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Send us a text Episode Description: Taking a break doesn’t mean you miss the good stuff. Last year, while I was on a much-needed vacation, I invited one of my favorite coaches back on the mic — Lynne Snead, Principal & Owner of Talent Evolution Systems. Lynne walks us through the Three Circles Model — a powerful framework for maintaining focus, expanding influence, and leading with intention. We explore what separates managers from true leaders, the role of mindset and energy in influence, and why how you speak to yourself matters just as much as how you speak to others. If people would follow you even if they didn’t have to… that’s leadership. Timestamps:  02:47 Meet Lynne Snead Again 03:47 The Circles Model 09:30 The Circle of Personal Control 14:05 It’s the How You Say It, Also to Yourself! 20:43 Thought Management 22:53 Leadership = Influence 25:08 A Manager vs a Leader 27:05 Mindset & Energy 29:59 Stress 31:11 Atomic Habits 33:47 Mindmapping 36:19 Emotional Intelligence 39:38 Read, Study, Learn or Be Left BehindReading List: Stephen Covey – The 7 Habits of Highly Effective PeopleJames Clear – Atomic HabitsJohn A. Daly – Advocacy: Championing Ideas and Influencing OthersGuest Links: Lynne Snead on LinkedInTalent Evolution SystemsSocial: #Leadership #ReplayEpisode #MakingDataSimple #ExecutiveCoaching #Influence #Focus #LeadershipDevelopment #MindsetMatters #PersonalGrowth #AtomicHabits #EmotionalIntelligence #CareerGrowth Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

External registration required at nyhackr.org.

Note the 7 PM start time.

We have a last minute change, with Daniel Lee, giving a talk about his favorite statistical model.

Thank you to NYU for hosting us.

Everybody attending must RSVP through the registration form at nyhackr.org. There is a charge for in-person and virtual tickets are free. Space is extremely limited and in-person registration closes at 3 PM the day of the talk.

About the Talk: Over the past 20 years, I've worked on a lot of different applications including models in pharma for estimating oncology treatment efficacy and PK/PD models; estimating vote share for state and national elections; clinical trials for rare diseases and non-small-cell lung cancer; satellite control software for television and government; retail price sensitivity; data fusion for U.S. Navy applications; sabermetrics for an MLB team; and assessing “clutch” moments in the NFL. Out of everything I've worked on, I have a favorite model and I'll discuss why.

About Eric: Daniel Lee is a staff data scientist at Teamworks, working on evaluation and projection for professional sports teams. He is a computational Bayesian statistician who helped create and develop Stan, the open-source statistical modeling language.

The venue doors open at 6:30 PM America/New_York where we will continue enjoying pizza together (we encourage the virtual audience to have pizza as well). The talk, and livestream, begins at 7:00 PM America/New_York.

Remember, register at nyhackr.org.

Send us a text I took a much needed vacation and invited my favorite coach back on the show. Lynne Snead, Principal and Owner of Talent Evolution Systems, discusses how to maintain focus and expand influence, using the three circles model.  Leadership is is influence; are you a manager or leader? Would people follow you even if they didn't have to? If the answer is yes, that's leadership. 02:47 Meet Lynne Snead Again03:47 The Circles Model 09:30 The Circle of Personal Control14:05 It’s the How You Say It, Also to Yourself!20:43 Thought Management22:53 Leadership = Influence25:08 A Manager vs a Leader27:05 Mindset & Energy29:59 Stress31:11 Atomic Habits 33:47 Mindmapping36:19 Emotional Intelligence39:38 Read, Study, Learn or Be Left Behind Lynne Snead Linkedin Talent Evolution Systems Katherine Mayne

Reading list: Stephen Covey, 7 Habits James Clear, Atomic Habits John A Daly, Advocacy: Championing Ideas and Influencing Others

Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Ready for more ideas about UX for AI and LLM applications in enterprise environments? In part 2 of my topic on UX considerations for LLMs, I explore how an LLM might be used for a fictitious use case at an insurance company—specifically, to help internal tools teams to get rapid access to primary qualitative user research. (Yes, it’s a little “meta”, and I’m also trying to nudge you with this hypothetical example—no secret!) ;-) My goal with these episodes is to share questions you might want to ask yourself such that any use of an LLM is actually contributing to a positive UX outcome  Join me as I cover the implications for design, the importance of foundational data quality, the balance between creative inspiration and factual accuracy, and the never-ending discussion of how we might handle hallucinations and errors posing as “facts”—all with a UX angle. At the end, I also share a personal story where I used an LLM to help me do some shopping for my favorite product: TRIP INSURANCE! (NOT!) 

