intellectual property rights in general to the protection of their property. That's protected under Article 17 of the Charter of Fundamental Rights as a fundamental right. On the other hand however at the same time we have the rights of the intermediary itself and of the users. So on the side of the intermediary what has to be protected is the freedom of the intermediary to conduct its business. That's protected under Article 16 of the Charter and on the part of the users of the intermediary. Their rights to the protection of their privacy, their data protection, these are protected under Article 7 and 8 of the Charter and of course their right to freedom of expression and information protected under Article 10 of the Charter. As a result what the court said is that when we have such clashes of fundamental rights what's necessary is to find a fair balance between them so that the protection of one right does not disproportionately affect another right. Now the court so far has not given us great deal of information with regard to how to go about striking a fair balance. In itself however this development is very interesting, it's very important. What it does is it reveals this sort of deeper dimension to the question of intermediary liability. It reveals intermediary liability as not simply a regular sort of mundane question of secondary EU law but one that sort of affects our basic fundamental rights, human rights, the basic building blocks of our legal system and our society. And it's important in that regard to also consider that this is also the approach that has been taken more recently by the European Court of Human Rights based in Strasbourg which has released a number of cases again in intermediary liability again taking this fair balance approach. So that's the way the situation currently stands. What indications do we have with regard to how this framework will evolve? At the moment things continue to be a bit murky. What we do have is a communication that was released by the Commission in May last year on the digital single market. There the Commission indicated that it is going to work on preparing a fit for purpose regulatory environment for intermediaries that will also contain rigorous procedures for the takedown of illegal content and avoiding the takedown of legal content. That to me suggests the introduction perhaps of a more detailed notice and takedown or notice and action regime and indeed in a different communication from December 2015 on a copyright, a more European modern copyright framework. The Commission did speak about the introduction of a notice and action framework and perhaps a bit more worryingly at least from my perspective even the introduction of a notice and stay down framework. In addition to that and its communication and digital single market the Commission also indicated the possible and this is very intriguing the possible introduction of a duty of care for intermediaries that will force them into taking more due diligence taking more responsibility for the information that they transmit or store. So that's how things stand at the moment. It will be very intriguing to see how things develop in the future and yes, thank you very much for your attention.


All right. Hello, everyone. Thank you for attending my talk today. I know it's the end of the day, so I really appreciate you all being here. And I'll try to keep it very quick, but also hopefully informative for all of you to walk away with something that you can learn about data-driven operations. So in this talk, I will mainly be going over a couple of things, starting off with what exactly is DevOps, why it's important, and how does measurement fit into the entire DevOps lifecycle, what gave rise to this data-driven development that we're currently seeing everywhere in this new data-enriched era of the world that we're living in. And finally, how can you get started with some metrics while you're on different aspects of your DevOps journey? How can you think about incorporating metrics through whichever phase that you might be in? And a quick demo or an example of how we try to do this at Red Hat. So I hope you find it relatively interesting, and also walk away with some new things that you can learn about. So with that, let me go ahead and introduce myself. So I'm Hema Viradi, and I work at the Emerging Technology Group, which is part of the Office of the CTO at Red Hat. And I work out of Boston in Massachusetts, originally from Bangalore, India. And I came to do my master's here at Boston University. And post then, I've been working as a software engineer at Red Hat, and now part of the Emerging Technology Group. So to sort of dive in, let's start by understanding what exactly is DevOps. So something that I found interesting was that DevOps is part of the Agile manifesto. And in terms of principle, what I think is very relevant is that our highest priority is to satisfy the customer through early and continuous delivery of valuable software. And now, with the data-driven world that we're in, this is actually changing to the continuous delivery of your valuable insights from your data that you have available at hand. So now that we sort of understand what is this essential DevOps and what it can actually lead to, let's try to understand how this DevOps culture came into existence and what sort of critical in order to make a strong DevOps culture foundation in any part of the organization that you're probably working with. So firstly, there are three major components that sort of build and make the strong foundation of a DevOps culture, one of it being the people. So this is mainly focusing on gathering and improving increased collaboration and also having a shared responsibility. So the more closely you sort of understand and work together as a team, the more collectively you sort of get ideas and the more coherent your team becomes. And that sort of leads into making your culture of your DevOps as strong as you can get it to be. And now we can look into the second pillar or the second concept and this is the process, right? So in every aspect of your sort of DevOps lifecycle, there are a bunch of workflows, there are a bunch of processes that are taking place and you are also trying to figure out what is the most effective way to have these processes in build, what are some effective ways you can automate some of these workflows and that's sort of also I would say one of the major components that's involved in this entire lifecycle. And finally, what kind of binds all of this together is of course your technology and that is something that's continuously evolving, continuously changing and as part of the emerging technology group at Red Hat, this is also something that we are highly focusing on or highly passionate about in the sense that there are so many new ways to do different workflows and things are always changing along the way. So whether it's from your security aspects or whether it's from automation pipelines or your quality assurance pipelines or even from a data science perspective, there are so many new ways that you can do a certain process. So having these three sort of are interconnected is what would make your culture as strong as you could get it to begin with. And sometimes it's hard to sort of adopt to this DevOps culture. It may not be existent in a lot of companies and you might have some challenges in ways to adopt to this, especially if the people on your team are not aware of what the end goal is or what the final outcome is. And how do we sort of make this transition easier? How do we sort of help adopt some of these principles? And one way to do that is of course with the help of data and of course powered through metrics. So data is sort of the heart of everything right now and ideally the more the data, the better the knowledge, the more ideas that you can help generate and the more effectively you can work towards creating these insights out of them. And once you have this shared data, the idea is now to have a shared knowledge out of it. So you powered the data that you have but now like we looked at the three pillars, that's what makes the people an aspect of it here because you're trying to make that a shared commodity or a shared entity to power all the data that you have and have some ideas that generate out of it which further again influences your processes. That's the second part of the life cycle. And finally, this can help you take all the necessary