Develop Yourself
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Develop Yourself
#221 - How AWS Used AI to Save 4,500 Years of Work – with AWS’s Director of Generative AI Applications
Send a text and I may answer it on next episode (I cannot reply from this service 😢)
I've been using AWS Q Developer for a few months now but it turns out I've barely scratched the surface.
Srini, Director of Generative AI Applications and Developer Experiences at Amazon Web Services, who leads the team behind Q, agreed to be interviewed by me.
We discuss:
• How AWS Q Developer helps software engineers with coding, testing, and debugging
• How Amazon used AI to save 4,500 years of development time
• Why AI won’t replace developers—but developers using AI will dominate
• The future of AI in software development and how beginners can stay ahead
🔗 Links Mentioned in This Episode:
• AWS Q Developer: aws.amazon.com/q/developer
• AWS Machine Learning University (MLU) Courses: amazon.science
• AWS Skill Builder (Free & Paid AI Training): aws.amazon.com/training/learn-about/machine-learning/
• AWS Generative AI Innovation Center ($100M investment): aws.amazon.com/generative-ai/innovation-center/
Shameless Plugs
🧠 (NEW) Parsity's The Inner Circle Program - a highly customized roadmap to take you from 0 to hired. For career changers who want to pivot into software.
💼 Zubin's LinkedIn (ex-lawyer, former Google, Brian-look-a-like)
👂🏻Easier Said Than Done Podcast
Already a developer? Check out 👉 Not Another Course
Serious about joining Parsity? Schedule a call with me ☎️
Welcome to the Develop Yourself podcast, where we teach you everything you need to land your first job as a software developer by learning to develop yourself, your skills, your network and more. I'm Brian, your host Today on the Develop Yourself podcast. I have a very special guest, srini, director of Generative AI Applications and Developer Experiences at Amazon Web Services. Welcome to the show and thanks so much for taking time out of your day to do this interview.
Speaker 2:Hey, thanks, Brian. Thanks for having me Very excited to be chatting with you and the audience that you have about all the work that we're doing and how AI is actually helping all of us.
Speaker 1:I would love to because, as you know, there's like two sides of the AI story now. There's YouTube videos that are basically like fear-mongering and saying it's going to, you know, basically be the end of software developers or jobs. And then there's people that are using it to maximize efficiency, productivity. I actually use a tool that Amazon's come out with Amazon Q, I believe it is. Yeah, a person showed me that recently and it's like so cool. It makes me move around at light speed in my terminal and just gives me all sorts of cool shortcuts. I showed somebody at work. So I'm using it. Like what is this? I'm like, yeah, this is. This is part of the cool stuff that you can do with ai now, like maximize your productivity and making coding a lot more fun and also just doing a lot of the repetitive junk that I don't necessarily want to do. Before we get into all that and talk about ai, can you share a little bit about your role? It's a very impressive title, but can you tell me kind of what you do at AWS?
Speaker 2:I'm the Director for Software Engineering for Amazon Q Developer, In fact, to what you just touched on Amazon.
Speaker 1:Q.
Speaker 2:Amazon Q is the generative AI platform solution that AWS and Amazon build overall we target, and there's different users that actually use and benefit from these tools. The specific system that I work on and my team works on is focused on the software development lifecycle for anybody who's working on it and providing generative AI solutions. So Amazon Q Developer is the most capable generative AI assistant out. So Amazon Q developer is the most capable generative AI assistant out there that helps software developers, DevOps, IT professionals throughout their software development lifecycle. And to something that you touched on, which is not having to do repetitive work, not having to do the boring, mundane tasks and that is something that we are focusing on and my team I manage the engineering team here at Amazon that builds the that we're focusing on, and my team I manage the engineering team here at Amazon that builds the generative way applications on top of it.