Highlights/ Skip to:

(1:05) I introduce a hypothetical  internal LLM tool and what the goal of the tool is for the team who would use it  (5:31) Improving access to primary research findings for better UX  (10:19) What “quality data” means in a UX context (12:18) When LLM accuracy maybe doesn’t matter as much (14:03) How AI and LLMs are opening the door for fresh visioning work (15:38) Brian’s overall take on LLMs inside enterprise software as of right now (18:56) Final thoughts on UX design for LLMs, particularly in the enterprise (20:25) My inspiration for these 2 episodes—and how I had to use ChatGPT to help me complete a purchase on a website that could have integrated this capability right into their website

Quotes from Today’s Episode “If we accept that the goal of most product and user experience research is to accelerate the production of quality services, products, and experiences, the question is whether or not using an LLM for these types of questions is moving the needle in that direction at all. And secondly, are the potential downsides like hallucinations and occasional fabricated findings, is that all worth it? So, this is a design for AI problem.” - Brian T. O’Neill (8:09) “What’s in our data? Can the right people change it when the LLM is wrong? The data product managers and AI leaders reading this or listening know that the not-so-secret path to the best AI is in the foundational data that the models are trained on. But what does the word quality mean from a product standpoint and a risk reduction one, as seen from an end-users’ perspective? Somebody who’s trying to get work done? This is a different type of quality measurement.” - Brian T. O’Neill (10:40)

“When we think about fact retrieval use cases in particular, how easily can product teams—internal or otherwise—and end-users understand the confidence of responses? When responses are wrong, how easily, if at all, can users and product teams update the model’s responses? Errors in large language models may be a significant design consideration when we design probabilistic solutions, and we no longer control what exactly our products and software are going to show to users. If bad UX can include leading people down the wrong path unknowingly, then AI is kind of like the team on the other side of the tug of war that we’re playing.” - Brian T. O’Neill (11:22) “As somebody who writes a lot for my consulting business, and composes music in another, one of the hardest parts for creators can be the zero-to-one problem of getting started—the blank page—and this is a place where I think LLMs have great potential. But it also means we need to do the proper research to understand our audience, and when or where they’re doing truly generative or creative work—such that we can take a generative UX to the next level that goes beyond delivering banal and obviously derivative content.” - Brian T. O’Neill (13:31) “One thing I actually like about the hype, investment, and excitement around GenAI and LLMs in the enterprise is that there is an opportunity for organizations here to do some fresh visioning work. And this is a place that designers and user experience professionals can help data teams as we bring design into the AI space.” - Brian T. O’Neill (14:04)

“If there was ever a time to do some new visioning work, I think now is one of those times. However, we need highly skilled design leaders to help facilitate this in order for this to be effective. Part of that skill is knowing who to include in exercises like this, and my perspective, one of those people, for sure, should be somebody who understands the data science side as well, not just the engineering perspective. And as I posited in my seminar that I teach, the AI and analytical data product teams probably need a fourth member. It’s a quartet and not a trio. And that quartet includes a data expert, as well as that engineering lead.” - Brian T. O’Neill (14:38)

Links Perplexity.ai: https://perplexity.ai  Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-Metric-Matters/dp/0593420586  My article that inspired this episode

Generative AI is transforming how we interact with technology.

Build with AI is a technical event hosted by Google in collaboration with GDG Berlin and GDG Cloud Berlin. This event aims to help developers learn and apply their skills on GenAI trends and products from Google.

Developers from all segments (including Backend, Android & Flutter devs) and all skill levels can benefit. 

Agenda

6:30 PM: Doors open, registration and welcome drink

7:00 PM: Opening by Katya Vinnichenko

7:10 PM: Keynote by Tim Messerschmidt

In this overview session, Tim Messerschmidt, Google's Head of Developer Ecosystem in Europe and Israel, will provide an overview of Google's Gemini AI models, focusing on Gemma, Google's open model. This session will cover the benefits of open models, explain how to use Gemma in your projects, and outline the benefits of responsible AI in practice.

