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Mohannad Arbaji: we're actually 20 times faster for making these types of courses than we were before. And the quality did not suffer. It went up because a lot of this work has to do with taste and judgment. But with a tool like AI, you're able to actually extract taste. I know a lot of people say taste can't be extracted, but once you see it, you can extract it and codify it.
Brian Hecht: All right, Mo Arabaji, welcome to the New Builder Podcast.
Mohannad Arbaji: Great to be here. Thanks for having me, Brian.
Brian Hecht: my pleasure. I've been wanting to talk to you. we've known each other for many years, but we reunited recently at a New Builder event, and I realized that we had so much in common. And I'll just put it out there. We both started sort of in the startup world doing a a pretty standard path. And then sometime in the last one or two years we kind of got the the AI bug and and it we tried to make it weave into the thing we had been doing before. And then at a certain point we just had to declare like b start a bankruptcy and said there's no point to doing
Mohannad Arbaji: Absolutely.
Brian Hecht: anything other than AI. And I am dying to hear about this journey 'cause when we met, you were you were a great founder, but you were in a tough space. And I'd love to hear a little bit about your company Chalk Talk as the sort of the beginning of the journey. So even before AI entered into the picture.
Mohannad Arbaji: Absolutely. But yeah, Fuddy Name caught the AI bug, it is a bug though, because it catches you out of nowhere and you're like, wait, all of this power of creation is now that took teams, is now compressed. And the just distance between imagining an idea and building like production quality solution just shrinks so much. And then people say it actually causes you to save so much time and work less. But as we both know, you end up working overtime because the world's your oyster. So it's been truly a bug that I caught. I'd say I caught it in February. I've always been, curious about this stuff. but since February, really fully locked in on mostly focusing on this. But just to zoom back out of how I even ended up here, so you mentioned founder of an ed tech company, and that ed tech company is actually grounded in my story of and as an immigrant of and how I came to the US. So I came to the US on a High school scholarship when I was sixteen years old and one way plane ticket seeking a better future and then from there did well in school. That Brown was very generous, they gave me a full ride, did a few degrees there, did electrical engineering, computer engineering, economics and business, which is called COE. Yes, absolutely. Yeah, yeah.
Brian Hecht: Just just just just a few just a few degrees at Brown, right? Straight off the plane from you're from you're from Jordan, right? Is that do I remember that rightly? Yeah. Yeah.
Mohannad Arbaji: it's the last place to get free stuff in college, so We milked it a little bit.
Brian Hecht: Ha ha
Mohannad Arbaji: And after college started in full stack software engineering. And you know, that was the last time I wrote code for a full time job, like my first job out of college, my first two years out of college. Also did some management consulting. My internships were in finance, investment, banking, and big four companies. So I kinda exposure to a bunch of different things. And then I came back and I said, you know. Education changed my life. And I believe that talent across the world is universal, but access to opportunity is not. And education is the world's greatest equalizer. is there a way that I can do this at scale? And I did tutoring in college where I got some additional side income. I actually bootstrapped the company when I started it was three hundred bucks, like just investment. By the time I graduated, it was a franchise in three countries. So I had that background, but it was mostly the business model morphed into helping, wealthier kids from these international families get into better schools in the US. And I'm like, no, I I want something for the masses. Where are there a lot of buildings? Because rent and teachers were the highest expense of the tutoring business. I'm like, where are there buildings and teachers that we can plug into schools? Let's go after the public school system and let's give them a software that's gonna help their kids read, write, and do math. And The reason I chose read, write and do math is if you want to model humanity's success, you want people to be good at four things. You know, reading, reading a book, reading an essay, writing, writing an email, a blog post, especially now in the age of AI slot. Math, at least arithmetic, some algebra, and then a fourth thing, which can be anything, whether it's marketing or sales or software. And if we can impact three of these four things in K twelve, I think we've done a pretty good job. So we did this thing, fast forward to today. Multi million dollars in revenue, nineteen states, you know, twenty dollars like less than two bucks per student per month, you know, about twenty dollars per student per year as a software that covers all the subjects for you know within ELA and math in high school and expanding since and we've got some of the larger school districts, like a Hillsborough, the seventh later school district as a partner all the way to and the smallest districts across the nation. So that's a little bit about how I landed here on my story.
