Jon Krohn: 00:00:00 For anybody in AI, software development, data science, things are moving so fast these days. It’s so easy to feel overwhelmed. But luckily there are people like my guest today who make it so easy to stay up to date and to have joy, while we are staying up to date, you’re going to love this episode. Welcome to another episode of the SuperDataScience Podcast. I’m your host, Jon Krohn. My guest today is Linda Haviv, who has recently gone off on her own to be full-time building as well as creating content in AI infrastructure, but she has an amazing background starting with singing, being a professional singer, to becoming a software developer at a major media corporation. And then that led to roles in developer relations at AWS as well as Anyscale. And so the first half of this episode, we talk a lot about careers in this space in general and what’s really exciting.
00:01:05 But then in the second half of the episode, we also get into a lot of specific technologies like the open source technology Ray that Anyscale produces and that is really important for distributed AI computing these days. So nice blend today of career guidance as well as technical tips. Enjoy. This episode of Super Data Science is made possible by Anthropic, Acceldata and Cisco.
00:01:29 Linda, welcome to the SuperDataScience Podcast. How you doing today?
Linda Haviv: 00:01:33 I’m doing great. We’re here in New York. It’s great weather.
Jon Krohn: 00:01:35 We are. It is great weather. It makes a nice change.
Linda Haviv: 00:01:38 Finally, we are waiting for this. It’s been a bit all over the place.
Jon Krohn: 00:01:42 I’m really excited to have you on the show because you are, I would say you’re an infotainer.
Linda Haviv: 00:01:50 I like that term.
Jon Krohn: 00:01:51 You make everything really enjoyable. All of your content, you have over 250,000 followers across your platforms, including at the time of recording, 99,000- Oh
Linda Haviv: 00:02:02 My God.
Jon Krohn: 00:02:02 … on Instagram. Can’t wait. By the time this episode’s out, you’ll surely have six figures on Instagram. Is that your biggest platform, IG?
Linda Haviv: 00:02:10 That is, and I would say the strongest community. I feel like it’s easier to reach that same community. I think 10 years … I mean, I’ve been making content for a while on there.
Jon Krohn: 00:02:19 Yeah. 2016.
Linda Haviv: 00:02:20 Yeah.
Jon Krohn: 00:02:21 10 years ago, you started making tehincal content for IG.
Linda Haviv: 00:02:24 Yep. When it was like you could count on your hand how many tech content creators that were on there at that point. It was microblogging with images.
Jon Krohn: 00:02:32 Right. And it’s really fun content to watch. You do get technical, but you’ve always got a big smile. Our video viewers will obviously see that, but even if you’re just listening to this, it probably comes through on the camera, which is fantastic. Yeah, really enjoyable to listen to everything that you have, watch all the content that you have. So it was really excited to have you on the show, and we put it together really quickly as well, so I appreciate you being flexible.
Linda Haviv: 00:03:00 Oh yeah. No, I’m pumped. I was like, I love your podcast. I’ve been listening for many years and I’m excited to be on here. I didn’t know you were in New York though, which I should have known. So here we are.
Jon Krohn: 00:03:10 We should probably, instead of just having this wood panel background, we should probably have
00:03:15 The New York skyline.
Linda Haviv: 00:03:16 The skyline.
Jon Krohn: 00:03:16 In the video version.
Linda Haviv: 00:03:17 Yeah. From the west side, I’m being vibed.
Jon Krohn: 00:03:23 So you recently, at the time of us recording, you’ve only just left your role as staff developer advocate at Anyscale.
Linda Haviv: 00:03:31 Yes.
Jon Krohn: 00:03:32 Which is a really cool company. And so we’re going to talk about the open source framework, Ray, for our listeners that aren’t already aware of it. It’s a really useful open source tool to be aware from Anyscale. But before we get into that, I want to hear about why now is the right time for you after a decade of content creation. It seems like … I realize that you’re not going just to content creation, but I think that’s going to be a pretty sizable mix of what you’re doing with all of your time now going forward.
Linda Haviv: 00:04:00 Yeah. I think for me, it’s been on the back of my mind for many years, but it wasn’t the right time for me, I would say. And there are multiple reasons. I think for me personally, I was 10 plus years in corporate and I think I had this corporate and now the startup experience and variety of different roles from like software development to DevOps to SRE to infra to cloud engineering. And then of course with the AI boom, shifting to AI in front and AI engineering. And I think through all of that, there is this part that you could connect the dots and you figure out over time where your strengths are, where you could actually help people. And I was a developer advocate through all that. And I was like, I think I’m at the point where I could be the most helpful working independently and being able to educate people where they need.
00:04:45 I think we’re in a point, one, with developers that are adapting and upskilling. I think there’s also a part that education needs to be joyful and inspiring and not feel overwhelming in a time that’s very overwhelming. Also very exciting. Two things could be true. And at the same time, also, I think it’s the best time to be an entrepreneur. I can’t tell you that I had years of experience doing entrepreneurship. I was always doing it on the side, but this is obviously, I’ve always been doing something on the side. I think it’s really important.
Jon Krohn: 00:05:13 Yeah. Let’s get into the entrepreneurship thing as well as the doing things on a side, on the side argument in one second. I think that’s really important, but something that I love that you just said. So I asked you a week ago before we started recording, if there were any topics you wanted us to cover or anything like that, and you wrote, For sure, no question, the longest email back that we’ve ever gotten. And it’s not crazy long, but people will usually be like, a few bullets like, “Don’t miss this paper or don’t miss this talk.” And you gave us this fantastic biography and in it, you have this line that as I read it, it made me feel relaxed and happy because even just knowing that somebody aims to do this or is doing this, it brings me some relief. So the sense that you wrote is that your goal is to inspire developers to adapt, build, and upskill confidently in an era that’s moving fast.
00:06:09 A lot of people feel it’s overwhelmingly fast, right? Yeah. And to make that journey a little less lonely and a lot more joyful. Just reading that, it makes me feel less lonely and more joyful. And I know that you are doing it. So congrats on doing it full-time.
Linda Haviv: 00:06:27 Thank you so much. Yeah. And I hope to live up to that. And I think now I’ll be able to also put more time into that. I think it’s a very exciting time, but I think there is an identity crisis happening also for, as a person who prided herself, but also the way the code I wrote. I know. I know. Interesting. There is this part that I miss. I’m glad I’m not of 2:00 AM pages in the morning and stuff like that. In that way, now that people don’t get paged today, they absolutely do. And cybersecurity is a whole other end that my friends who work in cybersecurity, I feel like they’re getting paged all the time, but there is this part where there are things that are being abstracted. It’s not new. We’ve gone through abstractions before in tech, but there is this human part that is more and more important.
00:07:11 And that combination of technical depth and understanding that humanity part and being able to … Education is always the way you deliver too. There’s a topic you learned in school that you could love or hate based on the teacher you had. And I always think about that because I was never a computer science major. I didn’t touch that because I thought that wasn’t for me until I saw someone on Instagram talking about it. No way. And it was in a … Yeah.
00:07:34 Instagram changed my … This is why I ended up doing a lot of content.
Jon Krohn: 00:07:38 I did not know that. So you can give us a little bit of this background here. So you majored in philosophy as an undergrad and you did that in New York, right?
Linda Haviv: 00:07:45 Yeah.
Jon Krohn: 00:07:46 CUNY?
Linda Haviv: 00:07:46 Yeah. CUNY, Baruch. I was in the McAuley program, the McCulley Honors program there, which was great. It was a great program. You get to do a minor with other folks and they give you full ride.
Jon Krohn: 00:07:56 Baruch is like right by where I live in Capspana,
Linda Haviv: 00:07:58 Right?
Jon Krohn: 00:07:59 Yeah.
Linda Haviv: 00:07:59 Yeah. You see you’re in the area. And it was great. The city was your campus for the most part. And I was a philosophy major because it does not have an answer. There’s no answer in philosophy. You
Jon Krohn: 00:08:11 Didn’t want to have it. You wanted to be in any exam
Linda Haviv: 00:08:14 And being able to answer. It’s almost like there was no … I was very good at studying something and repeating it back, but I wanted something that challenges me to not have an answer because it makes you think and it also opens your mind because I was like, what am I going to get the most out of in college? And I was also, I was in that point where I wasn’t locked in. I thought I was going to be a lawyer, but I wanted to be a lawyer actually to go to school, to be a law student. I think I just left school. And then I realized over time that tech is being a professional student, right?
Jon Krohn: 00:08:44 Right.
Linda Haviv: 00:08:44 Throughout my career, we all have to always upskill to stay relevant. And no matter what, whether you have to then shift into cloud or then shift into AIML or whatever, it’s a skillset of how you learn and how you always … It’s exciting if you love it, which I do. And very glad, zero regrets. Best move I ever made, I actually made my brother also shift into tech.
