The third tip was actively seek out inclusive perspectives. That's something we did not do in the beginning. We go and whoever is accessible in our part of our demographic, and it was usually younger people because those were people that I knew more about and those are the audience that I was embedded in the community I was embedded in. And it actually wasn't until I did the CUNY program where Sally who is the Executive Director of NPA actually gave us a session about equity and inclusion and I realized I went back to all the demographics that we had looked at, and I realized we were not really spoken to older audience. And so that was something that now I'm quite mindful about when I'm building products. And so that's how we build out and so I would really suggest looking at all of the different segments in the population that you have. And based on the feedback in the the learnings of the course, we learned about building sample personas for our audience. And this is what really helped us creating a sample persona describing them their age with their personality is giving them a bit of a human touch. This way we could also it was also helping us first of all, make sure that we had all of these different segments that we were covering within the diaspora population because it ranges quite a bit and you were mainly focusing on the North American diaspora because that they have their unique characteristics based on where the location is. They have the unique record the diaspora has their unique characteristics. And so we were like, Okay, this is somebody say somebody like a tech worker, an h1 B visa person. This is what a sample persona for him would look like. But what about a sample persona for somebody who's just landed in the country from India as an international student and they are now part of the Diaspora population. This informs now our current marketing strategy. We are just soft launching this product right now testing out all the technical kinks. And as we get into the population, and this would inform how we would get to them or where the tech workers are, where are the international students by creating these sample personas, we know exactly how and where to reach all of the audience. And my fourth tip, Chad's, GBT, I am a huge AI evangelist and a fan of AI because I think it can really, really help small newsrooms. Become a superpower because of its ability to help you. So Chad CPT came about in November, I was thinking about it brainstorming a lot about it, but really, it was in while I was doing this CUNY course, we were getting all of this theory and I was like, How do I apply this to my work that I'm doing right now? And GPT actually helped me break down concepts. I had a lot of back and forth conversations with it. Understanding how it would work for our newsroom. And so what I started doing was, I started telling it, what exactly we were building. We were building this digital media literacy chatbot and I was thinking about different things that I would need to do in building out this product. And so one of the use cases that we used it was we did a pre mortem analysis and so we were like, Okay, we're building this, what are all the things that could possibly go wrong? It was telling me Okay, poor user engagement, how you can do what what? What implementation strategies, what mitigation strategies can you have? If you're having a poor user experience, if you're having technical difficulties, these are all of the roadblocks that you would potentially reach and what needs to be done in order to what you would what do you need to do so that you don't reach that so this was just helping us think way in advance of, if you don't launch in time, what's going to be the reason and trying to prepare for that in advance so that we're not caught off guard. So that was one way we use there. Another way that we use it was creating user stories. So when you are so to back up a bit I'm also I have technical expertise. I am a data scientist I code but I don't know how to build out a whatsapp chat bot. That's a completely different technical expertise in one of the biggest challenges and that I've seen working as well with technical developers has been translating the vision of what you want as a person who doesn't know that tech properly, and translating that to somebody else. And developers so that they understand it, build out all of the features you want. You have to be extremely clear and concise. And so by building out this user story, and creating what the user onboarding should look like, I fed all of this to chat GBT and chat TPT was able to produce all of these insights. And so this is something that I looked over and I was like, oh, yeah, this is exactly what I had in my mind. And it put that all into words. And instantly, this is something that would have taken me a really long time. Plus, I don't think I would have actually done it to this level because this is what the first time that I was implementing product thinking in my work. And this is how charge GPD was helping me out. It was all about prompting, I was making sure it understood exactly. There was quite a few back and forth conversations I had with it to see understood what product I was building what exactly I was needing. And I told them that okay, this is how I'm building it. I have to tell a technical developer, so that's where I went back and forth with them. And I use it as I say, it's like a technical translator. Because you need to be able to have that conversation with your developer but if you don't really know what you need to be telling them, it could really lead to a product that's not that great. So this is how we used it. And my fifth and final takeaway is keeping data as your Northstar. I'm a huge data person and I think that's something that could really help inform and guide the way our product is being done. And now I'm not just talking about quantitative data. I'm also talking about like qualitative data. We had all of these user interviews we were doing all of these testings that we were doing. And so what we really saw was with these user interviews, we could also put them in so just to backup as well. I use otter, for transcription for all of our interviews, and otter came out with this feature called Auto chat where it's again, generative AI like chat, like chat GBT, where I could ask questions and based on the transcripts that could give me data. So again, we were held using that to help us with informing what our insights would be through these user interviews. What are they looking for, and this is this was all qualitative data that we were able to then input in. And while we were building out we also had like quantitative data for our project. And so we will this the first time we're building out a whatsapp chat bot. There's no guidelines on what product what data you have to collect for this. And so we had about like four different types of data points and metrics that we needed. And so I asked Chad GBT, what data metrics to ask as well to brainstorm with it, and it came up with like, 10 different quite a few that I was not actually thinking about, or I did not know about that I should be talking to our developer about. And so this is what he, we gave this to him. And so we made sure that all of the data points are being tracked in our database. And so this is now informing how our metrics would look like and so I would say, Try asking charge GPT, what data, what data you need to collect? And this is just how we used charge GBT to help us in our journey. And for me as well leading and building a product for the first time with product thinking and all of the knowledge that I got from ICFJ and q&a. And so yeah, so to kind of wrap up, I think it's really been being user centric has been the biggest. The biggest, I think guiding takeaway that I've gotten and something that I'm going to take away is constantly talking to your audience. We kept on changing one thing I need to say we kept on changing the courses content throughout. So we started really, we had the coast content by September, and what it looks like right now is completely different from what we had in September. And that's because we constantly changed it. GBT AI generated misinformation became a huge thing. And so we included that in our topic took away something else. And these were all possible because we were able to go back and forth with people who were already interested in our work and what we were doing, and this is how we kind of build out a product and I'm happy to share more and talk more and also just a quick plug about ICFJ is also having a leap news Innovation Lab focusing on AI and journalism this year, and I highly recommend being that and also the product immersion cohort. So if you want to talk to me about any of these programs, I'm happy to share about that as well. Later. Thanks.