actions or all the required decisions around your policies, around your systems, around your services and essentially helping the organization also make critical decisions whether it's around stopping a project because it's not going towards the right direction or whether some of the KPIs that you've defined earlier have now diverged and you're no longer looking at those things, right? So to be able to sort of do these right actions at the right time is also sort of critical and is what most of our developers and architects are so on looking into. And that's sort of what gave rise to this entire data-driven development. So a good data-driven development should not just collect a lot of data or have it made available, but you also sort of need to start aggregating all of the information that you have in various meaningful ways. So there's tons of data sources that you're looking at and oftentimes you tend to sort of keep them in silos but you also need to make sure that you aggregate them and that you're able to have some kind of correlations between them in order to make sure that you're tapping into all the right sources. And once we do that, this actually helps you to provide much more meaningful ways to convey and not just for your business leaders or your project stakeholders, but also for the ICs or the individual contributors because they're the ones who are driving some of these efforts. So it actually gives them an overview as to how is this being supporting the overall goal or the overall mission of the teams that they're doing? How is this one PR that I made actually being utilized into the wider organization or the wider picture of it all? And then of course, similar to how we had the entire test-driven development, it's very similar where you want to sort of answer questions really quickly. You wanna be able to go back in time, you wanna look at historical data, you wanna zoom in as much as possible, zoom out as much as possible, and then you also want to look at different type of visualization. So you no longer just wanna look at static reports, but you also wanna look at interactive dashboards, whether you're prototyping them in like Jupyter Notebooks, or you're prototyping them in some enterprise or open source related dashboards for a more higher level of the work that you're doing. So these are some ways which has actually led to this data-driven approach and why it's needed right now by most of the teams that we're looking at and how we're working it with it, right? And with all of this, now that we sort of know that we have all this data at hand, one sort of missing piece or one sort of secret ingredient that sort of ties everything together are your metrics or are your measurement, right? And one analogy which I think is very easy to relate to is just like how you're exploring and experimenting when you're cooking, it's very much similar to looking at your metrics because with the right metrics, you will be able to leverage all of the insights that you can and you'll be able to change course quickly. Like we said, you can make quicker decisions on the go and when you don't see a benefit in something that you've built, you can automatically validate that against the metrics that you've defined. However, one thing to keep in mind is that everybody is at a different pace or at a different phase in their DevOps journey. And just like cooking, it's an continuous learning process. It's a continuous iterative process. You'll probably not be good at it at the beginning, but the more you experiment, the more you explore, the more combinations you try out, you might end up getting really good at identifying what these metrics are. So don't be discouraged if you don't find the right metrics at the first pass because that's not gonna stay fixed forever, right?  So you have to keep that in mind that everybody's at a different stage of this measurement. So always continuously learning is I think the key to making any of these metrics work. And to actually start working on this metrics, oftentimes people ask, what is that right one metric that I should critically get integrated or what is that right metric that I should have for DevOps? Now, unfortunately, there's no easy answer to this because everybody's measuring things at various levels. Some are at a very high level, some are at a very detailed granular level, right? So there's no one size fits all solution for this. But one way to think about what are those right metrics is categorizing them. And again, similar to the cooking analogy or the food analogy, just like how you have all types of food out there for you to choose from your dairy products, your vegetable products and so on. Similarly, you should also think about your metrics and group them according to different categories. So you might ask, how do I start grouping them? And one way to do that is firstly, think about the outcome. So what is it exactly that you want to achieve by having these metrics? So an outcome, for example, here, I've said something like improving customer satisfaction. So let's say you have a service or you have a product that you have and one of the goals that you want to sort of look into is how do I gauge the customer satisfaction of this product, right? So that is your outcome, that is the goal that you want to achieve. Now, in order to come up with metrics to achieve those outcomes, you have inputs. So these are things which are actually under your control, essentially. So these are things which you can tweak, these are things which you can improve, which will help you get to the outcome of improving customer satisfaction. So some examples here would be, how do I improve the response time? So if it's an app, for example, is the requests that a user is making fast enough? Is there any lag? Is there any latency? Or if you're more interested in the UI perspective, so from a user experience, you want to understand, do I need to improve the UI for it? And are all of these metrics sort of helping me get to the outcome of improving the customer satisfaction? So keeping these two, I guess, the keys in mind, that is your outcome, and then the inputs that drive that outcome, will help you further narrow down what kind of metrics that I should start analyzing, or I should start thinking about incorporating into these services and applications. And one way to do this is the concept of valet metrics. So this was actually a concept introduced by Google's SRE team, and they were working with, I think they had a use case with Home Depot, where they were trying to figure out, how do I measure a particular application or a service? And what I found interesting was, they actually thought about these five major things that you should start looking into when you're coming about it from an operations perspective. And what exactly does this valet stand for? So we here stands for the volume. So how much business volume can my application or service handle? Now if you think about it like, an Android application that you download, or an iOS application that you download from the app store, the volume here would be the number of times the app was downloaded. Now when you think about it from an open source project, the volume here would be things like the number of stars that were there on the GitHub repository. So these are some indications of the way to gauge the traffic for your given application or service. And then we come to availability. So is the service up and running when I need it? Is it easily accessible when somebody logs in? And these would be metrics like uptime, for example. So how often when a process is submitted, how often does it get completed on time? That would be your availability metric. And then comes latency. So here this would be, does a service respond fast when I use it? So what is the average response time for the service to give back the request that the user wanted out of it? And then we have error. Of course, there will be cases where there are a lot of requests that might end up in failure, that might end up in errors. So how many number of times has this happened? And that will again help you gauge where you can improve, or what are the places and the gaps that you can do to avoid this on a frequent basis. And then finally, you have the tickets. So this is basically if the service requires any manual intervention. So nowadays you're trying to automate everything as much as possible, but are there still some existing places where you are