Brian Hecht: Yeah, so so so just to be clear, even though you had an academic background in it sounds like every possible permutation of of engineering and
Mohannad Arbaji: Yeah.
Brian Hecht: computer science and things like that, you were a the business guy. Like you weren't coding this. You had like a CTO and an engineering team. Is that is that right?
Mohannad Arbaji: Absolutely. I think, engineering was just a a a degree that or two of the degrees that attracted me further curiosity. Like one
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of my graduation projects is like building a full-on radio and we've put all the transistors and things. And my actually my main graduation project was this device that turns wind energy mechanical, you know, energy solar into off the shelf battery to power in ha remote households like in the Middle East desert. And it's it's cool. the process of tinkering with things and being curious and following that through logic and math was was a very curious thing. but it's it's not like primarily the coding was the thing that was attractive to me. It's it's it's it gives you directly access to tools that I can expedite the problem solving. So when I started Chop Talk, I had the vision and I'm like, what is version one Brian was a Google Drive folder with a bunch of worksheets and slides. And
Brian Hecht: I remember it. I I remember it.
Mohannad Arbaji: and if we if we do something like that and we can go to school districts and like this is amazing, then you can like start layering in software, be like, what's better than worksheets? Adaptive practice I meet you or what's better than slides, materials for the teacher. And it just builds on top of it from there. So I was focused on the thing that I had like a unique acumen for, and it's basically like the vision and Telling the story and talking to customers and figuring out what they want and building it and delivering it to them and talking to more customers and going back to product and just like that cycle. so truly a generalist wearing many specialist hats, depending on the year and the month.
Brian Hecht: Right, right. So this is an exercise I like to do. you said you started vibe coding in February, which is crazy
Mohannad Arbaji: Yes.
Brian Hecht: because that's that's when I did it too.
Mohannad Arbaji: Yeah.
Brian Hecht: and people people say like, you're such an expert, and I'm like, I I don't know anything. Like, I don't know, you could you could you
Mohannad Arbaji: Yeah.
Brian Hecht: could do what I did and be an expert in a month. but I like to ask people, go back to the day before you vibe coded your first whatever. What was the engineering technology infrastructure that you needed for chalk talk that day before before you ever vibe coded your first thing.
Mohannad Arbaji: So it was actually the the day I started vibe coding was I don't really take a lot of time off, but I had my brother in law's wedding in a all inclusive resort in Cancun and I'm like, well you know what, we're gonna hop on the flight, let me download a book. Everybody's been talking about this. I've been a power user of AI in the chat interface, the cloud.ai and the chat GPT desktop prompting and l prompt libraries and some of the workflows about cloud code in the world of connectors and MCP. actually writing production code and GitHub and getting an application set up. That I haven't touched in a little bit. So I went to Kindle and I like, What's what's the book out there? And I found one, Cloud Code for Beginners Made
Easy. And I looked at it and, you know, it enjoyed some chapters, sped through the others, but that was the thing that got me started. And to your point, like you don't need to s to ha like, this field is so, so, so new. The best practices on the playbooks are being written now. And the most exciting thing about that is you could be at the forefront of it so quickly. And it depends on what kind of forefront you want to be. You want to be on the technical forefront, you could just ask Claude to explain to you the latest research papers and get into technical research. You want to be on An implementation forefront, you tell us what are the MCPs that I need to do. But truly, whatever forefront means to you, which category you're interested in, you could be at that forefront really fast. So that gave me the beginnings and and then I was like, okay, let's let's dig into it. And one of the most magical powers of Cloud Code is you could just ask Cloud Code to explain things for you. So I'm very big on asking it to make artifacts and if I use like a a different, you know, harness like a codex site, they don't have artifacts. But I ask you to build an HTML page site with animations and toggles and things and it explains things. And then I learn them. And the just speed of flight of learning and just getting better at these things is incredible. And there is no authority for like the knowledge of of AI the same way the you know there's authorities in other areas. So you follow a lot of people on social media. You're one of the folks that I follow. I followed Tarek from Anthropic. He he just left yesterday, I was surprising for a bunch of us. And I follow a bunch of different folks on social media and I just get all this information and asking Claude and just I have Claude watching and reading podcasts and watching reels and doing things around the week and just learning organically. So that's kind of what happened. I caught the bug by picking up that book, going on vacation, sitting by the pool. And then I spent less time by the pool more in the room than I wanted to because it was just an addiction once you see the the power. And next thing I know, I'm I'm like talking into my phone even when I walk. I have a setup. I don't have a Mac mini, but I haven't closed down my screen in so like I
Brian Hecht: Ha ha.