Speaker 1:Wow, this is really cool. I did not know you actually were directly related to this change. I know you were somehow involved, but that is really cool. It's one of the best tools I've used and I'm not just saying that because you're on the show. It's been like a real, really cool tool to use, because there's so many things like Copilot for working within, like Visual Studio Code and things like that, but this one, specifically with just knowing like immediately, it was so intuitive when I'm flying around my terminal what to use, that was the real cool thing for me. I'm curious how has changed your personal workflow? You're a leader in a major company, one of the top tech organizations in the world.
Speaker 2:There is two pieces, and I'll touch on why. Some of the feedback that you've just given me is something that we're hearing a lot of the customers tell us as well. Something very natural that has been coming to us is figuring out what sort of areas that we can actually help developers software professionals, business professionals where we can add value. So the mantra that we had is find where the developer is and go help in that particular solution. So, for example, amazon Developer right now. It generates really highly accurate code, it can generate tests, it can scan your code. It is an AWS expert in terms of actually answering some questions. You can chat with Amazon Q developer as well. So what we did was we said let's not just put it in one place, let's figure out where the developers are.
Speaker 2:The developers use in their daily life. Some of them are coding, some of them are reading, some of them are managing cloud operations. Some of them are in the CLI, just like how command line interface that you're talking about. Some use VS code, some use JetBrains, some are in SageMaker. So what we are doing, and what we have been doing, is bringing the generative AI solutions to each of these modalities, each of these layers, and there are different reasons why we go to each of these. When you're in the CLI, you're probably managing a bunch of resources. You're doing some Git management. When you're in the AWS management console, you're operating on the cloud resources that we have. If you're in the IDE, you're coding testing. In fact, if you're in GitLab, you're coding testing. In fact, if you're in GitLab something else that Amazon Q Developer is integrated in it's almost like a DevOps CICD pipeline that you have set up, which means you're going there for a different reason, and so what we have done is bring Amazon Q Developer into each of these workflows for you, and that is something that we're constantly iterating on as well.
Speaker 2:Now, tying it back to how this helped me personally, there is two folds Me as the manager of the engineering team, that is, you know, helping the team bring together all these solutions. We are dogfooding ourselves. So we internally use Amazon users, amazon Q developer, in fact, my own, the team that we are on the developers who are building these systems and applications are providing feedback and learning from what it is and taking the benefit out of the tool themselves. In fact, some of Amazon Q developer has been built by Amazon Q developer itself Like that's how we write tests, that's how we write documents, that's how we write documents, that's how we update the documents, that's how code reviews happen, and so it is actually helping the team get productive.
Speaker 2:And I'll touch on something that you talked about as an example. Amazon QDeveloper has a feature for software upgrades. We can do Java upgrades. There is NET migration, mainframe migrations and VMware migrations. All of this is part of QDeveloper as well. Within Amazon, we used QDeveloper to transform and upgrade 30,000 production applications from old version of Java to the newer version of Java.
Speaker 1:And we deployed them.
Speaker 2:This is production applications Impressive and that saved us close to 4,500 years and $260 million a year because of performance improvements and the cost savings as well.
Speaker 1:John Harrison, Did you just?
Speaker 2:say 4,500 years? Sanyam Bhutani yes, If you did, 30,000 production applications that got migrated in the traditional old way. What would happen is we, would you know I, as a manager, as DM, would assign a team? Is we would you know I, as a manager, as dm, would assign a team and would spend months to be able to do this? And we go to the whole exercise generative ai with the systems that we have. Amazon q developer was able to fast track this, be able to do this faster. It says so. What what that helps me as the? You know, as you ask the question of how, how is it helping the manager here, it is helping the team get productive, get there faster so that we can actually bring valuable services and features to the customers who would really want to be upgrading software or writing.
Speaker 1:Yeah right, who loves upgrades?
Speaker 2:Correct.
Speaker 2:I'm like writing tests is important but at the same time, ideally I would want to get creative is important, but at the same time, ideally I would want to get creative and I think all the time that is saved in being and doing these mundane tasks.