Jon Krohn: 00:09:04 Oh, really?
Linda Haviv: 00:09:05 He was in music. That’s a whole other thing.
Jon Krohn: 00:09:07 Well, that is also, you also were for a long time, a professional musician. Yeah. And it wasn’t until you had your second of now three kids that you were like, this is … Moonlighting on music while also working a full work week on top.
Linda Haviv: 00:09:22 But it was always the people. I loved bringing joy to people. And I think there’s a way to do that with tech, with music. There’s actually a lot of parallels between music and tech that I find really fascinating. Even today, I think it’s amplified because now that we’re not doing as much coding day to day, we’re more systems thinkers. That was always a principle level job or a senior level job, right? System thinking. Now, just more people need to think that way. And there is a creativity to that, right? Just like anything. It’s all like how you interpret something, how you put a name that somebody else understands, right? So in a way.
Jon Krohn: 00:09:58 Yeah, it is interesting. So a lot of the best developers that I’ve ever worked with historically have been musicians and not necessarily professional like you were, but they play guitar or they play the trumpet or violin or whatever. And so there is something about that, I think continuous learning aspect. I think maybe recruiting kind of more parts of your brain.
Linda Haviv: 00:10:22 Yeah, left, right.
Jon Krohn: 00:10:24 There might have literally also been, maybe it makes it just easier to type.
Linda Haviv: 00:10:28 Also, it is mathematical. I mean, if you think about music, it’s like thethagorous, right? Created harmonies. It’s literally, if you think about it, there is a mathematical part to it.
Jon Krohn: 00:10:37 Nice.
Linda Haviv: 00:10:38 Philosophy
Jon Krohn: 00:10:38 Of math and music 101.
Linda Haviv: 00:10:39 There we are. I used to call it the jobless major because it was a bit … It’s like, what do you do with a philosophy degree? Well, today I would say there’s a lot more options for a person in the AI era, but …
Jon Krohn: 00:10:52 Oh, really?
Linda Haviv: 00:10:53 I don’t know. I feel like there’s more … I think more things that are philosophical are coming up as humans. So I can’t say for sure, but I would assume that I think Anthropic … I forgot exactly, but there’s a lot of non-technical background that are coming in.
Jon Krohn: 00:11:10 I still think it’s relatively niche.
Linda Haviv: 00:11:13 I
Jon Krohn: 00:11:13 Don’t think we’re going to start seeing every law firm
Linda Haviv: 00:11:17 And
Jon Krohn: 00:11:17 Recruitment firm be like, ” We need to get a philosopher in.
Linda Haviv: 00:11:20 “No, I think it’s more a way of thought. It’s good as a prereq for law potentially, I guess.
Jon Krohn: 00:11:26 Nice. So you must have done math or programming or something in high school or was there
Linda Haviv: 00:11:32 Any of that? So actually the funny story is how I started coding was I was working in TV news in between going to law school. And so right after college, I went into TV news and then I started learning how to code because I was building sites for all the anchors and started building their book sites.
Jon Krohn: 00:11:48 Oh, really?
Linda Haviv: 00:11:48 And so I actually taught myself through wanting to build their sites and I was doing everything because I was like a production assistant. So they’re
Jon Krohn: 00:11:55 Book sites. So they’re an anchor on the show. They write
Linda Haviv: 00:11:58 A book. They have a new book.
Jon Krohn: 00:11:59 Yeah. And so you,
Linda Haviv: 00:12:01 Just through conversation- And you have some custom code in there to make
Jon Krohn: 00:12:04 It look more
Linda Haviv: 00:12:04 Like HTML,
Jon Krohn: 00:12:05 CSS.
Linda Haviv: 00:12:06 CSS. I was using Squarespace or something. I was like, okay. And then I started going to meetups. So that’s what meetups played a very important role in Deverell. At the time, I think it was called Technical Evangelist. I think I remember seeing someone from Google at one of the meetups and I looked at that. It
Jon Krohn: 00:12:19 Was funny, you don’t see evangelist around as much anymore. No, because I used to be called that and now- Like Christian evangelism undertones kind of weird. I think the name changed. I don’t like that in a title.
Linda Haviv: 00:12:30 The name evolves. I think now it’s like- Developer vision. More developer advocates. But I looked at that person, I was like, oh, and down the road, not yet, I will do that kind of role. I felt like it was a combination of this people skills and tactical skills, but I was like, I first just want to be completely behind the scenes. I just want to go deep. There was something therapeutic about it. I think after working in news, I realized I really like depth. I find it actually very therapeutic. And this is why it’s also there’s an identity crisis of like, oh, I used to love coding. I mean, yes. No,
Jon Krohn: 00:13:03 I know, exactly.
Linda Haviv: 00:13:04 But there is part that’s also therapeutic about watching your agents work. You can build more. There’s more gratification I think that could happen, but also different kind of problems and you hit them faster, I think from a systems standpoint.
Jon Krohn: 00:13:16 Yeah. Let’s talk about this fast moving, not needing to code anymore scary business because I think it is … I don’t think anyone would argue that it’s still an advantage to be literate in code, even just for keeping track of what your agents are doing. But it is, as you said earlier in this episode, things have always been moving fast in this space. This is a really big change. It’s a good point. Where now theoretically you don’t need to be able to write code at all to be working in AI, to be training models, downloading open source model weights, fine tuning them to some specific task, labeling the data, getting that into production infrastructure, building the website that’s running that AI application in the backend theoretically now could be done all with natural language prompts. And that is a big change. And that’s really just a couple months now that
Linda Haviv: 00:14:13 You could do that. And I do think though the more and more we’re dealing with off toy examples, as people move to production and scale, we’re going to hit a lot more system thinking. And you know what stopped me in my tracks this week? Somebody who’s non-technical talking about memory, and I was thinking about that a few years ago, who would be talking about memory? It was always like an AI infra, you don’t see this part, it’s invisible. I used to build maps for elections, right? Nobody gave thanks to the DevOps engineers, okay? And I do think we’re in this phase where AI infra is actually becoming the problem that many people are just wording differently, that they need to figure out. And maybe they don’t need to know how prefilled deco desegregation or KV cache works or stuff like that, but they are saying the same thing in different ways, right?
00:15:03 They are saying, “Oh, this context is not working for me. Why does it feel like it’s lagging?” It’s things that even now that they’re building and vibe coding, whether it be the founder, the AI engineer or the AI infra engineer, they’re kind of all saying the same thing.
Jon Krohn: 00:15:20 Yeah. So in 2016, when you were getting … Well, actually, I don’t know if it was … Was it 2016 that you got started in your tech career or that’s just content creation? When did you start?
Linda Haviv: 00:15:28 Oh yeah, no. So it was 2015. 2016. Yeah.
Jon Krohn: 00:15:30 2015.
Linda Haviv: 00:15:32 I was teaching myself for a year and 2015, 2016 is when I got my first JavaScript. I was a JavaScript developer. That’s my first role.
Jon Krohn: 00:15:39 And you were in media still for a while
Linda Haviv: 00:15:42 Before- So the funny part, talk about transferable skills. I think people are all dealing with non-linear career paths right now, right? That corporate ladder, it’s not exactly … I don’t know how it’s going to play out for my kids. I don’t know how it’s going to play out, but I can say that I’ve navigated non-linear career paths, maybe not in the speed of now, but this is where I think transferable skills and people who have engineering background, it’s really, really a leg up and you have to lean into it. And what I mean by that is when I was working in media, I was working first on the other end of media and I was like, well, where would I have a leg up? If I go to a random tech company, I’m probably not yet … I didn’t have a traditional computer science background, but I do understand the user of media because I come from media.
00:16:24 And if I’m building the CMS for the journalist writing the article, I understand what they need because I wrote the articles also on the other end.
Jon Krohn: 00:16:33 Yeah,
Linda Haviv: 00:16:34 It’s the
Jon Krohn: 00:16:34 Content management system
Linda Haviv: 00:16:35 For … Exactly. Sorry. It was like my life for a while. I’m speaking of my hands and I always hit it with my chin. You know when I used to sing on stage, I used to always hit my teeth with the mic, sorry, tangent. There
Jon Krohn: 00:16:49 May have been a sound in the audio only version just now, and that sound was Linda hitting her face on the microphone.
Linda Haviv: 00:16:55 This is why I’m chaotic. I shouldn’t drink so much coffee. But that part of transferable skills, it is more about now you understand the user, you understand system thinking. System thinking, that is part of everything we’re doing. If you’re building an agentic AI workflows, there is a distributed systems layer here that you have to understand. And a person who is maybe … The people that are non-technical might be coming also with a very good transferable skill, right? They’re coming with understanding their niche. They’re understanding real estate. They’re understanding health. They’re coming from their niche. And what I think engineers bring is they understand where things are failing. It’s not completely like a mystery to them. Whether they come from software development, whether they come from the infra end, even someone coming from DevOps, they might not be like an AI/ML expert, but if you think about it, even the training of a model is an orchestration or distributed systems problem, mixture of expert models and how they work.
