“From SME To Published Author: Writing A Book As A Woman In Tech”: Danielle Barnes (Women Talk Design), Leemay Nassery (Spotify), and Margaret Eldridge (The Pragmatic Programmer) (Video + Transcript)

In this ELEVATE session, Margaret Eldridge (Managing Editor, The Pragmatic Programmers), Danielle Barnes (CEO, Women Talk Design), and Leemay Nassery (Engineering Leader, Spotify) discuss their experiences writing and publishing books and offer insights and advice for aspiring authors. The panel discusses the process of finding a publisher, managing time to write a book, and the doors that writing a book can open in terms of career opportunities. They emphasize the importance of knowing your audience, being patient with yourself, and sharing your ideas early on through platforms like blogs and workshops. 

Danielle’s book: Present Yourself: Proven Strategies for Authentic and Impactful Public Speaking

Leemay’s book: Practical A/B Testing: Creating Experimentation-Driven Products

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Leemay Nassery ELEVATE writing a book has made me a better writer for emails documents at work

Transcript of ELEVATE Session:

Angie Chang:

I’m Angie Chang, founder of Girl Geek X, And with us today we have some subject matter experts who are now authors. Margaret Eldridge is an accomplished writer and editor in the technology space. She’s worked with hundreds of authors at publishers like Wiley, Manning, and The Pragmatic Programmers, and she’s joined by engineering leader and author, Leemay Nassery, and Women Talk Design CEO Danielle Barnes. Welcome, everyone.

Margaret Eldridge:

Thanks so much for joining us. Our talk tonight is about writing a book, and it’s called From Subject Matter Expert to Published Author. Let’s start with Danielle. Would you like to introduce yourself and tell us a bit about your book?

Danielle Barnes:

Sure, yeah. Thank you so much for having me here. I’m Danielle Barnes. My pronouns are she and her, and as shared, I’m the CEO of an organization called Women Talk Design that’s focused on amplifying the voices of women and non-binary folks. We have a book coming out on Monday, actually, called Present Yourself: Proven Strategies for Authentic and Impactful Public Speaking.

Margaret Eldridge:

Awesome, thanks so much. That is definitely a book that I need to read because I speak every now and then, not that often. I feel like I’m a bit rusty, and I think even people who speak a lot, we need to polish our presentation skills, so I’m definitely going to be a buyer.

Danielle Barnes:

I’m glad to hear that.

Margaret Eldridge:

Leemay, what about you? Can you tell us a little about yourself and the book that you wrote?

Leemay Nassery:

Yeah, definitely. <y name is Leemay. I’m an engineering leader at Spotify. I wrote a book called Practical A/B Testing with Pragmatic, which gives you a practical approach into how you would incorporate A/B testing at your company, because A/B testing is not just about the platform, but it’s also about the cultural changes that go into incorporating A/B testing into your product development life cycle. There’s a lot of data and there’s a lot of analysis, so it’s like a practical introduction to get started.

Margaret Eldridge:

Wonderful. And you’re also working on a new book for us?

Leemay Nassery:

Yes. The second one that’s… Sorry, what?

Margaret Eldridge:

Is that a follow-up to the first one?

Leemay Nassery:

Yeah, it’s a little bit more advanced topic. We’re assuming that you’ve already built an A/B testing platform, and now you’re navigating some of the more challenges related to not having enough users or not having testing space or wanting to improve the data that you get from your experiments.

Margaret Eldridge:

It’s one thing to actually work in a particular domain and coach other people in that area, but it’s a whole other thing to write a book. Leemay, how did you actually get started? What was your spark and how did you go about finding a publisher?

Leemay Nassery:

That’s a really good question. I think I’ll put that question to two. For first, what sparked me to write the book? I’ve been working in tech for about 15 years and I’d say probably the most exciting aspects of my career have been related to A/B testing. It’s a pretty thrilling experience evaluating your change in production with millions of users and then getting data points that suggest it’s performant or it’s not performant, it’s increasing metrics or it’s not increasing metrics.

I shortly realized that my favorite aspect of working in tech is the A/B testing phase. It started with the passion. I was obsessed with the concept of A/B testing regardless of the context, if you’re A/B testing content on a video platform or a website or Spotify, any aspect of A/B testing was interesting to me. Then once I realized that I’m obsessed and I could spend a lot of time and energy writing about it, because we know it takes a lot of time writing a book. Then I experimented with the publishers.

I reached out to a few publishers and I’d say Pragmatic was the best fit from a ways of working and the history, the books that preceded me, I think I read personally. It was pretty cool to be part of company where I was contributing to their catalog, if that makes sense. The process, and we can talk about this later, I think was most compelling. How we worked with the editors, tools to push changes with your book, et cetera, is what attracted me the most to Pragmatic.

Margaret Eldridge:

I know Pragmatic has a workflow that’s a little more friendly for developers, it feels more like, yeah.

Leemay Nassery:

That’s what I love about it. It’s not a Google Doc. I have worked with other publishers where it was a Google Doc, and that was so stressful because I kept replicating the Google Doc. Because I was scared that it would become a single point of failure. Whereas I trusted a repository, which I committed to every day once I was done.

Margaret Eldridge:

Right, right. And Danielle, your book is not with a publisher. You published it sort of self-published through your company, correct?

Danielle Barnes:

Correct, yeah.

Margaret Eldridge:

How did that book get started?

Danielle Barnes:

Yeah, so Present Yourself actually started as a workshop. In 2017, we ran the first two-day Present Yourself program to help women and non-binary folks take an idea that they might have and turn it into a talk and practice it. We got such great response from that that we started running it in different cities, and then in 2020 we moved it online. Then in 2021, we launched it as an eight-week hybrid that was part asynchronous, part live.

We found when we had to make asynchronous sessions. We ended up writing out big chunks of the curriculum. There was someone who took that first program that said this should be a book, and the founder of Women Talk Design, Christina Wodtke, actually helped develop a lot of Present Yourself. She’s self-published several of her own books. When we first ran Present Yourself, she was like, “Let’s make it a book.” And I was like, “Absolutely not. That sounds like a terrible idea. I don’t want to write a book.”

After all this time and all this feedback we’ve gotten from alumni, I thought, I really want more people to be able to access this material. I never considered myself a writer. I never particularly wanted to write a book, but one of the pieces that I wrote, actually, I didn’t make it into the book, was that my why ultimately outweighed my fear.

I was like, I cared so much about this and I really wanted it to exist, but I decided it was worth it to pursue it. And we did talk to a couple of publishers early on. Even though Christina was a big fan of self-publishing, I wanted to do our research and make sure that was the right path. I spoke mostly to indie and hybrid publishers, none of the really big publishing houses., and I think ultimately we decided to self-publish for a lot of different reasons.

One was that we wanted to show the Women Talk Design community what was possible because so many people have book ideas in them, but it can be hard to find a publisher and get connected with one. We wanted to show the route of self-publishing, so we’ve done a lot to share our journey along the way.

We also really wanted to put together our own team, our editors and designers, and be able to make sure that that reflected the community that we had, and have a little bit more creative control in that way. I has been quite the journey. I have been troubleshooting some things that we’re getting ready to publish on Monday. There’s still things going wrong, so there’s definitely a lot of benefits to working with a publisher. But going this route allowed us to, like I said, share a lot of that journey, have some more controller pieces, and ultimately have more of the profits go back to the folks who are creating the content.

Margaret Eldridge:

That is the best reason for self-publishing is the creative control aspect, I would say. Because when you work with a publisher, we do want input. We want input on the content, on the cover, and there’s the whole rights thing.

When you’re self-published, you can sell it as many different places as you want and on your own, however you’d like for whatever price you’d like. The control thing is the number one, I think, self-publishing reason. I looked at your outline and I was like, “They definitely had an editor. This is a very sharp outline.”

Danielle Barnes:

Oh yeah. I guess maybe one of the misconceptions, and some people I guess sometimes do self-publish without an editor, but we still very much had all of people working on a book that might, we just paid them each individually. We had developmental editors and line editors and copy editors and proofreaders and designers, and so it was definitely not a solo project.

Margaret Eldridge:

I guess for some people who don’t have the money upfront to pay those sorts of people, traditional publishing can be a more attractive option just because the publisher takes on those upfront costs. Although you do pay because you don’t get to keep all of the royalties from it when you’re publishing. Although actually with Pragmatic, you get 42% on your first book and 50% on every book thereafter. So we’re a little better than other publishers who start around 10%. But still, it’s not a hundred percent, which is what you get to keep when you’re selling it yourself on your own website. So that’s definitely a consideration.

Danielle Barnes:

One thing I’ll add is we did, we had a community and we kick-started a lot of the initial funds. We ran a Kickstarter project and were able to bring our community in that way, and that also helped us pay for our editor. We had several contributors we wanted to pay for upfront. So yes, there are a lot of different factors that go into this.

Margaret Eldridge:

Now you have another book idea, which is self-publishing your book, right?

Danielle Barnes:

It’s true, that’s true. And anything you do, you have a book idea in you to inform the next one. Yeah.

Margaret Eldridge:

You didn’t actually have to write a proposal and present it to a publisher, but Leemay did. What was that like? Was it tough to write the proposal? Did we give you feedback that we asked you to change a lot of stuff?

Leemay Nassery:

That’s a good question. Was it tough to write the proposal? No, I would say it wasn’t. The part that I had to let go of a little bit was it evolved. My book evolved from the original idea to what it is today, which I think is a way better idea. Keep in mind, I truly did experiment with publishers, and one publisher was taking it a total different direction. Pragmatic was taking it another direction, and I picked the direction that I like.

The proposal itself is, for folks that are interested in writing a book, don’t be too intimidated. You talk a little bit about yourself, why you should write the book. You reference other competing books that are related in the topic, and then you drop an outline. Outline probably sounds a lot more intimidating than it is. It’s the chapters that you think that you’ll incorporate and that can evolve. Just because that’s what you propose, it doesn’t mean it’s what you’re going to end up with.

I think that’s something to keep in mind is it’s not set in stone forever. Just put pen to paper and see what you churn out. But the part that I think is most interesting is how it evolved based on the feedback and how letting go of what I originally had in mind actually helped the book be better.

Margaret Eldridge:

That’s a very interesting point because we get some people who come to us after they’ve already written their book and we’re like, “Oh, darn, that’s great. We’re happy that you wrote a book, but now we don’t have any chance to give you our feedback because it’s done.”

Leemay Nassery:

Yeah, I think I wrote one chapter. For my first proposal I think I wrote one chapter that accompanied the outline.

Margaret Eldridge:

I’d say my advice to people who are thinking about writing a book, especially a technical book or just a nonfiction book, is to go ahead and just write the proposal and maybe a few pages from the book to give publishers an idea of your writing style, but don’t go any further than that because publishers really do like to, on nonfiction books and technical books, they like to work with you on a chapter-by-chapter basis from the very start to shape the idea.

It’s really not that much work up front to propose your idea. And the worst that can happen is they say no. In the case of Pragmatic, I always try to say, “No, but. No, but how about this idea? Or would you be open to changing it around a little bit?” So yeah. [crosstalk]

Danielle Barnes:

I can add briefly. Even as a self-published author, I actually did fill out the proposal, a proposal template. I had talked to one of the publishers and they sent me theirs, and I found it to be really helpful in just helping me shape my own ideas. Like all of those things, if you’re going to be selling your own book, you want to have that competitive analysis and you want to be thinking through who is your audience. And so I particularly found that helpful, and we gave it to our developmental editor with our first draft of the manuscript so that she could say, “Oh, okay, well if this is who your target audience is and this is what you’re trying to achieve, that actually doesn’t match this of what you’re saying, or here would be my recommendation for some of this stuff.”

Margaret Eldridge:

That’s very important, your audience, you have to know who you’re talking to. And you have to realize that even though your topic might appeal to other audiences, like Leemay’s book, maybe it would appeal to some middle management people who aren’t actually doing A/B testing. But that’s not the primary audience, and you really need to laser focus your voice and who you’re talking to on the primary audience. If other people pick it up and get benefit out of it, great. I’m glad that you brought up the point of audience. Definitely. As far as when you’re writing the book, just basically how do you manage your time to do it? Because I know both of you have full-time jobs. How does that work?

Leemay Nassery:

I can go first real quick. Does that sound good? It’s a great question. Yeah. I love my day job. Definitely focus during the day on working at Spotify. To be honest, from what I’ve gathered, I think there’s two types of writers, and I’m curious where Danielle falls in this. I’m more of a marathoner, so I’ll spend weekends working, writing, editing. I’ll spend Saturday, Sunday, I’ll just allocate towards working on my book. I just don’t have the energy in me at 8:00 P.M. after a long work day to write. It’s just writing takes so much brain power.

Editing I think takes even more brain power because you’re meeting the needs of, or you’re trying to understand your development editor wants this, but it’s hard to hold in that change. Then you need to be in the right mindset, and so I focus on the weekends. At least for a year, almost every weekend, not every one, but almost every weekend was dedicated to working on my book, with breaks. I would go for a run, then work on my book, go for a walk, then work on my book. That was my style. Danielle, what was your style?

Danielle Barnes:

Yeah, I agree that I cannot work at the end of the day. I’m definitely a morning person. And it took me a while to figure out what was going to work for me. I am lucky that I was able to do this as part of Women Talk Design, which is my full-time job. I decided at one point I wasn’t making enough progress. I was going to take off a week of doing anything else, and I was just going to write.

I went to a coffee shop, I’m going to do this, and I write for an hour and a half. And then I was like, all right. I just couldn’t focus for that long. It was too hard. There was a lot of experimenting of trying to figure out what worked for me. I also had a baby in the middle of the process, so I also had to figure out who I was and what I was capable of before I had the baby changed once my family was very different.

The biggest thing that I learned in that process is to be patient with myself and to realize that there wasn’t one right way. I had someone recommend to me, “Oh, you don’t have to write an order, just write different ideas and then you can piece it together afterwards.” But that didn’t work for me, and I just kept getting stuck and I was like, “No, I need to write linearly, start again and write all the way through.” And so yes, very much appreciate that there’s different ways to do it.

Margaret Eldridge:

That’s interesting that you both didn’t find it possible to work on the book after work.

Leemay Nassery:

Yeah, I’m a manager, so it just, my day, emotionally, takes so much out of me that I just didn’t have anything. I’m an introvert too, so I just needed to sit in silence or something. And so writing, it’s just a lot of inner dialogue that’s going in, a lot of thinking and energy. I would love to be the type of person I would wake up every morning and just spend an hour working on it like they do in the movies. But weekends-

Danielle Barnes:

[crosstalk].

Margaret Eldridge:

I write a lot of blog posts and things like that, and I find that I really do my best writing early in the morning. I would wake up at 5:00 AM if I was going to do an article for just my own blog, like writing about baking and random things, I would do it super early at five o’clock in the morning and then start my day job.

Everybody’s completely different. Some people like to write in sprints where they spend 20 minutes writing and then go back and do something else. Pomodoro, whatever you want to call it. That’s interesting. It’s amazing that you both found the time to write a book while doing so many other things. I am completely amazed that you could have a full-time job, a baby, and write a book, Danielle, so that’s insane.

Now that you’ve written a book or are almost done writing the book, in your case, do you find that things have changed as far as, has it opened any doors for you? Has it changed your outlook on your career, your profession, what you’re able to do, things that, Leemay?

Leemay Nassery:

Yeah, I think, yes, definitely in two ways for me personally. One way, I’m giving workshops at universities. For example, I gave a workshop last year at UPenn, at Wharton about A/B testing.

The thing that’s really neat about A/B testing is it’s so applicable to anybody. Engineers build the platform, product owners leverage it to evaluate their ideas. Managers, same thing, CTOs, if their companies aren’t doing it, they want to do it. That’s the luxury with A/B testing is that it opened doors for workshops at universities, which is super cool. That’s always an avenue I want to venture into.

The other door that it opened up, which I think is such, I’m so grateful for this because I’m so obsessed with A/B testing, my manager sees that and then gives me the space to focus that more at Spotify. I do think that the book was a conduit for that change.

I think it more [inaudible] credibility, but it also, at certain point, if you’re writing every week about A/B testing, you’re thinking about A/B testing a lot. It’s just ingrained in me. I’ve said the word experimenting in different contexts at least four times today in this session. It gave me the credibility at work to then focus more on things that I’m more interested in. Again, I have a manager that’s amazing and lets me do that, but I think the book helped that case, if that makes sense.

Margaret Eldridge:

Danielle, what about you?

Danielle Barnes:

Yeah, and I just want to share that I had the chance to work with a lot of authors through Women Talk Design, and that’s something that I’ve heard as well. It’s like talk about and write about the work that you want to be doing, not just what you’re doing already, because-

Leemay Nassery:

I love that.