still not able to get back the service or there's something else that you have to reach out and the person has to manually go and maybe re-spin a pod or they might have to increase the resources. There's also many other things that can happen which require some manual intervention to take place. So these are again some metrics that you can capture here. So how many times have we created an issue out of this in your repository or if you're using like ticket management services like ServiceNow for example, you have a lot of SRE teams who try a job of their issues as tickets. So how many of these tickets are related to errors and things that users couldn't figure out by themselves is another way to think about the way to measure some of your operations and improve your product or your service. So that's one example which I found very useful when it comes to thinking about what kind of metrics you can start off with. And now that you sort of know what these metrics are, just like how again taking like the concept of cooking or whatever it is that you want to make, you need to also identify the right ingredients or the right data sources. So depending upon your outcome like here, the ingredients look like it's making some kind of Italian dish. So you're trying to figure out what would be the right combination of data sources to help you get those metrics, right? So for example, most of our data is getting collected through Prometheus and just curious to know how many of you are aware of Prometheus or have at least used it. Majority, yeah. So Prometheus is now one of the most widely adopted tool for any operations team or DevOps team or SRE team. Everybody has Prometheus, the reason being that it provides all of the right time series based metrics which help you understand how your service sort of is integrating in terms of like requests or availability and so on. So this is an example of like a UI as to how Prometheus would look like. It has, as you can see the nature of the data is all time series. It has timestamps associated with the values of these metrics. And there's actually like a demo Prometheus available for anybody to just look at some random metrics and play around with how this interface actually looks like. And another aspect about Prometheus is that it supports multidimensional data collection and also it helps you to analyze and diagnose some problems. For example, if an application was down or a service was down for a couple of hours, you should see some gaps. You should see some sudden decrease in values. So these are some aspects which can help you guide in terms of the metrics behavior over time. And Prometheus actually has a PromQL support so it's similar to SQL. It has, it sort of stores all of the data in a time series database and that is why a language called PromQL where you can query aggregate all of your metrics and you can actually do that right here on the UI and play around with some of the queries like sum over time, count over time, things like that to give a better picture in terms of these different metrics that you are looking at. And once you finally know all these different sort of metrics and I identified all the different data sources. Like I said, like Prometheus is one example but there are other various types of data sources that you should also be looking at especially for an open source project. Another data source that you would be interested in is GitHub since that's where most of your project tracking and project health and your releases and version control and everything happens inside of GitHub. So that's another data source that you could also be exploring to understand but just here taking an example of Prometheus for a simpler use case. And once you have all of these metrics sort of identified the next step is to actually start exploring these metrics. So the PromQL is great but it's just for a very high level overview of how these metrics sort of behave and one way to get better insights or explore these metrics in more detail is something called as the Prometheus API client. So this is essentially a Python wrapper for this Prometheus HTTP API and what it does is it provides you some additional ways of wrangling your data and not just limited to the PromQL or the SQL based way to analyze this API client and you can, it's available as a PIPI package so you can easily do a PIP install and get the Prometheus API client. A couple of our colleagues on our team have actually worked on this project very closely and we're trying to add a lot of new features into this where you can easily aggregate these metrics and store them into data frames and data frames is like the chosen sort of format for most of the folks like data scientists or data analysts who are more interested to know the structure of these time series data and the reason why we actually came up with this API client was since Prometheus metrics are all time series we also saw a scope for doing AI or machine learning on top of it. So it has some capabilities where you can maybe detect failures over time or predict anomalies over time on these various time series or timestamp associated values and that's why it was easier to interact with Prometheus metrics through this Prometheus API client and like I said, it's used easily inside your Python scripts or you can use them on top of Jupyter notebooks and give you a closer overview of how you can correlate one or two metrics together and how can we visualize them. So the Prometheus UI does have a small visualization when you query it you can sort of see the time series graphs but let's say you are more interested in seeing like a count over time or you want to compare to three different metrics or you want to do like a pie chart or a histogram so on. It would be easier to do that within your Python libraries and that is the reason why we explore it more closely using the Prometheus API client. And now that you sort of explored it, you've analyzed it just like how you are sort of done with your final dish or your outcome of whatever you're making you need to also make it presentable, visually appealing so that other folks can also understand it and easily digest all these insights that you've made, right? And for that we look at different visualization tools and particularly our team looks at two different type of visualization tools. The first one that comes to mind is of course Grafana, right? So Grafana is again very much compatible with Prometheus it's essentially nothing but a monitoring and operational tool and the use case here or the users here would be your developers, your operation team, your SRE teams and it supports a lot of other different data sources apart from Prometheus as well. So your Postgres SQL, you have influx DB and so on. And SuperSet differs in the sense that it's more of a BI tool. So for example like the GitHub data that you're collecting to understand different project releases or issues and PRs, things like that, it would be easier to visualize them inside of SuperSet. And this is again an open source visualization tool by Apache SuperSet. And it also is compatible with Trino, Postgres SQL, Database, Presto, DB and the users here would be your data analysts or your data scientists and more of the higher business leaders like project owners or project stakeholders who might be more comfortable with looking at these higher level dashboards and interactive dashboards. So that's why we tend to typically switch between these two different types of visualization tools. And with that I will just quickly show a quick use case that we did for our team, a given application or a service. So this is essentially a Jupyter book. So this is nothing but a static HTML rendering of our GitHub repositories actually. So what we did here was instead of having all your documentation inside of GitHub repository, sometimes it's easier to also host it as a standalone website. So we have a service called Meteor over here. So what Meteor does is it essentially allows you to provide a given GitHub URL that you want to build this Jupyter book for. And Jupyter book also comes with not just rendering standalone HTML web pages, but it also helps you to spin up a Jupyter notebook. So Jupyter notebook is the common interactive environment that most data scientists develop all of their work on top of. It's again supported through Python and we use that environment as our main coding environment.  