00:17:49 If you think about it, the large language models and how they’re trained, that’s also, in a way, a DevOps skillset. So I think a lot of it is getting abstracted, but what I think is bubbling up to the top is this AI in for challenges. And to bring it back to the transferable skills point, I think leaning into not feeling like just because, yes, people could vibe code, I think everyone brings strengths to the table. And just like the culture of open source, when we build together and we’re able to actually bring our strengths together, we will all win that way, right?
Jon Krohn: 00:18:19 For sure. So you’re saying your specific background, for example, in media prior to becoming a software developer in media, that was helpful. And that’s a useful tip for any of our listeners that are thinking, “Hey, I want to get into data science or AI or software development.” Where just like you said, Linda, it might not make sense for you then to try to be applying for jobs at Meta as your first tech job. At my first step. Exactly. But you could be thinking, “Okay, well, I actually have already been working in news media for several years. Maybe I can just stay at this company and start to have some of my time or all of my time be doing some kind of development work.”
Linda Haviv: 00:18:54 And I think people could bring their full self now to a lot of things because I think it actually matters. I think in the past, your career was always like, “Oh, here’s my separation.” As we look at also personal branding, as we look at how things are built that are going to be more intertwined with the human element, there is a part that I think we’re also being empowered even from, why am I going to entrepreneurship? I think it’s like there’s a lot that you could bring your full self today that you couldn’t in the past or creative things you could do that you weren’t able to because you didn’t have the $50,000 studio and you didn’t have … Even in the music industry, you had to be with a label. Today it’s very democratized, whether it be content, whether it be building something you don’t need to hire, maybe as many people, but also the software developer could take what’s in their mind, what they wanted to solve and do it for themselves.
00:19:41 And the cost is lower in some respects, right?
Jon Krohn: 00:19:44 Yep. In fact, it’s almost just as in music. So in music and performance probably in general, it seems to me now like there’s almost an expectation that somebody already has developed a social media following before they could get a record deal. And so that kind of reverse things. Whereas previously you’d get the record deal, you’d be on the radio and then you have
Linda Haviv: 00:20:09 A
Jon Krohn: 00:20:09 Following. And
Linda Haviv: 00:20:09 Then you were in the mercy of same with news, right? I used to work in TV news. And one thing I realized was, oh, I love media, but you know who the happiest people in my opinion were? The people who came as specialists, the lawyers, the doctors. Why? When they needed to not be on air that day because they weren’t feeling well, they could not be on air. But when you live a news cycle, you don’t have … I mean, there’s no balance in many things, but I think in TV news and it’s the 1%. And in a way, you have so much of your own authority of what you want to talk about, what you don’t want to talk about in your own media. Now at the time it wasn’t as common in 2016 to have your own media channels. But for me, I was like, I’d prefer to come in as an expert versus just … I think what journalists do are incredible.
00:20:52 I also realized what I enjoy versus non-joy. And I think you have to ask yourself that because I think you could curate your life today. You could be the artist of your life and curate it based on the things you want because you have the power today to take it. And I think it’s like everyone could build something, but what you build is really important. And going deep and figuring out where you bring that skillset, where you find joy, I think we’re in that era. I think there’ll be a creative renaissance in a way too. So live your wildest dreams.
Jon Krohn: 00:21:18 Yeah. So all of these things that we were discussing earlier in the episode as being scary from one perspective where as a software developer, as a data scientist, as an AI expert, we used to have this inbuilt moat that only this small percentage of us in the population could write the Python code or whatever, the Rust
Linda Haviv: 00:21:39 Code- Or design. You could be an engineer, but I’m not good at designing. I’m not good at project management, but AI helps me a lot.
Jon Krohn: 00:21:47 Right, right, right, right, right. So yeah, so there’s all of these different aspects where you still bringing your particular background, your particular expertise gives you probably more confidence to pursue any given direction. If you’re thinking, I want to build a media business with no experience in media, you can go to Claude and have the conversation, but are you going to have the same level of confidence in these recommendations you’re getting from the cloud chat than if you’ve lived that path in media, you’ve seen what works, you’ve seen what doesn’t work, and you’ve developed connections in that space as well. So you can say, “Hey, so- and-so from media company X, I’ve built this web app that I think solves these problems that you have. Would you like to see it? ” Them already knowing you, they’re much more likely to take the call to look at your app than if you send them a cold email.
Linda Haviv: 00:22:40 Right. I think for people especially who are many years in the industry and they’re toying with that, there is a strength to it. And I think of course, obviously you have the more junior developers who are struggling right now coming out of college. I think it’s harder to get junior jobs. So you’re seeing them also going very entrepreneurial as well. There is a democratization, there is an abstraction there. We’ve seen this happen though. In the age of bootcamps, 2016 time, I think I went to a coding bootcamp at the height of coding bootcamps. I went to the Flatiron School here. I think
Jon Krohn: 00:23:09 You did go at
Linda Haviv: 00:23:10 The height- That
Jon Krohn: 00:23:11 Was like the time. Because at that time there were also really great data science ones like Metis that was … I mean, that was a cool … I went-
Linda Haviv: 00:23:20 It was in person too.
Jon Krohn: 00:23:21 Yeah, in person. And I think that was a key to it be … Was it Flatiron in person
Linda Haviv: 00:23:25 As well? Yeah, Flatiron person.
Jon Krohn: 00:23:26 And I think to do an online bootcamp … The Super Data Science podcast, our name comes from superdatascience.com, which runs online bootcamps and there are absolutely advantages to doing it that way. We have instructors all over the world in Australia, in Asia, in New York, you can get the best people and you can obviously be anywhere in the world in any kind of personal circumstances and do the curriculum. So there’s definitely advantages to that. I didn’t mean to say that there isn’t. However, for me, and it sounds like probably for you, I think we might skew towards extroverted.
Linda Haviv: 00:24:07 Just a little.
Jon Krohn: 00:24:10 And that kind of experience for me of knowing that I’m going to be getting dressed, showering, going to Flatiron to go to Flatiron School, to be around these 20 people that are in different stages of life, different backgrounds, but all of us looking to become software developers, that is so exciting for me, having coffee with them and then they would have … The reason why I know Meta so well is because I would come and I would lecture once a term.
Linda Haviv: 00:24:34 Yeah. So you weren’t the lecturer.
Jon Krohn: 00:24:36 Yeah. And that even as the person providing the lecture, that’s a lot more fun for me than going on a Zoom.
Linda Haviv: 00:24:41 Totally. And I think there’s a part of it that’s also really mindset. When you’re trying to do a career change, so much of it is believing you could do it, especially when things get very overwhelming and when you’re learning something and actually sticking to it in three months. I think for me, I joined the bootcamp in person because I wanted community. I’m a people person. I learned better. And let’s say if you’re building something in a silo, okay, so you build something, but you know where you learn when you read somebody else’s code and then you have to have it work with yours. For sure. That’s where you’re reviewing each other’s codes. So when we had to do … And yes, we were able to do that virtually, but it was a different vibe for sure. And I think we’re going to find this layer, in my opinion, again, I think in- person events are going to be very popular right now because as we are able to do a lot digitally, I personally, again, this is my personal opinion, I think we’re going to need the mental part of it to build together.
00:25:28 It’s not that we can’t do everything virtually, it’s that we’re going to need it in some respects to reconnect with our human element. And even if we’re struggling to figure out what to build, I think by being in person, you find your answers through not just sitting, taking a walk, going, taking a shower. There usually have to be doing something and I think community is so crucial right now, especially as we navigate and we learn from each other. We each come with different expertise. There’s no way to be an expert in everything, but we all bring different strengths and we all maybe also want to get better at certain things. And by leaning into that mindset and by being with people who are building and are doing then finding your community, I think it helps you up level as well. And that’s just as important as learning everything, building.
Jon Krohn: 00:26:18 For sure. Yeah. And for me personally, there’s just a lot of fun and joy. I mean, just like us being able to record in person here today, it’s a lot more energizing for me, even though I have to … It takes a bit more time to come in in person to get dressed, but it’s worth it. I don’t know. I really enjoy this in- person stuff. Anyway, we ended up talking about this. This has been a really interesting 20 minute long conversation. But this all started with me saying that you’d recently left Anyscale.
Linda Haviv: 00:26:51 Yeah.
Jon Krohn: 00:26:52 And you have touched on some parts of the answer here in the last 20 minutes of conversation, but why is now such a great time for people to be doing things independently or to be building things on the side, even if they’re not ready to go completely independent yet?