Danielle Barnes:

Yeah, folks start to see you for that and reach out to you and you can get more opportunities. The book, it comes out next week, so we’ll see, I guess what comes from it. But just already. one of the things that I’ve noticed, we’ve been talking about the book a lot, and when people know that you’re working on this thing, they want to support you. We’ve gotten connected to podcast hosts and I’ve spoken more podcasts than I’ve ever had getting to share this idea and have gotten to meet just so many wonderful people.

I’ve had a lot of folks from throughout my career also reach back out when they’ve seen that I’m working on the book and use it as an opportunity to reconnect. I’m excited to see what comes from this. I think we’re still very much at the early stages. But something that we did that I would recommend to others is to talk about the book even before, I know there was a question, I think about marketing too before you release it, which I think both helps with marketing, but then also can help build some of those opportunities before the book even comes out.

Margaret Eldridge:

Awesome. Well, we did get a question. “How do you prioritize writing a book and working it into your schedule?” From Michelle. And I think we answered that. I’m not sure if maybe Michelle wasn’t around. But yeah, all of us say that we can’t put any energy into writing after work. We all seem to need a dedicated time set aside for that.

I don’t know if that’s true for everybody, but in this room it is. Any other questions? I don’t see any. I think that was the only one. We only have about five minutes left, so if you guys want to share anything about your book, maybe where to get it or maybe something, just an insight that you gained through writing the book. Why don’t we use the last few minutes for that? Danielle, do you want to go first on that?

Danielle Barnes:

Yes. Sorry, can you repeat the question one more time because-

Margaret Eldridge:

Oh, I’m saying just wrapping it up, tell people where they can get your book and maybe share just an insight that you gained from writing it that you didn’t have before.

Danielle Barnes:

Yeah, sure. Let’s see. Right here. This is what our book looks like. Oh, it’s a blurry background, but Present Yourself, if you go… And there’s my dog because we’re getting book deliveries. I’m going to pass it to Leemay because my dog’s going to take a second and then we’ll come back.

Leemay Nassery:

Sounds good. I can go real quick. My book, Practical A/B Testing, you can get on the pracprog.com and on Amazon. Check it out, please. I have a few jokes in there, so if you read through the chapters and see, will spots some jokes. You’ll get an insight into my humor. And then insight that I would share, I’d say writing a book has made me a better writer for everything, for emails, for documents at work. I’m constantly writing documents to pitch ideas, proposals, push ideas forward. I would say all of the insights that I gained from working with my editor, Vanya, at Pragmatic has made me a better writer. And the better writer you are, the better you’ll be able to do in so many facets of life, just convincing and persuading people [inaudible].

Margaret Eldridge:

Awesome. So apparently a bunch of more questions popped up that, I guess they were in the chat, but I was on the Q&A. Any platforms or mediums you all recommend to start writing on before you test the idea of a book? So I guess blogs and that thing. Are you guys doing any blogging, and have you found a preferred platform if you are?

Leemay Nassery:

Substack, like I have a newsletter, it’s called Experimenting. Do you have something similar, Danielle?

Danielle Barnes:

Yeah. For us, it’s really great to be able to run the workshop first for years. I think that was a big way that we found that there was interest in this topic and we refined the topics a lot and it informed the outline of the book. That was big. Even now I’m writing on LinkedIn. I’m trying to share more about what I’ve learned in the process.

For anyone who’s curious about self-publishing, if you want to find me on LinkedIn, I’ve been writing lessons that I learned about the process. I think it’s a great and really important idea to start sharing your ideas early and by writing, by giving talks that can help develop the book. And that was, I think one of the big lessons that I’ve had through writing the book is just how important it is to bring in people to support you – and by sharing your ideas early, you can do that.

Margaret Eldridge:

Right. Very good. Leemay, there’s one directed at you that says, “As a technical female, how have you found the marketing side of writing a book to be?”

Leemay Nassery:

That’s a really good question. It is, and a vulnerable answer is, I was shy at the beginning, because it is a very technical concept. I had a little bit of imposter syndrome, but I hate those words. But that left. I think once it was out in the wild, and once I got pretty good feedback from colleagues and from friends, I then started pushing marketing a lot more. But I was timid at the beginning. I was, truthfully, I was more timid than I probably should have been. And I hope not to be like that in the second book. But it is hard. There’s [inaudible]

Margaret Eldridge:

And I think the thing that some authors don’t realize is that people like to celebrate with you your accomplishments.

Leemay Nassery:

Yeah, I had a lot of support.

Margaret Eldridge:

Don’t be shy about it. And also while you’re at it, boost other people. If you see somebody who’s written something, they’re not your competition. Support everybody and good things will happen. So it looks like we’re out of time. Angie is back. Hi, Angie.

Angie Chang:

Thank you all for all your insights and encouragement for us to become such experts that are recognized and published. We’ll be looking forward to reading your books, the ones that are coming out soon, and the ones you’ve already published.

“Explore How Generative AI Can Enhance Your Career”: Eileen Quan with 2U (Video + Transcript)

In this ELEVATE session, Eileen Quan, Senior Director of Data Science at 2U (the company behind edX), outlines five key skills for working in generative AI: technical proficiency, model understanding, ethical AI, problem-solving and creativity, and communication and collaboration, and recommends resources and courses for learning these skills, including edX.org, Realpython.com, and huggingface.co. 

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Transcript of ELEVATE Session:

Eileen Quan:

Thank you and welcome, everyone. I’m here to talk about elevating your career through generative AI and a high level overview of the skills that you need to work in this new industry.

I’ll quickly give you a recap of what I do and who I am. As was mentioned, I’m senior director of data Science at 2U, a publicly traded edtech company based out of Maryland. We partner with universities and colleges as well as corporations to create and deliver digital content education offerings on our learning platform, edX.org, which may ring a bell for some of you who are familiar with MOOCs.

At 2U, I lead a small team consisting of a data science manager and analyst on product initiatives such as platform feature experimentation, learner behavior analysis, and lead prioritization modeling. I worked at several edtech companies, recently serving as an advisor for CareerFoundry, which is another tech boot camp based out of Germany.

My career path to working with LLM models was obviously not a linear one. I have a background in journalism, which I found to be a very useful skill to have in crafting compelling stories using data.

I’ve switched careers over the last decade, working at a TV station to managing, backing up server banks and writing PHP scripts for e-commerce sites. I eventually fell in love with working on large data sets and validating business assumptions using natural language processing, which ties back to my love of words.

The benefit of working for an edtech company is that I have first-hand knowledge of the type of jobs that are in demand and what skills people are eager to learn. Here at 2U, we have over 4,000 digital offerings, ranging from master’s degrees all the way down to free MOOC courses, working with over 230 partners across the global university and corporations.

For the last several years, we’ve tracked the growing interest in AI landscape and found that the fastest growing courses that adult learners are interested in and enrolling in are in the AI and data science field.

We’re all aware of how generative AI is accelerating at a very fast pace. As you can see, the growing popularity has exploded in the last couple of years due to faster computational capacity and neural networking algorithms. They’re way more efficient than they were five years ago.

Many of the big tech companies that you’ve obviously have heard of have launched their own LLM models to the world for us all to experiment with. And as you could see from this graph here, there’s hundreds of startups that have launched applications and tools built office technology, and they’ve released open source software and APIs for others to build upon.

A lot of these industries, healthcare to finance, retail to entertainment, and of course education, we’ve seen how generative AI can boost our productivity and automate some of the more mundane tasks that we do right now and make us more efficient at work.

At a business level, the growth and automation means increased demand for AI specific skills to design, build, and maintain these systems.

What does this mean for you? Employers are prioritizing creativity and problem-solving alongside expertise in generative AI tools for roles in software development and data science, and product managers and business stakeholders are expected to understand the nuances of this technology and apply it to specific use cases.

Using AI also requires recognizing its limitations. The tools for us that are in use out there right now are still more experimental than production ready. It necessitates human oversight. Hallucinations have occurred with chatbots and computer vision tools that misinterpret data, outputting responses that don’t align with the intended prompts.

One specific example that came out recently, I think a week ago, was Google’s Gemini chatbot featured a diverse version of America’s Founding Fathers. You might have seen some of the news on that and how interesting it was. People have created other memes off of this. Obviously, this technology still needs human intervention to verify the outputs generated to detect and address these issues.

What skills do you need to keep up with demand? When I think about the skills, there are five large buckets that I feel are important: technical proficiency, model understanding, ethical AI, problem-solving and creativity, as well as communication and collaboration.

I designed this developer skills roadmap that I’ve used in my work right now, and many of you are likely already using some ChatGPT or LLM model like Claude or Perplexity. Prompt engineering or crafting instructions for AI systems to output the best possible result is a great starting point and a good skill for everyone to learn.

EdX.org has a free Intro to Prompt Engineering course that highlights some of the strategies to use when tailoring your responses, and it’s also crucial to have a good foundation in machine learning and Python programming.

It’s good to learn about basic neural networks and gain practical experience implementing the simple models, and TensorFlow and PyTorch are two of the more popular deep learning Python libraries that data scientists regularly use to train their neural networks.

Realpython.com provides tutorials, explain the difference between these Python libraries in the best use cases for each.

Huggingface.co is an open source community platform with thousands of ML model variations and open source data sets and sample applications and code that you can actually try out on your own. It’s also good to have a strong grasp of mathematical concepts such as linear algebra. Probabilities and statistics is more or less fundamental for machine learning models because those models are essentially mathematical equations.

Deeplearning.ai offers a free Generative AI for Everyone course, which I’ve taken several months ago. It’s a really great course that’s run by some people that have been working in generative AI for years, and it covers how LLM models work and how the technology can be applied to any industry.

It’s also a good idea to read up about the underlying architecture in the three common models that’s permanently used generative AI. There are GANs, VAEs, and transformers, and I’ll talk more about these models in the next two slides.

But lastly, it’s good to have a specialization. There’s a lot of learning that’s out there, and there isn’t obviously much time to learn everything, and things are changing so fast. So based off the three models I mentioned, they all work very differently and they won’t apply to every business case.

For example, our data science team has used NLP models to analyze students’ learning data such as their performance on assignments and homework and projects. And we use these data points to train our model to auto-generate personalized learning sessions, to provide resource recommendations, additional reading material or exercises tailored to our specific learner’s strengths or weaknesses.

What are these models I mentioned previously? There’s generative adversarial networks, variational autoencoders, and transformer-based models. These three frameworks power most of the applications that are out there right now. You probably have used some of them in some context.

GANs are commonly used for generating media like images and phrases. It consists of a generator, a neural network, and another neural network called a discriminator that work adversarially to generate new data. They’re probably used in entertainment, design, and fashion industries to produce creative content. Whereas VAEs, or variational autoencoders, generate new data by training on existing data sets, expanding those data sets without compromising the quality.

Architects and industrial designers, for example, would use VAEs to create 3D images for new products by training it on 2D models. And stable diffusion is one example that incorporates the VAEs to encode images into a latent space for training and then decoding them back into realistic images during generation.

Lastly, transformers. This has really revolutionized natural language processing. Transformer-based models are what most of us probably are aware of and are learning as well. These models can generate text-based content from diverse sources and create images based on written text descriptions.

Transformers are designed for processing sequential data, especially in natural language processing tasks by using sub-attention mechanisms. ChatGPT, Claude, a whole host of other chatbots are popular examples of applications built using transformer-based models.

Some of the work that I do requires wearing a product manager hat. So I found that some of these strategies are helpful in framing the use of gen AI tools for stakeholders. And as with any project, it’s always a good idea to evaluate your business’ current AI capabilities, like should you build a chatbot for your site or should you just buy one that’s already out there? Taking a look at your data infrastructure, for example.

Do you have enough server space and memory to train and maintain a model? Or do you need to invest in more capacity and resources? And then your engineering team proficiency, how big of a technical lift will this be? How much time will it take for your team to actually learn a new skill? And will gen AI add value and solve a business problem or just be a distraction for your teams?

And if everything is a go, introducing generative AI gradually is a good starting point. You would probably look at the non-critical processes that can be easily automated to allow your teams to adjust without major disruption.

It’s also a good idea to take a specialized course like AI for Leaders or AI Chatbots Without Programming, both of which are on edX.org platform, to enhance your background in AI product understanding. Strategic alignment is very important to ensure that the integration of AI models actually match your broader business objectives of product innovation, customer experience, enhancement or efficiency.

Lastly, embracing innovation within limits. The technology is still experimental stage as we have seen with hallucinations, chatbots not giving you the right information, they’re making things up.

It’s a variety of different issues that have come up. There’s bias in some of the data sets that are used by chatbots as well, so always a good idea to keep that in mind. Then ensure that any kind of models that you do eventually work on, you put in guardrails to make sure that there are certain ways that the results that come out are more accurate and then less of a hallucination or inaccurate.

Some key takeaways here based off of what I’ve talked about. Foundational concepts in mathematics, statistics, and programming fundamentals are a good starting point to learn. Learning about data pre-processing and model training and those different data sets, text, images, audio, and video.

Practice coding in Python along with TensorFlow or PyTorch frameworks. Those are the two frameworks that data scientists use pretty regularly with Python.

I know that people have mentioned R at this point or they ask me about R. R is a good tool to use primarily for research papers and high level analysis because it’s very mathematical intensive, but Python is one language that a lot of businesses regularly use.

I strongly suggest learning Python as a good starting point rather than R.

Understanding neural networks, training algorithms, and model architectures like the three that I mentioned, GANs, VAEs, and transformers, and knowing which one to use and which one would be the best use case for your business. Follow AI blogs.

I’m signed up for a number of AI blogs, so there’s a lot of duplicate information that does come through, but most of the AI blogs I sign up for are actually at specific companies, like OpenAI has a wonderful blog that covers a lot of the things that they’re currently working on. They also have a GitHub open source…GitHub open source site that you could take a look at some of the models they’re working on and they also provide you with some specific examples as well.

It’s a good idea to keep up with the new technology since it’s constantly changing and new developments are occurring, new startups are actually launching very similar things, but really based on more or less similar models that are out there. And also keep in mind about the ethical considerations around AI usage.

There is a lot of bias, a lot of stereotypes that are floating in the data space, so it’s good to keep an eye on that and then definitely call it out and then make sure that these companies are aware that they might be giving out misinformation based off of their tools or products that they release.

Also, good idea to work on personal projects with others in the field or participate in open source AI initiatives to build your portfolio that showcase your new skills. AI projects often require collaboration with a host of different teams at your company, so problem-solving and adaptability are just as important.

Lastly, I think we all have an opportunity to drive innovation by pioneering these new gen AI tools and applications across a host of different industries, and we can all work together to ensure inclusivity and prevent biases in AI systems.

I’m really hopeful that most of us will steer in the direction of AI research and development, building new tools that will combat a variety of different things versus just creating tools that would automate some of our processes that we have.

I hope that this would inspire more women to join the dynamic field. Thank you. I do have an appendix that I can share out with everyone that has a list of all the links for the companies’ blogs as well as additional reading material and organizations that are very AI-specific that you can take a look at.

Sukrutha Bhadouria:

There’s a question for you in the Q&A section. It says, “Please share the list of courses you are recommending, especially the ones for leaders or for those that are not coding directly.”

Eileen Quan:

Yeah, I can share that actually right here. I put in a Google Doc. Hopefully you all can see it. Perfect. Great. On the appendix, there’s some AI specific organizations I listed. There’s a variety of them, so please take a look at that. The gen AI models, that’s specific to, there’s a white paper in there as well as introduction to variational auto encoders, but the other three above that are really good explanations of how transformers and generative adversarial networks actually work so it’s very clear. Those are really good links right there.

In terms of the learnings, I linked the deep learning AI. The edX.org site has a number of classes that you could take, and a lot of them are free. Huggingface.co is also the open source platform dimension that has a number of different ML models on there as well. That’s really useful because you could take a look at that and see what other people have built.

A lot of it is open source, so you could download or take a look at their GitHub account and then make a duplicate of whatever work that they did and then build upon that, which is really nice. It’s a good learning. There’s a lot of people that put the tutorials on huggingface.co as well, and then link it to their GitHub.

AWS, Google, OpenAI, NVIDIA, they all have blogs which are very useful. It spells a lot of things out about what they’re building and how they’re developing their models. I think those are a lot better than some of the other ones that are out there.

Definitely take a look at that, and then realpython.com, that will teach you from the basics of how to code in Python all the way to the more advanced stuff. And I highly recommend that site as well.

Sukrutha Bhadouria:

Thank you.

Eileen Quan:

And then-

Sukrutha Bhadouria:

Yeah. Sorry, you were saying something?

Eileen Quan:

Oh, no, no, go ahead. I think I might be close to time.