And once you sort of give this and submit this request, it'll take a couple of minutes to go through the builds and so on, but essentially what it does is it should finally show you this Meteor that's created. So it's gonna show you what's the repo. And as you can see here, you can either open as a Jupyter hub or you can open as a website.  If you've added that into your repository, it's also gonna build that notebook environment for you up over here. And what we did here was look at an example application or a service that we had within our team. This is called the Kebushet tool. So Kebushet is a source hop bot that essentially automates and updates dependencies in your Python projects. So most of your Python projects or GitHub repositories will have all of the packages that it needs inside a PIP file or inside a requirements.txt file and this project was actually done by one of our correlated teams called the Thought Team and they basically created this bot which would automatically open a PR against your Python repository to say that these packages that your project is using have now been updated to so-and-so version. So you need to update your project versions as well. And this is nothing but a service that essentially running against all your repositories and it's looking at the entire PIPI index to search for all these different Python packages and try to update to the latest version so that your project is also up to date with the latest dependencies. And they wanted to sort of visualize how this Kebushet is performing. So they wanna know how many repositories are we maintaining, how long is this entire trigger of workflow staking for Kebushet to actually update all of these dependencies, right? So this is there when we were working with them this is sort of the architecture diagram they shared with us. I don't wanna go too much into detail about this but essentially to understand the service a little more and to actually come up with metrics we wanted to know what are the various components involved in this service. How are they interrelated? How are they talking to each other so that you can better recommend where what metrics can be tracked? And once we got an idea of their architecture some of their workflows they have a bunch of different sequence of workflows that they have based on how they're doing this package release or the Python dependency release and so on and then what are some ways they're existingly monitoring this service, right? So all of the tooling that they have we sort of identified how many of these service components so it has service components like Kafka which is the streaming platform they're using they have Argo which is what they use to run all of their workflows doing all of their GitOps work essentially and then they're using Ceph as their S3 storage system and finally they also have a lot of monitoring components here so they're using Prometheus of course to track all of their metrics and they also have various other components involved like a metrics exporter, a SQL metrics exporter and so on so these were the different components that they've sort of integrated with their service and once we sort of understood their metrics as we recollect from the slides the one way to go about thinking of these metrics so you sort of understood their objective that the bot's aim was to update all their package dependencies on various Python projects now how do we think of metrics for this? So one way to do this is to start grouping it or is to start thinking of categories for your metrics so what we did here keeping the concept of the valet metrics as well we also started looking at the different personas who are involved to understand the working or the performance of Kebyshet, right? So there are a bunch of people who would be interested so some of the personas would be a product owner, a project manager, you have your technical architects, you have your operations team, you have your analysts or your data scientists and then you have the end users who are actually using the service so once we sort of identified these personas we also started prioritizing who out of these personas are most important for us and who of these are looking for what type of metrics and try to categorize them even under that valet concept of your metrics so for example, if you look at it from a product owner perspective since Kebyshet is maintaining and looking at a lot of Python projects on GitHub one way to metric that would be relevant for product owners is a number of active users but in this case it would be number of active repositories maintained by Kebyshet so how many Python dependencies is it trying to look at based on all the repositories that we have how many of them have actually enabled this bot on top of their repository and that again would fall under the V of the valet so that would be the volume that you're trying to capture over here and similar there are a lot of other related metrics like this like daily active users like DAU, WAU, things like these are very much accurate from a product owner perspective and then also thinking about it from an operations perspective so here we try to identify which is the persona and then also which is the component from which this metric can actually be fetched from so here for example we are looking at average time taken by Kebyshet to run the workflows average time taken to process requests so these are again falling under your latency aspect of your valet metrics and also looking at like errors for example that's another thing that you also want to capture and then availability here would be all those components that we saw in the architecture diagram you need to find out the availability of all these components so finding out the uptime for each of these components would also be the metrics that you can sort of start working with and start experimenting to analyze these different type of valet metrics so these are some ways to get started of course the more personas you're targeting the more specific you can get in terms of the application also that you're looking at and once we sort of jotted down all of these metrics we sort of identified how we can fetch them how we can aggregate and collect them and so on so these are some steps that we followed to export on these Prometheus metrics and to further work on it from calculating it or making the required calculations and so on and once we had all of this we also had to connect it to a Grafana dashboard so again looking at categorizing the dashboard so looking at only the overview row here would only capture all the uptimes so out of all the components how many of them are up how many of them are having issues and then from an operational metrics point you can look at these different workflows here so you can figure out how many succeeded how many had an error status and so on and you can track that over time and then things like the latency or the average time or the response time these are things that you would want to look at like bucketizing these response times so looking at it from a histogram perspective what you would do here is basically each of these buckets are nothing but the time taken like in seconds how many seconds or how many minutes that it took for a particular workflow to complete it has a lot of workflows in their service so among those how many were categorized into these different duration times and then of course looking at it from more of a product owner lens like we talked about the active users or the active repositories that it's maintaining so Kebyshet currently I think maintains approximately 200 repositories where it's observing and maintaining all these Python dependencies and so on and then also this number of stacks that it's maintaining as well and you can also go like I said when you're thinking about a data-driven development you want to make decisions quick you want to go as far back as you can you want to drill down you want to zoom in zoom out whatever and that's how you can do with the help of your dashboard so you can sort of go back in 15 minutes ago how were my metrics compared to like a week ago how did they behave and so on and that's one aspect of the approach you're taking to drill down on these different granularity levels of the metrics that you want to finally end up with and the other visualization tool that I was talking about was Superset some of my colleagues in the previous session walked through an example where they were tracking metrics for like GitHub projects another