Linda Haviv: 00:27:12 Yeah. I think one of the reasons I went into software development in general is because it’s something you could do on your own. It’s a skill that no one could take from you and it’s not reliant on a company, right? Because companies, the people could be great, but you’re a number. The people you work with might not be, and the relationships I have from companies are my lifelong friends and people who change my life. But the company as a whole, you’re a number and-
Jon Krohn: 00:27:35 You’re a human resource.
Linda Haviv: 00:27:37 Yes, you’re a human resource and it’s like the system is built. It doesn’t matter. No one really controls that really. So I think for me, I learned that early on. I always, in order to show up as my best self, even in my full-time job, I always liked not having all my eggs in one basket. And for me, it was also because I just loved community. It started as something that I wasn’t actually doing as … Sometimes if you pursue your passions.
Jon Krohn: 00:28:00 So yes, you’re talking about this is why it’s valuable to have a side hustle. It’s like insurance.
Linda Haviv: 00:28:04 Right. It’s insurance and it also ended up adding to my full-time job because by creating a personal brand, my job at Amazon happened because they found me talking about cloud computing on TikTok. So sometimes life is not … You can make a plan, but life does not work that way always. Especially right now, I think also being an artist of that direction is really important. So back to your question on why I made that shift, I really believe that one, we’re living in obviously an unprecedented time. And I always think to myself, what would I have done when the internet came out? And we’re living right now in that equivalent. And I’m like, well, I’ve already, I’ve worked 10 plus years. I actually want to continue working with the companies I’ve worked for in other ways, but I think I could be a bar raiser in combining my skillset and communicating.
Jon Krohn: 00:28:58 And I do have here a list of some of the brands that you’ve worked with independently, which includes Anthropic, Dell, Amazon, GitHub, Microsoft, Nvidia, and quote unquote, many more.
Linda Haviv: 00:29:12 Yeah. And that’s the thing, I was like, oh, I love corporate. I love working for a company. And part of me was like, but I could still do that in other ways and consult. And actually I could be much more intentional because sometimes when you work inside a company, you are pulled into a lot of things that just take time and understandably they’re like red tape that happen and that’s normal, but when you’re not in that scenario and you’re not full-time employee and you have a project you need to commit to and a campaign you need to do, it actually ends up being more efficient for me. And at this time I was like, this is the right time for me. But I also think that, I don’t know the percentage, but maybe even 50% of jobs will be like small companies because right now everything’s getting leaner, right?
00:29:56 I think things are getting … And again, this is not some scientifically backed metric I’m giving, it’s very much where I personally see I think there’s going to be a leaner obviously way to work. I think we’re going to see two to three person billion dollar companies, maybe even one person billion dollar companies. And I also think obviously it’s not just money, it’s really empowerment of how do we navigate this next phase? Do I want to be in the forefront? And can I do that in a company? Absolutely. Where do I personally think I’m at the point that I could give more? And I asked myself this, is it the right time or the wrong time in my career, in my family’s life also? I have three kids and I wanted certain flexibilities as well. Not that I’m not going to be working more for myself, but I think there is a point that I felt like I could actually do better, more good for developers working independently and still advocating for all the things I love, including Ray and AWS and all these things in different ways because I’ve gained enough that I’m able to combine and bring a different interpretation to things and help people navigate.
Jon Krohn: 00:31:01 Yeah. I had a similar journey to you and actually in some ways on similar kinds of timelines where it was also 2015, 2016 that I started creating content. And it was also, it initially started with meetups in person. So doing talks at data science, computational statistics, meetups in New York, that’s how I got started with content creation. And then somebody from Pearson happened to be at one of these talks and said, “Would you like to create a video course?” And then that created this really popular deep learning video course. And then that same Pearson team says, “Would you like to write a book on it? ” And then that ends up being a bestselling book. And then I got invited to be on the SuperDataScience podcast as a guest.
Linda Haviv: 00:31:46 No way!
Jon Krohn: 00:31:48 On the back of my bestselling book. And about six months later, Kirill Eremenko, the original host and the founder and still co-owner of this show said to me, “Do you want to become the host of the world’s most listened to data science podcast?” And then now several years later, after doing that for about five years, about four years, I got to a point where similar to you, I had enough kind of different clients, different ways of impacting people where originally I got started in the content creation because I wasn’t doing it for the insurance aspect necessarily. I was doing it because as an extrovert, it’s hard for me to stay motivated to learn when it’s just for myself. The weekend comes, and specifically the content that I was creating in Deep Learning 10 years ago, I wasn’t creating that content because it was popular at the time. I was because it was obvious to me that there was a huge amount of potential in deep learning.
00:32:54 And for about a year, weekend after weekend went by where I was like, “I need to spend this weekend learning about deep learning,” but then ended up going to the pool instead.
Linda Haviv: 00:33:02 This sounds so similar to my AWS journey because I was talking about AWS before I ever joined AWS. And I just, for me, was finding other people that were also learning with me on the certifications. And then it was not touched upon enough.You could count how many people on TikTok or Instagram, especially in those areas were talking about cloud computing. And because of that, I met a lot of people that we first met on social media, became friends across the ocean in different countries, and ended up working together. And it’s like how the world is so much smaller also. And we think about open source and we think about developer twolling and how it’s actually the same thing in a way where you contribute to different things and you’re able to find that community. And I think in that way was, especially I think right now that exists in AI info a little bit.
00:33:46 I don’t think enough people are actually, let’s say on short form video talking about AI infra and the depth that needs to be there. It’s on X, it’s on YouTube, but I don’t think it’s like … I had videos go viral on TikTok that are about AI infra, and I think people assume, but until you don’t try and you don’t go technical there, people are still there. It’s just that you’re dealing with different audience and maybe you need to make it something that they could watch at 2:00 AM, but they’ll learn something. And how much of education is also how you retain something? If you’re reading something from a paper, you’re not going to remember the same way as you listen to it on a podcast or you watch it visually on a YouTube video. You know what I’m saying? There’s different ways people learn.
00:34:24 And I think there is a part that I was like, oh, this is the way I love to learn. I love X2 and Twitter and stuff.That was where most tech content was, but I realized that the women in tech preferred Instagram and that’s where I was finding my community of women too. And I was in DevOps and for me, I was looking for my people also throughout that and cloud. And I think the amount of community I found in cloud on those platforms was incredible. But so similar to you also, what is your one lesson also that you feel like today for people kind of navigating that or entrepreneurship, what do you think people should be thinking about?
Jon Krohn: 00:35:02 It does seem like a time where you’re saying the barriers to entry to be building applications because we can be orchestrating teams of AI agents to be building products for us. Especially if you’re not able to get that in your day job as somebody in our space, if for whatever reason your role in a business, you have this very specific narrow niche, or maybe you’ve even … Actually, it applies at all stages of the career. It could be that you aren’t in the technical role that you eventually want to have. You’re in a non-technical role. We have a regular listener, Adriana in Germany, who is a flight attendant, but she’s been studying data science courses for a while and at some point soon, I’m sure she’s going to be going for her first technical role. But so whether you’re in that kind of situation coming from your resume up to date doesn’t have that line that says software developer at Meta yet.
00:36:08 So yeah, whether you’re that kind of person, whether you are in a specific niche, because maybe you’re in a very big corporation and so you have a specific niche carved out for you and even though you’re really excited about all the new, exciting things happening with agents, you don’t have the opportunity in your nine to five. Or it could be the case that maybe now you’ve actually grown really far in your career. You used to be hands-on, but now you’re a manager.
Linda Haviv: 00:36:36 Yeah.
Jon Krohn: 00:36:36 And so regardless of which of those three situations you’re in, which together are probably the majority of our listeners fit into one of those three buckets, whichever you’re in, if you’re excited about this new co-gen, agentic moment that we’re definitely in where it has never been so easy, so powerful to be building AI applications, production quality, we have guests on all the time talking about open source frameworks or proprietary products that you can be using to get secure, reliable agents into your hands and into potential customers of yours hands. Or potentially, it doesn’t even need to be a customer. You could be thinking about, what kind of project can I do to make a social impact with these kinds of AI agents? And so regardless of what situation you’re in career-wise, you can on the side be experimenting and there’s great courses. My friend Ed Donner has great courses on getting into agentic coding and also-
Linda Haviv: 00:37:42 Fantastic. Follow him.
Jon Krohn: 00:37:43 Yeah, he’s incredible. So we co-founded a business together.
Linda Haviv: 00:37:48 No way.