Mohannad Arbaji: shut down my la laptop once a quarter now. I have a setup where I'm always talking to it.
Brian Hecht: Well, I f I feel I feel guilty. I I'm I'm here technically on vacation and I I can't see the laptop screen when I'm out on the porch here. So I've sort of
Mohannad Arbaji: Yeah.
Brian Hecht: settled for keeping the door open and sitting here and looking out at the at the water, but actually coding while I'm here and with multiple screens open and talking a whisper flow and things like that. But I found it my God. You can speak
Mohannad Arbaji: Whisper flow cloud code combo is incredible.
Brian Hecht: so quickly. So much more
Mohannad
Arbaji: Yes.
Brian Hecht: quickly. And and when I demonstrate it for people, people think still, even with Claude Code or with Prompting for Vibecoding, that you need to be so precise in every word you say. And while it is true that there's kind of a mode of thinking about how to tell it, like, don't think about this, I don't care about that, I only care about this, and and that's helpful. But you can talk to it and say like I'm thinking about having this artifact thing that's gonna help me find podcast guests, but I'm not really sure. And you could just riff like that, like you're brainstorming
Mohannad Arbaji: The best.
Brian Hecht: with someone and people don't get that. They think you have to, you know, have to have some kind of like formal typey typey thing, and you don't even need that.
Mohannad Arbaji: Hundred percent. Riffing is one of my favorite things that I do with it. But to be honest, I just started with wanting to be curious about this. Then I found the business problem because you told me what does the day before look like. And then I was able to generalize it. And I think most businesses probably have the same three things. You either use it to renew your customers, to find new customers, or to complete projects. And then I develop best practices around how do we do that, right? I needed to ingest all my company's enterprise data to tell me which customer is at risk. we're talking the backend for Postgres, we're talking all the Slack messages, like the of course anonymous, PII compliant, the whole thing. There's a way to set it up, but we're talking the Google Drive, the Notion for the Wiki, the Jira for the tickets, the intercom for the chat, and you're able to create a company brain that Or they call it now a semantic layer on top of everything, where you can the entire company becomes queryable. That's step one. And then step two, now that the company's queryable, you can now create playbooks the same way that humans can follow the playbooks to do tasks. Now you have playbooks for your agents. And the company collapses to two roles. People who build these things, build these agents to, you know, off the semantic layer to just do the tasks, or people who talk to customers to make them. to establish the connections and help with the renewals and and getting new partnerships. But really the world is collapsing into builders and I don't like the word sellers, but I I guess it's a little bit of that. Could be renewals, could be sales, but builders and sellers. Yes.
Brian Hecht: Not the every everyone everyone's everyone's selling something. There's no sh there's no shame in selling. Someone someone's gotta put food on the table for the builders. what what's that
Mohannad Arbaji: Hundred percent.
Brian Hecht: what's that ratio like now? Because is there sort of a diminishing like marginal return on building tooling? 'cause I'm sort of wondering about that. Like our new builder is just me and my right hand man Nico, and we've got sort of second brain going for All of us, this semantic layer. And at a certain point I find myself just fine tuning all these things for the point of perfection. And then I realize okay, we kinda have what we need. Let l let's just do the job we're here to do.