Sukrutha Bhadouria:

Yeah, I think we’re at time. Thank you, everybody, for dialing in and thank you, Eileen, for the wonderful content. I hope you all continue to have a wonderful and enriching rest of your day. Bye.

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“Leading With Quiet Strength, Overcoming Layoffs, & Surpassing ‘Not-Enoughness'”: Wen Hsu with Wen Coaching (Video + Transcript)

In this ELEVATE session, Wen Hsu (Founder, Wen Coaching) shares her own journey of initially doubting her ability to be a leader due to her introversion and inclination to stay in the background. However, after a conversation with her manager made her realize that her introversion could actually be a strength in leadership, she started showing up differently, embracing her introversion, and focusing on listening and creating space for others. 

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Wen Hsu ELEVATE conflicts are beginning of us understanding each other

Transcript of ELEVATE Session:

Wen Hsu:

Thank you, Angie, for that warm introduction. Before I begin, I actually like to get to know you a little bit better. Use the emojis or chat and I have a few questions for you. Who self-identify as introverts here? All right, I see quite a few. Thank you. Who here has experienced layoffs? Thank you, I see a bunch. Who here sometimes struggling with the feeling of not enough? Thank you.

Lastly, what is that one thing that’s preventing you from leading, given it be your team, your career, or even your life? Put it down in the chat, this is important because we will get to turn it around in the end, okay? Thank you for your openness. My leadership journey starts about 10 years ago in a one-on-one conversation with my manager. Basically, he caught me off guard by asking me question, “Hey, Wen, what do you think about getting into a leadership role?”

My knee-jerk reaction is a mix of disbelief and panic, honest. Like, what are you thinking…? I scream inside my head. I’m not like those leaders I see in the news or even at work, they’re outspoken, charismatic, they can talk to people with ease and confidence. I’m not like that. I’m an introvert. I claim to my boss, it’s not possible for me to be a great leader.

You see, as a first generation Taiwanese-American, introvert, lesbian, and woman in tech, my natural instinct is to blend into the background. My cultural upbringing and my social cues have taught me well, keep my heads down, stay quiet, work really hard and don’t challenge authority.

I have perfected the art of keeping myself invisible and safe, or I thought. Until this very conversation with my manager, and before I can dismiss that idea from his head, he started telling me what he saw in me, what I already did naturally as a leader, those qualities I didn’t see for myself. As I slowed down and looked at him, he’s one of the best bosses that I ever had, yet he’s way more introverted than I am. He is actually the living proof that my belief, introverts can’t be great leaders, is simply not true.

With that, it sparked a radical thought. What if my introversion actually makes me a great leader? And that’s always so foreign to me when I first saw it. But since I see it, I might try it on like trying out a new lens and it’s the first time I looked at the world through that lens. And then I start to show up differently. I take on more responsibilities. I lean into my introversion as a strength, not a hindrance.

I listen attentively and I create a space for people to be heard and to grow. When I see things that I really believe in, I fight passionately. And all this time I see this rule, it is the truth, but it’s only when I wear this new lens I see for what it is. It is basically an outdated script that no longer work for me. The best thing is I get to rewrite it.

As I step into the leadership journey, it’s not without its challenges. After a while, I start to get feedback like, “You need to be more aggressive. You need to speak more loudly.” Or, “You need to show less emotions.” Basically, act like a traditional leadership archetype. And I tried, I adapted, I prioritized what’s expected of me over my authenticity and it worked.

I got promoted, but I feel like it’s not me who got recognized and promoted, but a carefully crafted image of me where I really left a part of me out, feeling rejected in the process. As I grew in my responsibility, grew my impact. I also became more and more unhappy. At my lowest point, I started seek out help. At this time I actually found myself a coach, a coach who helped me to see what’s behind all these challenges that I face.

What are some of the rules that stopped working for me and the rules that have given me so far, but really left me feeling unfulfilled. I started to really stay open and curious to challenge each of them. One example, I need to take on more to prove myself over and over again. Sound familiar? I changed that to, okay, I can voice opinions and I can push back as needed.

I went from, I’m just so uncomfortable with conflict. I want to avoid them at all costs to conflict are actually the beginning of us understanding each other for real. And the other, I need to be the expert in the room, probably do everything alone to reach the highest standard and to I see asking for help as a strength, not a weakness. I get to leverage everyone’s talent.

Each of the mindset shift, each of the rule that I turn around and not only earn more respect, but I get more time, more space to focus on what’s truly important.

I teach those around me to do the same, and when I start doing this, every rule that I turn around, I show up more congruently and powerfully and as things are going well. But my leadership really ran into the biggest test in April 2021. I was about to be promoted again.

One day I was called into a Zoom to have a one-on-one with my skip level for the very first time since he joined two months ago. Little did I know that was also my last one-on-one with him because I was let go in that mass layoff. He didn’t tell me the reason why I got let go.

So my self-doubt went crazy. Was it my gender, my accent, how I look, how I sound, how I lived? Basically, all these questions really crushed my confidence. Regardless of my track record of success. This layoff is like slapped in my face telling me you are not good enough for someone. And for months after that, I clung onto anger as if that makes me right. And the bad guy wrong.

He was the one who didn’t even take the time to know how good I am, how dare him to make that judgment call? And I was in pain. And after a while, I just feel like I couldn’t do it anymore. So I really open myself up and examine the situation. I take a step back. And now I see it. I literally gave my power away to a guy that I don’t even know and I make sure it’s his responsibility to validate my worthiness.

And it turned out I was the one who mistreated myself. I was the one who keep feeding myself anger and resentment when what I need is actual acceptance and love. I have fallen into the rules that I learned since I was a kid. I relied solely on external validations, like money, like title, like productivity, to feel my self-worth.

Oh man, that realization really hurt. Yeah, that’s what I did to myself for months, years, or even decades. The good news is once I know I’m the one that outsourced my self-worth, I can stop it. I can apologize to myself, forgive myself, and hug myself without judgment. I can take that responsibility back to make myself happy and own it completely.

And at this time, I have been developing my coaching business on the side for a few years, but I was too afraid to take that leap. And I see this layoff as a not-so-gentle nudge from the universe saying, “Wen, it’s time. It’s time to follow your dream and do what you love.” And that is when I decided, okay, I don’t want to work for other people anymore.

As scary as it was, I decided to launch into solo entrepreneurship, be my own boss of my own career and coaching business. For the longest time I’ve been following those old games and old rules as if they’re the guiding principles to success. But whose success is it really for?

It’s only when I’m open and curious to challenge my own stories, my very own identities and those old beliefs with each questions, I carve out a path for myself that’s more authentic to me. I get to lead with love, integrity, and creativity. And don’t get me wrong, those rules and expectation, they are still there demanding me to following them all the time.

But it’s only when I’m open and be curious and be courageous enough to try out something different, I find a life that’s much more fun for me. It’s not easy, but it’s so much more fun to live this way. This is important because I know when we are able to question what we blindly follow is the new rules that no longer work for us. We get to take the ownership back to create our own rules. When we do that, everyone benefits.

We get to create a world that’s more inclusive, empathetic, creative, and prosperous. That is why, regardless of my introversion, nervousness, I’m standing here in front of you sharing my truth and also sharing that possibility with you. Remember the last question I asked you, what’s that one thing that’s holding you back?

What are the rules behind it? Are they still working for you? And if not, are you open, curious and perhaps brave enough to create new rules so we can reinvent ourselves? Thank you. Oh, and I think thank you all for all the emojis and chats. Thank you. And I know when I say this, probably a lot of excitement, disbelief, confusions, or anger that might show up. I am having a mentorship office hour tomorrow at eight. Feel free to pop in and we can certainly talk a lot more about this very topic.

Okay, so I don’t see any question yet. If you do, you can ask now. Actually, I thought I went over and I guess I was just so into it. Thank you. Thank you. And I think one thing I want to call out, when we take that first step, it really seems very scary. Okay, so let me finish that thought. The thing is though, like I said, trying out a new lens for the first time and look for evidence of what’s fitting you better.

In that way, we can make it again. Question, how do you own your LGBT identity in the workplace? Great question. I’ve been hiding my own identity for, I don’t know how many years. It’s interesting, the first thing that I learned actually was from another lesbian who just casually out to me, “Hey, me and my girlfriend.” Blah, blah, blah.

I was like, it blew my mind. I’m like, oh my God, I cannot believe someone could be out like that. I think the first thing and most important thing is to not feel like it’s something that you need to hide. And of course, I think I’m lucky and in this tech space where I feel like people are definitely more inclusive, open to sexuality like this.

I show up as if and now it is true that it is actually something that adds to the richness of my life. Even on LinkedIn, what I talk about me and my partner…we just got married last year…all the time. And I think when we show up as that’s the best thing happened to us, for us, then people feel that with you. So yeah, thank you.

I don’t know if there are other questions. Oh, how to bring your whole self into interview process? Great question. I always think about interviewing is like dating. If you hide part of yourself, even if you get into the relationship, it’s not going to work out. Initially, maybe we want to present our better side, but in a way, you really want to know and ask yourself, interview is about fit.

It’s not about who is better, who is higher, who have more power, but am I here to bring out the best of me and help to achieve better good? When I approach interview in that sense, I not only do my best, I show up because I don’t want to make the wrong judgment in a way when we pretend, that’s not the case. We could waste years, right? Similar to dating, I would just show up. Yeah, thank you.

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“Navigating the Engineering Manager Odyssey”: Megha Krishnamurthy (Adobe), Namrata Ghadi (Workday), Nono Guimbi (Airbnb), Seetha Annamraju (Cash App), and Joya Joseph (Hinge Health) (Video + Transcript)

In this ELEVATE session, Joya Joseph, an engineering manager at Hinge Health, moderates a panel with Megha Krishnamurthy, a senior engineering manager at Adobe, Namrata Ghadi, an engineer and tech lead at Workday, Nono Guimbi, an engineering manager at Airbnb, and Seetha Annamraju, an engineering manager at Cash App. They discuss various topics related to being female managers in the tech industry, including transitioning into and out of management roles, maintaining career development for ICs, building team culture and trust, and building confidence in team members. They also provide advice on preparing for interviews in the current job market, emphasizing the importance of networking, research, preparation, and having a support group.

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Seetha Annamraju ELEVATE request career check ins with your lead

Transcript of ELEVATE Session:

Joya Joseph:

Hi. Welcome everybody. I am Joya Joseph, I am an engineering manager here at Hinge Health. And I am joined by some awesome people and some awesome women who are going to also introduce themselves. We’ll start with Megha.

Megha Krishnamurthy:

I’m Megha Krishnamurthy, I’m a senior engineering manager at Adobe. Until recently, I’m in the transition phase to move to my next role. Which, connect with me on LinkedIn and you’ll see more about it. I’m so excited to be part of this panel. And thank you Angie and Sukrutha for giving me this opportunity.

Joya Joseph:

Thank you. Namrata?

Namrata Ghadi:

Hi, I’m Namrata. I am an engineer and tech lead at Workday. Not too long ago I was also an engineering manager, but I transitioned back into an IC recently. And I hope to share some of my experience both as a manager as well as IC.

Joya Joseph:

Thank you. Nono, can you introduce yourself?

Nono Guimbi:

Yes. Hi everyone. Hello. My name is Nono, I’m an engineering manager at Airbnb, and I have been there for three years. And I have been an engineering manager for about seven years now, and I’m really super excited to be here with you.

Joya Joseph:

Great, thank you. And not least, Seetha. How are you?

Seetha Annamraju:

Hey everyone, my name is Seetha. I am an engineering manager at Cash App. I’ve been here for close to four years now, and before this, I was mostly doing Android development. Really excited to be here.

Joya Joseph:

Now that introduced ourselves we will start talking about… As you can see, most of us, or all of us really, either been in management or have moved out of management and back into tech. We’re going to talk about all the things that are important to us as managers, especially as female managers in the spaces, what we have gone through. Some things to help you understand what management looks like, and also, how to transition in and out of management.

To get us started, let’s talk about the transitions. All of us have either transitioned in or out. How did you transition into management or tech leadership? If anybody wants to take that?

Nono Guimbi:

I can start.

Megha Krishnamurthy:

Go ahead, Nono.

Nono Guimbi:

Okay, thank you, Megha. I actually wrote a post about it recently on my LinkedIn, so if you find me, you will really see the long version there. I transitioned back in 2000, I think it’s ’17. Yes. I joined this company called Pandora, they do music, and I was a senior software engineer. I joined around May. In August I was an engineering manager. And if you had told me that it would happen this way, I would have never thought that this was possible. What were the steps there? When I joined Pandora, I joined as an engineer, software engineer, so my expectations were to do a software engineer role. I joined just before the career conversation time. I really loved the company, I really loved my manager.

This was the first time for me I was witnessing a manager I could really relate to. I was like, “Oh, I could do his job. I really like what he’s doing. It doesn’t seem really that scary.” During our conversation he told me, “Hey, Nono, what do you want to do later?” I’m like, “Oh, I think I want to be a manager.” Back then, I’m like, “No, but not now, I know I just joined. So it’s not now, but maybe later, maybe in a year or so. Maybe later.” Then he said, “No. You know what? I can really see you as an engineering manager. You have all those skills, so you will do fine.” I was like, “Oh, you really think that?” I said yes. That was the end. And times went by, and I think in June or July there was an opportunity in one of our sister team and he sent me a Slack message and say, “Hey, Nono, there is an opening right now for an engineering manager position. Do you want to take it? Do you want to try?”

Because I had to go through the interview process. I was so excited, so scared, but I said yes. I did all the prep work to understand, what is the interview process to become an engineering manager? What do I need to know? I know that there were a lot of questions where I wouldn’t be able to answer, but at least I tried to think about what would I do if I was put in the situation. And really, I know that many people tend to think that, yeah, it takes time, you need to demonstrate this or that.

There are always sometimes some opportunities. I think that I was there at the right time. What really played the role for me that I really shared what I wanted with my manager. I think the piece of advice here is really to share what you want, to talk about it. Because if you don’t talk about it, nobody is going to know. If I have not done that, my manager would have never proposed me this opportunity. I created my own chance when I did that.

Joya Joseph:

Thank you, Nono. It’s really important to know where you want to go. Let somebody know, and then they will advocate for you and help you get there. That’s basically the takeaway from that. And then that’s the IC to management pivot, now we’re going to look at the other way. Namrata, how and why did you transition out of management? We heard about Nono transitioning into management, but how did you do it? And what should a manager, going back into the IC role, understand about that move?

Namrata Ghadi:

Right. I’ll talk about me personally on why I made that decision to go back into the IC role. When I decided to become a manager, I was already part of a team. I had good understanding of the product that we were building, and I felt confident that I can survive as a manager within that team. Sometime back AI took over, and I quickly realized that because everything is becoming fast-paced, and we all have to upskill ourselves to the new technologies in the AI space, I personally feel very confident of leading a team when I have hands-on experience with it. That gives me a sense of confidence.

Also, the role in my org, at my company, requires the engineering managers to also contribute towards code, to brainstorm with the senior lead engineers and tech leads to design the solution, and also show the direction or show the path forward for the teams and the feature. Considering all of these things and the fact that I wanted to get more experience in AI, I decided that I wanted to transition back into an IC role.

Joya Joseph:

Because you have sat between both, you’ve been managing, you’ve been IC, what are the things that you feel that managers often forget about being a tech lead or being an IC in a team?

Namrata Ghadi:

Yeah. When I was a manager, one of the things I always felt was that there is always this sword on your neck, or whatever they say. That there are timelines, there are customers that you have to satisfy, there are roadmaps that you have to align to, and deliverables. As a manager, I thought that it was sometimes difficult to convince the engineering teams to be on the same page with those timelines. Considering that now pretty much everybody has to adapt to the fast pace that our current industry is currently experiencing, sometimes that you will experience as a manager a pushback from your engineering team. That’s when you have to be a little understanding towards where they come from, and also understanding of where the upper leadership is coming from, and try to establish that balance between the two. I think that was one of the things that I personally felt was hard to work around and get the team onboard with those things.

Joya Joseph:

Awesome. It seems like the communication, it’s almost a different job. The communication though is the one thing that is constant between those two jobs, and so if you don’t have that communication, it all falls apart. And we’ve all worked on teams where it all falls apart.

Namrata Ghadi:

Yeah, definitely. Communication and transparency, being transparent both ways is really important.

Joya Joseph:

Very important. Megha, now you had raised your hand a little earlier for that question. How did you navigate the IC to manager transition? What was the epiphany that, “Megha needs to be a manager”?