Jon Krohn: 00:37:48 Yeah. So Ed Donner, there’s a few people on the planet I know better than him. I left a very comfortable, amazing corporate job in 2015 to join him at a startup that he had just founded in 2015. It was a startup called Untappd. He was the co-founder, CEO of that business. I was chief data scientist, not a co-founder, but in 2020, that business was acquired. And then him and I co-founded another business together. And so from 2020 to 2024, late 2024, him and I were co-founders of a business called Nebula
Linda Haviv: 00:38:28 And- That is so cool. I had no idea.
Jon Krohn: 00:38:30 Yeah. And he’s in New York as well.
Linda Haviv: 00:38:32 I need to interview you. Okay. We have a lot to talk about here.
Jon Krohn: 00:38:36 But yeah, Ed is amazing and he has about 18 months ago, he made the leap that you and I made in 2016 to creating content on the side. And oh my goodness,
Linda Haviv: 00:38:48 His
Jon Krohn: 00:38:48 Stuff has taken off in a way that I don’t know if I’ve ever seen before from anyone.
Linda Haviv: 00:38:52 Crazy.
Jon Krohn: 00:38:52 He has half a million paying students on Udemy for his courses. In 2025, he released about a new 20 hour Udemy course per month. And so whatever aspect of agentic engineering you’re interested in, so that’s from engineering the agents, so writing code to create agents, or the flip side of that, using agents to engineer. Yep. I like it. Whichever of those you’re interested in, Ed can sort you out. And actually, you know what? I have a surprise for you, Linda.
Linda Haviv: 00:39:28 Oh, uh-oh.
Jon Krohn: 00:39:29 Because this is, given how much you love Ed, something I’ve never said on air, even though this has been in the works for a long time, is that him and I are writing a book.
Linda Haviv: 00:39:39 Drum roll.
Jon Krohn: 00:39:40 And it’s about … So we’re basically, with my experience book writing, we’re taking his content, these wildly successful Udemy courses.
Linda Haviv: 00:39:52 Incredible. And
Jon Krohn: 00:39:53 Packaging them into a book format.
Linda Haviv: 00:39:55 That’s amazing. Well, first of all, we need to do the clap right here. I feel very honored that this was shared here, by the way. I didn’t know that. That’s amazing. And I think books, courses are fantastic and having … I always say you need multiple ways to learn the same thing and having that other format as well. And I will need to pick your brain because I think writing well-written books is a whole art in itself.
Jon Krohn: 00:40:20 Well, and you may not know this about me, but I am now for … So going back to 2016, when I started creating content and writing books for Pearson, I’m now the series editor for all of the AI books that they publish for professionals. Oh,
Linda Haviv: 00:40:33 No way.
Jon Krohn: 00:40:34 And so- When
Linda Haviv: 00:40:35 You sleep.
Jon Krohn: 00:40:36 Well, and so we can talk about you having a book for sure. Oh,
Linda Haviv: 00:40:40 That’s amazing. First of all, I’m very excited for this book that’s coming out.
Jon Krohn: 00:40:44 And that also, that goes for any listeners who think they have a book ready to write, feel free to reach out to me and we can get you with the-
Linda Haviv: 00:40:53 So many skillsets.
Jon Krohn: 00:40:55 Well, but it’s all about communication, right? It’s all about taking technical concepts and being able to in different formats. Like you say, different people learn in different ways, but whether it’s a book or a video course or a one minute long YouTube short or Instagram short-
Linda Haviv: 00:41:11 People consume in different ways and learn in different ways. I think that’s the thing. There are actually more and more people who actually need to also learn. And we’re all also going on that journey, building in public, right? Where it’s a very big cultural thing in tech, where you build in public. Why? Because when you’re at a certain point, you’re able to explain something that in a year from now, you will not explain the same way. And so sometimes I always said this to people when they were … I would give certification, cloud certification courses. I started an internal one when I was a software developer when I was upskilling to cloud, and I was always one step ahead, right? But the reason it was helpful was because the way I explained it then would not be the way I was so deep after that, that how I would explain it to someone would not sometimes be the best way for them to grasp it at that time.
00:41:59 And there is always this part where your perspective is also really important and the way you’re educating is you have a voice in that and not to feel like this part where like, “Oh, I don’t know. Just make sure obviously everything you’re saying is accurate.” But I think there is a part where it’s like learned experience is much more raw when you’re able to be hands-on and freshly explain something after you were hands-on on something. Even if you don’t understand every single thing, say that, be honest about it, but I think you will be surprised how much you’re teaching someone that is very much close to where you are. Maybe you’re one step ahead of that.
Jon Krohn: 00:42:28 Yeah, 100%. Yeah. So really exciting for anyone, hopefully maybe a little bit of extra encouragement here from us around how doing stuff on the side can lead to unexpected positive consequences, who dares, wins and just by … You never know. I do encourage you if you, for whatever reason, feel like creating content or studying is something that’s useful to you. Doing it in public, even before you have an audience, I think is really important because that knowing that you’re going to be pressing publish on that LinkedIn post or that Instagram video or whatever, knowing that someone might see it. And in the beginning, not everyone’s going to be an edner, but in the beginning it’s going to be maybe very few people looking, but it could be an old colleague or something. A few people look, you get some feedback and just knowing that someone’s going to be watching, it forces you to have this extra level of making sure you understand what you’re doing.
Linda Haviv: 00:43:35 They say the best way to learn is to teach.
00:43:38 Kind of puts the deadline for you and you also have a responsibility for someone else. And to add to that, I think you find also that the feedback, as you were saying, and the community, so much of it is about sometimes two people in the same spot as you that you will interact with are worth more than the 100,000. It’s never really about that. It’s about finding the right people. Great if it’s like 100,000 people that are also in that. But I think even for, I’m sure you and I both, it was kind of like that personal branding part and that content part comes from also finding like- minded community and individual as extroverts. But this is also for everybody else. If you’re an introvert, this is probably the best time too, because you could be not having to go to social events and be able to do that from your house.
00:44:22 And in whatever format that works for you, just because it worked for person A, if you don’t like doing that kind of content, you like doing this kind of content, do what’s right for you. You’d be surprised how much sometimes what you … There’s not one way for it to work. And I’ll give you an example. When I was making tech content in the beginning, I had two young kids at home and I couldn’t do talking head videos. So I would do like music with text videos and I would still educate in other ways.
Jon Krohn: 00:44:45 Music with
Linda Haviv: 00:44:45 Text videos. Over text overlay videos. And those end up really working well for shorts. But in a way, that was also the only way I was able to film. I was able to take B roll of what I was doing and as much as I wanted to talk on camera, that was just not the format that worked for me at that point in life because I was like hiding in the closet trying to edit these videos after. So I think my point is lean into like also the things that bring you joy in education. So whether it be … And start there. Don’t overwhelm yourself on the 10 steps. If you build something, take a screenshot at first and write out a caption. It doesn’t need to be the full video if you are nervous. Usually you have to build that comfort level, but I do want to get into that.
00:45:24 That personal branding is also really important. And it’s not because branding or to be an influencer. It’s to find like- minded individual and community because you’re building with more people around. It’s the same concept as contributing to open source, right? It’s like it’s open because we’re all innovating together and you’re much more likely to bring the right things to yourself and opportunities if you put it out there than chase them because you don’t always know what’s out there.
Jon Krohn: 00:45:46 Yeah, spot on. All right. So wow, this has been great. And none of this has been in my plan. Almost 100% of the episodes so far. So some episodes, and I don’t know, maybe listeners can tell in some way or other, but some episodes, we have a research plan. We have an amazing researcher, Serge Masis, who’s a great data scientist
Linda Haviv: 00:46:06 In
Jon Krohn: 00:46:06 His own right and content creator, in his own right, author.
Linda Haviv: 00:46:08 I was following him on LinkedIn.
Jon Krohn: 00:46:10 Yeah. Brilliant guy. And so he comes up with a rough conversation flow based on everything that his AI agents can find about guests online. And yeah, so some episodes, I have a plan and I relatively stick to it. In this episode, this entire conversation so far has been- On a win. Yeah. Which does happen, sometimes
Linda Haviv: 00:46:37 It mixes. Yeah, it’s a vibe.
Jon Krohn: 00:46:38 But I will get to you now the first question that we had. So I started by talking about how you’d recently left your staff developer advocate role at Anyscale, but I want to talk about what you were doing in that role because the open source framework, Ray, that you were advocating for
Linda Haviv: 00:46:56 There
Jon Krohn: 00:46:57 Is sensational. I mean, we have been-
Linda Haviv: 00:46:59 I love Ray.