Mohannad Arbaji: Yeah, a hundred percent. So I think when you when anybody first starts with AI, especially if Nu with the Kore Oktu, you you start with I started at least like with the to-do list. I started with a lot of these things and then you enter this trap of AI just can just keep optimizing things for the sake of optimizing and th everything is infinitely optimizable. And that's the trap that that you're talking about. But then you when you switch that And you try to plug it into an existing company's workflow. From our end, we said, what are big projects that are taking a lot of time? And can AI accelerate it? So it's it's it wasn't, when we're first learning a thing, at least for me, when I was first learning a thing, I was I was finding a project to apply the skills. But then when I looked inside the company, I found actual problems that we could just apply this to an expedite. One major one is like Courses. In I'm in the space where a textbook publisher like Ahotin Mifflin or McGraw Hill spends seven years on five million dollars to make a single algebra one textbook. That's wild, because that's content. And of course it has to be held to high rigor, high stoch stochastic analysis, all these things, but it's it doesn't make sense that in today's age it it looks like that. But if you use the AI and you say give me algebra textbook, it's gonna look like AI slop. So We're able to go into our manual SOP, our process of how we start from state standards and then align them to the specific topics and create a master taxonomy and create slides and then insert AI into each of these pieces. And that's how you get a 20x acceleration in the work. we're actually 20 times faster for making these types of courses than we were before. And the quality did not suffer. It went up because a lot of this work has to do with taste and judgment. And when it comes to taste. You can't you can't key way taste, right? You br bring in another contractor and or another hire and what if their taste is not like how do even give that feedback? But with a tool like with w like AI, you're able to actually extract taste. I know a lot of people say taste can't be extracted, but once you see it, you can extract it and codify it. And the quality bar is becomes more consistent and higher. So not only are we twenty times faster, but the quality is higher and more consistent. So that's been my My my pivot from learning into applying it to the business and of like finding the biggest bottlenecks and be like, can we turn it into an agentic end to end workflow? which in many cases are thousands of files across many repos, but it it does eventually work. And to do that, I literally just was so busy with doing this.
Initially I was thinking of getting a COO or getting some other support, and I ended up settling on, you know. bringing a CEO to help manage the board and run the business and do other things and then give myself myself the title president and just lock in fully on AI transformation within the company 'cause there is nothing higher importance as a founder you can be doing than this thing. And it truly requires the founder to be involved top down. Otherwise it's not gonna pick up.
Brian Hecht: So you d you you did the you did the ultimate anti founder move. most founders
Mohannad Arbaji: Yeah.
Brian Hecht: cling with their dear life to their CEO title w either, you know, when they're either no longer the right person to do that job or they've lost interest in it, but you know, feel like they or they should be doing something else, but they feel like it would be a demotion to be anything other than the CEO. You just kinda like fell on your sword and you're like th this This is the the sort I wanna be on. I don't need the title of CEO. I wanna do the work I wanna do. Is that that's fair or
Mohannad Arbaji: 100%. And and I'm not going to even make it sound easy because it's like you have to, for a long time, that is basically the thing that I'm able to do. that's how I move the business forward. But once you experience the magic of these things and you realize like that if you take any company, and this doesn't apply to startups, it applies to even like the largest Fortune 500 companies, the companies that PE companies try to buy somewhere between startups and and and a large public company if if you just take those and you say there's two questions. If we can h help give them to AI, there's literally like no bigger impact on the company that you can do. And it's a mistake to focus on anything else. So what are two questions? Now question number one is what does this number mean? Or what is this fact? Like how many active students do we have? What's our runway? Which customers are at risk or where are the top ten hottest leads? If these things can go from subjective to objective, because you have a a glossary layer, a semantic layer with defined rules and things, you're fifty percent of the way in. The second thing is like what does good look like? what are we producing? Are we producing leads for an HVAC roll up company? Are we producing in my case like courses? And then we turn those into playbooks, then that's it. The AI knows what everything means, it knows your vocab, and it knows what good looks like. And then it can just start ripping through things and executing and helping you move things at not ten or twenty but a hundred X faster. And I could only do that if I lock in. And as a founder, I have the generalist brain where I know all the departments and all the areas. And that's what you need with AI. That's why people started hiring forward deployed engineers. It's hard to have like a team of twenty working on a thing. One person with the context and how the business works without AI is the best suited to build it if you arm them with AI. So when I saw the impact, that's what really turned me around. And I saw it on some of our customers and the bottom line for like the initial POCs and like this is it. This is what I want to do and this is what's best for the business.