Speaker 2:Like I have a doc file that I have to go, I have a markdown file with documents. Constant problem that we all have is the code gets updated and the markdown file never gets updated. That's right and you have to go spend extra time to be able to do this, versus now you can ask Amazon Q developer to update your Markdown file with the latest code and it'll just update it for you, so that time saving all of that effort is actually going to I can ship amazing solutions to customers a lot faster, and that is the value that it is bringing to the table and that's kind of the value add even for me and my team as well. I love that you said that Also value that it is bringing to the table and that's kind of the value add even for me and my team as well.
Speaker 1:I love that you said that Also. That's amazing. I mean, just that number is wild. But then I think about how long an upgrade takes. I'm not familiar with Java, but anybody that's done a significant upgrade of a library or framework of things. It can be stressful, error prone and super time consuming. So in retrospect I'm not shocked that you said it could say 4,500 years. But that's just such a wild, fantastical number. Hey, I really hope you're enjoying this episode Now.
Speaker 1:As you may know, I've joined forces with an ex Google engineer, zubin Pratap, who's also an ex lawyer and learned to code at the age of 37. He and I have very similar stories and we've combined forces to create a highly customized and personalized coaching mentorship program for career changers who are serious about breaking into tech. If you know that the outdated coding bootcamp model won't work for you, you're serious and realize that this transformation will take time. We want to speak with you. Our program is not easy, it's not short, but it's highly effective. If you're a listener of this show, you're likely the kind of person we want to work with. If you're ready to apply, click the link in the show notes and you'll be talking to Zubin or I on the phone about the program.
Speaker 1:Now back to the episode. But yeah, I use it to writing tests. Now I've offloaded a lot of the manual coding I would do and I've actually tried to switch to more prompt-based engineering. I used to kind of laugh at that phrase. I thought, okay, what is that? But now I'm like this is a thing, but I still see that it requires some knowledge. Obviously you can't have somebody that's completely unknowledgeable with something like coding or specific language or domain or something or a problem space to just spit out a prompt and deliver something. What parts of the development process do you still see that require a lot of human oversight?
Speaker 2:There's two or three pieces to how this is set up. As we're designing these automated systems, we have a bunch of agents that can do the work. You can ask QDeveloper to review the code for you and all the changes in your IDE that you have. It actually reviews the code for you and identifies security vulnerabilities. It can understand performance issues. It can understand you know best practices and give you recommendations. You, as a developer, you get recommendations and when the recommendations happen, you can also ask QDeveloper to say can you also fix this for me? It'll give you.
Speaker 2:Here is the fix that you should take for your code. You, as the developer, are still in control, the human in the loop to say I do want to accept it or not, and that is the decision-making. And this is where the fundamentals of how we build software and how we're understanding services, distributed services, availability of the systems, keeping them very secure, performant those don't change. You still have to have the knowledge of doing this. In fact, one of the analogies that I keep using all the time is I learned and again when I went to computer science, engineering, I understood binary numbers, punch cards, how dot matrix printer worked, how do you do the? You know, microprocessing for me was 8086 and 8088 kind of programming that I had to do.
Speaker 1:Oh, wow, okay.
Speaker 2:Now at that point. Since then we've moved on from understanding the fundamentals of bits and flops, but the code doesn't really change. I still have to understand the logic around all of this. Now what we are up, leveling it by bringing in the creativity aspects of it, how do you communicate Not just with your peer, but how do you communicate with a system that is also intelligent right now and that comes back to your prompt engineering that you're talking about? It's not just prompting, you're having a conversation.
Speaker 2:One of the other examples I have is in q developer. There is a software assist software agent where, through a natural language command, you can say something in english and say ask q developer to perform a task. It first understands your repo, it understands your code base, it understands what you're trying to solve and then generates code. And it's not just generates code, it is a multi-reasoning, multi-step agent, which means it thinks and tests the code for you. You can also ask it to say as you're giving me the response in a code format, can you make sure that it actually is testable? It is tested and it can run. Yeah, it goes through that journey First, understand, is it testable? Oh, it's not. Let me go generate it again, it will emit the code.