colleague of mine will also be presenting tomorrow more closely on a community health aspect of different metrics that you can look at so similarly we try to do this for one of our teams or one of our initiatives if it comes up but essentially what the dashboard was doing is of course it has some errors but oh I should log in okay thank you maybe but yeah while it loads and logs in Superset is another instance that we've deployed on top of our operate first cluster hosted on OpenShift and what you can do with Superset is again it's a BI based tool so you run your SQL queries and you create like interactive charts and dashboards to work on it and you should get let me give it one last try if not it's fine yeah so the UI this is how it looks you can see like a list of dashboards here you have an option to connect databases connect various data sets upload CSVs you can also explore SQL queries so you can open up a tab to actually run some queries look at past queries saved queries and so on and these are some dashboards that our team created essentially it's like intuitive for any other dashboard that you would work with it has a bunch of ways to filter things and you can look at metrics that you want to track right so for a GitHub project for example if you're interested in a particular repository or you're interested in a particular organization within GitHub you can drill down here are some repositories that our small team manages so we wanted to just look at some of these repositories and understand some key metrics which track the behavior or the activity on this repository so some examples would be number of open issues number of closed issues mean time to close issues ratio of open to closed issues so among all those that were open how many were actually closed out and then looking at it from more high level things like who are the main contributors in this repository and how many PRs were we creating over time the different type of activities that go on in the repository so you know you have events like how many people commented on an issue or how many people reviewed an issue how many people opened a pull request how many people reviewed a pull request so these are all GitHub events that are captured inside of any GitHub repository and we also wanted to see sort of the contributions made by each of these team members so like I said at the beginning of the slide you also want to provide insights not just to the senior leadership but also to the ICs you want to know what work you're doing and where am I contributing the most and this sort of gives them a picture in terms of so for every we have like each of our GitHub handles here not to pinpoint on anybody or anything but more to keep it more transparent in terms of who's working on what project who's making what contributions where so just to keep it more transparent and more accountable so that people also feel encouraged to work on more projects as they come up so that was just for like one repository but if we just unfiltered it you should see like a wider distribution in terms of the different organizations or the different repositories they're working on so for every user you should see like a bucketization on what are the different SIGs or this is how we categorized it so we have different special interest groups so based on these special interest groups which contributors working on what type of SIGs and so on so as you can see these two visualization tools are very different in terms of the use case more operational would be Grafana and more high level more project owner specific would be used in SuperSet and more easier to aggregate that kind of data inside of SuperSet so that concludes a little bit of the demo yeah I think I just wanted to conclude by also mentioning about Operate First oh now my slides don't wanna come up okay but yeah lastly just wanna briefly mention Operate First so Operate First is what we used to build most of these things all the dashboards the SuperSet instance the notebooks for exploring the storage the databases that were managing is all hosted on Operate First you might have heard about this in some of the earlier sessions in this conference but the concept was that we wanted to create an open source community cloud environment so that we can open up the operations and show how you can run your software or your services in a production grade environment so typically when users get any software or a service it goes through a bunch of CI CD checks so in the top most part of the diagram here we see that whenever they face any issues with the software that they have they usually try to reach out to the upstream development teams or they work with some vendors who are more closely associated with these upstream development communities and with the concept of this Operate First community cloud it allows the users to sort of play with these different versions of the software that you are providing and they can test it against this community cloud environment and it also allows them to mirror some of the way we are operating and look at the services that we are making and look at the dashboards we're making look at the metrics we're making and not just learn from the community cloud but also contribute back to the community cloud and they can still work on contributing to upstream of course they still have all the access to do that but this is an easier way for them to get faster access into a very much like a production grade environment that they can test and influence some of the work that we eventually want to integrate into all the applications and services so if you wanna learn more about how you can be part of this initiative which is within Red Hat we also have a website that you can go to it also has a lot of resources about like a Slack community that we have we have a YouTube playlist which has a lot of demos from the past of the people who worked on this project and a very active community who's trying to improve all the various services that we provide and enabling more users to replicate this data-driven operation and open up these practices and best ways to monitor any of your applications and services so with that these are some references in terms of some of the material that I covered one being operate first and then the Kebyshed application or service that was managing all the Python dependencies the repository where we were tracking all these metrics making that standalone Jupyter book rendering and so on these are some URLs to get started with all of this work and thank you for attending my talk feel free to connect if you have any questions but happy to answer anything now as well thank you, any questions? If not, thank you all for attending one of the last sessions for today and I hope you enjoy the reception if you're attending it, but thank you


What we have done is we've incorporated STs, STM32 microcontrollers, their sensors, as well as their connectivity. We are showcasing at this booth our STM32 Discovery Kit IOT node and only one Starter Kit that helps developers jumpstart their design and bring their idea to life. Here we can show that we can detect a cat. For instance, this is one of the objects that it will detect. It says a cat. It also can detect other objects like a plane. It says here 100% it thinks that's a plane. We worked with ST to build this demo and we're using an STM32. In this case, we have a UHF tag embedded in all these clothing and we have a UHF reader which is inventorying the entire storefront. In this case, you can see when the medium shirt is removed from the shelf, it highlights that the medium shirt is no longer on that shelf. We also have a light sensor and an infrared thermometer sensor to be able to detect all the different aspects of your pet that might be of interest to you. First, the IOT makes it a great design solution for pet trackers, asset trackers, kid trackers and those devices that are small battery powered with other electronics, sensors, you name it. We also have jointly developed the ST Discovery Board, a developer kit with ST, which you can see here. So if we take a look at the features, it's a 25mm diameter board with all the different sensors that ST offers. We've broken it up into connectivity, inertial and environmental and proximity as well as microphone sensing. So we have all these different sensors built on to the device itself. The main heart of the device is the Blue Energy Dash to Cortex-M0 microcontroller and BLE radio.


