Podcasts about programmatically

In this episode, Jake and Surma chat about web components. Why they were invented, what they're useful for, and how they would improve. Resources: Surma showers his eyeball. The old custom elements 'v0' spec. The old shadow DOM 'v0' spec. The old HTML imports spec. The initial version of Polymer. Lit (formally lit-html). HTML attributes vs DOM properties. Issue looking at ways custom elements could have  behavior. The ElementInternals API, for making custom elements interact with forms. The is attribute. WebKit's position on the is attribute. Programmatically assigning children to slots. Issue looking at ways to slot children that aren't direct children of the shadow host. Declarative shadow DOM. Custom element support in React 19. pinch-zoom custom element.

On episode #23, Jimmy speaks with Jan Erik-Asplund from Sacra. They discuss:- How to create content when it's the product- Using repeatable content formats to increase velocity- Programmatically repurposing long-form content into useful snippets- How to use publicly available to create novel content- How to find and use "golden nuggets"You can signup for the free Sacra newsletter here → https://sacra.com/Also, Sacra is hiring an Analyst → jobs.sacra.com

Welcome to the Tearsheet Podcast. I'm your host, Tearsheet's editor in chief, Zack Miller. Today's guest is Michael Shaulov, co-founder and CEO of Fireblocks. Michael strikes me as one of the smartest guys in finance – Fireblocks, for its part, is a platform to create blockchain-based products and manage day-to-day digital asset operations. Banks like BNY Mellon and BNP Paribas are using the firm's wallet technology for digital asset custody.  For an industry in flux, Michael is positioning Fireblocks as an important layer in the digital asset technology stack. Our discussion happened before the failure of SVB and the closure of Signature Bank. Michael's pragmatic approach is refreshing and speaking to him, you get the feeling that he's building something for the long run. Michael Shaulov is my guest today on the Teasheet Podcast. You can access the audio and a full transcript here: https://tearsheet.co/podcasts/you-can-programmatically-use-blockchain-to-reduce-counterparty-risk-fireblocks-michael-shaulov/

This new trend has become popular in the last few years. Companies are using AI to automate video production. They have created their own AI writers that can create videos by analyzing text and then produce a video based on the content of the text. This is not only used for marketing purposes but also…

Advertising is taking a hit from the economic slowdown. For some advertisers with podcast and audio budgets, they want to reach more listeners efficiently rather than invest in expensive custom branded content. For Wonder Media Network, however, programmatic advertising isn't part of its inventory. There are certain instances where programmatic advertising in audio makes sense, according to Shira Atkins, CRO and co-founder of podcast company Wonder Media Network, such as targeting people who are in one specific region versus running national ads. But on the latest episode of the Digiday Podcast, Atkins said she still believes that programmatic is “a tragedy for the podcasting ecosystem at large." Her team does not sell any of its ad space programmatically. Instead, the podcast network uses its branded content studio to make bespoke audio ads, which Atkins said creates memorable ads that listeners are less likely to skip over.

Sometimes SEO and other marketing integration can be overlooked when you're doing migrations or other developmental projects, especially on the ecommerce side. Holistically, you can't do one without the other. So the search engine and conversion pieces are equally as important as the technology pieces. Ethan Giffin, the founder and CEO of Baltimore-based Groove Commerce, joined me on the SEJ Show to discuss overall SEO for ecommerce & DTCs, Google Merchant Center / Shopping, the importance of CRO & Upsells for SEO (and other channels), and ways to prepare for the 2022 Holiday Shopping season.   Shopify Plus can be a good fit if you have a simple business. However, you need more customization if you have a more complex interaction with more business rules and logic. Big-commerce people generally float over to the big commerce side of things. –Ethan Giffin, 12:38 Programmatically create the best framework. The best framework is thinking about how the template is laid out. What is the hierarchy of the template, what are the components, and how do the sites link together from an internal linking standpoint? So I'm just a believer that many things we did back in the day still work. –Ethan Giffin, 16:11 Suppose you're new to a company and you hear a migration. In that case, hopefully, there are archives within the company that people used before you. There are lists of the redirects that were integrated and implemented into the site. Just cleaning that history up can be incredibly impactful. But again, it gets into human behavior and the ability to make things more efficient. –Loren Baker, 20:37 [00:00] - About Ethan. [12:14] - Most commonly overlooked issues during migration. [24:44] - An essential factor to consider in building an ecommerce site. [31:48] - What to do when a product is no longer sold. [35:18] - FAQ schema recommendation. [37:23] - What is rendering on the ecommerce side? [41:29] - Recommendation on optimizing from an ad script perspective. [43:01] - Optimizing schema integrations on the catalog side. [48:55] - Ecommerce companies Ethan has worked with.   Resources mentioned: Groove Commerce: https://www.groovecommerce.com/   Every app that you install into your store has overhead. Every kind of front office sales-related app, reviews, upsells, cross-sell, site search, out-of-stock notifications – everything has a component to it that loads into the browser, and that creates a heavier page. –Ethan Giffin, 25:15 People don't think about margin enough. So how do you promote those products and get people to buy things with the best margin versus the most popular ones? So really, it's thinking about how to optimize the catalog. –Ethan Giffin, 47:32 If you have a business and you're moving to another building, you don't just bring the storefront with you. You got to bring everything you got to bring the offices, the desks, everything. So if you're migrating a site, you don't just redirect the front page and the top like collections or categories. You must take care of everything –the blog, subdomains, subfolder structures, etc. –Loren Baker, 22:56   For more content like this, subscribe to our YouTube channel: https://www.youtube.com/user/searchenginejournal Are you looking to keep up with current and effective digital marketing today? Check out https://www.searchenginejournal.com for everything you need to know within the digital marketing space and improve your skills as an internet marketer.   Connect with Ethan Giffin: Ethan Giffin, GrooveCommerce's CEO, has been a leading ecommerce and web analytics expert for most of his career. Conversion rates, search engine visibility, and web analytics are all things he knows inside and out. However, he is passionate about helping businesses succeed online without having to become experts. There are only a few people like Ethan. His love for SEO, conversion rates, and ecommerce go hand in hand with his passion for traveling and DJing in between - not to mention those pocket squares! Connect with Ethan on LinkedIn: https://www.linkedin.com/in/ethangiffin/ Follow him on Twitter: https://twitter.com/opie   Connect with Loren Baker, Founder of Search Engine Journal: Follow him on Twitter: https://www.twitter.com/lorenbaker Connect with him on LinkedIn: https://www.linkedin.com/in/lorenbaker