Brian Hecht: How do you think about strategy in that framework? So I'll give you a personal example. So, I've got this new builder community, I do some coaching, I do some consulting, I do all these different things. And I've decided, you know, sort of as an ongoing effort, I kind of have a a strategy repo and I have all these conversations speaking to it, you know, and trying to help me plan out what the next year should be in my career with respect to my life. And it knows, it knows everything there is to know about it. And whenever I push it, it kind of gives me the same answers over and over. sort of, you know, based on a false premise or something it assumes I care about, but I don't actually care about and I have to correct it. And my takeaway from that could be one of two things. Either I'm prompting it poorly or I've structured the infrastructure of that of that repo incorrectly, or I'm just not prompting it very well, or it's bad at strategy.
Mohannad Arbaji: No, th that that that's a really good question. So there's a few ways, you know, few really big use cases for AI. Like one is the strategy, which is really good. And then the other is like, you know, the execution of the work. I man, I forget the third one. It was like on the AI Daily Brief they talked about three big categories. I was mostly talking about like the execution of the work and you talked about strategy, which is like a really big piece. For me, what helped me with the strategy is at the start Of at the start of every session I have a hook that r like reads you know relevant top level folders in my you know obsidian vault. Like this is my focus, this is all of it doesn't read everything, of course, because we have progressive disclosure, it only reads the top level. And at the end, it runs this thing that I call like session end ritual, which allows which goes into the transcript and finds all the relevant projects, updates them, even does decisions, brags, logs, all of these pieces. So when I open a new session, it sees it in context. And one of the la one of the steps in session and ritual, anytime I give it a tweet or a website or anything to explore a podcast, it logs it as a source with metadata. So now you have an onion layer, progressive disclosure of here's the big project, here's decisions, here's this, and it's all related and I run a search at the start of every session to look into it. So it starts to develop a an a taste for my version of what the strategy would look like. Cause if you start from scratch and I tell it, I'm trying to do a semantic layer, it's gonna aggregate into what just the web corpus does, versus I have at
least like twenty-five artifacts about semantic layer and a lot of work I wanna cumulatively build on top of. That's been 'cause I I did notice a problem you're talking about in my workflow and that has helped quite a bit.
Brian Hecht: Yeah, I think I'm missing that obsidian layer because it'll just come back and say, Yeah, but you dis I'll be asking it a question today and it'll say, Yeah, but you decided on March twenty twenty six, March thirtieth, that you were never going to do X. And then like, you know, I don't know, maybe I should do X. Like the world changes. And it just lacks that level of I guess taste and judgment in a way, unless you set it up the way that you're setting it up. So I'm learning Learning a lot. I wanna go into some of the conversation we had, which is what does the future look like? and I think you and I share the the notion that we're having fun now and we are exploring What for a lot of people is the frontier, and is the frontier for us, because every day we know more than we did the day before. But there's a big picture there. This is a society transforming movement. and as people who have taken it upon ourselves to understand a little bit more, a little bit earlier, we have the opportunity to lead the way into whatever that new world is going to be. And you're a guy who has much bigger thoughts than I do. So I'm just gonna let you, I'm gonna I'm gonna let you riff on that.
Mohannad Arbaji: What does the future look like post AI?
Brian Hecht: No, no, no, no, no. That's too broad. That's too broad. I
Mohannad Arbaji: Okay. Tell me.
Brian Hecht: mean, how how how do we move from, you know, little clusters of people like us who are
Mohannad Arbaji: Yeah.
Brian Hecht: embracing it into that sense of agency
Mohannad Arbaji: Yeah.
Brian Hecht: that we can use not just to make, you know, you know, people in offices more effective or companies more effective, but to sort of really make those sort of transformative changes in society. that w we maybe that we can't even conceive of right now? That's a very difficult question.