Megha Krishnamurthy:

Sure, Joya. In my case it was a very intent-driven transition. One of the things we spoke, like Nono mentioned, is knowing what you want in your career. I had that very clear in my mind that I want to move towards this role for multiple reasons. As an IC and a tech lead, I enjoyed mentoring other engineers. I enjoyed having broader conversations, learning about the business. The collaboration aspect of it, bringing the team together, and all of these things.

I look at what my manager is doing for the team and I knew that’s the direction I want to go in, and I started taking baby steps towards it, finding opportunities, being willing to take up challenges outside of your comfort level. And communicating to the leadership and my management, this is the direction I want to go. When there is an opportunity, they can provide that to you and consider you as one of the candidates.

I also intently took some of the… Learning is a very important aspect. These two are very different roles. I invested time into taking some leadership courses at that time provided by eBay, and also tools like LinkedIn Learning, Coursera, and so on.

In order for that to happen, that transition, there has to be two things. There has to be a readiness from your end, and there also needs to be a role available. There are two aspects that are required to make that happen. I was working towards that, and interestingly enough, I completed around five years at eBay and I was looking for a role within for the last one year there. Since I was not finding those opportunities within my org or in peer teams, like Nono had that opportunity, I ventured outside.

I looked at other companies and other roles, and definitely building strong relationships and partnerships across orgs in the company, and having the mentorship definitely helped there. Because applying to an engineering management role without having the experience, people usually don’t even look at your resume or consider it. Given that relationships that I had and when I applied to this role I was interviewed for, and I moved into that role. That was my journey into management, very intent-driven, and I took all the steps that I need to get myself ready for it.

Joya Joseph:

Right. If you want to move into it, basically I’m hearing is sometimes it’s not at your company. And sometimes, as they say, you hit the glass ceiling at that company, but there’s more movement that you can find outside your company.

Setting yourself up to get into that role, putting yourself intently into that role sometimes is a path. I know some people unfortunately also get thrown into that role, but having that understanding of where you want to go. Keeping in with career development, now we’re managers. Now, other people’s careers are important for us to lead them forward.

Seetha, coming to you, how do you maintain the career development of your ICs as a manager? How are you best making sure that they are able to achieve success in what they want to do in their professional endeavors, as a manager? What are some good best tips for that?

Seetha Annamraju:

Yeah. One of the things that I like to do… Background is that I didn’t have a lot of good managers. It felt like the most important thing for me should have been career development, and I wasn’t really getting that, so when I became an EM, I was like, I’m going to make sure that this is my top priority.

One of the things that I do is I set up quarterly career check-ins, this is across all of my directs. There is always a recurring career check-in. do a red, yellow, green exercise with each of them against the career ladder that we have at our company. This is what I would recommend to other EMs as well.

There’s almost always some type of career ladder or promo-readiness evaluation that’s available. Doing a red, yellow, green with your directs and providing feedback during those check-ins, finding opportunities for where they can expand in those gaps are all good ways.

Then the other thing I would say to ICs is, understanding the career ladder at your own company, because almost every company will have some version of this. Taking the time to understand the process and then looking at past packets, if they’re available. All of these will be really beneficial for your own growth.

One of the expectations for almost every engineering manager is to help directs improve in their careers. You can request career check-ins with your lead. Set an expectation and hold them accountable, because this is one of the core responsibilities for your lead. And then request feedback at a recurring cadence as well. You can ask, “What gaps do you see for me at the next level?” And if they’re not able to answer, that’s probably a red flag, but at the same time you can go in and talk to your peers and try to figure out what those gaps are.

Then one of the other things I would say is, advice for ICs, sometimes I have ICs come to me and say their EMs haven’t been supportive of their career or their path to promo.

Always ghostwrite your own packet. You don’t have to wait for your EM to write a packet for you, especially if you’re not feeling that support. Because if you ghostwrite your packet, you’ll understand where your gaps are. You can find a trusted group of people that you can get some review done with and get some feedback at least to begin with.

These are my tips, request career check-ins. One of the things I do is identify gaps for my team. I try to find opportunities not just within my team, but outside of the team. Because in order to fulfill a gap, an opportunity may not always exist on your team.

Maintaining your peer relationships with other cross-functional leads or your peer EMs will help you find opportunities that your team can jump into to help in an advisory capacity, or one quarter a year or something like that.

One last thing is I try to offer, take at least half a day every week. Or take Fridays, especially when we don’t have too many strict deadlines that are coming up. Take a day and focus on the things that you want to improve for the company. And so that helps people venture outside of the team as well.

Joya Joseph:

Great. Thank you, that’s some great advice. Sometimes you have to take initiative for your own. Especially, I think we all have had bad managers at some point in our journeys. And so, instead of waiting for that manager to do the things that you need for yourself. Because your professional development, it’s also in your hands as well. And as managers, we’re also being managed as well. Nono, do you have any additional insights on managing your performance and making sure that you are seen, heard, and all that stuff?

Nono Guimbi:

Yeah. Another point I would add is adding career conversation. I don’t need to wait for my manager to set them. Every trimester we talk about, how am I doing? Where I need to improve. And yes, we use a career ladder as an example of the things that we want to demonstrate.

One thing which is important is that not everyone want to move to the next level. Sometimes we want to stay in the level where we are, and we want to grow, we want to acquire more skill to solidify some skills. Being intentional and talking about that, asking for constant feedback for improvement, or to improve on the things that we are doing well, not just with the manager but actually also with the peers. It also means, especially for the people who are trying to grow, understanding the process.

If, for example, you want to move to the next level, you need to understand, what is the process of this promotion? Who are the key actors? You can maybe start developing some relationships. There is a lot of thing that is going in the relationships that you have with people, and you can’t go into this process not knowing what will happen. And of course, get feedback.

Get constant feedback. I love the idea of writing your own package. And even if you are not ready for a promotion, asking feedback on that. A, if tomorrow I would go with this, what would it be? What would be the gaps? So being prepared. And it’s all in the intention that we set for what we do.

Joya Joseph:

Thank you. Thank you. Yeah, that wraps up performance, because I think it’s for everybody. It doesn’t matter if you’re IC, doesn’t matter if you’re a manager, we’re all held to a performance metric. Making sure that you’re aligning with what is expected of you and what you expect of yourself as well. You should be giving yourself performance management courses. Yeah, keep yourself true to yourself as well. You don’t want to get lost.

And that puts us into, we’re all women on this call, and we have been or currently in management. One of the things, how do you make sure that team building and hiring and team culture, and trust and all of that stuff doesn’t get regulated to us because we happen to be the women in the company and we’re good at that? How do you get that from you becoming the secretary of the management team essentially? How do you avoid that? Seetha, you want to take that one?

Seetha Annamraju:

Sure. I might have a slightly different framing for this question, but I don’t necessarily see this as a women-only problem. It is possible that women care about this historically, or tend to gravitate towards it. But to be clear, I think caring deeply about your team, team culture and building an organization where trust is high priority are all signs of an effective leader, or an effective team.

People underestimate the value of building a strong team culture and the trust within the team. But we also, at the same time, see that teams that have high trust and a strong team culture tend to be more resilient. They tend to be higher performing, they tend to create broader impact.

There’s two ways that we can probably ensure that women aren’t solely focused on team culture and trust. One of those is to hold leaders accountable. In opportunities that come up ask, how do we plan to address a lack of trust or strong team culture?

As an engineering manager, you tend to have a little bit more leverage than an IC in talking to your skip lead, or bringing this up as a recurring conversation. And this is something that I do, which is not comfortable at all. But, “Hey, here’s a gap that I see on this particular team.”

Then I see from that team, a woman I see has reached out to me and said this is a problem that they’re facing. Go outside of your comfort zone and mention it to… Luckily, we have a culture where I feel mostly safe to go to the head of mobile engineering and say, “Hey, this team doesn’t feel like it has the right combination of trust and safety to operate effectively. And I’m wondering if there are things that we can do here.”

Bring it up in a productive way that improves the organization. That can work sometimes, it depends on how well your leadership is aligned with this. But the other way is to just become that example. I’m not saying it’s the easier way, but it is a very clear way.

Because when you become an example and you showcase the impact of a high trust team or a strong team culture, you see higher performance, you see resilience and impact, and that does show up. If someone were to say, “Hey, Seetha’s team is shipping all this stuff, but nobody looks like they’re burned out, everybody looks happy and they’re a strong team,” that shows up, and why that happened, you can trace back. When people ask, I can talk about all these. I found that tends to be a little bit more of a motivation, the impact of having these, than the work to create that trust. Yeah.

Joya Joseph:

Do any of our other panelists have anything to add to some of that good insight there from Seetha? Since we all are women in these spaces, any examples of how you’ve become not the queen of morale, basically, at your companies? How to spread that culture, that team culture, so the other engineering managers that are not women can also adapt to that. Do you have any examples?

Megha Krishnamurthy:

I do. Interesting enough, I became the morale queen back at org, Adobe. It started out as, “Hey, we need to bring the team together.” Especially, I started working there in the beginning of the pandemic, and everything was virtual. New hires are not able to connect with the team, they’re all in our homes. Building that strong relationships takes a lot of effort to gather the team together. We are not meeting people in person. I suggested a few things we can do as an org. For instance, as simple as celebrating people’s birthdays, and in a virtual setting. Not necessarily on the day but a monthly one, because we can’t just have too many meetings all the time too. A monthly birthday celebration. I did start off.

After a few months I felt like this shouldn’t just be my onus to do this, we could share that burden and across different leaders in the org, like ICs, whoever, and share that. I’m not the one responsible for always setting these up, getting people in, sending those invites, whatnot. Then I brought it up in my staff meeting with my leadership saying, “Hey, I started this. I did this for a while. Now I need someone else to take this up. Another volunteer, could be one of you or one of someone from your teams to continue this.” We started sharing that responsibility. Because it’s not just my responsibility, it has to be a culture across the org and teams.

Joya Joseph:

Just to follow up on that, how do you improve trust? You need your team to trust you. You need the team to trust the company. You need the team to trust each other. How do you, as a manager or a tech lead, improve on the team culture and the trust in the team? Because I think trust is very, very important both ways. How can the team trust you? Anybody feel like they want to feel that one? Nono?

Nono Guimbi:

Yeah, I think that trust needs to be heard. We have to earn the trust. My way to build the trust with the people around me is to show a real interest for who they are. Not just as IC engineers, but even outside of work, because those people have a life. To really care about showing interest about what they want. To me, they’re where they are exactly. Whatever, what they are doing, whatever their last performance is, understanding exactly where they are with the company right now and building this trust and showing them that their success is actually my success.

I want them to really be successful. Because what people don’t often realize is that when we are able to promote engineers, or when people are doing well, it does reflect on us. Their success is completely directly tied to my success.

From there, it’s really building a relationship where I first can be vulnerable. I’m not afraid to share, not my doubt, but the things where I’m unsure. Being able to say, “You know what, I don’t know, but I’m going to figure this out. You know what, this also impress me, but we are going to find a way.” Being vulnerable and sharing about our weaknesses so they can feel that, okay, this person is not here just to be the one who has all the answer, but is also human being going through stuff. It allows them also to be vulnerable when they are going through certain things. The vulnerability, it’s tricky because you want the trust to be vulnerable, but you’re also being vulnerable builds the trust. So it can be difficult to know where to start. Use your intuition when you meet people, given what’s going…read the room. Read people, their face, what they are showing, how they are expressing themselves.

At the end it can only reflect on your relationship with those people, but also with the team. In team meetings, when you are showing up, really be true. It doesn’t mean that you need to be the one with making jokes and everything, but really care. If you see something, say something. If there’s an elephant in the room, talk about it and say, “Hey, I’m noticing this. Let’s talk about that.” Always share what’s your intention behind. People will really thank you and really get to know you, and get to understand your style and know that, okay, this person really care.

Joya Joseph:

Yeah. One of the things that I wanted to pull out as a small thread, is as leaders and as managers on teams, how do you help your ICs or other managers – because I will say other managers as well – build that confidence?

Because one of the things that I have noticed in my time is that many times the women engineers don’t have the confidence. It’s that, I am not supposed to be here feeling that we all get, or I’m not good enough feeling. As managers and as leaders, how do we build that? Because that’s part of that trust as well. And part of that development is, how do we build the confidence of our ICs and our fellow peer managers and leaders? Anybody can take that. That’s a big question.

Seetha Annamraju:

I have a thought on this. I think the question is around, how do we build confidence for peer EMs and also for senior ICs? One of the ways that I do this is I look at the way that I’m roadmapping or planning projects, and I always make sure that any women on the team, or basically ensuring that everyone has equal opportunity, and that almost always impacts in a positive way for the women, because they may not have gotten the same opportunity prior.

The career development check-ins really help with this because I’m honing in on the gaps that I’m seeing. I always say, “Here are your strengths, and this is where you can stretch in these strengths. For example, you write really great documents. I would love for you to speak up more in meetings when you feel comfortable. How can we create an opportunity to do so?” And try to create that opportunity.

Just giving a really easy way to provide confidence for ICs or peer EMs is to give them opportunities and say you trust them to run with it, and have their back even if something doesn’t go as well. There are sometimes situations where you may have someone own a project, and it’s not going exactly as it could. You use that opportunity to provide feedback, but always create the background necessary so that they can succeed.

One thing is you might say, “Hey, go talk to the senior leader to figure out how to do this piece of the project.” But before they do that, you reach out to that senior leader and say, “Hey, just a heads-up, this person is going to come to you for feedback. I would love for you to cooperate and help them. This is an area that they are going to do really well in if given the opportunity.”

Prefacing and setting that structure up for them to succeed, and then pointing back in these career check-ins or feedback, recognizing areas that they’re doing really well, finding peer feedback to support that.

I find that things like imposter syndrome or improving confidence, it’s usually just a data thing. The more that you just say, “Hey, you’re really good at this. Did you notice you’re really good at this? Oh, did you hear this person say you’re really good at this?” And then requesting peer feedback. We use something called Lattice, but there’s other ways that you can do this. Just electronically requesting feedback that you can document in some way, whether it’s public or private. And then showcasing that, allowing kudos, doing recognition and channels.

Those all I have found to help build confidence. But more than anything, trust comes with trusting them with a project. That’s really what ICs and peer EMs want. For peer EMs, I’ve noticed giving them opportunities to speak has really helped. I’ve gone out of my way to ask a peer EM that’s a woman to just say, “Hey, I think this is a strength of yours. I think you should share it with the org. I’m going to set up a really informal discussion, would you mind leading this?” And then gathering the people necessary, the audience for it. And then panels like this and things like that, those are ways that you create confidence as well.

Namrata Ghadi:

I would also like to add to that. It is also, I’ve seen what works is if you call out the work that they have done, the contributions they have made, that also helps in elevating their confidence levels. I would do that in my team time and again, especially for the engineers, be it women or not, who I would think were lacking in their confidence. If they have made some contributions that are worth calling out, then I would definitely make sure I do that.

Joya Joseph:

Yeah. What I’m hearing is mentorship through feedback, very important. And then visibility and call-outs, because you need them to see themselves as the great engineers that you can see. And getting to understand that, yeah, you’re great. Let everybody else see that they’re great. And also let them know that they’re great and where they can improve and expand.

I’m going to move us a little bit more into something that’s very more recent. We’ve all heard of the layoffs, there’s a lot of interviews and everything. As managers, because we are always on the other side of the table, we’re the ones that are interviewing people.

I know there are many people in our chat that probably are at that phase of their time. Speaking of hard and strategic things, how do you prepare for such interviews? What advice do you want to give to mid to senior-level women in tech about preparing for these interviews?

We all know the interview space has changed a lot recently. I know that, recently just got out of it. Any advice from the panel about interviewing right now and how to prepare, how to get into those interviews and get that job? Yes.

Megha Krishnamurthy:

I can go. I just switched jobs in this economic conditions. One of the few things that really helped me is getting… The first step of the challenge that I saw in this time is getting your profile noticed. The few ways I went about to make sure that at least happened, that at least they consider your resume for that role, is research the company.

Review the job description well to understand what is it exactly they’re looking for. Have your resume ready to showcase the contributions you have made and your experience relevant to that role and company. And connections, networking, reaching out to connections.

Referrals really go a long way, because companies are getting hundreds and thousands of applications for those roles now. So getting all these in place so your profile stands out.

And of course, being very persistent and resilient… Taking the whole process of interviewing and taking it more as a learning experience and feeling empowered. That’s the step one, getting that first interview call or the recruiter call. And then on it, really prepare, sparsing up the technical skills. Especially for managers, having all the situational examples, behavioral questions, examples for them handy.

Really understanding what aspects of that role can you fill in, and how can you bring your experience and strengths to the table. Preparing questions for the interviewers ahead of time. Really meaningful, thoughtful, insightful questions. I’m showcasing that interest, showcasing why you are passionate about that role.