Alex Ratner - Programmatically Building & Managing Training Data with Snorkel One of the key bottlenecks in building machine learning systems is creating and managing the massive training datasets that today's models require. In this talk, I will describe our work on Snorkel (snorkel.org), an open-source framework for building and managing training datasets, and describe three key operators for letting users build and manipulate training datasets: labeling functions, for labeling unlabeled data; transformation functions, for expressing data augmentation strategies; and slicing functions, for partitioning and structuring training datasets. These operators allow domain expert users to specify machine learning (ML) models entirely via noisy operators over training data, expressed as simple Python functions---or even via higher level NL or point-and-click interfaces---leading to applications that can be built in hours or days, rather than months or years, and that can be iteratively developed, modified, versioned, and audited. I will describe recent work on modeling the noise and imprecision inherent in these operators, and using these approaches to train ML models that solve real-world problems, including recent state-of-the-art results on benchmark tasks and real-world industry, government, and medical deployments.

Kellie Straub is the Vice President of Growth & Innovation, National Wellness Institute. The National Wellness Institute (NWI) is the leader in providing professional development and engagement opportunities that support individuals from a variety of disciplines in promoting whole–person wellness.At the core of NWI's offerings is its Six Dimensions of Wellness model, developed by NWI co-founder Dr. Bill Hettler in 1976.NWI enriches the lives and careers of wellness professionals by:Serving as the global professional network for connecting to all disciplines of wellnessProviding education and training that promotes life-long learningIdentifying and representing inclusive whole-person professional standards and competenciesThe Vision of the National Wellness Institute (NWI) is to be the worldwide voice of the wellness community.The National Wellness Institute, Inc. (NWI) was officially formed in 1977, but held two wellness symposiums (1975 and 1976) prior to the official organizational launch. Originally, the organization was launched under the University of Wisconsin-Stevens Point (UWSP) Foundation, as the Institute for Lifestyle Improvement. Three UWSP faculty at that time—Dennis Elsenrath, EdD, CWP, director of counseling services; Fred Leafgren, PhD, director of student life; and Bill Hettler, MD, CWP, director of health services—joined together with the idea that we, as humans, could live better, healthier lives through the principles of balance and awareness. In 1976, Dr. Hettler released what was to become the base philosophy for the National Wellness Institute: the Six Dimensions of Wellness model. The six dimensions are intellectual, emotional, social, spiritual, occupational, and physical. By balancing these six dimensions and actively seeking to improve them, the organization's founders believed individuals could improve their overall well-being.In 1985, the name of the organization was changed to the National Wellness Institute to reflect a more global mission. Then, in 1988, NWI separated from the UWSP Foundation and applied for its own nonprofit status, which was granted August 3, 1989. The National Wellness Institute is a registered 501(c)(3) organization headquartered in Stevens Point, Wisconsin.Each summer NWI welcomes well-minded individuals to its annual National Wellness Conference. Patch Adams, Geoffrey Canada, Mike Huckabee, Naomi Judd, Robert F. Kennedy Jr., David Katz and many other notable individuals have given voice to the wellness movement at the National Wellness Conference. The NWI Foundation, launched in 2003, has actively brought individuals to the National Wellness Conference on scholarships.Programmatically, NWI began accrediting undergraduate health promotion and wellness programs in 2008. It began certifying professional wellness practitioners in early 2009. Today the organization has an active membership and educational outreach program and continues to expand its offerings to meet the growing needs of wellness professionals globally.For more information, please visit www.nationalwellness.orgSupport the show (https://paypal.me/Optamiz?locale.x=en_US)