7:30 PM: Dinner

8:00 PM: Hallucination Prevention by Jakob Pörschmann

Jakob will deep dive into preventing hallucinations from an engineering perspective. In his keynote he will discuss the tech behind the techniques outlined by Eva. He will provide detailed insights in how Google customers utilize and implement them. Finally, Jakob will show a hands-on demo and connect his findings to the latest LLM research.

8:40 PM: Generative AI for gaming and entertainment industry by Nikolai Danylchyk

In this talk we will dive deep into generative AI tools and solutions aimed at creating virtual characters for games that take advantage of Large Language Models, Visual Assets creation as well as Audio Synthesis.

9:20 PM: Networking

Speakers

Katya Vinnichenko - Google (Program Manager)

Katya is a Program Manager at Google Developer Relations team. Currently she is leading the Google Developer Groups (aka GDG) and Women Techmakers Ambassadors programs across Europe.

Tim Messerschmidt - Google (DevRel Program Manager)

Hello there! My name is Tim, and for the past ten years, I've been advocating for, serving, and enabling developers across several Developer Relations teams. In my current role, I oversee the regional execution and program strategy targeting developers and startups for Google's Developer Relations Ecosystem team in Europe, Russia, and Israel. My favorite technologies are Flutter and anyt…

Nikolai Danylchyk - Google (Cloud Customer Engineer)

Nikolai works as a Cloud Customer Engineer at Google, specializing in application and infrastructure modernization, and AI solutions. His work in the past few years is focused on customers in the gaming industry.

Jakob Pörschmann - Google (Cloud Customer Engineer)

Jakob Pörschmann is a self-taught coder and ML enthusiast working as a Customer Engineer at Google Cloud.

Hosted By

Abhinav Kulshreshtha, GDG Organizer

Eugenia Zigisova, Organizer

Jerome Mouton, Organizer

Louis Tsai, GDG Organizer

manjula dube, Organizer

I am Software Engineer & teacher. I'm a world renowned tech speaker.I am from India currently living in Berlin with my husband Sahil Mhapsekar.

I work at The Vanguard Group Europe. I am Founder of Geekabyte that aims to deliver in person tech workshops on Web Development & organises international conferences, React India & JS Conf India. I'm also a Google Developer Expert. I have been obsessed with coding ever since I graduated out of college.

I am founding member of Mumbai Women Coders that aims to encourage more women in tech & provide an avenue into the technology world. I love contributing to open source in my free time. I love Javascript, React & my family ❤️ In coming years I see my self teaching people to code.

Alex Mir, GDG Organizer

GDG Berlin co-lead and Software Engineer

Complete your event RSVP here: https://gdg.community.dev/events/details/google-gdg-berlin-presents-build-with-ai/.

The word "model" is used a lot by data professionals. There are dbt models, machine learning models, relational models, and conceptual, logical, and physical models. My concern is we're missing the bigger picture of what data modeling was initially supposed to accomplish, which was to represent reality and structure it as data. The bigger implication is that our various "models" will become too myopic and miss the larger broader context of the reality of how we use data to serve our organizations.

If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

In this episode, Conor concludes his conversation with Jason Turner! Link to Episode 105 on Website

Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Jason is host of the YouTube channel C++ Weekly, co-host emeritus of the podcast CppCast, author of C++ Best Practices, and author of the first casual puzzle books designed to teach C++ fundamentals while having fun! A list of Jason’s content: C++ Weekly YouTube ChannelThe [Fill in the Blank] Programmer YouTube ChannelC++ BooksTalk PlaylistShow Notes