Speaker 2:At that point of time you as a developer can say oh wait, I need a little more than what you just did, and it's more of a conversational exercise that is happening with the agent, with the multi-reasoning agent, and then I get to accept the changes. I get to say yes, I would like I, as a developer, or you as a developer, get to decide what you want the output to be. So this is more of a tool, uh, that speeds up, improves the productivity, gets you there faster. To be able to do this yeah, I, I totally agree.
Speaker 1:I think most people that are writing code for a living, doing something revolving around software and using AI tools, have noticed this. It's like it's a great, great enhancement, for sure. But to offload all of your thinking and logic onto the AI tool is not really possible yet, and also the amount of bugs you could create, like at speed, would be pretty wild if you didn't you know kind of question and, like you said, have a conversation, and that's what I really appreciate too. A lot of times I use like an ai tool to determine like, hey, is this a good um approach to this? Like I'm looking at a database steamer thinking I'm coming to change this.
Speaker 1:What are the pitfalls here? Give me some pros and cons of this approach. It helps me think through things, kind of like the old, old term rubber ducking, and now I feel like AI has become a lot of people's rubber ducks where you can get some pretty good feedback. I'm also learning how little I'm using Q. I've been using it pretty much strictly for my command line and it's been amazing for that. I need to use a lot more of this. You're schooling me on a lot of the things that I'm missing out on here.
Speaker 2:I'll touch on something that you just mentioned, where it generates a lot of things. You have to be very careful about what it generates and how you're introducing. This is where what Amazon is doing is something that is in our DNA consistently. We have done this for a while. The availability of the service responsible AI is something that a lot of people talk about. As an example, we have a feature called Reference Tracker. What it does is when code is generated. If the code that is generated is a match or is similar to some code out there in the public repos, we attribute to the author and to the source, and you, as the consumer, can choose to use it or not use it, because you could say, hey, I don't like this code snippet that I received or I don't like the license that is used in here, that I want to be able to do that. So that is the responsible AI piece that we bring to the table.
Speaker 1:That's something that we Very cool. Yep, I've never actually heard of anybody. That's a really amazing thing because I'm sure one you can avoid maybe potential legal issues, but also can help you explore your curiosity. Maybe you find something like oh it's here and you can go explore further that particular person or organization that created the code you're using. That's really cool.
Speaker 2:For us, the guardrails that we have in place on top of the generative AI piece itself is something that is very important and something that we prioritize consistently for developers, because there is a lot of intellectual property that goes in when you're working with, you know applications and code and stuff like that. That's one. The second is the program analysis and the automated reasoning layers that we're bringing on top of artificial intelligence too, Like one of the things that we announced at reInvent last time is mathematical correctness of the output. So this goes beyond AI. Where is the output correct? If it is, it is performant enough, and this is where some of the skills and the capabilities that the code review features for QDeveloper has come into picture is. It goes beyond just the AI aspects of it and what I broadly call the scaffolding in my head. When I say scaffolding, this is the smarts, the 20, 30 decades and decades of experience that AWS and Amazon has over software building, bringing that program analysis and automated reasoning into the applications for developers to use QDeveloper as well.
Speaker 1:Very smart Also I just thought of this too, because so many developers I forget you probably know this like the market share that AWS has pretty much everybody uses AWS, so having a tool that I can only assume must be intimately familiar with like all AWS cords and knowledge and things like that having it integrated. So when you're doing things like creating I don't know a YAML file or deploying a Lambda or setting up some sort of infrastructure as code, you can probably get really really good feedback on what you're doing in there.