F finna bit supplement review. Hey what's up guys it's Josh here today I want to do a little first impressions review of F finna bit. Now I don't know if you guys have been following my channel but the original finna bit is one of my favorite go-tos for a new tropic slash anti-anxiety. It's something that I've used several times probably 25 to 30 times. I personally only get effects from it when I have anxiety so I can't really take it for recreation or just take it just to take it but at the same time every single time that I have taken it it has completely helped me not one time has it ever let me down. Again this is me personally maybe you're not going to have this experience maybe you're going to have a bad experience I don't know but finna bit just really really really works for me and it's literally my go-to for anti-anxiety. I couldn't really think of another supplement or new tropic that I would prefer over finna bits. The only other thing that can really pull me out of a really bad day is cratum and it's still not as anti-anxiety as something like finna bit but they both help. That's kind of like my background with finna bits and I didn't really need to try this version but out of curiosity it's like you know let's try it out finna bit works so good. So I don't know if you guys have also been following my channel but I've been doing these reviews on these medaffinal analogs one of them like chloromodaffinol the other one fluoromodaffinol and I've been doing a lot of research and there is a lot of pharmaceuticals that have these floros and these chloros. I think they said like 80% of American pharmaceuticals contains some sort of a chlorine or chlorinated something so it's very very commonly used however when I looked up the fluoro and the chloro I think they're like the number one and number two most toxic things most toxic things on the periodic table although they probably increase the effectiveness of the drug or maybe make it last a little bit longer. When you have something like finna bit that works with you don't need anything else at least in my opinion I don't really need to add a floral or a chloro to it especially if it's toxic. I know that they do it in American pharmaceuticals I don't know why that is but I just would prefer not to do it so I kind of figured that out after I got this so I was hoping that I would get only positives and not negatives with this. I was pretty much blown away by how close finna bit is to so many other things you know when I think of finna bit I think of oh it's just a drug in Russia which it is you know it's a prescription drug in Russia but I don't know if you guys you probably almost everybody's heard of GABA GABA is just something that makes you feel relaxed content makes you feel just overall good I think that's the number one thing that kind of like Xanax targets although it's like GABA 1 there's like GABA 1 and GABA 2 but Xanax targets GABA so GABA just makes you feel relaxed makes you feel like everything's good finna bit is just basically it's like the finna bit that's basically GABA with one phenol group to it so it's essentially like the molecule of GABA was just something on it that makes it more potent so I'm like wow I had no idea it was exactly the same and pretty much as GABA which is one thing added and then I found out that although I just went over that finna bit is a drug in Russia but there's something called tola boot or tola boot I'm not sure if I'm pronouncing that correctly but that is essentially finna bit with just one methyl group so it's like that's very close to being a pharmaceutical here since it's just one methyl group added which is just going to make it a little more potent and then if you have finna bit with an added chloro you actually get something called bactylaphen or bacylaphen something like that and bacylaphen is like I'm supposed to be like a muscle relaxer but it's essentially finna bit with just one chloro group this is finna bit with just one chloro group so it's very very I would think gotta be in the ballpark or at least very similar you know what I mean so I found that kind of interesting like finna bit is directly related to GABA if you add a methyl to finna bit it becomes tola boots and if you add a chloro to finna bit it becomes something else as well so I just kind of want to throw that out there I was kind of blown away that finna bit was so close to not only GABA but two other pharmaceuticals so let's jump into my experiences with the fluoro finna bit so the very first time I took it I kind of just I wasn't only planning on taking it I just basically did a trial I don't really remember too much about it the second time I took it I was actually feeling some pretty bad anxiety I was getting like one punch after another and I just needed a week off a week or two off from the blows you know what I mean I had another one come and I was just freaking out you know it's probably one of those times I should have taken finna bit so about halfway through the day I decided to try out the fluoro finna bits and I really didn't know the dosage the dosage for finna bit is about 250 to 500 milligrams the fluoro finna bit is going to be a lot stronger but I didn't know how much stronger so I did about two slightly rounded scoops and each scoop was about 30 milligrams so it should have been probably I would guess about 70 milligrams and right after I did that I looked what bacillafin is because bacillafin is essentially finna bit with a chloro this is a fluoro so I'm like it's got to be kind of close to this bacillafin right so I looked it up and bacillafin is in like 15 or 20 milligrams and I'm like oh my god I took like 70 I think the fluoro finna bit is a little bit weaker than the bacillafin but still I wouldn't want to be too much higher than what the bacillafin is because I have tried fluoro and chloro medafin on there very similar to me personally you know what I mean took the 60 milligrams I started to feel like definitely better things were kind of going in the back of my head normal finna bit takes about I think like four hours to peak so I took this near the end of the day I was watching a movie I got about 10 minutes left and I was like I am out I am so tired I'm just done which is really good a lot of times I won't you know decide to go to sleep early even though it will benefit me I was super anxious I felt great went to sleep had a great sleep I woke up and I had probably this really noticeable anti-anxiety feeling for maybe five or six hours after I woke up I woke up in such a good calm centered head space so I didn't really have any headaches or anything like that I didn't really have any noticeable side effects with the fluoro finna bit I would say that it seemed a little bit more sedating versus finna bits and I kind of will have to dance back and forth when I have anxiety I'll try finna bit I'll try the f finna bit see if I can kind of fine-tune what I like and dislike about each one but yeah I was very very blown away absolutely pretty much did everything finna bit did it almost felt a little bit stronger in some aspects and a little bit more of a sedative so then the third time that I took it was actually yesterday I was kind of feeling a little weird kind of want to just have a relaxing into my day I ended up putting 30 milligrams and like a big jug of water and I went to work I hadn't even finished the jug of water and I was just already super tired wanted to go to sleep so I will have to fine-tune the dosage and again I don't know the dosage so be very very careful here I would say try to start with about 30 just to be safe because 30 totally had me going to sleep had some anti-anxiety effects for sure very strong very noticeable I think if I'm having really bad anxiety my go-to will probably about be about 60 because I