Especially the moment you get a call with the recruiter, bringing in all those things to the table so you can actually move forward to the interviews. Because that I think is the bigger challenge right now, just getting that first call. Those are some of the things that really help. And definitely the network, LinkedIn network was very powerful.

Joya Joseph:

Yeah, lots about networking. Oh, go ahead, Namrata. Go ahead.

Namrata Ghadi:

And along with that I also feel, given the current times, it is also important to have your support group by your side when you’re interviewing, because it’s not going to always be a positive outcome. What you want to look at such outcomes is go through those interviews in your mind and think about where you could have done differently, and take those learnings with you into your next interview. But having that support group is also extremely crucial in these times.

Joya Joseph:

Yeah, most definitely. Having your…squad, I think I heard at one of our last ones, is having your squad with you along the way. I see Angie’s here.

Angie Chang:

Thank you so much for being part of our squad. I’m sure everyone here got some micro mentorship out of this panel, so thank you for sharing so much wisdom and insight from your career, the tactical management things to finding a job. Which, if you’re not doing it yourself, you definitely know someone who is, so feel free to pass on all the advice. And the fact that we’re doing this tomorrow as well, so stay tuned for more. Thank you so much, ladies, for joining us for Elevate. And see you at the next session.

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“Data-Driven Product Development: Leveraging Data Analytics To Build Great AI Products”: Kelsey Brown with Sourcegraph (Video + Transcript)

In this ELEVATE session, Kelsey Brown, a data analyst at Sourcegraph, shares her journey of building an AI product called Cody and the lessons she learned along the way about working with the product team, focusing on a leading indicator of product quality, and running A/B tests to improve the product.

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Kelsey Brown ELEVATE data and data teams need to be maximally flexible particularly in this AI environment

Transcript of ELEVATE Session:

Kelsey Brown:

Hi everyone. I’m so excited to be here and thanks to everyone for coming out to this talk. Now I want to start with a disclaimer. The title of this talk may be a bit misleading because, while I would love nothing more than to sit here and tell you that I have built the one correct way to use data to build AI products, the field of AI is just so new and it’s changing so fast that I don’t think the one correct way really exists yet.

What I’m going to do to is tell you a story about my journey and how I helped to build a new product in the midst of this AI boom that we’re in right now, and some lessons that I learned from my amazing coworkers and the overall experience along the way.

I hope that you all can take the story and adapt the learnings to wherever you’re at in your data journey or your AI or your product journey and one day even build on it.

First, a little bit of background information just to set the stage for our story here. As I mentioned earlier, I currently work in data analytics at Sourcegraph, which is a software company, and we sell a developer tool that helps engineers understand, fix, and automate code and coding.

The way this tool works is by indexing, or in other words, cataloging, all code repositories. This index makes all sorts of things possible. For example, it makes it possible to search for a specific piece of code and the way that you might search for a specific word or specific phrase in a document using control F. It allows you to even replace that specific piece of code with some different piece of code the way you could find and replace in a Word document. It does a lot more than this, and it does it all at really massive scale. Think thousands and thousands of files across a company’s entire code base.

When ChatGPT was released kicking off this huge AI boom, the team at Sourcegraph wondered, well, what if we combined Sourcegraph’s ability to index an entire code base with an LLM’s ability to ingest and interpret large swaths of data and built an AI coding assistant named Cody? It doesn’t just know general principles about coding and software, like any LLM might, but it also knows anything and everything there is to know about your specific code base and the repositories that you work in for your job or your side project or whatever the case may be. That would be pretty cool, at least that’s what we thought.

For me as a data analyst at SourceGraph supporting our product and engineering teams, this presented a really unique challenge. I had at the time, never helped our team or really any team for that matter, built out a brand new product, and I’d certainly never done it in a brand new industry.

There was no industry standard or roadmap or Stack Overflow thread for me to fall back on or to save me here. I also didn’t necessarily have a clear directive or ask. The goal was just for the data team to come in and help. It was hard to know where to even start, and that brings me to my first learning.

Now at this point, SourceGraph has been around for a while. We have an established data team and with that comes established processes for interacting with our stakeholders. You submit a ticket via our tracking system, we’ll respond based on our bandwidth and the urgency of the issue, and we’ll scope out a solution and get back to you.

Processes like this are really important for any team really, but in the early stages of building this new AI product, they were just too slow. I probably don’t need to tell anyone here that the most successful teams in AI right now are moving at warp speed. Everyone is trying to get their hands on the latest technology and build something better than anyone else has seen before. If our data team couldn’t keep up with that pace, then we risked just being left behind altogether.

What did keeping up look like? Well, it looked like abandoning some of our existing processes and being present wherever the team was. I often attended their stand-ups and planning sessions, which was a great way to understand their questions and priorities and blockers and figure out where data could help.

Being present also meant meeting them in their preferred tools. In our case, it was GitHub and Slack rather than asking them to go through our process of filling out a ticket in our ticketing system when they needed something from us. Even though it meant abandoning all of our regular processes, being embedded as a member of the team meant that I could more easily keep up with the pace of development, focusing on the most important thing that week or that day, or sometimes even that hour.

Being an embedded member of the team also meant that I could provide some immediate value by proactively anticipating team needs. When I first joined the project, I didn’t have a clear task or request or roadmap of any kind, so I just started with what we had. I searched through Slack and GitHub issues, documents, looking for conversations people were having, and then the questions that people were asking.

Then, I’d look to see if we had any data to answer those questions. If we did, I’d write some queries, create a data visualization, store everything in a single dashboard where it was easy to find and access repeatedly. If we didn’t have the data, then I’d let the team know, and I’d helped them scope out how to build telemetry to get the data we needed to answer their question. Then, I’d transform that data and make it usable.

I did this really haphazardly at first, just small things here and there, because everything was still so new and it was hard to know what mattered most. But even small things like this paid off in really big ways because after doing this repeatedly over and over again, a few things happened.

The first was just that I got to know the team and they got to know me and how they could leverage me and the data to help them iterate on the product. The second thing was that some patterns started to emerge. Some questions kept resurfacing and what mattered became a lot clearer and easier to focus on, which brings me to my next learning.

Now by this point, we were tracking a lot of what I’d consider pretty traditional product usage metrics like daily active users, for example. A metric like this is an important indication of growth and the success of the product, but it wasn’t enough to help us determine the quality of what we were building and where we needed to improve specifically.

This is because this metric is affected by things the development team couldn’t necessarily control, like marketing campaigns that would bring in new users. And also because it’s a bit of a lagging indicator, product improvements might not immediately lead to more new users, for example.

We needed a metric that would be a leading indicator and a measure of the quality of what was being built.

For this reason, we picked a single metric that measured the quality of one of our most popular features in this new tool, which at the time was code autocomplete. The code autocomplete feature is the same as when you’re in your favorite documents tool and it finishes a sentence in that ghost text.

As you’re typing, you can usually hit tab to accept the proposed text if it’s accurately predicted whatever you had planned to say, and save yourself the hassle of typing it yourself. Our tool could do this, but it did it for code instead of language, and it was a really popular feature at the time. We wanted to focus on making it great.

The metric we picked, which is called Completion Acceptance Rate, or CAR for short, measured the percentage of our suggestions that were ultimately then accepted or tabbed to accept by the user. This was a leading indicator of the quality of the product. The higher the acceptance rate, the more value we were providing to the user, the more likely those users were to stick around or better yet tell their friends about our product.

Once we had decided on this metric, that was a leading indicator and a measure of quality of what we were building, we started obsessing over it. It featured prominently on our team dashboards, team members would often check it first thing in the morning and over and over throughout the day, it was regularly reported on and discussed at the company level and company meetings and other communications. Small jumps and dips in CAR were always investigated and interrogated, and we were all focused on this important goal. How do we improve our CAR, and by extension, make our product more valuable to the developers who use it?

We weren’t exactly sure what the answer was to that question, but the team had a lot of hypotheses and the only way to definitively prove which ones were correct was to test them.

The team started running A/B tests to try to understand which product changes would improve our CAR. When we decided to run the A/B tests, we discussed setting up one of the many great A/B testing services that are out there, but we ultimately decided not to.

We at the time were already tracking a lot of product usage metrics and had a lot of telemetry set up, and we could leverage that data for experiments, and we could run calculations on the data to test for statistical significance ourselves relatively easily.

The method we came up with for testing was pretty clunky compared to what an A/B testing suite could offer, and the processes definitely wouldn’t scale, but at the time we didn’t need it to. This homegrown solution was ready to go in just a couple of days, whereas the process of selecting, purchasing, setting up A/B testing software could have easily taken a week or maybe more. And that brings me to my last learning.

While testing is really important, especially for a new product, especially in a new industry, the speed still matters. At times like this, it’s often better to opt for a quicker solution rather than the most robust, most scalable one, just in the interest of time.

We are coming towards the end of our story here and lucky for our heroes, we do have a happy ending. After we defined this metric, obsessed over it for a while or ran tests after tests in an attempt to iterate and improved our Completion Acceptance Rate, we were able to increase it by about 50% over the course of a month when in the past, it really hadn’t budged much in the months prior. And because we’d chosen this leading indicator and this measure of quality, we saw improvement in our other more traditional product metrics like daily active users and retention as well.

Within a couple of months of this effort, we formally made our Cody product generally available, confident that we were providing a high quality code AI tool that developers everywhere would love.

Like I said at the beginning of this talk, this isn’t a definitive solution. It’s just a story of my journey of being a data analyst in the world of AI products and what I learned along the way from my smart coworkers and a little bit of trial and error.

I found that being embedded with the team so I could move at their pace, picking a leading indicator and obsessing over it and testing until we found what moved the needle, really helped us to create a high quality product. And now we’re even trying to replicate this framework for other products and features where it makes sense as well.

And at the end of the day, he most important lesson that came out of this, and it sort of ties all of these other points together, is that data and data teams need to be maximally flexible, particularly in this AI environment, and we need to adapt to the stage of the product life cycle that our company is in.

When we first started building Cody, moving fast and narrowing in on specific focus areas was the key to our success, and the data team needed to figure out how to enable that, even if it meant throwing away the things that we had done before.

And as the Cody product now moves into this sort of next phase of general availability, I can already see how our team and our data need to adapt again. The quick solutions we set up in the interest of time will need to be reworked to scale with our team and our user base as they grow. Telemetry that we implemented without strict standards or haphazardly will need to be standardized. Tooling will need to become more flexible so teams can answer their own questions and run their own A/B tests without a data member needing to be involved.

Wherever you are in your data or AI or product building journey, the best advice that I can offer from my experience is really just to always be ready and willing to adapt and adjust to meet the needs of the teams that you’re working to serve.

And with that, if anyone has any questions, I’m happy to answer them.

Amanda Beaty:

Okay, Priya, go ahead with your question.

Priya Shastri:

Kelsey, first of all, thank you for that amazing presentation. You have highlighted some of the important aspects of software development and how AI is coming very fast into the software development lifecycle and how we are incorporating the AI tools into it.

My question is, what are the AI tools that you use for analytics, and which ones are the market leaders? Which ones would you recommend to use for a product? And the third question is, how fast do you see the adoption of AI in the analytics market?

Kelsey Brown:

Yeah, great questions. I’m actually not sure I’m even the best person to answer what AI analytics products are the best out there. My company’s been building an AI tool that’s for developers, but it’s not specifically focused on analytics. I’d love to say that I have an answer for you there, but I really don’t.

In terms of how quickly I see AI affecting the analytics space, I think it’s going to be a huge game changer. I think the one thing that we need to solve for first though, is how we make sure that our data is structured in such a way that it can be readable and ingested properly by LLMs.

Right now most of our data infrastructure is really built for people to ingest and query, and so I’m really interested to see, and I think the speed at which we can adopt AI and analytics really depends on how quickly we can build infrastructure in such way that a robot could essentially interpret and give us results.

A lot’s depending on that. I think if some of the existing AI or existing data tool companies come out with interesting new tools to make that more possible, I could see it happening really fast, but if not, I could see us getting hung up on how to build data warehouses and data infrastructure for robots instead of people.

Amanda Beaty:

All right, Priya, I hope that answered your question. Kelsey, thank you. It’s time to end the session and hope everybody can join us at the next session.

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“How To Give A $H*T – Drive High Performance & Engagement”: Hannah Hosemann with Affinity (Video + Transcript)


In this ELEVATE session, Hannah Hosemann
(Director of Onboarding and Implementation at Affinity) discusses her philosophy for leading high-performing and highly-engaged teams, which she calls SHIT (sincerity, honesty, integrity, and trust).

She shares personal stories and experiences to illustrate the importance of these principles in leadership, and emphasizes the need for authenticity, transparency, clear communication, accountability, and building trust with team members. 

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Hannah Hosemann ELEVATE find a trusted mentor or friend to help you practice the principles of shit security honesty integrity trust

Transcript of ELEVATE Session:

Hannah Hosemann:

Thank you so much. I’m excited to be here. Thanks everybody. Well, I’m going to dive in. I want to get as much goodness out of this as possible. First, as Angie mentioned, my name is Hannah Hosemann and today I’ll be sharing my philosophy for leading high-performing and highly-engaged teams. Before I do so, allow me to introduce myself.

I’m the Director of Onboarding and Implementation at Affinity, the leading provider for relationship intelligence software. I lead a team of implementation experts. We are the first stop in the customer journey for Affinity, and our role is to help customers set up the Affinity solutions.

In addition, I’m a co-founder and co-CEO of a wellness beauty brand named Element Candles, an Othos brand. We have been featured in Martha Stewart, Women’s Wear Daily, New Beauty, and we’re ranked some of the cleanest burning candles on the market by beauty bloggers of mindbodygreen. As mentioned, I have nearly two decades of experience specializing in global award-winning SaaS customer success, support and implementation.

But who am I really? Well, I’m a daughter, I’m a wife, I’m a sister, I’m a dog mom to three amazing miniature Schnauzers, Ella, Patch, and Zeke. I’m a business owner, hobby crafter. I do glassblowing and ceramics. I’m a travel connoisseur, a foodie, a music lover who absolutely knows no lyrics at all. So don’t quiz me.

Before I dive into my philosophy, allow me to share a little bit about how I got started and my journey overall. I went to college with the ambition of being a thriving visual artist. However, my desire to eat outweighed my love of art. I had an opportunity for me to enter the nonprofit community aiding arts organizations, and I grabbed it.

I then landed a job with a software provider who created software for nonprofits to manage their organizations, and I began as a phone operator. Through extensive mentoring, I worked my way up from a phone operator to a customer success leader.

Nine years ago, I discovered that I’m actually allergic to most candles on the market, so my husband and I set out to make candles, which I would be able to burn in our home. But with all my experience being an employee and leading teams, I found one consistent.

Your leader can literally change your life for positive or negative. Love it. Thank you, Kelly. I also love the fine arts. Over the next few slides, I’ll be reviewing what SHIT is, why SHIT matters, how SHIT has impacted my life, and how SHIT can impact yours too.

Transparently, I’ve made a lot of mistakes over the years. When I first was selected to lead a team, I tried to be everyone but myself as imposter syndrome actually is a real thing. It wasn’t until I put my personality forward, becoming authentic, that I realized how I could lead my team effectively and actually creating my own principles, which I’m calling SHIT.

To start, let’s review what SHIT is. It is comprised of four pillars. Just like Captain Planet, when you know we have earth, wind, water, we have sincerity, honesty, integrity, and trust, and you need to have all four of them. If you just have one, it’s not going to work.

We’ll be reviewing each one of these principles and sharing some stories in how I identified these components to be important pillars for me and actionable ways you can incorporate them into your life.

I’m seeing a lot of people on here having lots of different experiences and it’s important to call out you don’t have to be a people leader to leverage these. You can be an IC, an aspiring people leader, an active team lead. Anything goes. They’re just basic principles that will help you be better relationships across the board.

Let’s dive in and let’s start with S. S is for sincerity, being genuine and authentic without an agenda, but with a goal to produce authentic connections. There’s quite a few ways that you can show sincerity. A few which stand out to me are – transparency in communication, consistency, respect for others, authenticity, empathy and compassion, inclusivity, accountability and genuine interactions. But I’m going to start by sharing my experience, and I’m going to be humble here for a moment, so apologies in advance.

When I first became a leader, I made a lot of mistakes. I mentioned that earlier. I tried to be everyone else but myself. I became someone that I wouldn’t want to work with and quite frankly, definitely would not want to work for. I became serious. I failed to give my team grace. I coached like everyone was everyone. One size spray coaching. I took other people’s mistakes as my own failures, and I felt that because I was good at my job, quite frankly, I knew best.