Source: https://www.thecloudcast.net/2021/06/automated-data-labeling-for-ai-apps.htmlSee also: https://softwareengineeringdaily.com/2020/04/09/snorkel-training-dataset-management-with-braden-hancock/Software 2.0 is Andrej Karpathy's idea that instead of coding business logic by hand, the applications of the future will be trained by data. In other words, machine learning. But ML is limited by the quality of data available, and there is a lot of unstructured, unlabeled data out there that is still being manually labeled today. Scale.AI is a well known startup that has done very well offering a scalable manual labeling workforce, however they are still bottlenecked by the number of subject matter experts available for labeling critically important data, like cancer diagnosis and drug trafficking rings. In order to get labels from subject matter experts, you typically have to put them through a very tedious process of labeling to build up a useful structured dataset upfront before any useful machine learning can be done.I did some very minor ML work about 5 years ago and found Christopher Re's work on DeepDive at Stanford. It takes a revolutionary approach by making it easy to write the labeling functions themselves. This turns the labeling process into an iterative, REPL like experience where subject matter experts can suggest a function, see its impact right away, and continue refining it, assisted by AI. DeepDive is now commercialized in a startup called Snorkel.AI, so I was very excited to find a clear explanation of Snorkelflow from its CEO, Alex Ratner. Here it is!Transcript[00:01:15] Alex Ratner: [00:01:15] SnorkelFlow is a platform that's meant to take this process of building machine learning models and AI applications. And I get all starting with buildings, the data that they rely on that fuels them and make it, in a nutshell, look more like an iterative software development process. Then you know, this kind of 80, 90% upfront just, hand labeling exercise.[00:01:34]And so snorkel flow supports that entire iterative loop of, actually laboring data. Can be by hand in the platform, but also most centrally programmatically by letting users, what we call labeling. Basic idea, is that rather than say asking your, legal associate at a bank to, or your doctor friends to sit down and, label a hundred thousand contracts or a hundred thousand electronic health records have them, right.[00:02:00]Sharistics are bits of their expertise look for this keyword or look for this pattern or look for this, et cetera. I'm like a bridge from old, expert knowledge type input. Modern machine learning models using one to power. The other. So a snorkel flow is an IDE basically, and has a no-code UI component as well, but let's not people either via code or by pushing buttons for even, non-developer subject matter experts say to.[00:02:24]Programmatically labeled their data by writing these labeling functions and then uses a bunch of modeling techniques. A lot of which was actually, the work that, that the co-founding team. And I did in, in, in our kind of thesis work around how you take a bunch of programmatic data and clean it up and turn it into a final.[00:02:41]Instead of clean training data for machine learning models, and then actually in snorkel flow, you can, autumn, basically push button train best-in-class open source models. You can then analyze where they're succeeding or failing and, and use that to go back and iterate on your data.[00:02:54]And there's a Python SDK throughout the whole thing. So many of our customers will mix and match. Will you start.  Create the training data set and then train the model on some other system, et cetera. But what's normal flames of support. Is it basic iterative development process where, you know, rather than just spending months to label a training at once and then being stuck with it and having to throw it out and start all over again, anything in the world changes your upstream input, data changes your downstream objectives.[00:03:18] Change, making it again more like an iterative process where you push some buttons or write some code. That label the data. You compile a model or train it, but you can think of it like compiling and then you go back and debug by, by iterating on your data, everything centers and snorkel flow around looking at your data and iterating on how it's labeled to improve models.[00:03:38]Brian Gracely: [00:03:38] I'm curious. So you mentioned you mentioned in there's a there's a Python SDK, which for anybody who, works in data science, data modeling, right? Python is your language to Frank sort of the language you use or are you a couple of them, that's the language that, you how you do your program, but I'm curious, like in today's world, Do data scientists consider themselves programmers or is there still Hey, look, I work on the numbers, I'm good at building models and the numbers, but I don't think of myself as a programmer.[00:04:08] Like how do you bridge those two worlds together or do you not really have to bridge them together? How much does the data scientists have to go? I have to focus on numbers and models versus I have to focus on programming, something to do stuff. What's their world look like?[00:04:21]Alex Ratner: [00:04:21] It's a great question. I think I, I haven't been are currently I'm part of four or five different data science institutes or something. And I don't even still know. I mean, the data science is such a broad umbrella term. There's so many different varietals of us and, and types.[00:04:35] And so I do think there's a very broad spectrum of, the data scientists. An ML engineer and just, loves writing codes are the one that, to your point really just wants to push some buttons and get back to the numbers and the modeling and the outcome. And, we definitely, try to support the range through a layered approach.[00:04:50]And, we, we have , but on top of that, we have a a no-code UI that allows you to write these wavelength functions without writing code. So for example, if you're trying to train a CA a contract classifier and snorkeled flow, you can, write Lateline functions based on clicking on keywords or pressing buttons with kind of templates for types of patterns or signals you want to look for.[00:05:11] So, No we try to support basically, if you want to move fast and you're a non developer, or you're just not looking to spend time there, you can just do it in push-button way. But then if you want to go and customize or inject custom logic or really get creative, you can always fall back to the Python SDK.[00:05:27] And so, I mean, I think a lot of the what we're trying to accomplish in the very beginning, right? Raised me abstraction know level at which you're interfacing with and programming your machine learning model or your AI application. And the first step is the hardest, right?[00:05:39] If you think of the way that hand labeled training data is, it's like the machine code, or really actually, just so you know, I think of it as like the ones and zeros, literally for binary classification cases. Yeah, a lot of the effort behind the circle project and the company is just, or was just getting from that layer to the layer of, assembly language day.[00:05:57] But once you get there, you can build all those layers on top and you can go up the stack and down the stack, according to the application of the user type, right. Actually, my co-founder Braden who was, who also did his PhD around, snorkel related stuff, had a paper actually on how you could use natural language inputs.[00:06:12] You could explain in, in natural light. Just speaking to the computer, why a certain data point should be labeled a certain way and then use off the shelf semantic parsers to parse that down to code, which then would get dumped into snorkel. So basically once you make this leap from labeling data, by ham kind of zeros and ones to labeling your training data with code, then the sky's the limit in terms of building layers of abstraction on top of it.[00:06:35] And that's actually a lot of what the company does and has been doing over the last two years is. Building a flexible interface through our platform, snorkel flow for different data types and use case types and user types. [00:06:45]Brian Gracely: [00:06:45] Yep. Well, and, and I think you, you really answered my question in there.[00:06:49] The reason I brought it up was on one hand you have this you have this language level SDK in terms of Python, you can get into, Some pretty granular level stuff. And then you have, on the other end, you've got application studio, which you said, like you said is this sort of low code graphical way of, building templates and building applications.[00:07:08] And I was like, There must be like, I think sometimes there's just perspective of there's one profile of a data scientist. And I think what you really highlighted is it, it's like a lot of things there's a spectrum of, those that specialize in one part of the job, others that don't care about it and want it, certain things to be easy.[00:07:25] And so that, that was useful because I think sometimes like in my head, I'm thinking, okay, Data scientists is served a certain sort of task the same way you might say okay, they're a Java developer. So they, there's a tool set that they always use. So that was super helpful.[00:07:39]Alex Ratner: [00:07:39] Yeah. And it depends on what the problem is too. I mean, the other thing also that I think goes under, emphasized in the air space big. Points number one. And I don't think it's that avant gardening where to say it was maybe more back in 2015 is, Hey, AI is about the data, not the models or the algorithms, which I think, fewer people will find a controversial statement today.[00:07:57]Even if it's phrases in a somewhat reductive way. But the other thing that I still think is under emphasized in practices and necessity of lupus. What we often refer to as subject matter experts into the process. And so I think w and I won't ramble here too long, but just for some perspective, and this is actually the very first  funding that, that the snorkel project ever had was specifically about looping what they call SMEEs and the government subject matter experts.[00:08:20]Our original partners were some genomicists at Stanford. How do you loop them into the. Of AI in a better way than just saying, Hey go label data for eight months for me, please. And this idea of how do you get subject matter expertise from a human's head into a scalable machine format has been the focus of AI for, decades, but the answer of modern machine learning today for the last, five, 10 years.[00:08:44] Okay, just sit them down, have them labeled data points one by one, nothing else. They've got all of this rich domain knowledge, a doctor, a lawyer, a cyber analyst, network, technician, and underwriter. Throw that all away, just have them literally just, give zeros and ones labeling data. And that's a nice abstraction.[00:09:01]And it has been actually a very productive one for the field, because that means the ML engineers can totally abstract the way the messy realities of real-world data and real world subject matter experts. And just focus on optimizing, a fancier model architecture. But I think we've reached a point where it starts to become silly and impractical to have this wall.[00:09:19] The subject matter expert and the data scientists. So I'll let us loop back and say, but a big focus of circle flow is about making these interfaces in this process, accessible to a non-developer who's, a legal associate or an underwriter or a network technician and have the process too. And that's another motivation behind the kind of, layers, including no-code UI.