just had a great experience on it it kind of put me to sleep but not like it I could have stayed up if I needed to but I mean I was just out and that's kind of what I wanted to so I think 60 milligrams is the perfect kind of you know middle ground for me on this one but anyways guys that's been my initial first review first impressions of f finna bit I'm so far I'm actually really really liking it I like that it's a little bit more of a sedative and I'll be interested to see in the future fine-tuning of if I like finna bit more if I like f finna bit more or do I want them for specific uses I know some people say that they get really really awake on finna bit which is crazy apparently it happened to my friend and I know finna bit is supposed to boost a little bit of dopamine but it's more of an anti-anxiety effect so it's like you get a slight bit of dopamine boost but more anti-anxiety this one it's not that it felt bad but I just didn't really feel as much of a dopamine boost in the f finna bit so anyways guys let me know what you think of finna bit let me know what you think of f finna bit some interesting stuff here we're on the road to 50,000 subscribers and I couldn't do without any of you guys so I hope you guys are the best I'm having a great day out here hopefully having a great day at home see you all in the next video peace


Well, welcome to another video guys if you're new to the channel be sure to smash that subscribe button hit the notification bell and also Comment down below if you guys are new to the channel smash that freaking like button you guys better pepper this thing up I'm here with fishing with norm a dub fishing and flutter the fisherman if you guys want to go check out any other channels I'll leave them down below. I really suggest you guys do that because we're gonna be filming a lot Throughout the next year. So that's gonna be awesome. But yeah, we're gonna have different perspectives So it's gonna be a dub in norm on a different boat, you know, they're gonna have a whole different perspective So if you guys want to go check out that on their channel, you can if you want to go check out our version on fletcher's channel You can but we're gonna catch some big fish and we're gonna have to put a boat weapon on them We cannot take the L today boys. We cannot do that especially to norm and a dub like we Come on, man. If we take a L to norm and a dub, I'm gonna be mad.  What are you doing? I forgot my phone. I got to take pictures of the bass Such a fool norm. All right guys, let's go ahead and hop into this. This is fletcher by the way Come here, buddy. Let me get you in the frame. You guys need to go check out his channel Oh, yeah, he's been wanting to do this full time for a while. You're good, man And um, so yeah, go check out his channel. Go give him a subscribe Um, and yeah, we're gonna catch some big fish. Let's go ahead and hop in the boat We're about to give it to these boys. Yes, sir. Let's get it. It's a beautiful day outside, man. Whoa What's going on? You better calm down You're gonna get all the ladies from that one right there a dub. Oh, yo any girls in the comment section Norm in his natural habitat. I'm gonna make him really nervous right now.  You're stressing me. I know I'm trying to stress you out Go mess up norm Look, he's trying to do his intro but he always redos it like 15 times This is my fishing teammate for today adob.  I said go mess up norm This bag is norm doing a jump. What is what is norm doing? I don't know what he's doing But I don't know about my partner. I don't know about that. But look at my new scale So do you think if you lose today, it's because of norm? I'm not gonna blame it on him because it's a team effort because I could I could catch 40 pounds by myself. I'm not good I'm just kidding. But no, you just say that you're gonna catch the most fish on our boat No, I said I could catch 40 pounds. Noah's trying to tell bad about you. I'm defending you. Yeah, no That's no dude. He's always trying to paint me like Every single video I know you're gonna put me stumbling in because I'm trying to rip my feet off And I was like, what can I do to make norm look bad? All right, little friendly tournament, we're gonna get it. We're gonna get it. We're gonna have to try to put a boat with Best five, yeah best five we weigh them on the scale take a picture put in your phone Whatever we gotta do get my scale out somebody gets one first five minutes. All right. That's fair Let's figure it. All right, Pletcher. Let's get in here. Let's make it happen Yep, five biggest fish best five wins. All right Losers losers have to buy the other one's dinner.  Yeah, I had another one. He broke me off. It's all good. You need help or you got Oh, all right, two pounder. Yeah Oh, no It's all right. It's all right. We good. We we on them. We on them Yeah, one broke me off right there. I'm up for this. Oh, it snapped. Yeah, I broke that dig right there's Really down there that hard There's one Uh, that's small Yeah about same size. Yeah, this one i'm making sure I get in You want me to help you or you got them? Dude, that's a decent two and a quarter two pound. Yeah, let's go. It's all a little two pounder probably I don't even know if you you might be pushing it. I don't know They're gonna eat that underspin good. I didn't realize how clear this water is out here. I wish I would have brought some Yeah, they're stacked right here It's super faded. Yeah, it is fit. Yeah, it's one. Yep. 185. Yep. 185. All right. Cool. Cool. Cool Fletcher with the first fish today had two bites back to back on the underspin. There's clear water out here That's gonna work good. I'm hoping they'll eat this trap that I got on Um, I broke off my jig. I had one hit me. I hooked that he missed it And then I had another one grab it and I broke them off We lost like two fish before that so There's a lot of fish in this area. See if we can hook up on a few more try to get a limit real quick There it is finally Oh Let's go All right boys, we got our second fish today solid three pounder right there. We're gonna get him on the scale Two pound two eight six or two nine All right two eight six Uh, you put it we put it on there. I was like, oh, that doesn't look like it's on there very well All right what we did so We've been fishing some of those big boulders on the bottom and what what were you saying like pipes and yeah, it's just pipes trees Stuff like that. Yeah, that's what we started off with we came over here Started flipping a fluke under these trees and two bites right away I mean I broke that first one off They were back in a pocket and I'm thinking that they were doing the same thing with like a spinner bait a fluke And uh, I'm pretty sure they were catching them pretty good But uh, yeah right up in these shade lines skipping a fluke up under there. See if we can catch another one Oh There goes my fluke it freaking launched it. I watched him grab that gosh They're just not committing the keeper. It looks like he's probably we we didn't even say a limit 12 inch. Yes keeper He's good enough. He's good enough to get five in the box All right boys fish number three. He's not big but guess what we just got to get a limit in right now We missed way too many fish earlier You cannot do that fish in a little tournament. So we're gonna have to grind the rest of the day 167 It's so faded. Yeah, it's 167 though Hopefully you can see that on the film.  You guys are lying. We're guys You're not going to do that to you, but that is a beautiful fish fish number three. Let's go a flutcher Let's get it for more fish number four I watched him come out of that you look how you ate that one bro He choked that that's just a one pounder. But like I said, when you fish in a tournament You weigh them all in boys. You don't ever take no chances Now we're gonna get this one on the scale. What was that last one? 167 167 I gotta put these in my phone because I did not there's literally a Second after the last one.  