Jon Krohn: 00:47:00 AI software companies that I’ve had four years have been using Ray as a key part of our stack. So tell us about
Linda Haviv: 00:47:09 Ray
Jon Krohn: 00:47:09 And why listeners need to know about
Linda Haviv: 00:47:10 It. So to level set, if you don’t know what Ray is, Ray is a Python native open source distributed computing framework. And what’s great about Ray is … It’s open source, Python native. Spark is probably something that people use that’s … I think for AI workloads, Ray works really well, especially AI infra. And the reason is, one, you have more multimodal data you’re dealing with, right? Video, audio, text. And I think as a gentech AI systems get more complex, you need that distributed computing part. Many engineers are not coming to start being orchestrators or everything. An ML engineer wants to just build something. They don’t want to start being a distributed computing expert potentially, and it actually was born that way. And I think Robert would be a good guest by the way on your podcast. Robert Nichara actually- Co-founder of this, yeah, co-founder of AnySkill and co-creator of Ray.
00:47:58 They were at Berkeley, Jan Soeka’s lab. And they created it because of reinforcement learning at the time for reinforcement learning at the time, they were ML researchers and they were spending more time on distributed computing and distributed systems over building and working on ML research. And so a lot of it was to help that. But the funny part was that because they made it work for reinforcement learning, today it ends up … I mean, reinforcement learning for a bit was kind of not talked about and now it’s back because a lot of the-
Jon Krohn: 00:48:27 RLHF.
Linda Haviv: 00:48:27 Right? A lot of the biggest models today, DeepSeq, all that, they’re all using RLHF and different variations of reinforcement learning. So I think now it’s … And it’s not just for reinforcement learning, the library’s expanded. So the thing about Ray is it actually works on multiple different parts of the stack. So Ray Data, RayTrain, as it sounds for training, RayServe for serving, and it’s the same language, Python native. It’s fantastic. I definitely recommend checking it out. There’s also a lot of native integrations if you use it. If you’re using, for example, VLLM and Hugging Face and all that, there’s a lot of native integrations. And maybe if you’re doing distributed training, you’d be using Ray data and retrained. There’s a lot of different libraries pretty much built on top. I think I’m throwing off information, but for the most part, it’s helping engineers not have to be distributed computing experts and writing in the language they’re used to building in.
00:49:21 And whether you’re building an MCP server and you want to serve, you could use Ray, or if you’re doing distributed training, you could use Ray to distribute the workload because a lot of this does not fit … The problem you’re solving is it’s not fitting on one GPU, right? You have multiple GPUs, TPUs, and all the multimodal data we have in the world, 80% plus of it right now is important because that’s your unlock for businesses.
Jon Krohn: 00:49:46 And it’s open source, so you can get on it
Linda Haviv: 00:49:48 Right
Jon Krohn: 00:49:48 Now as you’re listening to
Linda Haviv: 00:49:49 This
Jon Krohn: 00:49:50 Episode, obviously at no cost with a full functionality. Now, in your response there, as well as actually many times throughout this episode already, you’ve used the term AI infra. And I’m not sure if we’ve used the full word yet, which is infrastructure as opposed to say maybe people thinking inference possibly.
Linda Haviv: 00:50:07 Yeah, because I swallow my vowels. I’m from Brooklyn.
Jon Krohn: 00:50:10 No, that isn’t what I mean at all. This has nothing to do with you. No,
Linda Haviv: 00:50:13 I know.
Jon Krohn: 00:50:14 I know. But what is AI infrastructure exactly? That is something that you are expert in.
Linda Haviv: 00:50:20 Yeah.
Jon Krohn: 00:50:21 And how do you define that?
Linda Haviv: 00:50:23 You know, that’s a very good question. I don’t know if I’m going to define it in the way that maybe the industry wants to define it. And just like anything, I think DevOps has the same problem. If you know the definition of DevOps, people say, oh, I’m a DevOps engineer, but it’s really a culture, right? And as it expands, it gets complicated. I think we do that a lot in tech. I find AI infrastructure to be still infrastructure, but the thing that actually is more aligned to the problems that are different with AIML workloads. And there are a lot of things that happen that are infrastructure-like, right? Same thing as any other infrastructure that’s also not AI, but that you’re dealing with specifically with AI. So Ray, for example, is built for AI because it’s the way it’s actually working under the hood. One, because it’s Python native and a lot of things that machine learning are built in Python, it works better for that.
00:51:09 But additionally, because of the wave functions with GPUs, where you’re not letting it just waste as much money pretty much in the way it’s filling the workload, because AIML workloads are … There’s a lot of variation of issues that come up with AIML that are not in traditional infra, especially deterministic ways, deterministic or not deterministic. Is it like … Let me backtrack for a second because I think I’m going very granular, but- No, it’s good. AI infer in general, if I had to define it, and again, don’t kill me here, but for me, how I look at it is anything that really is also infrastructure specific to AIML workloads versus your traditional infra. However, there’s overlap.
Jon Krohn: 00:51:49 And so traditional, you mean like AWS EC2 instance would be traditional compute infrastructure.
Linda Haviv: 00:51:55 Well, it’s why Neil clouds exist now because you have- Like
Jon Krohn: 00:51:58 Lightning
Linda Haviv: 00:51:59 AI. Yeah. Anything because you don’t need as much of the virtualization, you need direct bare metal access. So I think there’s a lot of things that are happening that are challenges in AIML workload. That’s why you have frameworks like VLLM that came out that are open source for inference. Inference is the first place you’ll hit it because that’s probably the more common place you’ll hit it. Inference is whether you’re doing it for a finance company and you’re trying to do batching of inference or you’re doing this as trying to serve a lot of users, you’re dealing with inference every day, right? But that’s the first place you’ll probably see those challenges that are different than regular inference than traditional inference. So
Jon Krohn: 00:52:37 It’s in inference that you’re likely to see the first infra structure
Linda Haviv: 00:52:41 Challenges. From like a user standpoint.
Jon Krohn: 00:52:43 I
Linda Haviv: 00:52:44 Guess AI, infra engineers have always … I think there’s also a lot of new roles all the time. You have the ML platform engineers and then when you’re a DevOps engineer, you might be an SRE or you might be an infrastructure engineer, you might be called a DevOps engineer doing all of the above and you might be a CI/CD pipeline person. I mean, every company, again, and I’ll say this because there’s a lot of debate around it and I’ve seen it, in a corporation, you might have more silos of what each role does. In a startup, you might be doing all of the above. And I think we are going to see new terms. We’re seeing this now all the time. There’s new terms that didn’t exist because what we’re really trying to do is give it a term to explain the problem. But AI infra is pretty much any infrastructure that I think is really associated with building out the workloads we’re using today, which do behave differently, right?
00:53:28 They are much more compute heavy. In the past, we were probably more reliant on having data, input, output, but today we’re really dealing with a lot more compute heavy workloads. And so that’s a different challenge than traditional. So when I say that, I think the compute stack is also changing a little bit and that’s why Ray exists to distribute that load. You needed Spark for other use cases, but for AIML workloads, you needed to integrate with VLLM and you needed to also distribute the workloads in different ways.
Jon Krohn: 00:53:56 Yeah. So to do a bit of an analogy here, so Spark was kind of analogous to the EC2 instance in terms of providing you with the infrastructure you needed for traditional compute loads that didn’t involve GPUs But now that we do have GPUs as a key part of serving any AI capability or training an AI model, that it now counts as AI infrastructure. And so open source tools like Ray become useful relative to something like Spark. A neo cloud like Lightning AI becomes useful relative to AWS cloud compute perhaps, although of course they’re also developing …
Linda Haviv: 00:54:41 I call it the specialist. I’m actually doing a video tomorrow. I’m going to do this in the airport where I’m doing an analogy between traditional cloud and neoclouds because I think they each are kind of overlap … Traditional cloud are actually building a lot of stuff that neo clouds do. However, neo clouds are just, they’re specialists and they’re pretty much really building out what you need for AI infra. And I think there is a good analogy there in a way, which I will work on. I’ll put this video, but there is this part that like you have this problem that’s growing very fast and you need to solve it faster because we’re moving very fast. So NeoClouds are really solving that and you have the Nebus and Coreweave and Cruzo and all of them that we’re seeing more and more. We’ll hear a lot of their names for sure.
00:55:26 And I look at AWS, GCP, Microsoft is like, they do everything in the airport, right? And then you have that specialty route that’s probably … You’re filming
Jon Krohn: 00:55:35 This at the airport
Linda Haviv: 00:55:35 For the purpose of- Yes, of psychotic for this video, yes. Just
Jon Krohn: 00:55:38 For the video. You’re
Linda Haviv: 00:55:38 Not
Jon Krohn: 00:55:39 Flying anywhere.
Linda Haviv: 00:55:39 Well, I am tomorrow I’m going for one. Yeah, yeah. I was like, I take advantage of the situation. I’m like, you know, I should do something in the clouds.
Jon Krohn: 00:55:46 Right.
Linda Haviv: 00:55:48 Analogies help.