SPACs (Special Purpose Acquisition Companies) or “blank check” companies have been in the news recently, so I used some real SPAC data for this episode. Your spreadsheet has empty cells in column A, and these empty cells should be filled with values. Your task is to fill values down up until you find another cell […] The post Dear Analyst #42: Filling values down into empty cells programmatically with Google Apps Script & VBA tutorial appeared first on .

Jim Spanfeller is the CEO of G/O Media, CEO and President of Spanfeller Media Group, and Former CEO of Forbes.com. He helped shape the advertising and publishing business and continues to be a driving force in using a sound approach to marketing. He joins Kevin in New Orleans for a talk about how to get people to engage in the publishing space, what is still broken in marketing, the future of cookies, and how to find the right people for the right message.   Takeaways: ● Prior to joining Forbes, Jim also served as chairman of the Interactive Advertising Bureau (IAB), publisher at Inc. magazine and also held senior positions at Newsweek. ● Third-party data gave us the opportunity to buy and scale, but nefarious things such as buying bots, no ads being seen, or multiple ads on a page came to fruition. This led to verification sources, but there is still much to be changed about the current practice. ● We must acknowledge that the model is broken, and it calls for an awareness of new privacy regulations. ● Programmatically buying contextual advertising will drive the overall push for impressions into a more controlled environment, which will take away the notion that there was an unlimited supply. ● Jim explains why his view of advertising is still optimistic, despite the current broken state of the industry.   Quotes: ● “When we really get right down to it, it’s a large number, but it’s a defined number of ad impressions that anyone would want to buy.” ● “I don’t know what these people do for a living where they have time to go deep shopping for Nikes.” ● “People don’t go to the web to look at ads. They go to be entertained, informed, and delighted.”   Mentioned in This Episode: Jim Spanfeller: LinkedIn | Twitter Forbes.com The Daily Meal GDPR Ghostery Bad Boys For Life Irwin Gotlieb Amazon