Speaker 2:Yeah, in fact, the other flip completely. If you are using AWS one of the things that we every any developer you know you get paged. If something is an alarm. The first thing you do is you go paged. If something is an alarm, the first thing you do is you go look at your logs. In this case, let's say CloudWatch logs. You go look at the CloudWatch logs, you run a bunch of queries and then you say you know, somebody with some expertise of the system and infrastructure will understand the CloudWatch log, decipher them and go back to fixing. What we did do is we brought QDeveloper there as well. Oh, back to fixing what we did, do is we brought.
Speaker 2:QDeveloper there as well. What QDeveloper can now do is it goes through your CloudWatch logs and when the alarm goes off, it actually says here is potentially what is going on and if there is a runbooks associated, it can actually tell you what the mitigation steps are as well. It saves so much time both from fixing the issue and also getting somebody onboarded as well. It saves so much time both from fixing the issue and also getting somebody onboarded as well.
Speaker 1:Oh man, that's really good, because everybody's had this experience. You know, I'm like I've done more front-end stuff, more web dev stuff, so sometimes I've launched like something simple, like a Lambda.
Speaker 2:I'm like what is wrong?
Speaker 1:I'm looking through the logs and trying to decipher things. Having a tool like that is really good. This is super cool. Now, which kind of leads me to what I think a lot of people are hearing. There's a lot of people that listen to this show and they're earlier in their career. They're pretty early, and they hear this stuff and they're like well, what am I going to do? So I'm just curious what do you see, what is your advice for new developers that are coming up, and how do you see ai potentially impacting?
Speaker 2:their careers. First and foremost, we are going to tectonic like change in how we are evolving in software development, application building with generative ai, with some of the tools that we're bringing. Have fun. I, I again, this is so much. It's it's amazing to be part of this journey, to be going through the journey. I have a nine-year-old son and I'm very, very thrilled and excited, looking forward to seeing how his life is going to change and get better. And over time we have. You know this is slightly philosophical, but over time we as humans are evolving to be becoming better and better. So one is to have fun, embrace the technology and go through this journey.
Speaker 2:The second, which is probably a little more relevant, is these are powerful tools. Adapt to your needs. Yeah, if you are a doc writer or if you are, there is something called Q Business in the service that we have, where enterprises can pull in all their enterprise data and start asking questions. Internally we use q business as well, where I actually don't search anymore but ask you business and q gives me that information. Start embracing these tools. Think of it as a tool, uh, in your arsenal, to be able to help get more productive and get there faster. And one quick example I'll give you.
Speaker 2:Traditionally, when a new developer joins an organization or a team, you give them a bunch of documents to read, you assign them a peer mentor, give them small tasks and they take a couple of months to go through the whole journey and then they hit the ground running. That's kind of how traditionally that happens. The world has changed Now, in fact, one of the developers on my team she recently joined and she started using QDeveloper to understand the existing code base, understanding the documents, use that time to update the documents itself. So she was able to hit the ground running so fast faster than normal and we observed this pattern consistently going on. So for anybody who is embarking in the journey right now whether you're a computer science grad, like I was, or whether you're in electronics engineering or you're doing biomedical engineering the time we used to spend, or we are spending, on researching topics is shortening because of the various technologies that AI is bringing, and it's about how we will bring up the creativity and how you collaborate.
Speaker 2:Now. Not only you're collaborating with your peers, you're collaborating with many, many people out there if in a world. Another analogy that I keep using is um. We used to apply to schools and apply to schools from India, where I come from, means you'd mail in the application to a school in the United States. I was in that transitionary phase and that has changed. From now you just apply online and you send an email. I mean, that's become just you know nobody sends it.
Speaker 2:That is the transition that we're in, where we use these AI-based tools Amazon Q Developer, amazon Q overall as a tool in your arsenal to get productive, to be able to help customers, to help yourselves and come up with creative solutions. I'm like you know. There is so much we can do as a humankind and this is going to help.