Jon Krohn: 00:55:50 Oh, for sure. We did some research on a popcorn analogy that you had.
Linda Haviv: 00:55:54 Oh yeah, batch inference.
Jon Krohn: 00:55:55 Yeah. I mean, well, now that I’ve set it
Linda Haviv: 00:55:57 Up, you might as well just tell us
Jon Krohn: 00:55:58 About it.
Linda Haviv: 00:55:58 Because there is popcorn. It’s like imagine you’re eating pop. I think I was trying to compare … I think the video was all about explaining batch inference, right? And so I think I was sitting there with a pan and I was like, so when you’re talking about regular inference, you’d be like just maybe putting one … Prepping data is usually on CPUs, right? So you’re prepping the data and you need to distribute that, but you put the butter, you put the salt and your kernel. And then if you’re only taking one kernel and you’re popping it on the pan and then you’re whatever, that’s great for getting something in a fast way, right? So you’re dealing with that’s latency optimized. But then if you need to batch a bunch of popcorn, that’s not the best way, right? Let’s say you’re in finance and you have so much data that’s going to take you years to do it that way.
00:56:51 You might want to do a bunch of them. So you might go to a microwave and put the full bag of kernels in there. The problem becomes that that microwave is always on and you’re paying for it. So what happens? You pay a lot of money and you need the pre-processing to happen at the same time. So it was kind of an analogy of pretty much trying to show how you need kind of the pre-processing of the CPUs. You need the prep of the kernels to be ready for the amount of microwave popcorn in the … So you’re not wasting money and not starving your GPUs, but …
Jon Krohn: 00:57:24 I like that. And the visuals make it easy.
Linda Haviv: 00:57:26 So
Jon Krohn: 00:57:26 Even just like as you were speaking there,
Linda Haviv: 00:57:29 I was imagining the visual.
Jon Krohn: 00:57:30 Single kernel of popcorn and how inefficient that is if I need to do hundreds of thousands of popped corns.
Linda Haviv: 00:57:36 Yeah.
Jon Krohn: 00:57:38 Yeah. So the visuals there are super helpful. Going back a few minutes to something you were saying, you were talking about all these specialized roles that are developing, which
Linda Haviv: 00:57:46 Is
Jon Krohn: 00:57:46 A natural consequence of so many more technologies, so many more capabilities, so much more demand in the AI space. And actually, so something, the reason why I’m bringing this back up is because today, the day of recording, Monday, April 13th, I made a post on LinkedIn that regular listeners might be interested in checking out-
Linda Haviv: 00:58:06 Check
Jon Krohn: 00:58:06 It out. Because this show, the SuperDataScience Podcast, has been around for 10 years. And what was originally one role, data science, and maybe at that time we had like data analyst, data scientist, there weren’t that many different kinds of related roles, but now 10 years later, all these AI ops, ML engineer, AI engineer-
Linda Haviv: 00:58:25 LLM ops, everything.
Jon Krohn: 00:58:26 LLM ops, just so many different careers. And you could potentially, depending on how you wanted to categorize it, you could say these are all subspecializations under a big data science umbrella. Not necessarily everyone would do that, but I do. But the thing is that we are thinking, the show is almost 10 years old and we’re thinking, is the name SuperDataScience Podcast, does that make sense given how much this field has evolved? And so, yeah, check it out. So I might make this post again, but if you go to my LinkedIn, and definitely if you scroll to Monday, April 13th, I’ll probably post at least one more time before the survey closes, but if you want to have a say on the SuperDataScience podcast brand, does this name make sense in this day and age, given the stuff that we talk about on the show, let us know what you think about the brand, about potential alternative brands.
00:59:22 We will make the changes based on the feedback that you provide us.
Linda Haviv: 00:59:27 I think this is such a great topic because the tech overlap role thing has, I think even from a DevOps standpoint, that has always been such a pain, but the speed in which it’s going in right now because of like AI ML is like … And it’s also, I think the best way to also understand that evolution is write the story behind it, whether it be like big data and the internet came and then you had like machine learning, you had to make sure that your models also have their own ops problems and so you have jobs just for that. Yeah. I will weigh in on the That post.
Jon Krohn: 01:00:00 Fantastic.
Linda Haviv: 01:00:01 But I
Jon Krohn: 01:00:02 Love
Linda Haviv: 01:00:02 The name. I
Jon Krohn: 01:00:03 Love the name. Well, I’m glad you do. It’s
Linda Haviv: 01:00:05 Good for SEO.
Jon Krohn: 01:00:06 Well, yeah, we’re not necessarily changing it, but we’re evaluating.
Linda Haviv: 01:00:11 We’re
Jon Krohn: 01:00:11 Doing an exercise. And so let us know your thoughts. Speaking of posting and models, you’ve been doing a lot of posts recently on new exciting developments in the AI space, like open source models, rapidly closing the gap with proprietary ones, which is exciting. And you’ve also been posting a lot about protocols like MCP, model context protocol, moving toward vendor neutral governance under the Linux Foundation. So how will these kinds of developments, open source, accessibility, how will that redefine the competitive landscape?
Linda Haviv: 01:00:47 I think especially around … I know we were talking a little bit about AI. In general, I think open source will catch up. You have also a lot of contributions that could happen today with Agentic AI in a different way. But I think especially when you have governance with Linux Foundation, Ray actually recently also joined the Linux Foundation and you have VLM, Ray, Kubernetes. It’s like the stack. It’s like the AI compute stack. And I think it’s crucial, especially there’s also with MCP joining as well, I think it’s crucial for it not to be owned by one company and for it to have governance. So that’s obviously … And also from the community standpoint, I think there’s processes and all that are really important. And as far as open source, I think it opens … Funny, it’s fun, but it opens a lot of opportunity even as we see more people build servers in their house.
01:01:34 Using open source models like Gemma four that just came out and DeepSeq and all that, I think it allows you to also build in different ways and fine tune and update things. And that allows for a lower barrier to entry. And as we were talking about a lot, it’s democratization. I also am very big on encouraging people to contribute to open source in their career. I think that you also find your community and you innovate in different ways and you’re able to learn a lot through seeing the code there too. And yeah, MCP is also a great example. In general, I’m very passionate about open source. I think one, from a community standpoint, as you can tell, I’m a people person. I think the open source … I think the culture of innovation and being able to build on each other will always win in the long run.
01:02:16 We have to make sure that’s the case. I think also it allows us to all be able to keep up as well. But yeah.
Jon Krohn: 01:02:24 Yeah.
Linda Haviv: 01:02:24 And allows- I was trying Gemma for on OpenClaw.
Jon Krohn: 01:02:26 Yeah.
Linda Haviv: 01:02:26 It was good.
Jon Krohn: 01:02:26 Nice. What have you been doing with OpenClaw?
Linda Haviv: 01:02:29 So at first, before Claude got really … I feel like, okay, now I’m kind of more on Claude than OpenClaw. But I think initially I was trying to make it my AI assistant that was just at home and building out a bit of a newsletter I want to do and some automations there. Although I would say that Claude works really well for most of that right now for me. As I’ve been building a lot more skills and plugin, just all the stuff I’m also trying to always keep up. And as of recently leaving AnySkill, I get to build a lot more. It’s very new, but there’s a lot of building for myself as far as systems. I think that’s also my tip. Always first thing, solve your own problems. And then you’ll be able to probably share that and teach. And so lots of skills, lots of mistakes too.
01:03:14 Some of my skills were overlapping a little bit. So I think the best way to learn is to always build, but now you could also watch your agents work. But yeah, I kind of have been in, I call it the experimentation phase because I try a lot of different things. Gemma4, I really like for my phone because on airplane, my wifi is really bad and now I could just talk to AI from there. I also have used Ollama and put it on my laptop and have that running too. But I think sometimes I don’t want to switch the wifi from the phone to the laptop and I’m like with kids, so I can’t always open the laptop. So got my handy dandy phone.
Jon Krohn: 01:03:51 And you and I are both, this podcast is sponsored by Anthropic currently and you’ve had Anthropic as a sponsor, but we both love Claude genuinely. Love, love, love
Linda Haviv: 01:04:01 Them. I’m Claudia.
Jon Krohn: 01:04:02 It’s one of those things when a broker of these kinds of deals came to us and said, Anthropic is interested in sponsoring your show. And
Linda Haviv: 01:04:12 You’re like, great. Right, because this is so aligned. I talk about you all day. Yeah.
Jon Krohn: 01:04:17 So yeah. And then this open source ecosystem thing, agents, tying back to earlier in the conversation, this is so great for anybody, regardless of where you are in your career, regardless of if you just want to be having some fun, making an impact, learning skills in your free time or developing a side business or going off on your own to develop your own business, all these kinds of open source innovations pair really nicely with the Agentic capabilities that we have today. Now, final career question for you. Yeah. Given that you have now left your role at Anyscale, you’re getting things going on your own. It sounds like you might not have decided on exactly what the name is of your next thing, at least at the time of recording. It may be something that you know by the time this episode is published. But what specifically are you doing and what kinds of people amongst our audience should be reaching out to you for help?