Neil Pasricha on Coaching For Leaders, Corey Quinn on On Call Nightmares, Craig Daniel on Build by Drift, and Bryan Liles on Hanselminutes. I’d love for you to email me with any comments about the show or any suggestions for podcasts I might want to feature. Email podcast@thekguy.com. And, if you haven’t done it already, don’t forget to hit the subscribe button, and if you like the show, please tell a friend or co-worker who might be interested. This episode covers the four podcast episodes I found most interesting and wanted to share links to during the two week period starting January 6, 2020. These podcast episodes may have been released much earlier, but this was the fortnight when I started sharing links to them to my social network followers. NEIL PASRICHA ON COACHING FOR LEADERS The Coaching For Leaders podcast featured Neil Pasricha with host Dave Stachowiak. Neil described his first professional role, working at Proctor & Gamble. He had graduated from Queen’s University in 2002, one of the top business schools in Canada and, at the time, a job at Proctor & Gamble was one of the top marketing jobs you could get. Neil felt like Charlie Bucket winning the golden ticket.  But he was horrible at the job. He had been expecting to spend his days creating PowerPoint presentations and instead was asked to create spreadsheets to analyze trucking, gasoline, and a million other variables to determine how much to increase the price of mascara. As a high achieving adolescent, he took his failure to be his own fault rather than a factor beyond his control. He worked late, came in on weekends, and started grinding his teeth. A few months in, the company wanted to put him on a performance improvement plan. He couldn’t handle the notion of being fired, so he quit nine months in. He catastrophized this event. He thought, “If I can’t work here, at the best company, with the most supportive culture, kind people, and a lot of structure, I can’t work anywhere.” He thought, “If I can’t do marketing, my highest mark in business school, I certainly can’t do finance,” and, “If I look for another job, they’re just going to call P&G who will say ‘This guy is horrible.’” He pictured the worst-case scenario: he thought he would go bankrupt and thought his life was over as a working person. He calls this, “pointing the spotlight at yourself”. High achievers have a tendency to think, “It’s all about me and I’m terrible.” He was a low-resilience person. He wrote his new book, You Are Awesome, about resilience because he identified himself as lacking it. Like most of us these days, he grew up without famines, wars, and other sources of societal stress. He got the gold stars and participation ribbons and didn’t have the tools to handle failure. He didn’t see for years that the P&G blow actually was his first lesson in resilience. He says we look at successful people and think their lives were a string of successes, but the most successful people are those that have also seen the most failure. He cited Cy Young, who has won the most games in baseball ever. He also has the most losses. Nolan Ryan, who has the most strikeouts, also has the most walks. Dave talked about his first full-time role as director of a center that helped students learn math and reading skills. He was average at the job and the culture wanted people to show a lot of initiative. He struggled, got passed over for promotions, and the feedback he was given was that he wasn’t moving fast enough, wasn’t taking initiative, and wasn’t meeting deadlines. Like Neil, Dave dropped out and started his coaching business. Neil says that Dave’s and his own feelings of incompetence are a result of the spotlight effect. The spotlight effect is the feeling that we’re being noticed, observed, and judged more than we really are. Nobody at P&G probably even remembers Neil, but the spotlight effect had caused Neil to feel that everybody had watched him fail. To help reduce this effect, Neil asks himself three questions: 1. “Will this matter on my deathbed?” 2. “Can I do something about this?” And 3. “Is this a story I’m telling myself?” For example, if you fail a biology test, the story you might tell yourself, “I failed my parents.” Dave asked whether Neil is now comfortable with being uncomfortable. Neil had thought that he had reached a point where he was finally comfortable with being uncomfortable, but when he left Walmart to take his side hustle full-time, he suddenly felt uncomfortable again. He says you have to treat it like yoga. You have to keep learning it until you learn it. Apple Podcasts link: https://podcasts.apple.com/ca/podcast/448-the-value-of-being-uncomfortable-with-neil-pasricha/id458827716?i=1000461086169 Website link: https://coachingforleaders.com/podcast/value-being-uncomfortable-neil-pasricha/ COREY QUINN ON ON CALL NIGHTMARES The On Call Nightmares podcast featured Corey Quinn with host Jay Gordon. Corey started his on call career in what he called an “abusive” environment. One year in, a new manager was dropped in and the first thing this manager decided was that he wasn’t going to be on call himself. The number of people on call dropped from four to three and then another person left. So Corey was on call 50% of the time and he could never schedule his life around it. At the end of that time, Corey swore he would never put himself in this situation again. When he started the Duckbill Group, he decided that anything he did would be “business hours only”. Jay asked Corey what in 2019 most excited him in the world of cloud. Corey said that it was the awareness by the providers that building the fastest, most exciting, far fetched, far flung technologies was not going to be what won them the hearts and minds of their customers. Instead, he saw the large providers speaking to enterprises about migrating from data centers to cloud environments. They talked about Microsoft’s advantage in selling the cloud to enterprises. Corey says one of Microsoft’s big advantages in cloud is that they have forty years experience apologizing for software failures. Explaining these failures to non-technical audiences is something Microsoft excels at and Google and Amazon have had to learn. Jay brought up that the embrace of managed Kubernetes was a big trend in 2019. Corey says that his objection to it is that if you run everything on top of Kubernetes, you’ve abstracted away what you’re doing from the cloud providers’ built-in primitives so much that it becomes challenging to do workload attribution of cost. Programmatically figuring out which workload is the expensive one is surprising difficult. Jay talked about Hashicorp’s rise in 2019, providing tooling around cloud agnosticism. Corey said that one of the best conversations he had on his own podcast, Screaming In The Cloud, this past year was with Hashimoto. Hashimoto argued that Terraform provides workflow portability rather than workload portability and that one is worth pursuing and the other one is not. Jay said that this was the first year that Amazon talked about multicloud. Corey says they talked about hybrid but still avoided multicloud. Corey believes that every cloud provider hates multicloud until they realize a large customer is going to go with a different provider, then multicloud is wonderful. Apple Podcasts link: https://podcasts.apple.com/ca/podcast/episode-46-year-in-review-corey-quinn-duckbill-group/id1447430839?i=1000460596737 Website link: https://www.podomatic.com/podcasts/oncallnightmares/episodes/2019-12-23T11_00_16-08_00 CRAIG DANIEL ON BUILD BY DRIFT The Build podcast by Drift featured Craig Daniel with host Maggie Crowley. Their topic was “What does Drift look for when hiring product managers?” Craig says that the product manager role is unique in that you don’t have direct reports but you need to be able to influence the engineering team, the designers, and a slew of stakeholders that includes customers. Regarding technical skills, Craig says they look for systems thinkers with the ability to break down a problem, articulate their breakdown, look at data and combine that data with qualitative research. Maggie asked about hiring for associate PM roles and Craig says it goes back to a core principle that a person’s aptitude and attitude outweighs their experience. Craig defines aptitude as a combination of ability to learn and curiosity. These people are those who can grow faster than normal. Maggie added that these people are paying attention to the world around them, are asking questions of the tools that they’re using, and are not just assuming things are the way they are. For more experienced hires, Craig is looking for results. Sometimes there are good people who were in bad companies or joined a startup prior to product-market fit that never got off the ground. If they don’t have results, you want to see outputs: shipping things, building partnerships, and media coverage. People who are successful in product are those that can build coalitions, roll up their sleeves, do the hard work, and get stuff done. Maggie says that PMs can be afraid to be accountable for the end result. It is easy to say, “I wrote my one pager,” “I wrote the spec,” or “We stuck to the timeline.” There are so many excuses that you lose sight of the fact that you need to be accountable for results. Regarding the interview process, Craig says Drift’s process consists of a design leader interview, a product team interview, an executive interview, and something called, “The Who method.” Craig himself is looking for fit. This is not culture fit. It means, “What is this person great at, what do they want to do, and what do we have available or can make available?” To get at fit, Craig asks about their superpowers. If they’re at a company with, say, five product managers, what would everyone say they are the best of the five at? What are they worst of the five at? He also wants examples of truly exceptional work. This tells him what they think exceptional is, what their emotional intelligence is (are they a braggart?), and what their value system is. Craig says he has made a lot of mistakes over the years hiring PMs and, as a result, has learned to be more systematic about it. You have to have a practical part of the interview where you have the candidate go to the whiteboard, break down a problem, or tell the interviewer about work they’ve done in the past. A particularly good practical problem is to have them talk about a product they use everyday and describe both what’s great about it and what needs to be improved about it. They talked about the Who method (https://www.amazon.com/gp/product/0345504194). For example, if the candidate tells you they were responsible for a half-a-million user product, the Who method lets you find out how much of that was their responsibility versus something they are just taking credit for. He talked about an aspect of the Who method called the threat of reference check. You ask the candidate, say, who their previous manager was, and then say, “When I call your previous manager, what are they going to say about you?”  Apple Podcasts link: https://podcasts.apple.com/ca/podcast/what-really-matters-when-hiring-a-pm/id1445050691?i=1000461459804 Website link: https://www.spreaker.com/user/casted/what-really-matters-when-hiring-a-pm BRYAN LILES ON HANSELMINUTES The Hanselminutes podcast featured Bryan Liles with host Scott Hanselman. Scott started out by asking Bryan what he means by “a complete engineer”. Bryan says he has rules for everything and rule #1 is that there is a competition out there but you are only competing with yourself. You can watch what other people do and you can emulate them, but don’t compare yourself to others. Regarding being a complete developer, he says you have two aspects of being a developer: 1) writing software for money and 2) providing an impact to the world. That impact may be helping other developers level up, providing a role model, or simply doing no harm. Growing up, Bryan’s dream was simply to have a better life for himself than his parents had. Now, he wants to show people that his life was not a fluke but is a result of preparing himself for opportunities. The world Bryan wants to live in is one in which we’re trying our best and we’re also looking out for the people that come after us. Bryan talked about his recent project Octant that is a console for showing what’s going on in your Kubernetes cluster. He says that often we start to make a product and we start listing a bunch of features we want it to have. He says this is like that friend that talks too much and tells boring stories that go on for too long. We all prefer the friend who tells simple stories and it is the same with software products: you need to start off simply and solve one problem at a time. The interview ended with Bryan’s three pieces of advice that he gives to all black males that he meets. First, he says to ignore the advice that says you have to be the dumbest person in the room. That works when you have privilege and you have a safety net. Instead, be the smartest person in the room, but don’t tell anyone. Second, opportunity is rare, so when it comes, be ready. His third piece of advice is to get used to, but not comfortable, with failure. Apple Podcasts link: https://podcasts.apple.com/ca/podcast/being-a-complete-engineer-and-bryan-liles-rules-to-life/id117488860?i=1000460922091 Website link: https://hanselminutes.simplecast.com/episodes/being-a-complete-engineer-and-bryan-liles-rules-to-life-BiK2k99r LINKS Ask questions, make comments, and let your voice be heard by emailing podcast@thekguy.com. Twitter: https://twitter.com/thekguy LinkedIn: https://www.linkedin.com/in/keithmmcdonald/ Facebook: https://www.facebook.com/thekguypage Instagram: https://www.instagram.com/the_k_guy/ YouTube: https://www.youtube.com/c/TheKGuy Website:

Dan Faggella is the Founder & CEO of Emerj the only market research & company discovery platform focused exclusively on artificial intelligence and machine learning. He regularly speaks for audiences of businesses & government leaders, with a focus on the critical near-term implications of artificial intelligence across major sectors including presentations for the World Bank, the United Nations, INTERPOL, and global pharmaceutical and banking companies. https://emerj.com https://danfaggella.com Twitter ► https://twitter.com/danfaggella LinkedIn ► https://linkedin.com/in/danfaggella Podcast ► https://emerj.com/artificial-intelligence-podcast Episode 1 featuring Dan ► http://bit.ly/DanFaggella1 ******* Simulation interviews the greatest minds alive to inspire you to build the future ► http://simulationseries.com Design Merch, Get Paid, Spread Thought-Provoking Questions ► https://yoobe.me/simulation ******* Subscribe across platforms ► Youtube ► http://bit.ly/SimYoTu iTunes ► http://bit.ly/SimulationiTunes Instagram ► http://bit.ly/SimulationIG Twitter ► http://bit.ly/SimulationTwitter Spotify ► http://bit.ly/SpotifySim ******* Facebook ► http://bit.ly/SimulationFB Soundcloud ► http://bit.ly/SimulationSC LinkedIn ► http://bit.ly/SimulationLinkedIn Patreon ► http://bit.ly/SimulationPatreon Crypto ► http://bit.ly/CryptoSimu PayPal ► https://paypal.me/simulationseries ******* Nuance-driven Telegram chat ► http://bit.ly/SimulationTG Allen's TEDx Talk ► http://bit.ly/AllenTEDx Allen's IG ► http://bit.ly/AllenIG Allen's Twitter ► http://bit.ly/AllenT ******* List of Thought-Provoking Questions ► http://simulationseries.com/the-list Get in Touch ► simulationseries@gmail.com