It wasn’t until my mentor looked at me and said, “What the heck is wrong with you? We did not hire you for you trying to be everyone else or whatever this is.” That was the shakeup that I needed.

I looked inside to identify who I wanted to be as a leader, and at that moment, I began incorporating active listening, genuine thoughtfulness, and understanding that most people actually want to do a good job at work. With that, I began approaching conversations differently.

I began approaching coaching conversations as heartfelt interactions where I’m here to help you grow. Not I’m reprimanding you and telling you you’re doing something wrong. I found that I was able to build stronger, more authentic relationships with both my peers and my team by leveraging that.

Both my team and I worked harder for each other because we both cared for each other. We both wanted to see the person succeed. I was able to identify their strengths, where and how to foster them, and then also learn what their goals were and give people opportunities through that.

Let’s go into H for honesty. Honesty is being transparent in communications, providing clear expectations and building respect. Some of the principles of honesty in leadership are truthfulness in what you say, transparent in your motives, good intention, fair in feedback, constructive and quite frankly, genuine intent.

Over the years, people have honestly looked to me asking for advice on how to become a leader. While there really isn’t a one-size-fits-all program, honesty in coaching and mentoring is probably number one in my book.

Allow me to tell you a story of an individual contributor’s experience on my team. They wanted to be a leader. This person was amazing at their role, but being a leader really wasn’t quite in the cards for them at that moment. Because they were really good at their role but like I said, just a couple moments with my mistakes, it’s more about your abilities to get somebody else into that role and less about you doing that role.

I needed to be honest, but in a constructive and not hurtful way. I shared that I saw potential and that they weren’t quite ready to be a manager. However, with some coaching, I was sure that we could get them ready for leadership.

Instead of just saying, “Hey, you’re not quite there,” I offered to help. We developed a leadership program focused on those leadership essentials, which we’re discussing today. The SHIT ones, the sincerity, honesty, integrity, and trust – and together we grew. I met them with weekly meetings. We were a team.

We wanted both of us to be successful. I wanted the person to be a leader and they wanted to be a leader. With this, we were able to make a difference. Foreshadowing, the person actually became a successful people leader. Bringing it all home, what really made this difference for this person is that it was all about honesty. Setting the expectation of what is going to set them up for success and understanding where they are and how to get to that next level.

I is for integrity, consistency of actions and values to create relatable and ethical leadership. Have you ever been at a restaurant, remember, I’m a foodie, that was incredible the first time you went and then you went back a second time and it was awful? You didn’t rush back. Now let’s pretend that you go to the restaurant and instead of having just a bad experience, somebody comes out and apologizes and recognizes that it really wasn’t their good night. I’ll bet you’ll give them another chance.

Integrity in leadership’s a lot like that. Let’s be real. Just because you get the leadership title doesn’t magically make you a perfect leader. In the workplace, integrity can show up in many different ways. Consistency in how you show up to work, accountability to work, admitting when you made a mistake, reliable to getting things done and hitting your deadlines and also, transparent and vulnerable, fair and equal to all. I fail. We all do.

Allow me to share one of my failures so you don’t have to. One of my team members provided some feedback. It was meant to be constructive, but all I could hear was criticism, frustration, and it was likely me. But at that moment, it didn’t matter whether it was the delivery, whether it was me, what I just had come from.

What mattered was is that I found myself getting defensive, and that’s not normally my style. I realized I was the one putting up walls. My body language was like a cat ready to pounce. Despite the delivery, this person was genuinely trying to help in giving their honest feedback, and quite frankly, that’s really hard to do.

I paused the conversation. I thanked them for sharing, and I also admitted that I really wasn’t in the best place to receive the message. I suggested that we hit pause, giving me some time to digest the words. We wrapped up the call and I replayed this feedback over and over, sifting through to get to the core of the concerns.

When we reconnected, I kicked off with an apology for my reaction and expressed my gratitude for their honesty. I offered to coach them on delivering feedback as well, as what they said was really important. We as leaders needed to hear this, but how they said was where I think we could have massaged it. They were superbly grateful for this opportunity to partner.

Integrity became a critical principle in my leadership style, as it’s not that we may make mistakes or if we will make mistakes. There’s a guarantee. It’s when we’re going to make mistakes and there’s nothing more concerning than not giving people the honesty back and knowing that we’re humans.

T is for trust. The foundation allows us for vulnerability and risk taking. I am terrified of heights. I am also the slowest rock climber in the history of the world. But when I’m harnessed in and my husband’s belaying me, I feel like I can literally tackle any mountain. Now it is strictly because I trust him.

Same thing for leadership. People are naturally scared to fail, and when you’re working with a group of people who want to be successful, the fear can actually stall success. In the workplace, trust can show in many ways. Reliability, respectful communication, understanding that each person is a unique person and brings different skill sets to the team, fair and equal to all.

Allow me to tell a story of what this may look like in the real world. In technical support, every call is a new adventure. Client on the line fuming over what they think and expected versus what the actual product capability or whatever it is capability that you have. Their demands were extremely high.

On our end, we had a team member trying their best to keep up, make the customer happy. The team member was terrified that this customer would escalate, causing them more work or worse, penalization. That’s when I decided to jump in on the call, not to take over (the associate was doing phenomenally) but to lend a hand in navigating this situation.

I listened to the client to ensure that they had an outlet to articulate their frustrations, but I was upfront with the client acknowledging their frustrations, but also, gently setting the record straight on what was doable and what’s not. We set expectations on the next steps and followed through, which in turn reinforces trust to the customers.

In this instance, it wasn’t about fixing the issue at hand. It was about showing that we cared and that we were going to be their partner. Most importantly, as a leader, my actions that I took showed my team member that I would be vulnerable. I would put myself in the line of fire alongside with them, and I would always have their backs.

The last story of today is about my favorite leader. They set the bar high for their team and then even higher for themselves. They knew what to do and held you accountable. They knew what you could do and they held you accountable. They were all in all the time. No matter how late and what holiday it was, they were there for you if you needed.

You always knew where you stood. They laid everything out on the table. They coached privately, praised publicly, but always were jumping in to provide feedback and look for opportunities to mentor, to help you grow. Not just at your job, but as a person too. They weren’t afraid to show their human side, owning up to their mistakes. They were the first to cheer you on when you nailed it, and they genuinely cared about what happened to you and your world, both at work and to beyond the office walls.

As my parting gift to you, I encourage that you each find a trusted mentor or a friend to help you practice the principles of SHIT: sincerity, honesty, integrity, and trust. Wrapping this up, it’s clear that giving a SHIT isn’t about being your team’s best friend. It’s about being there, showing up when you need somebody, when they need you the most. Having their backs, your team’s growth, celebrating their successes, and always making it personal. That is the shit that makes the difference.

With that said, I would love questions, and so feel free to chime in. I think there were some in the chat. Some actionable items to be a manager. I love that question. Thank you. I will tell you the actionable items to being a manager, a lot of it is coaching.

Our job as leaders is to make your lives easier, is to remove obstacles and give you the freedom to have the abilities to do your job effectively without any burden. In this instance, a lot of it is understanding how to de-burden and how to make people’s lives better, but also to give recommendations, feedback, coaching along the way.

Feel free to connect with me if you have a specific… I would love to talk more about your specific role, what you’re aiming for, and how do we get you there.

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“AI & The 2024 Election”: Susan Gonzales with AIandYou.org (Video + Transcript)

In this ELEVATE session, Susan Gonzales, founder of AIandYou, discusses the importance of AI literacy and the impact of AI on elections. She explains the difference between predictive AI, which predicts behavior and makes recommendations, and generative AI, which uses prompts to generate content.

Susan highlights the issue of misinformation in elections, particularly through deep fakes, and the importance of researching and verifying information before believing or sharing it. Be curious and cautious when engaging with AI and to seek out trusted sources of information.

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Susan Gonzales ELEVATE there are no rules regulations or consequences related to creating false information and to protect voters

Transcript of ELEVATE session:

Susan Gonzales:

Hey, everyone. Thank you so much for having me join this great conversation. I’m super excited to know that there’s just a lot of women out there. I love that. Let me give you a snapshot of where I’m coming from. I was at… I was in tech at Facebook where I learned about AI, believe it or not, almost nine years ago.

I thought it was really cool technology because it was allowing the blind community to access the platform. And so I became really intrigued with the idea. when I left the company about five years ago, I launched AIandYou specifically to educate marginalized communities about AI, its challenges and its risks. And marginalized communities really are defined as women, people of color, LGBTQ, disabled and others.

I’m super excited to be having this conversation with you today about AI and misinformation in the election and also to plant the seed about AI literacy. I’ll talk a bit about that and then we’ll jump into the election.

AI literacy really is about creating a basic understanding of AI. For example, we’re not talking about becoming a data scientist or anything like that. What we’re talking about is to encourage you and hopefully plant a seed of curiosity to learn more about what’s happening. AI, as we know, it’s not coming later. It’s here now and there are a couple things to understand.

There’s predictive AI, which is the AI that we use every time we touch our device. Someone asked me, “Well, what time did I start using AI, Susan?” I said, “Well, what time did you log on?” The minute we start clicking, we are engaging in AI. It’s made a lot of things really convenient for us. That’s called predictive AI because it predicts our behavior and then it recommends the movie or it recommends the jeans we like or that type of thing.

The other AI you may have heard about recently is generative AI. Generative AI is a large language model and that is based on prompts that we type in and they pull from essentially a virtual library of information. It’s not an internet search, so it’s not a cut and paste situation. There’s not articles. You could type in like write me a thousand word essay on World War II, or what’s the best recipe for whatever, or what’s the best way to entertain five year olds on a scavenger hunt? I mean, literally everything you could possibly imagine.

I share that specifically to encourage everybody to explore generative AI. Most people may have heard about ChatGPT. That’s a fantastic one. There’s also Google Gemini, which was just released. They had a little kinks, but I want to share something about ChatGPT. There was an article recently that noted that 70% of the users to date, which is about 18 months of ChatGPT, are men. That’s a bad sign.

I just want to encourage you and I’d be happy to talk some other time more in depth about generative AI specifically, but that is directly related to the election, is to really explore what’s out there and not be afraid of it. The fear can be paralyzing, especially for educators. I mean, it’s very scary, but now is the time to learn. For people who are admin assistants, or paralegals, or any kind of repetitive skills, translators, they will be replaced. Those jobs are starting to be replaced by AI.

Think about it. When was the last time you actually called customer service and you actually spoke to a person? All those jobs are changing. Now is the time to really have curiosity and just dive in and just start looking around.

Definitely feel free to visit our website. It’s aiandyou.org. We offer free content. It’s in small bits and pieces. It’s not meant to be a long course. It’s very simple, short and easy to understand language and to help you understand about all different aspects of AI.

Today we’re talking about misinformation in the election and what I’d like to do is show you a very short video. It’s about four and a half minutes. This will give you a clear snapshot of what we’re talking about and then I’ll be back. Give me one minute. We’ll load that up. Okay. Let’s see. Well, that didn’t work. I’m not familiar so much with what this is called, Airmeet. Huh. I think it’s this. Yeah, this is a problem. I cannot get to it. Let me see. No, it’s not working. Okay.

Amanda Beaty:

Susan, I can do it. I have the link you sent me.

Susan Gonzales:

Yeah, sorry about that. Unfortunately, I didn’t practice this, but now I have to get out of that. I want to stop presenting. Can you access that, Amanda?

Amanda Beaty:

I can, but I’m not sure I can share. Let’s see. Oops, I have too many links up in. Okay.

Susan Gonzales:

While Amanda’s looking into that, a couple of things I do want to share is when it comes to AI overall, it’s affecting and impacting us in so many different ways.

There are some beautiful benefits, especially in medicine. Look, I’m a breast cancer survivor and diagnosing it is getting better. We’ll come back to that. Okay, here we go.

Amanda Beaty:

Okay.

Susan Gonzales:

Thank you.

Narrator on video:

Elections have always been about connecting with voters. Campaigns will try almost anything to get your attention and vote. The 2024 election will require you to protect your vote because of recent advancements in artificial intelligence, AI. So how exactly is AI influencing the 2024 election? Well, today we will cover three key things you should know about protecting your vote, but let’s begin with the big picture. Tools for voter engagement have evolved from telegrams to the internet. Enter 2024, and we find another player in the arena: advanced AI. The 2024 election is all about AI literacy. Voters need a basic understanding of how AI can impact their vote. AI is more than just technology. It reflects our society’s evolution. AI has been used in past campaigns to do things such as improve the accuracy of voter registration and voting systems, help campaigns target voters and optimize their messaging to reach voters.

Have you ever considered how recent technological advances have made your lives easier? Think about it. Today, you can grocery shop without ever going to the store. You can do many things today you could not do four years ago during the last presidential election. Well, how is this happening? AI powers all the new things we do on our devices. And how does AI work? AI needs data to work. So let’s dive into AI in your vote. Number one, instead of a one-size-fits-all campaign, AI enables hyper-personalized messaging, reaching voters with issues that matter most to them and swaying voters with true or false information. Analyzing voter data is invaluable. By analyzing real-time data, campaigns can adjust their strategy, making outreach more effective. The efficiency of AI is unmatched, but it’s not without its challenges. And the 2024 election will bring unprecedented amounts of disinformation to you, the voters. So what is misinformation or disinformation and how is it created?

To put it simply, misinformation is false. It is information created to trick voters. It is a campaign video created with AI to make you believe something about a candidate that is not true. It is a phone call that sounds like a candidate, but it is actually AI. It is a personalized fundraising letter created with AI. The most prevalent tool for misinformation is called deep fakes. Number two, know deep fakes. Deep fakes are hyper-realistic, but entirely fake content pieces that can be used to spread misinformation. In the past, creating a deep fake would require someone in technology, like someone who writes computer code. Not anymore. Deep fakes can be created by someone from their laptop at home. It’s becoming harder to differentiate accurate content from AI-generated ones and we won’t get there in time to navigate truth from fiction during this campaign season. The most troubling aspect of deep fakes is there are no guardrails to protect you as voters.

There are no rules, regulations, or consequences related to creating false information during the campaign to protect voters against false news, disinformation, or false narratives. Voters must independently research key issues to determine what is true and false. Undecided and new voters are expected to be the targets of political misinformation, which is critical given that the election is expected to be decided by a small percentage of voters in 2024.

Remember that not all AI in elections is misinformation. Campaigns also use AI for positive engagement. For example, virtual assistants or chat bots help answer voter inquiries, ensuring accessibility and connection. And local communities can easily leverage new AI tools to organize and mobilize voters. It begins with searching for the best options to meet your needs. The challenges of election misinformation are global issues at all levels of political campaigns. In Toronto, a candidate in the mayoral election who vowed to clear homeless camps released a set of campaign promises illustrated by AI, including fake dystopian images of people camped on a downtown street and a fabricated image of tents set up in a park.

Number three, what must you do to protect your right to vote based on accurate information? Well, the answer is do not believe anything you see, read, or hear until you have researched it. In the 2024 election, you will not be able to rely on any one source, whether it is online, on TV, or in the press. Deep fakes are not online only. Broadcast TV networks cannot alter political commercials, which could also be deep fakes. It won’t be easy, but do your research to protect your right to vote. To wrap it up, do not believe anything you see, read or hear in any election. Do your research. Seek trusted sources confirming political deep fakes. Most importantly, protect our democracy and protect your right to vote.

Speaker on video:

If you like this video, check out aiandyou.org. See you next time.

Susan Gonzales:

Thanks, Amanda. I want to point out one thing in that video, is that’s not my voice. It’s an AI augmented voice. I used an AI tool to create the voiceover and it took about five minutes. That’s just illustrative of how easy it is to create things this election.

As we stated, unfortunately, the technology has advanced so quickly that there has not been time to regulate it. It is somewhat of a free-for-all without any consequences. The good thing is some companies, very recently, within the last weeks, have announced that they’re not going to allow certain types of content, particularly using large language models. But then at the same time, people are still being allowed to use it.

The opportunity to target women and people of color, the two groups that have decided recent elections, is very high.

The question is how would “they”, how would the internet know I’m a woman? What’s our behavior? Our behavior online illustrates, it doesn’t tell somebody my name and address, but my behavior online likely communicates that I’m a Hispanic woman living in the Bay Area simply because of my clicks.

I just want to offer to be very careful this election on what you’re clicking on because the minute you click on something that you find intriguing and it’s a deep fake, then you’re going to continue to get more deep fakes from that particular source.

This is great timing to have this conversation because literally within the last 10 days two things have happened. One is there was a fake robo call from President Biden’s voice, encouraging people actually recently to not vote in the New Hampshire primary. After much research, they found who the person was and it was a magician who created the deep fake.