Speaker 1:I like your outlook. I tend to have a more optimistic outlook too. I think what a lucky time for us all to be alive on this planet. We can see these kind of massive changes. It feels like overnight. It is wild, and I think just two years ago how I was writing code and now what I'm doing now. It's like night and day. I mean, coding obviously is a skill probably won't go anywhere in the near future because we'll need people to somehow construct code and software. But what other skills do you think will be more important going forward, in the AI age, where you don't spend as much time physically typing into a keyboard?
Speaker 2:This is where the creative aspects of it I think one of the frameworks that we have is working backwards. In Amazon, we use this quite a lot. What are we working towards and what is the customer value? Whether it's the enterprise customer or an end user customer doesn't matter. What is the customer value? The creativity, the manual tasks tedious ones are all gone, are all going away, and that's the transition that we're in, which means the skills that I'll have to develop or I am developing through this journey is collaborating with a group of people, which, again, is nothing new. We have been doing this for a while, only that now you get a lot more time and energy to be able to do this and tools to be able to do this. Come up with creative solutions, a little more creative than what we were or have. What was not possible at one point of time is now a little more possible. That's the creative solution that you're able to do. Those are the things that we will continue harping on and solving for customers as well.
Speaker 1:Okay, I mean that kind of tracks to what I see. I think skills of communication obviously are increasingly important. I feel like there was a developer stereotype archetype, like the quiet developer in their little code hole, and I think that that's probably a little more difficult to do that. I think, going forward, that communication skills, whether humans and learning how to write and communicate yourself through text is probably going to become increasingly important.
Speaker 2:You are chatting with the QDeveloper assistant right now, which means that is, even in this case, almost like QDeveloper is a peer software engineer or a DevOps for you, and you're having a conversation with a system that is intelligent enough, that is built with all the security paradigms and the responsibility of guardrails. I think that is very important as well, as we're embarking on a lot of this. With great power comes great responsibility, and this is something that is very natural to how we do this internally is when these tools can do that. You're having a conversation, asking the agent to do the work for you on your behalf, configuring it to be able to do this, and, as you trust the system more and to your end like, for example, you're using it in the command line already, you seem to like it and you're actually finding it useful, then if you, if you are somebody who also codes in the IDE, then you will experiment there and then bring it into your workflow. I think that's the important piece.
Speaker 1:Yeah, for sure. I think I feel like there's a bit of a digital divide happening and it's accelerating really quickly, with people that are either adopting AI and I think, obviously if software, if you were to get Amazon, we're surrounded by people that use it. I'm in San Francisco area, but I feel like there's a lot of people that are slow, slower to adopt and it's like they're going to probably really want to before this divide gets really really big.
Speaker 2:As an example. We only talked about developers. We talked about Q business, but there is a lot of applications Like, if you go back to the medical field right now, you're at the doctor's place the cancer research that we're talking about, where we have customers who are building AI, gen AI applications, leveraging. You know, bedrock is a platform that brings many, many generative AI models with it, like whether it's an anthropic model or a meta model. So Bedrock is a platform and you can build applications.
Speaker 2:We've seen many customers actually build applications that leverage AI and generative AI to help their workflow. So if you're a doctor or you're a nurse which in theory, you could say they have nothing to do with the computer science piece of the puzzle but they're leveraging the benefits out of this too. The research, in fact, you know the research time for you to be able to understand all the data is getting there. So, as this technology becomes ubiquitous and it is available everywhere, right now, qdeveloper is a developer tool. So I, as a software engineer, I'm more probably paying attention to it. Yeah, right, but as it goes through, my nine-year-old son has a bunch of software tools that he's using. When we bring in some AI skills in there. What is happening and this goes back to the tenet that I talked about is we want to bring these applications to where people are. That way it is not. There is a separate tool that you have to go to learning, but it is built into the systems that we have as well.