Subscribe, 5 ⭐ And Please Write A Review! The funniest or biggest hater reviews are likely to get a shout out on the show.   Listen, Watch, Review, and Share With A Friend!   Law Smith is an SMB Consultant, Digital Strategist, Stand Up Comedian and President of Tocobaga Consulting. Eric Readinger is a Website Producer, Video Editor, and Partner at Tocobaga Consulting.

The ideas keep flowing this week, as we invent a sort of device ecosystem of parental sentinels for keeping children in their beds at night. Comforting body heat? Programmatically generated existential rhetoric? Bluetooth connectivity? Cheez-it dispenser? Sleep Daddy and Cuddle Mommy have it all. Patents pending.

The O’Reilly Security Podcast: Compounding security technical debt, the importance of security hygiene, and how the speed of innovation reintroduces vulnerabilities.In this episode, I talk with Dave Lewis, global security advocate at Akamai. We talk about how technical sprawl and employee churn compounds security debt, the tenacity of solvable security problems, and how the speed of innovation reintroduces vulnerabilities.Here are some highlights: How technical sprawl and employee churn compound security debt Twenty plus years ago when I started working in security, we had a defined set of things we had to deal with on a continuous basis. As our environments expand with things like cloud computing, we have taken that core set of worries and multiplied them plus, plus, plus. Things that we should have been doing well 20 years ago—like patching, asset management—have gotten far worse at this point. We have grown our security debt to unmanageable levels in a lot of cases. People who are responsible for patching end up passing that duty down to the next junior person in line as they move forward in their career. And that junior person in turn passes it on to whomever comes up behind them. So, patching tends to be something that is shunted to the wayside. As a result, the problem keeps growing. Reducing attack surface with consistent security hygiene We don't execute on the processes, standards, and guidelines that should exist in every environment for how you're going to do X, Y, and Z. Like SQL injection. If we are making sure we're sanitizing inputs and outputs from our applications, this attack surface by and large goes away. Is it 100%? No, but nothing in security is 100%, sadly. For patching, again, you have to have a proper regimen in place. It's sort of like this: I could build you a house if I have a hammer, but if I don't have the context of the larger plan to build that house, I’m stuck. There are tools available that can help you execute patch management. The tools and the abilities are there, but we need the processes to follow, and we need to execute on them. But the thing is, patching is not something that most people find enjoyable. We need to do a better job of seeing patching as an important part of protecting our environment and take pride in that. Innovation’s role in reintroducing previously solved problems Well, the Internet of Things (IoT) has really devolved into the new bacon. Any device you can get your hands on and slap an internet connection to is now IoT. I've seen kettles, I've seen toasters, I've seen toothbrushes that had internet connectivity. Here’s a question for you: if you have a device with an internet connection and you pull that connection, does your device stop working? I worry about this because we're getting so bogged down in the crush to create IoT devices that we're really, again, bypassing fundamentals. I've seen devices that are out on the internet using deprecated libraries, and in some cases reintroducing Heartbleed. This is abjectly silly. It's a problem we tackled a few years ago, only to see it reemerge in IoT devices that are online. Or conversely, with the Mirai botnet, we saw default usernames and passwords. Programmatically, there's no good reason for that. That is an easily fixed problem.