Then within the last week there was a deep fake with former President Trump surrounded by members of the black community. That posting got one million hits before anybody acknowledged and labeled it. It was AI generated. We are very important votes this … Well, every election, but particularly this election. This is a nonpartisan issue. This is a global issue. Not only can our US campaigns get involved, but also bad actors globally, foreign bad actors.

Be aware, protect your vote. Ask a lot of questions. Ask your friends if they have seen or what they have seen. And maybe it’s someone said, “Well, I’m going to rely on my local little newspaper. That it’s actually reporters writing the news.” I’m like, “Great.” Someone else said, “Well, I’m going to rely on the debates.” Everyone needs to have different sources this year because the election is unprecedented. It’s a digital election in an unprecedented fashion given the acceleration of AI.

I hope that was helpful. We don’t have much time for questions, but I certainly would be open to that. And again, feel free to reach out to me via aiandyou.org and stay in touch. We are launching new content for AI in education, for teachers and students. Also, AI in jobs for workers, followed by AI and health. Thank you so much for having me today and I hope this was helpful and gives you food for thought, but be curious. Be curious about AI. Thank you.

I don’t really see. Let me see. Be curious and careful someone said. That’s absolutely right. Yes, that’s the most important message of today, is to be careful. It’s interesting and it’s sad in a way that this is where we are, that we’re going to need to question everything. I’ll give you an example.

I am deeply embedded in the AI ecosystem. I’m living AI every day and about two months ago I saw on Instagram Taylor Swift promoting Le Creuset, the cookware, which I like. And for a moment I stopped and I thought, “Why would she be doing that?” And I almost clicked, but I didn’t. And then the next day I saw on the news that was a deep fake. There was another instance where it was Oprah and it was a deep fake about her promoting some weight loss gummies.

The other thing to look out for in general, beyond the election, but including the election, is if you notice, especially on different social media platforms, is there will be a celebrity and then there’s a voiceover like you just heard my voiceover, but they’re not matching the lips of the person. There’s this commercial of the voice of the celebrity, and it looks like the celebrity is, that’s what they’re saying, but it’s actually not them. As you can see, this can go a long way.

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“AI In Exercise Optimization, & Why It Matters”: Shikha Tandon with svexa) (Video + Transcript)

In this ELEVATE session, Svexa Chief Resilience and Partnerships Officer Shikha Tandon discusses the role of AI in exercise optimization and its importance for health and wellness. The current landscape of human performance is heavily influenced by data, but there is a need for dynamic approaches to performance and health analytics. 

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Shikha Tandon ELEVATE leveraging training optimization models allows you to adapt your training plans

Transcript of ELEVATE Session:

Shikha Tandon:

Thank you, Amanda. Hello, everyone. My name is Shikha Tandon. As Amanda said, I am the Chief Resilience and Partnerships Officer at Svexa. I’ll share a little bit about AI in exercise optimization and just discuss briefly why it all matters.

I plan to keep this high level, but more than happy to deep dive with anyone one-on-one after. Send your questions in. Happy to get to them at the end. If not, happy to chat individually. I’ll preface all of this with the fact that exercise and activity has a direct impact on a person’s health and wellness.

Before I begin, I was asked to provide some snippets of my personal journey thus far and how it has brought me to my current role. I was also told that most speakers don’t spend enough time as they should on this slide. I’m going to spend a few minutes here.

I grew up in India. I spent most of my school and college days as a student athlete. I represented India for almost 15 years in swimming, including at the Olympics in 2004 in Athens. And through my journey as an athlete, I was always fascinated with the bioscience.

As an athlete, you’re constantly trying to push the limits of what’s possible. You are introspecting a lot, and so that’s what I ended up choosing as my majors in college. As an athlete, I had also been drug tested a lot. I knew that… That was something that I wanted to work in in anti-doping science. The only problem there was that there was absolutely no such career option in India, and that prompted me to move to the US for a second masters.

As you can imagine, anti-doping science is not a course available in college. During this time, I had to customize my projects to gain specific experience in this field that would set me up for hopefully a role after. After I graduated, I was fortunate enough to get my dream job and work at the USADA, which is a United States Anti-Doping Agency as their science program lead.

It was a research and education focused role, but I also had the opportunity to dabble a little bit in product management as I built one of their first online education modules for health and medical professionals that were working with athletes.

Fast-forward a few years, I moved to the Bay Area, suddenly surrounded by everything tech and something that I had no immediate interest in at the time, but it grew on me. As one does, I attended multiple conferences like this, some in-person, trying to figure out what next.

During one of those conferences, a presentation on human performance and analytics got my interest. I mentioned to the speaker after that if he did anything with that research that he presented, I would love to be involved.

Leveraging my experience at USADA and a little bit of the PM work that I did, I worked in product for a few years as a product manager at different fitness wearable companies, and that was a time the wearable industry was picking up. I had a great chance to just learn a lot in a short span of time.

After that, I went on to work as a product manager for TechCrunch, a tech media company where I learned a lot about the startup ecosystem. By now, it had been a few years since that conference that I just mentioned. Turns out the speaker co-founded a company, Svexa.

For the past few years, I’ve been at Svexa now… was one of their very, very first few employees moving up and across different roles within business and partnerships.

My current role, the way I see it, is the resilience aspect is just a really nice mix of learnings from my time as an athlete, but also while driving the company business and the partnerships forward. I know this session was listed on the tech track and that there is a separate one on career, but if anyone wants to learn more about how and why I made all these seemingly unrelated career moves across titles and roles, please feel free to reach out to me and happy to go deeper.

What is Svexa? Svexa, it stands for Silicon Valley Exercise Analytics. It’s an exercise intelligence and human performance company. What we do is we develop proprietary algorithms that we then license out to support health and fitness companies.

Think of us as an ingredient brand similar to Intel Insights, but for human performance optimization. We are a B2B company. Our algorithms are applicable to athletes, sports teams, irrespective of level, sports tech, wearable tech companies, corporate health, virtual health, hardware, or software. Just essentially anyone that has health and wellness data on their end user and is hoping to offer some sort of individualized analytics and hyper-personalized insights from this data.

The current landscape when it comes to human performance is already pretty heavily influenced by data, but the key, really, is in terms of understanding this, is that this landscape is continuously evolving and that really requires a dynamic approach to performance and health analytics. Bit of background, over the last 10, 15 years, there’s been a huge tech revolution in terms of what’s out there.

It is really never been easier to gather data, whether you’re an athlete or just someone looking to stay active and healthy. This has led to an overwhelming amount of data with very, very limited insights and recommendations that are personalized to the specific user in their context. Today, most technology displays aggregated data or just provides insights and recommendations that are based on either general population or just limited data streams. And this is not scalable or even flexible across industries.

When we look forward in terms of what is this need, we’ve seen that there is a trickle-down effect from elite sport across industries. There is this need to deliver actionable hyper-personalized insights that are scalable across industries. As an example, 15, 20 years ago when I was competing and training, heart rate monitoring was accessible only to elite athletes. And even within our team, we had one or two heart rate monitors that we would share among everyone on the team within that session.

Today, almost every smart device has this capability and it’s almost expected as a minimum feature for users whether or not they know what to do with it.

Diving a little deeper into the tech landscape, as I mentioned in the previous slide that wearables and heart rate devices are a great way to gather data, but primarily, or most of them, are single or limited source. These could be smartwatches, CGM trackers, smart rings, and many of these have accompanying software or hardware which then enables data visualization for their B2C clients and end users.

If we go a step further, so you have platforms such as athlete management systems that can import data from multiple sources, but typically, they still all fall under a data visualization tool. They may offer some sort of trend analysis or population-based comparison analysis for their end users.

When we move towards really individualized insights, this typically falls on some level of human intervention and expertise. And in the sports world, this may be a professional coach. For fitness apps, this may be a coach on the platform. This, however, is not scalable and there’s only a certain number of athletes or users, a coach or trainer, can manage.

While many of the existing tech could be used by these professionals, personalization is, on some level, limited to the knowledge of the individual to interpret the data. This may or may not be relevant across industries. For example, the coach may be in a position to deep dive, may not be in a position to deep dive into the health metrics, while the health professional may or may not feel comfortable with training optimization.

Today, we have… AI solutions are leveraged to counter some of these issues, but just adding AI to the mix is really not the answer. That’s the part that Svexa is building. The technology is a combination of both AI and human domain expertise. The AI aspect is utilized to scale the algorithms and the offering.

If you think about from the competitive lens, we have hundreds of algorithms that we can license out and different combinations of these could be licensed to add value to most of these existing solutions, whether they are hardware or software. These algorithms then can be used to either make all of these offerings more scalable or just applicable across industries or providing deeper insights or just looking into them at a very, very hyper-personalized lens.

When it comes to exercise optimization, multiple factors such as sleep, nutrition, mood, stress, activity, travel, injury, illness, and any other existing health conditions, they all play a role in an individual’s readiness and ability to perform and be productive on any given day. And today, we have access to so many different technologies. These inputs could really come from various different sources. A lot of them are reported differently. Some could be subjective in nature; some could be objective in nature in terms of the data.

The key is to be able to handle an account for all of these, keeping in mind the individual at the heart of all of this. And in some instances, similar metrics are gathered by multiple devices for the same individuals. For some of us, we may have multiple apps on our phone that are giving us daily readiness scores, for example. How does a score of an 85 on one app compare to a score of a 75 on another? And what does one even do with this data?

As we think about addressing this need to deliver the scalable intelligent solutions, at Svexa, for just ease of licensing, we refer to groups of our algorithms as products. One of these products is called the Athlete Passport, which is essentially a concise representation of all the data available for an individual. Unlike existing tech, this is not just a visualization tool, it goes well beyond it. It highlights key response patterns and correlations between metrics for that specific individual. For example, it could be correlations between mood and stress, or sleep and nutrition. This enables output and insights that are more actionable.

For example, some people have great sleep after a hard workout, while others may have restless sleep if they’re very tired. If that person has travel somewhere, so add travel and jet lag to the mix and then it impacts these metrics. If you add suboptimal nutrition and water intake due to the said travel, the equations change again. Instead of just stating that sleep quality is trending a certain way, we can explain specifically for that individual what is driving these changes and how and what to do with it.

Again, building further on this Athlete Passport, we have a Digital Twin technology as well. While Digital Twins are gaining traction across biomechanics, you also have some for in-game and on the field strategy, and also maybe some in the medical field. Ours focuses on the overall physiology of the individual. These Digital Twin algorithms enable us to simulate millions of possible scenarios to generate optimal recommendations for training performance and health.

Some examples of how powerful these optimization models and recommendations engines are… Just last month, the technology predicted a half-marathon time for one of our team’s data analyst with 0.2% accuracy and he just became the second-fastest European ever in this event. Pretty exciting. For the past few years, we’ve had similar accuracy across sports, not just with elite athletes, but also with recreational athletes. And not just with event timings, but also for personalized heart rate zones and things like that.

We’ve talked a bit about exercise optimization and how we go about it, but why in all of this, how does it even matter outside of elite sport? We are functioning as an intelligence layer between the data layer and the interactive layer. We can tell anyone what the optimal amount of activity is for them at any given point of time.

This intelligence is really the product and it’s contextually driven by a combination of algorithms. Think of Intel, it’s a seal of excellence for laptops. Similarly, you have GORE-TEX, which then functions as an ingredient brand for others like Patagonia, North Face, Arc’teryx, which are all similar products, but GORE-TEX is able to service them all and doesn’t really play any direct part in their final design, but essentially powers all of these different brands.

Svexa tech is data source and device-agnostic, and we can work with as little or as much data that is available. This is licensable to both, as I mentioned, hardware and software. Essentially, we’re democratizing access to health and wellness optimization through our team’s deep understanding of human physiology. You have generative AI and voiceover tech, it’s getting a lot of traction within the health and fitness space. Fom our perspective, all of these fall under the custom front-end implementation.

Think of all of these talking to that Svexa intelligence model before they provide any sort of output. And of course at a much, much broader level, the United Nations Sustainable Development Goal 3 aims to ensure good health and wellbeing for all at all ages.

Individualized recommendations for physical activity and exercise are pretty powerful tools when we are trying to achieve this milestone, and so we are striving to play a small part in this.

Going beyond, when you think of elite athletes, at one end of the spectrum, you have learnings from this can be extrapolated to recreational athletes, corporate health, and general health. Whether you’re recreational athlete training for your first 5K or running a marathon, leveraging some of the training optimization models allows you to adapt your training plans in a way that you can maybe prevent over-training or prevent injury fitness enthusiasts.

We’ve heard the generic recommendations of 10,000 daily steps and 150 minutes of activity a week. While all of these have been great in terms of getting people active and moving, going forward, I think adaptation within this that factor for everything else going on in your life, all of that has been shown to be more effective than just following a one size fits all model.

Even if it means helping you pick the next workout on one of your favorite fitness apps. When we look at corporate health, if we take a step back and replace performance for productivity, we could potentially utilize the same algorithms that we use for injury and illness prediction to positively make an impact on burnout and productivity within corporate health.

The next piece of the pie is health and wellness, and exercise and activity plays a pretty crucial role in management. The same algorithms that are used to assess an athlete’s daily readiness, for example, via the multiple input parameters that I mentioned, they can and are being actually contextually applied-

Amanda Beaty:

All right. Sorry, Shikha, we’ve got to head over to the next session.

Shikha Tandon:

Okay. Thank you so much.

Amanda Beaty:

If you have time, when I close this, you can pop over to the Q&A and look what’s in there. And the attendees won’t be able to see it, but you can, if you want to put the relevant answers on LinkedIn or something like that then…

Shikha Tandon:

Okay, sounds good. Thank you.

Amanda Beaty:

All right, thanks everyone. We’ll see you in the next session.

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“Decoding What Recruiters Are Looking For In A Resume”; Nora Hamada with Recruit Rise (Video + Transcript)

In this ELEVATE session, Nora Hamada (founder of Recruit Rise) highlights the basics that a recruiter will be looking for on a resume, such as education, skills, and experience. She provides a practical exercise for analyzing job descriptions and identifying patterns to tailor resumes accordingly. 

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Nora Hamada ELEVATE use data to make your accomplishments more credible

Transcript of ELEVATE Session:

Nora Hamada:

My talk is on what recruiters are looking for on a resume, and if you’re here in attendance today, it’s probably because you’re on an active job search or about to kick one off in the short term. Welcome, and I hope you have a pen and paper and ready to take notes. Who am I? Angie gave my bio, so I’m going to skip this one. But hello, nice to meet you.

Expectations for this talk. This talk is for people who already have a good idea of what job they want. If that’s not you, if you’re still figuring out what next step you want to take, this talk is probably not the best fit. So just letting you know that in terms of expectations.

My objective is to make sure that you all leave by the end of this conversation with actionable takeaways that you can do on your own time. And then we’ll have a practical exercise that we go through together at the end of the talk. So we’ll go ahead and get started.

First I’m going to go over the basics, just following best practices. Likely you’ve heard all of these before, but I’m just going to run through them just in case something seems new to you.

Use the standard resume format. I know that there are a lot of resume templates out there that are very fancy and creative, but it’s much easier if it’s just a PDF file. That’s what I always recommend.

Make sure to highlight your relevant experience. If you’ve done a lot throughout your career, maybe an untraditional path, I think about what jobs you’re applying for and how your experience aligns, and make sure to highlight the most relevant experience. We’ll go through that in detail later on in the talk.

I can’t stress this enough, please double check, triple check for errors, spelling errors, grammar errors. It does not look good if there are multiple spelling errors in a resume, it definitely comes across as a yellow or a red flag.

And again, just going to run through these really quickly before we dive into the meat and the potatoes of the talk. I’ve seen six, seven-page resumes. Please don’t do that. Just distill down your experience to one page if you can, two pages maximum.

Recruiters are looking at your resume for seconds. They’re skimming for information that they’re looking for to make sure that it’s a good enough fit to keep reading and spending the time investment to do that.

One page makes it really easy to skim. Same with hiring managers. It’s a lot more friction if it’s a lot longer and hard to parse. We live in the digital age, so make it easy for people to connect with you online. Include your LinkedIn URL, especially great if you have common connections, and your email.

This is a good segue for my next little check mark here, which is, you may not want to list your phone number, and I’ll tell you the reason why. If you are on job search sites like Indeed and Monster and all of those, if you list your phone number publicly, you will get unsolicited calls.

What’s great is that there are tools out there: I use something called OpenPhone, and that allows you to create a phone number for a specific purpose like a business, or in this case, job search that you can then get rid of at the end once you get into your next position. Definitely, this is just my opinion, but I think it’s great to just have a box with a skill section in it.

If you’re an engineer, for example, just highlighting right away, making it super obvious that you have practice in specific programming languages, or if you’re a designer, what tools you’ve used in your jobs, just making it really easy for someone skimming your resume to see that super clearly and decide to keep reading if it’s relevant that you need certain skills to be qualified for the job.