Linda Haviv: 01:05:17 Yeah. So I think first step for me is I’m continuing with the tech education I’ve always been doing on content. I do have some consulting, especially around DevRel. I’m very passionate about helping companies with developer advocacy and B2B companies on that.
Jon Krohn: 01:05:31 So in that case there, given all of your experience in developer relations at AWS, at Anyscale, and then of course all the stuff that you do independently, you will now help enterprises, not just literally with the content creation, which you’re also available for, but also setting up the systems, the operations, understanding how to succeed at Deverell as an enterprise in general. Yes.
Linda Haviv: 01:05:52 And especially around digital communities and building that. I think there’s so much accessibility that happens. Developers are all over the world and there is a part that you could really make a sticky community through social as well as through other ways and it needs to all tie in, as well as enabling people internally. I think a lot of people, founders especially are the face of a company and their brand is actually a DevRel version, right? Especially technical founders, there’s a lot they bring that no one else could bring. And that’s a really important, I think, part that I also coach on and specialize in and have done also for companies. But I think that has a different art because there’s a thought leadership part to it. There’s a part that they don’t have time also to deal with scaling themselves, but that is very much a part of the why of their company and not something that’s a company page thing.
01:06:47 It’s more of a personal story. It’s their own technicality and it’s their own thought leadership that also drives … A lot of the times that’s what drives the company. So being able to take that in different ways for people to understand, visually explain it and scale that message is really also important. So specialize in those kind of things as well. And yeah, I’m excited to build a lot. So I think this is the most exciting time in history to build. As we were saying earlier, I always ask myself what I would do. So I’m building a bunch … First, I’m of course building your typical applications. I’m building an app right now that will be just for my family to keep track of my kids’ schedules and birthday parties and everywhere in one place and Agentic workflows. I have my morning briefing for even contracts I’m doing, I have my agents and my scheduled tasks that are happening.
01:07:36 So using Claude for all that. And yeah, so of course initially also building a lot my own systems to be able to scale as well as consulting and there’s more to come, but I’m super, super excited about it.
Jon Krohn: 01:07:51 Fantastic. Yeah. Very exciting. So great to have you on the show at this important point in your career.
Linda Haviv: 01:07:59 Thank you for having me on.
Jon Krohn: 01:08:00 Yes, of course. I mean, love to have you on again soon. This has been such
Linda Haviv: 01:08:04 A fun
Jon Krohn: 01:08:05 Episode. Yes. But before I let my guests go, I always ask for a book recommendation. What do you have for us?
Linda Haviv: 01:08:11 All the chip books. Okay. All of them. Every single one. Chip in the end. Yes. AI engineering probably is the first one. I definitely think it’s a required read. I know people have different visual learning styles. I think that one was fantastic. So I know you had her as a guest here too. Yeah. I think
Jon Krohn: 01:08:30 We had her on the show talking about that book.
Linda Haviv: 01:08:32 Yeah. So look back at that episode.
Jon Krohn: 01:08:35 Should link it back. And I think that is one of the most popular episodes we’ve ever had.
Linda Haviv: 01:08:38 It’s fantastic. I felt like when I was trying to get a holistic view, we’re always upskilling and for me, I needed that book. I have it on my desk, has a bunch of bookmarks in it, written in it. So thank you. Thank you,
01:08:54 Chip. Thank you, Chip. Yeah, definitely recommend. And in general, I think reconnecting with more like human stories and I personally just love, I love tech and I love everything. Tech and I think finding our elements of how we solve with technology human problems. I think this is the time. And so I’m even working on trying to reconnect with certain things as well. While I feel like there’s not enough time in the day because I am very much in the rabbit hole, like everything from reading the whole paper about Methos to every new thing that’s happening and trying to actually make demos about it. A lot of my passion is also making like hands-on visuals for people to actually set it up and try to help people actually adapt without having to waste as much time or giving them crash courses. So that’s kind of my mission right now.
01:09:43 Initially kind of take that information and then show you, okay, let me save you time, here’s what you’re going to do. And yeah, I think most important message is be the artist of your own journey and lean into what you actually enjoy doing tech, you’re able to do that with that today. Don’t get overwhelmed. We’re all professional students at this point. Just new terms every day, new roles every day.
Jon Krohn: 01:10:10 And yeah, so for people who want to stay close on the pulse of all of this content that you’re creating, we will have links to all of your social media profiles in the show notes, but do you want to list out for us all the various places we can find you? Sure.
Linda Haviv: 01:10:23 It’s a little confusing because my LinkedIn name is Linda Haviv and my socials are all Linda Viva, which is Haviv Backwards, V-I-V-A-H. At this point, I didn’t update it for SEO reasons because I actually found out that I actually show up in quad search. So I’m like, okay.
Jon Krohn: 01:10:42 Yeah. So what you’re saying there is that you’ve left. So on Instagram, on various other social media platforms, you have YouTube. Yeah, all of them are
Linda Haviv: 01:10:52 Linda Viva.
Jon Krohn: 01:10:52 Everything except LinkedIn is Linda Viva. So people who have been wondering why I’ve been calling her Linda Haviv
Linda Haviv: 01:10:59 In
Jon Krohn: 01:11:00 This episode. That is in fact her
Linda Haviv: 01:11:01 Real name. My work name was always my …
Jon Krohn: 01:11:03 And her LinkedIn name. But yeah, for finding her album- This were the days we
Linda Haviv: 01:11:06 Separated content from
Jon Krohn: 01:11:08 Working full time. But now you say that you’re leaving it with the backwards Haviv Viva. It was
Linda Haviv: 01:11:14 Actually my stage name for music.
Jon Krohn: 01:11:17 Well, it’s good because it’s like life.
Linda Haviv: 01:11:18 It’s life. Yeah. There’s something about the ah. Yeah. I don’t know what it is, but I was like, it’s a good album name at the time. Yeah, no has
Jon Krohn: 01:11:26 Great SEO and AI agent EO.
Linda Haviv: 01:11:29 Yeah. EO. But if you like AI engineering, AI infra, a lot of those topics and in general, upskilling in this age.
Jon Krohn: 01:11:39 Fantastic. We really appreciate you doing all that work and also taking the time away from all of your kids and all of your business ventures to spend time with us and my listeners on the show. Thank you so much. Really appreciate it. Linda, thank you.
Linda Haviv: 01:11:53 You’re amazing. Thank you.
Jon Krohn: 01:11:57 Sensational episode with Linda Haviva. I think we’ll have to have her on again soon. In this episode, she ibuliently covered her path from philosophy major to TV news production assistant to self-talk coder illustrating that non-linear career paths are not a liability. They’re a source of transferable skills, especially now that system thinking matters more than raw coding ability. She talked about how AI infrastructure refers to the compute stack tooling and frameworks purpose built for AI and ML workloads, how Ray is a Python native open source distributed computing framework that lets engineers distribute training, data processing and model serving across GPUs without needing to become distributed systems experts. She talked about how open source models like DeepSeq and Gemma are closing the gap with proprietary models and key projects like Ray, VLLM and MCP are moving under Linux Foundation governance so they aren’t controlled by any single company.
01:12:50 We’ve got links to all of those technologies for you in the show notes. And finally, Linda talked about how building in public, creating content and contributing to open source are not just career insurance, they’re how you find your community, attract unexpected opportunities and learn faster through teaching. All right. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Linda’s social media profiles, as well as my own at superdatascience.com/987. Thanks, of course, to everyone on the SuperDataScience podcast team, our podcast manager, Sonja Brajovic, media editor, Mario Pombo, our partnerships team Natalie Ziajski, our researcher, Serg Masís writer, Dr. Zara Karschay, and our founder Kirill Eremenko. Thanks to all of them for producing another buliant episode for us today for enabling that super team to create this free podcast for you.
01:13:41 We are deeply grateful to our sponsors. You can support this show by checking out our sponsor’s links, which are in the show notes. And if you’d ever like to sponsor the show yourself, you can see how to do that at jonkrohn.com/podcast. Otherwise, please do help us out by sharing this episode with someone that would love to have a laugh and learn from Linda. Review this episode on your favorite podcasting app or with a YouTube comment on the episode. Subscribe if you’re not already subscriber. But most importantly, I just hope you’ll keep on tuning in. I’m so grateful to have you listening, and I hope I can continue to make episodes you love for years and years to come till next time. Keep on rocking it out there, and I’m looking forward to enjoying another round of the SuperDataScience Podcast with you very soon.