The O’Reilly Security Podcast: Compounding security technical debt, the importance of security hygiene, and how the speed of innovation reintroduces vulnerabilities.In this episode, I talk with Dave Lewis, global security advocate at Akamai. We talk about how technical sprawl and employee churn compounds security debt, the tenacity of solvable security problems, and how the speed of innovation reintroduces vulnerabilities.Here are some highlights: How technical sprawl and employee churn compound security debt Twenty plus years ago when I started working in security, we had a defined set of things we had to deal with on a continuous basis. As our environments expand with things like cloud computing, we have taken that core set of worries and multiplied them plus, plus, plus. Things that we should have been doing well 20 years ago—like patching, asset management—have gotten far worse at this point. We have grown our security debt to unmanageable levels in a lot of cases. People who are responsible for patching end up passing that duty down to the next junior person in line as they move forward in their career. And that junior person in turn passes it on to whomever comes up behind them. So, patching tends to be something that is shunted to the wayside. As a result, the problem keeps growing. Reducing attack surface with consistent security hygiene We don't execute on the processes, standards, and guidelines that should exist in every environment for how you're going to do X, Y, and Z. Like SQL injection. If we are making sure we're sanitizing inputs and outputs from our applications, this attack surface by and large goes away. Is it 100%? No, but nothing in security is 100%, sadly. For patching, again, you have to have a proper regimen in place. It's sort of like this: I could build you a house if I have a hammer, but if I don't have the context of the larger plan to build that house, I’m stuck. There are tools available that can help you execute patch management. The tools and the abilities are there, but we need the processes to follow, and we need to execute on them. But the thing is, patching is not something that most people find enjoyable. We need to do a better job of seeing patching as an important part of protecting our environment and take pride in that. Innovation’s role in reintroducing previously solved problems Well, the Internet of Things (IoT) has really devolved into the new bacon. Any device you can get your hands on and slap an internet connection to is now IoT. I've seen kettles, I've seen toasters, I've seen toothbrushes that had internet connectivity. Here’s a question for you: if you have a device with an internet connection and you pull that connection, does your device stop working? I worry about this because we're getting so bogged down in the crush to create IoT devices that we're really, again, bypassing fundamentals. I've seen devices that are out on the internet using deprecated libraries, and in some cases reintroducing Heartbleed. This is abjectly silly. It's a problem we tackled a few years ago, only to see it reemerge in IoT devices that are online. Or conversely, with the Mirai botnet, we saw default usernames and passwords. Programmatically, there's no good reason for that. That is an easily fixed problem.

In episode 123 of the Office 365 Developer Podcast, Richard diZerega and Andrew Coates talk to Jakob Nielsen about the Excel Bot. Weekly updates Office Developer Program. NEW value, FREE perks! by the Office Dev team SharePoint PnP webcast—Programmatically creating and updating modern pages in SharePoint Online by the PnP team New Microsoft Teams training content available by Todd Baginski Microsoft Teams Extensibility Training by OfficeDev GitHub Gotchas writing Tabs for Teams by Andrew Coates Microsoft Teams Extensibility by Collab365 Live Richard diZerega on developing for Microsoft Teams and Bot Framework by Microsoft Cloud Show Microsoft Graph adds SharePoint endpoint for Groups in the beta branch by Mikael Svenson How to run SharePoint Framework Pattern and Practices Samples through Docker by Stefan Bauer Get to know who is tracking your emails via the Microsoft Graph and Azure Functions by Elio Struyf Controlling invitation of external members to an Office 365 group programmatically via the Microsoft Graph by Mikael Svenson Singleton object in JavaScript by Paul Schaeflein Show notes Excel Bot GitHub Repo Got questions or comments about the show? Join the O365 Dev Podcast on the Office 365 Technical Network. The podcast RSS is available on iTunes or search for it at “Office 365 Developer Podcast” or add directly with the RSS feeds.feedburner.com/Office365DeveloperPodcast. About Jakob Nielsen Jakob Nielsen is a principal designer for the Microsoft Office team working on Excel and Office for professional developers and makers. In his 20+ years at Microsoft, he has worked with enterprise customers and partners in Microsoft Consulting Services and on the Dynamics and SharePoint products. About the hosts Richard is a software engineer in Microsoft’s Developer Experience (DX) group, where he helps developers and software vendors maximize their use of Microsoft cloud services in Office 365 and Azure. Richard has spent a good portion of the last decade architecting Office-centric solutions, many that span Microsoft’s diverse technology portfolio. He is a passionate technology evangelist and a frequent speaker at worldwide conferences, trainings and events. Richard is highly active in the Office 365 community, popular blogger at aka.ms/richdizz and can be found on Twitter at @richdizz. Richard is born, raised and based in Dallas, TX, but works on a worldwide team based in Redmond. Richard is an avid builder of things (BoT), musician and lightning-fast runner.   A Civil Engineer by training and a software developer by profession, Andrew Coates has been a Developer Evangelist at Microsoft since early 2004, teaching, learning and sharing coding techniques. During that time, he’s focused on .NET development on the desktop, in the cloud, on the web, on mobile devices and most recently for Office. Andrew has a number of apps in various stores and generally has far too much fun doing his job to honestly be able to call it work. Andrew lives in Sydney, Australia with his wife and two almost-grown-up children.

Programmatically transforming Ruby code is easier than you think and has several interesting applications: automated refactoring, coding style/best practices enforcement, DSL translation, and more. Ben Hughes explores how to transform ruby code with available tools.

Can music be perceived beyond its extramusical associations? This week, we explore the dichotomy between "Absolute" and "Program" music, and their varying degrees of applicability depending on composer intention, performer agency, and audience reception. And believe it or not, the two of us disagree... Keep up with the continuing discussion: visit www.perennial-dissonance.tmblr.com And subscribe to us on iTunes!