Date Recorded: 2022-10-26 Date Released: 2022-11-25 The Best Parts of C++ - Jason Turner - CppNorth 2022The Power Of Compile-Time Resources - Jason Turner - CppNorth 2022Making C++ Fun, Safe, and Accessible – Jason Turner - C++ on Sea 2022Your New Mental Model of constexpr - Jason Turner - CppCon 2021C++ AdressSanitizerC++ UndefinedBehaviorSanitizerC++ Sarter ProjectCircle CompilerVal Programming LanguageJakt Programming LanguageCarbon Programming LanguageCppFrontADSP Episode 97: C++ vs Carbon vs Circle vs CppFront with Sean BaxterJT on TwitterCppCast Episode 242 is AWESOME!Chapel Programming LanguageCppCast Episode with Adreas KlingChaiScriptRhai Programming LanguageC++ Pattern Matching ProposalC++ Pattern matching using is and asC++ Code Smells - Jason Turner - CppCon 2019Conor Hoekstra — ITM: My least favorite anti-patternIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0

"I come from a T-SQL background, so when I first laid my eyes on SQL Server 2005, I was shocked--and then, I was scared! I didn't have a CLR or XML background and suddenly had an urgent need to learn it. SQL Server 2005 is too big of a release to learn from the books online. Fortunately, now there is a book for developers who need to go from SQL Server 2000 to SQL Server 2005 and to do it as painlessly as possible. Basically, it's one-stop shopping for serious developers who have to get up to speed quickly. I'll keep this one on my desk--not on my bookshelf. Well done, Bob and Dan!" --Dr. Tom Moreau SQL Server MVP and Monthly Columnist SQL Server Professional, Brockman Moreau Consulting Inc. "A SQL book truly for developers, from two authorities on the subject. I'll be turning to this book first when I need to understand a component of SQL Server 2005." --Matt Milner Instructor Pluralsight "An excellent book for those of us who need to get up to speed on what's new in SQL Server 2005. The authors made sure this book includes the final information for the release version of the product. Most other books out now are based on beta versions. It covers key areas from XML and SQLCLR to Notification Services. Although the wide variety of information is great, my favorite part was the advice given on when to use what, and how performance is affected." --Laura Blood Senior Software Developer Blue Note Computing, Inc. "SQL Server 2005 is a massive release with a large number of new features. Many of these features were designed to make SQL Server a great application development platform. This book provides comprehensive information about the SQL Server features of most interest to application developers. The lucid text and wealth of examples will give a developer a clear understanding of how to use SQL Server 2005 to a whole new class of database applications. It should be on every SQL Server developer's bookshelf." --Roger Wolter Solutions Architect Microsoft Corporation "While there will be a lot of good books on SQL Server 2005 development, when people refer to the 'bible,' they'll be talking about this book." --Dr. Greg Low Senior Consultant Readify Pty Ltd "SQL Server 2005 is loaded with new features and getting a good overview is essential to understand how you can benefit from SQL Server 2005's features as a developer. Bob and Dan's book goes beyond enumerating the new SQL Server 2005 features, and will provide you with lots of good examples. They did a good job striking a balance between overview and substance." --Michiel Wories Senior Program Manager, SQL Server Microsoft Corporation Few technologies have been as eagerly anticipated as Microsoft SQL Server 2005. Now, two SQL Server insiders deliver the definitive hands-on guide--accurate, comprehensive, and packed with examples. starts where Microsoft's documentation, white papers, and Web articles leave off, showing developers how to take full advantage of SQL Server 2005's key innovations. It draws on exceptional cooperation from Microsoft's SQL Server developers and the authors' extensive access to SQL Server 2005 since its earliest alpha releases. A Developer's Guide to SQL Server 2005 You'll find practical explanations of the new SQL Server 2005 data model, built-in .NET hosting, improved programmability, SQL:1999 compliance, and much more. Virtually every key concept is illuminated via sample code that has been fully updated for and tested with the shipping version of the product. Key coverage includes Using SQL Server 2005 as a .NET runtime host: extending the server while enhancing security, reliability, and performance Writing procedures, functions, triggers, and types in .NET languages Exploiting enhancements to T-SQL for robust error-handling, efficient queries, and improved syntax Effectively using the XML data type and XML queries Implementing native SQL Server 2005 Web Services Writing efficient, robust clients for SQL Server 2005 using ADO.NET, classic ADO, and other APIs Taking full advantage of user-defined types (UDTs), query notifications, promotable transactions, and multiple active result sets (MARS) Using SQL Management Objects (SMO), SQL Service Broker, and SQL Server Notification Services to build integrated applications