Listing education could help or hurt. There is a very strong education bias in the tech industry. I don’t agree with it, but it is what it is. It’s out there. You should be aware of it. If you have that competitive advantage, I would definitely list that toward the top of your resume. If you don’t, it’s fine, but just something to think about.

The reason I put that it could hurt potentially is, I’ll give you a specific example. In my career, I’ve worked with a lot of different people in their job searches, everything under the tech umbrella, designers, product managers, engineers, et cetera.

For engineers specifically, there was this kind of common trend where sometimes hiring managers would see that engineers had MBAs, and then the question would come up of like, “Well, does this person really want to work as an individual contributor or are they trying to get a job as a product manager?” That question came into play of like, “Well, is this job really for them? Is this the path that they want to take?”

Questions will come up that people have and they’ll just make assumptions. Those are things you want to think about too. Is your resume telling the story that you want to create? And if you think those questions might come up, really trying to tell your story in the right way so it doesn’t even … I guess no one has a specific assumption that may or may not be true. You’re already telling your story the way you want it to be told.

Keep your best information at the top. The most recent experience is what you want to be the meatiest, I would say, if it’s the most relevant for the jobs that you’re applying for. The top of your resume is prime real estate, so I would definitely put your strongest assets there. Again, these are probably things you’re aware of, but….

I’d recommend starting bullet points with action verbs in your experience, talking about your experience. I just put an example there, like grew our user base 75% in one quarter – that is a very clear and concise sentence, easy to read, very clearly tells, “Hey, this person is capable of doing customer acquisition, like helping with customer acquisition and growing our user base.”

Then the next point is, use data to make your accomplishments more credible. You want to show people that you can do the thing with numbers. Quantitative data and metrics makes that more salient and credible in a hiring manager and recruiter’s mind.

We’ll pause there and we’re going to get into the practical exercise. If you have a pen and paper handy, I’d recommend taking notes, and then we’ll leave room for questions at the end if we have time. Because this is such a short talk, I might be running through something quickly, so feel free to ask me at the end if you want me to go into more detail about it. So we’ll go ahead and get started.

I get a lot of questions about how to put together a resume, and this is a simple trick that I like to use. In this example, I’m going to use a growth product manager role just as a hypothetical example in this scenario. Say you’re a growth PM, that’s the job that you’re looking for.

For this practical exercise, I would recommend taking 10 job descriptions. I just found 10 using this site called Levels.fyi that you may or may not be familiar with already. And I took 10 job descriptions that fit a narrow scope. I was looking for growth PM positions at tech startups and specifically high growth tech startups and in a certain locale. At the location, I would say it doesn’t really matter that much, but it could potentially.

The more narrow your scope, the better. And you can try this at home after the talk with the job descriptions that you have in mind for your next step.

Find the patterns. All of these job descriptions likely have things in common. What are they? That is your job to figure out, so you can feed it into ChatGPT. That’s what I did, and I just double checked it to make sure that it was correct because it is not always 100% accurate.

The common patterns that were found in these 10 job descriptions were that, there was an emphasis on having qualifications around these items like conversion rate optimization; collaboration across teams, dealing with all these different stakeholders like marketing teams and engineers and customer support teams, things like that; data-driven decision-making, making sure that there was an emphasis on using data analysis and experimentation to influence other people and to drive decisions to be made for product.

Strategic leadership, shaping user journey was really common and leading growth strategies. A customer-centric focus, talking about the customer journey, understanding that customer journey and prioritizing that customer experience was a common thread among these job descriptions.

Continuous iteration and experimentation came up a lot, involved running experiments and testing hypotheses, iterating on features to improve outcomes and achieve growth goals. Metrics ownership, driving those KPIs and growth metrics that the company had set with that product manager’s influence.

Once you have that, once you have those patterns and have a really clear idea of what those are, and you will notice patterns, believe me, then you can take your resume and say, “Okay, what am I missing here? What’s not on here that I found in the patterns and what is?”

For the ones that you already have in your resume that fit, maybe talk about them, you might decide, okay, based on how I was talking about them on my resume versus how these things were coming across in the job descriptions, you might want to change the verbiage to match the kind of wording that was used in the job descriptions, or keywords, for example.

Ten if you don’t have some items that you saw as common patterns, that’s your chance to either say, “Actually, I do have this experience and I need to tell my story to convey that I have these qualifications.” Or if you don’t have those things, decide, “Okay, how can I maybe bridge the gap there?” Maybe with supplemental education, maybe pet projects that you can do on the side if you have the time. There are a whole host of things that you are able to do that you can do within your control.

It’s important to remember that companies care about their bottom line. Corporations care about profits, they care about making more money and saving money. It would be helpful to tell your story in a way that shows them that you can make an impact on what they are likely to care a lot about, these two things.

In that growth PM example, you can demonstrate, “Hey, in my experience, this is how I’ve contributed to building a large user base and making the company more money through that top of funnel, driving customers, driving new users, or perhaps really helping with customer retention, user retention, and thereby saving the company money on customer acquisition costs.”

These are things that are definitely going to really wow someone, and you might have a competitive edge against other applicants if you can really demonstrate your aptitude for certain things that are going to make them believe that you can help them make more money or save more money.

I’ll pause here and let you guys ask some questions. I’ll stop sharing my screen and show my face again. There we go. Okay. So yeah, any questions? Let’s see. What is the best way to highlight relevant experience? Okay, so there’s a little bit more context from Eileen here. What if you’re applying to a job, but the most recent jobs are not the most relevant jobs? Okay, can you share an example of a resume format that would help highlight the roles that are more applicable even if they’re less recent? This is hard because it requires a lot of context sometimes, but if the most recent jobs are not the most relevant, then I would put the most detailed … So I’ll try to give an example here.

Again, just using the growth PM example, if you’re a growth product manager and those are the jobs that you’re looking for, but perhaps you’ve had to take a job recently or a contracting experience recently that was kind of a shift away from that, and no shame, it’s a tough market right now, then I would definitely make sure to keep that experience minimal on your resume and put a lot more detail on the most relevant career experience that you’ve had for the jobs that you’re applying for. You really just want to condense your experience down again to that one page ideally and tell your story the way you want it to be told. Sorry, just quite a morning for me already.

Let’s see. Can you share an example of a format? Yeah, it’s hard without a specific context. I’m going to come back to that Eileen, and if you want to message me directly, I can spend some time with you. Okay. So Eva has a question.

What are your strategies to stand out when more and more candidates are using AI bots to submit resumes, resulting in recruiters getting a thousand more resumes? Yeah, that is a really big issue right now.

Here’s the thing. I’m going to share how AI gets used on the other side with recruiters.

The way that I’ve seen AI tools commonly used right now for recruiters’ positions is that they typically use AI for contacting candidates, finding candidate, sourcing. Whereas when looking over resumes, even at big companies like TikTok for example, they will take the time to go through every single applicant, really truly, or if they don’t have the time for whatever reason, then they’ll use their applicant tracking system to look up specific keywords.

This is where that practical example of finding those common patterns and seeing what likely comes up will help you decide, “Okay, what sort of verbiage do I want to include on my resume that matches that or emulates that?” so that those common keywords that might be really important in the job come up when recruiters are having to look through their database and say, “Okay, I need someone with user optimization experience, or something, or having a specific tool that they’re familiar with, like Figma,” or whatever it might be, just highlighting specific keywords in that batch of resumes to have that person, the most applicable candidates come up. But yeah, it’s a lot more work on recruiters’ plates right now because it’s just a ton of applicants coming through more so than in the past. It doesn’t mean that all fit though. There’s a lot of noise, there’s some signal, but there there’s a lot of noise.

I hope I’m pronouncing your name correctly. How do you keep best information at the top, especially if the layout is chronological? Yeah, that’s also a really good question. Again, everybody’s story is different, everybody’s competitive advantages are going to be different, but using the education example, again, I don’t agree with this, but there is an education bias that’s out there. If you went to a top 10 computer science school or something, and if you’re an engineer, I would definitely put that at the top, or perhaps you were … Oh, I think we’re at time already. But if you were a YC founder, you might want to highlight that right away in the top and have that in your bio. Okay, I’ll stop there, Angie.

Angie Chang:

No worries. Thank you. This was really helpful. I especially liked the insight about AI and how things are working today. But yeah, this was a really great talk, and I’m really excited to see you. Thank you for sharing all of your knowledge, and we will see you in the next Elevate session.

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“Is Crochet Turing Complete?”: Christina Burger with Runway (Video + Transcript)

In this ELEVATE session, Christina Burger (Runway Senior Software Engineer) discusses the intersection of computer science and crochet. She explores the idea of representing a Turing machine as a crochet pattern and demonstrates her attempts at creating crochet symbols for the different states.

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Christina Burger ELEVATE Crochet is Turing complete

Transcript of ELEVATE Session:

Christina Burger:

Thank you so much. What a wonderful intro. I guess that makes my next slide less relevant. I’ll just introduce myself quickly. I’m Christina. I not just love making software, I also enjoy all sorts of other things, including hanging out with my cats, who ignore me, and also, I have a art supply shopping problem, which has recently extended into a yarn buying problem.

I also recently made a zine with my good friend Erica, and I’ll tell you a little bit more about that at the end if we have time. All right, so this is where you find yourself at the intersection of computer science and old lady crafts. I really love this place that I find myself in. I love both of those things equally.

I’ve also often felt like I can … it’s hard to not see them as similar things because when you’re looking at something like crochet or knitting, it’s actually quite a technical endeavor.

I have lots of family members who always claim that they’re not technical or they’re not smart, but they’re able to make amazing, wonderful things from scratch, and so I think that there’s something to be said for respecting the technicality of certain crafts and hobbies.

To kick off this talk, let’s talk about what is crochet. I just recently learned crochet myself. Previously I had tried to learn knitting, and I found it a little bit overwhelming with all of the counting and dropping stitches and things like that. I tried crochet, and I really loved it.

I feel like it’s really soothing, if you have a very active brain at the end of the day, you can just do some crocheting to wind down. The history of crochet is very long, and there’s lots of different countries that claim that they invented crochet. It’s been around for a while. If you haven’t crocheted before, you use a hook like this, and yarn. And you’re just basically making fabric from yarn.

Crochet consists of a few stitches. You can go from different heights, so you have single crochet, which is the lowest stitch, and then it goes all the way up to a super high stitch, which is a quadruple treble crochet. These are US stitch names. I think the UK uses a slightly different system, but I learned how to crochet in Canada, so these are the ones that I use.

Crochet can be used to make really intricate, wonderful laces. It can also be used to make a little hat for your cat, so it’s very versatile, but learning crochet and learning crochet patterns, I felt like something about them was very familiar in that it almost felt like I was reading an algorithm, and I think that’s because it is an algorithm.

For the next part, we have to talk about the next part of this talk, which is what is Turing completeness. The first time I heard this concept was at university, and it was very foreign to me to understand why it matters. I’ll endeavor to explain that a little bit better.

To introduce the concept, Turing complete just means that a language or a thing can be used to make a Turing machine in theory, and so I guess what we need to talk about more is what is a Turing machine?

A Turing machine is a theoretical model of a computer, but before we had computers, and it’s very important to be able to think through something like a Turing machine because it explains to us how we could solve general problems with one machine, instead of making a new machine for each problem that we have to solve, which was the approach that we had before we had this way of thinking.

A Turing machine is a very theoretical thing – it’s not a physical thing. It was never built to prove that it could exist. I think people have built ones in modern times just to show that you can, but it’s more of a way of illustrating what a computer could be.

The parts of a Turing machine are very simple. It’s this tape that you see here, and it has symbols written on the tape, and you’re able to read and write onto that tape.

To illustrate a little bit more, because it can be a little bit hard to understand that just from a quick introduction, here is one algorithm that we will be stepping through in a Turing machine.

First of all, we’re going to start with 110, that’s the number, and our goal is to invert the number to 001.

You can see that for this machine, we have three symbols, a zero, a one, or an empty tape or an empty cell in the tape. We have the tape and we have the ability to read a symbol under the head, and so how this program works is we have 110, and we have this state table that tells us what to do in case of each symbol that we read.

If we read a zero, we’re going to invert it, so we’re going to write one, so write zero, write one. Then we’re going to move the tape to the right, and we’re going to do the same thing. We’re going to read one, and then we’re going to invert it and write zero; move the tape to the right, read one, write zero; move the tape to the right, and now we read an empty cell. That means we can stop. That’s pretty much how a Turing machine would solve this particular problem.

Right. I was thinking about this and how familiar it felt. And I thought, could you represent all of that as a crochet pattern?

It turns out you absolutely can, and it also turns out that it’s really fun to figure it out.

Before we go through the pattern that I ended up making, I will show you a few of my attempts. I tried to make various ways to represent the different symbols in crochet, and all of them turned out a bit wonky. One I tried to do with color, and I think you could totally do that with color. But I gave up after one row because I realized that it’s cheating, it’s not really thinking too much about crochet stitches and more about the colors. But yeah, you could definitely do that if you want.

Let’s go back to what we had before, the Turing machine that we were or the problem that we were solving with Turing machine in this previous slide. Now we’re going to solve it in crochet, and I did end up with a more acceptable crochet block.

All of you are welcome to use my pattern to make your own crocheted Turing machine.

So what is our algorithm here? To start with, we’re going to chain 15. Chain just means putting in the foundational stitches to start, so these ones at the bottom. We’re going from left to right. From row one, we’re going to add a few stitches for height, and then we’re going to chain one, and do a double crochet, chain one, do double crochet, all the way through. And that’s just to set up the tape.

These shaded areas of double crochet, these are just structural to keep the piece in a sort of grid. I shaded them so that you can ignore them and understand that only the ones that are not shaded are important for our tape. Okay.

In the second row, we are going to write or not, I guess, not write, we’re going to make a double crochet to represent a one, another double crochet to represent another one, and a picot, which is this little piece at the top, to represent a zero, because we need three states, we need to be able to also represent an empty cell, which is a chain.

So yes, we’ve done that. Then the crucial part is that we need a stitch marker, which looks like this, and that’s how you can mark a place within your work. You can mark, oh, I’m here, yeah? That’s going to be our head. In our crochet Turing machine, we’re going to not move a tape, but move a stitch marker. We are putting the stitch marker at the picot, and that’s going to be where we start. And then we’re going to go over to row number three.

For row number three, four, and five, we’re just going to follow the algorithm, which is this part here at the bottom. The algorithm goes, copy each stitch that you see from the previous row unless you’re at the marker. If you’re at the marker, if you see a picot, do a double crochet. If you see a double crochet, do a picot. And if you see nothing, then you’re done.

Let’s go through. To the next row. Copy, copy, copy, copy, to the marker. We see a picot. We’re going to do a double crochet. Copy the rest. Chain three to turn. Then we’re going to remember to move our stitch marker over two stitches to the right.

In the diagram, they’re all to the right, but if you were doing actually the crochet, you’d have to remember to move it to the left, because you turn your work every time you add a new row.

So yes, for row number four, we’re going to see that we have double crochet, so we’re going to do a picot, double crochet, double crochet. Move our stitch marker to the right. And then copy everything. Chain three to turn. Copy everything. Then we see a double crochet, so we do a picot, a picot, and double crochet. We’re just copying again from row four.

We know that we’re done because in theory we moved the stitch marker to the right and saw that it was empty, so we were done with the algorithm. And yeah, this pattern is available, I will share the link in the chat, if anyone wants to follow it. And just wanted to show a bit more visually, it can be really hard to see.

Also, I’m not the world’s best crocheter, but this is my final product. I’ve tried to highlight the different stitches and what they mean both in the diagram and in the finished product.

If you look at the colors, to represent the one and the zeros, that might also help for you to see that we started with a picot and two double crochets, and we ended with a double crochet and two picots.

That was a really quick run through both of Turing machines and crochet. I guess, my conclusion is that crochet is Turing complete, as long as you don’t think too hard about the fact that a person has to still do the crocheting.

There’s no such thing as a crocheting machine, we have knitting machines, but crochet is actually really complicated to automate. And so mostly it’s done by humans. I guess, in that way it’s not Turing complete because it’s not a machine at all.

It’s still a really interesting thing to think about, how we have algorithms in our day-to-day lives that we’re following, and how we can integrate computer science more into our granny-like crafts.

Yeah, so that is pretty much it. Just one final note, I wanted to share my zine. Oops, sorry. Wanted to share my zine here, which I made with my good friend Erica. It’s filled with really positive thoughts and fun puzzles, we have a crossword.

Follow the link in the slide here if you would like to buy one. Or if you have any questions, now is the time.

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