Speaker 1:Yeah, yeah, really good point. Yeah, you're right, it's more than just like the software field. It's leveraging these kinds of tools for sure, before I let you go here, I think a lot of people listening to this also are like I want to get into the field of AI. Your director at Amazon obviously is one of the big companies everybody knows and a lot of people want to get in there and they're thinking how do I work in AI? What do I do? What does that even mean? Are there some skills degrees, just things you would suggest people do that are really curious and want to begin working in the field of.
Speaker 2:AI. There is two or three ways of doing this. I think, for example, amazon has machine learning university that is open for anybody to be able to do this. There is AWS skill builders that are free of cost or low cost courses that folks can actually go and learn. If you are, there is a generative AI innovation center that we started, where, you know, it's $100 million of investment from AWS for anybody to be able to do this too. So there are systems in place and this is more intentional. I want to learn this intentionally. I want to be able to do this. In fact, we are. You know, we have an AI ready commitment that we have in. You know we set this up in 2023, where Amazon announced that we are going to provide AI skill training for 2 million people by 25.
Speaker 2:Intentional upskilling opportunities that we provide, and there are. That's one. The second, going back to your other, depending on where you are embracing the technology and having fun you are embracing the technology and having fun. I think that is the second very important piece of the puzzle and learning it. In fact, five years ago, I did not know much about AI myself. I was working in supply chain. I learned through this and I went through that. So there is intentional learning through upskilling that you could do, which we provide. Number two is embrace the technology while you are in there to be able to do this too. I think that's number two to get there and number three related slightly to how we're embracing the technology as applications come with this functionality, starting to adopt them and actually contributing back to the system, being able to add value. I think all of these, these two things, will help anybody get ready. I'm like you know, it's more like, rather than being ready, go with the journey and then we'll all have fun too.
Speaker 1:I think I like that. Yeah, Thank you so much. Really appreciate you taking the time to talk with me and hope everybody got a lot out of that. I did Any last words for the audience.
Speaker 2:First of all, I mean like, just because I specifically know a lot about Amazon Q Developer, if you are a developer out there, or learning to code or understanding systems and software services, give it a spin. This is one of those tools where we've taken utmost care. We hear the feedback from customers, both internal customers my own, like you know, my own team. We all use it, get feedback. That's one. There is a going back to the responsibility and security guardrails and security systems that we have in place. So give it a spin, test it out. That's one. Number two something that you earlier asked is embracing the technology and understanding how this is going. I think that is very important. Number three not just Amazon Q developer. Look for upskilling systems that exist out there with AWS that we have and you know get onboarded?
Speaker 1:I need to look into that. I totally forgot that. Amazon has quite a few courses out there that are pretty well made. I've heard a lot of great reviews of those. I need to check out the ones you just mentioned. I'll have those in the show notes. Actually, I have links to all those in the show notes. And what about you? Are you active online? Is there anywhere people can find you? I am on LinkedIn.
Speaker 2:Of course I am that way active with you know. What we are also doing is because the world is changing so fast. We're updating the service queue developer on a constant basis. A couple of weeks ago, we announced a feature enhancements to the Amazon queue developer software agent. This is where I was talking about. Not only does it generate the code, it actually tests the code for you on behalf and make sure that the code is ready to be tested and executed. That feature, so any updates that happen, we are constantly posting on LinkedIn. There is an AWS developer forum that actually does this Amazon queue. These are all the systems that you could follow to get the latest and the greatest news as well.
Speaker 1:Sweet. I'll have those links in the show notes as well, and I'll start following them also, because I'm getting a lot of use out of that tool.
Speaker 2:Thanks again.
Speaker 1:Really appreciate you being on the show today, thank you, thank you, thanks. Use out of that tool. Thanks again, really appreciate you being on the show today, thank you, thank you, thanks for having me. That'll do it for today's episode of the Develop Yourself podcast. If you're serious about switching careers and becoming a software developer and building complex software and want to work directly with me and my team, go to parsityio. And if you want more information, feel free to schedule a chat by just clicking the link in the show notes. See you next week.