He's got a little gut on him. Oh, it's 106 106 okay There we go 106. So we got 167 106. I need to put that in my phone. So now we got four fish Now let's hunt for that fifth one.  That wasn't a carp. Like I actually saw one just look like yeah Did you see carp? That was a bass man. I wouldn't one of them. I got a good look Yo, that's a decent one. That's what we need. There we go. That's what we need. You want to grab him for me? Yes, sir number five. Let's go. Let's get it. There we go boys. That's uh, what we're talking about three pound fish This is number five Now we're getting them together. So to give you some little insight I'm throwing the fluke around these trees. We got a pattern with this and they're biting it really good Um, Fletcher over there is throwing a spinnerbait looking for a big bite So if I can just catch some three and four pounders and he can pull up a five six seven That'd be good two nine nine Two nine nine here almost three Two nine nine or three. Wait, does that say that's three or four? I can't even see it from this angle Yep, three or four That's our biggest one right there Well three pounds four ounces biggest fish today. That is a beautiful beautiful thick and chunky bass Let's get him back in the water. Now we got to start calling out those one pounders. We have we have two threes or one That was almost three All right, let's get it boy You got big He's gonna call one. Oh That's a two and three quarters two and a half. Is this on the chatterbait? Oh, wow. That's a good one, man There we go. Number two for me. That's number six for the boat. This should definitely Call one. So we're gonna go ahead and get this on the scale and our smallest is 1.06. So you just have to call You're definitely calling that out It's sneakily A lot less than that. Which I doubt it I think this is uh, definitely gonna bump us up a little bit for sure I think he's probably two and a quarter for if we're lucky Two two two two. I don't know if you can see that but it says two 20. Yep 2.20 On the dot Saw a little fish like get her back on in the water Bye pretty lady Good job, bro. You got that coal. He ain't much but he's a pound. He's a pound coal every every ounce counts, brother Hey, we got we got about 11 pounds Maybe the bigger bite He ain't big. I don't think he's on coal We can double check Because we just got that one one six 106 and but already you just got that one out Dude, that's the second time that I'll let it sit just not move it He's ate it. That's something we need to pay attention to. Yeah Right there Let me know I don't think he's gonna I think everyone's 167 165. All right. I'll put in my phone or check it Just see that is a beautiful little fat bass Guys, that's the last two. I just haven't even moved it. Just threw my fluke up there and dead stuck it and they already had it Um, yeah when I hooked him man, I thought I had your line whistles. You know how that goes All right, this is how we're gonna be adamant norm If we just keep covering water how we are They don't even have a chance They're they they fish very like adam fish is so slow man like very slow which can be a good thing and a bad thing That's a decent one Yeah, that's a cold hundred percent. Oh, it's gonna be close. That's gonna call that smallest one 100% hopefully that's the two Really doubled up. Yo Biggin. Oh, do we about a cold? Well, we have a 185 and 167 so these two could potentially do it You got it, bro Awesome Let's get it, dude. How can we get any better than that? Get a double up. Let's go. Let's get it But what I was saying is like like adam fish is really slow and norm I don't know about normie. He might just a little slower, but I'm like really fast. So if we both just Cover it like this. Yeah, I mean you see how many fish are in these trees. Oh, that's a two 205. Okay. All right 205 that's gonna cold out that one Either one six seven or one eight five This is One seven six.  Let's go. Nice. They weren't much, but that's important Or I mean not nothing enough one seven six. All right Yeah, dude, that that cold out are too smallest right there. Not by much, but Heck, yeah, double up Yeah, I need to get you out on the river, man Maybe each year the Savannah river Yeah, well, I'll take you out man.  Oh Oh, that was a good one. That was actually a good one Like four. Yeah. Yeah, that that was over three It's uh, oh good one. Uh, no, I don't know if he's gonna help My help is worth a he's worth a way 182 182 is that a cold? Wait, what was your last one? That is a cold Yeah, because your last one was smaller than that, right? Yeah, 176 182 another cold Look at these boys.  Oh man Bill dance Oh, I can't tell He might call by like a freaking quarter ounce, but oh actually that's a two pounder, bro Do you have some meat on him? Do what? That's a two and a half, bro. There you go. Definitely call. They look a lot smaller than the water Dude, why did he look so small? That was like the smallest looking fish Okay, where's that skill at?  Oh, that's a two pounder. He might Yeah, try it. It's worth the shot Like I said, man, we might have the dang identical We would regret every fish we threw back if we didn't weigh them in 182 Oh It's close. We might have our smallest 185, but I can check Not as it has it tied it Didn't call but nevertheless a little bit of fun doesn't hurt Oh That's that's that's a better one. That's got it. Oh, oh, yeah. Oh, maybe I don't know. God. He hit hard, dude Oh Dang, I'm just I swear this one not that big of a fish, but man, he hit it so good What he hit it at first. I thought it was I thought it was like a three or four 198 I don't know. That's a call Get a little fish Appreciate it boss later There he is That's probably a good one. That's a big one. He's gonna call. He's gonna call. That's a three Oh, man, you want to grab? It's a 30 pounder. Okay. All right, so that's uh, that's gonna help One step closer All right boys right there. I don't know if he's quite three. He's gonna be close But that is one step closer what we need. We got the competition right there. See him over there See him sitting down. See him being lazy. Me and flesh are like, man I don't know what these fools are doing I don't know. I don't know if they got a big bag. I hope they don't but that is a good little fish That's gonna be a great call A great call We need just a couple more of these to have a really decent bag and then we need one kicker We really need a four five six pounder 268. Yeah, 268. I'm gonna leave it at that All right, 268 not quite a three pounder, but beautiful bass Good little upgrade. There she goes Oh He's surfing in. Oh, yeah, he's calling That's a two Hey I thought yours was like a four for a second there. Yeah 224. All right, that's a small cool. Yeah a little bit Every every big count She's pretty on to the next. Oh god, that was a terrible release Sorry pal That's gonna that's gonna bend over quite a bit. There's one. Oh, oh, just awesome. Oh, please back on. Yeah, that's a better fish Big that's a better fish. Um, I don't know how big but it's definitely better Oh, yeah He's pulling pretty good. That's that's definitely one of the better ones For sure. Oh my god. Yeah, he's got some meat to him. Yeah, I'll let you get him You got him. That's a good one Yes, sir Whoo, there we go.  Okay And that is more like it. Look at that an absolute football bro That's a good fish. That's a good fish Let's get this that might be one of one of the better ones for sure. This is three all day That might be over. I think I think we're thinking three and a quarter Three five seven beautiful fish man three and a half pounder Off the depths I'm going with this S waiver.