Mohannad Arbaji: Yeah, no no I'm no that's a it's a it's a great question. And when you think about like the power of this AI at our fingertips, like I forget what metric it is, like how few people only just use the chat interface and of those like how many even use like a like at harness a codex or or cloud code and how many of them are like set up files with their context and like think like then you get into a very small layer and I do think there's a couple of notes here. I do think for that reason the business of implementing AI is going to be so much larger than the business of making these frontier models. we are ab we're I'm not about to enter we are in the beginning of like an AI education and implementation like era that it's going to be really intense in the first two to three years, and there's gotta be a lot of first movers who expand and build platforms and tools on top of this, cause there's no clear winners in that space. And then after that, the models will start eating up some of this stuff, but it's always going to be there. you need an implementation, an education angle. And part of why I don't think there's a clear winner, like I've taken the courses. on the anthropic site and their academy and some of the other academies. And my wife's preschool teacher, and then I'm taking these courses and I showed her a lot of the things where they are and she uses cloud codes right now, but it's very different the way they're teaching it. Like when you log in, they talk about a transformer and neural networks and like she doesn't care about these things. She just wants like, build me a website. Like what is the right tool? I'm like, okay, you should download the Figma, you should do the connectors. There aren't, you know, a lot of Simple, here's an applied AI intro. Even the book that I read, it was talking about how to install plot code within the cursor harness and get repos and all of these things. And you're either seeing this or you're seeing prompt libraries, right? Like the word GitHub is scary for a lot of people. Like there isn't somebody explaining, GitHub is to code, like Google Drive is for your files. Like that sentence is missing. Alright, so I think what we what we need is an educational angle that's designed for the preschool teacher and the and the plumber and the everyday the the policeman, everybody. Not just for the bubble of tech that exists in Silicon Valley and a few other cities in the US. And not a lot of people are putting out their content. But I think, you know, whoever puts out the content will play a really important role in its adoption and it's some of the work that I've been working on personally as well. It just doesn't exist.
Brian Hecht: d yeah, yeah. T tell me more about that. it seems like educate with with your wife as a preschool kid teacher and you with your passion for education, it seems like you are very well suited to point your nose in that direction.
Mohannad Arbaji: Yeah, absolutely. I've been definitely going very deep in this. I this week I have you know two P funds reached out 'cause I've recently started writing stuff on LinkedIn on my blog. It's blog.mohanedarbaji.com and talks a lot about how to operationalize AI in your company. And not just Do this tool. It it's a different approach than the book I learned, which is like do it so you can gain these skills. It's like here pick a problem that you have in your company and we're gonna make it go away ten times faster or more efficient and kind of creating these frameworks for education in a way that's gonna actually solve a problem that you have in your business that you can walk away with. And it's we talked about some of the facts here. so or some of the levers here, whether it's a semantic layer or AI that understands all your playbooks and does them and how can it be a deterministic thing. But it's it's been definitely a field
that I've been digging really deep into. I think it's there's definitely a race for who's going to build like the AI curriculum that teaches people how to think. And if you think about, you know, I I recently also wrote about how China retired, I think thirty six hundred degrees and they added new ones, mostly around AI, but that's a whole field that doesn't have it's a new field that doesn't have people with a degree in AI. So people are all moving towards like what does this curriculum look like? Who's gonna write it? How can it be effective? And it's an area that I've taken a lot of interest in, for sure.
Brian Hecht: Yeah, th yeah, you're right. There's there's definitely degrees in like I mean there
Mohannad Arbaji: I looked at the number, ten thousand two hundred degrees completely. But yeah, go ahead, sorry.
Brian Hecht: there I mean the degrees that exist, they're the sort of scientific degrees. It's like how do LLMs work and things like that. But the sort of it's almost like there needs to be like with it within the sociology department, within of course with the business department, like they should all have like a an AI track or an AI requirement, or it it should just let's sort fade into the background. It's just assumed. The same way every math course you use computers and calculators or every course you use cal now you know, there should just be AI baked in. One final question for you before we wrap, I'd like to ask this of people, when you have free time, which I know you may not have a lot of, give me one fun thing, just for your own amusement, that you do building with AI.
Mohannad Arbaji: Let me show you one thing. So I have an English bulldog and she is very she bites things very heavily. And then there is this thing that you put one of those treats in, like the peanut butter ball, and then she licks it. But then I ran it through the dishwasher and it just wouldn't open, and you're still having it right here. So I looked at the specs and then I went into Blender and designed a 3D model that literally and You could put it around here to help open it because it's the lever is really hard. And then I asked Claude to go and 3D print it and ship it and find the right filament lane. And it did it, and there you go. Now it opens. So when I say I use the eye for all the problems, literally for anything you can think of. And this is my 3D printed opener for anybody who has a little wolf toy. Let me know if you're stuck, I'll send you the model for free.
Brian Hecht: Now just now yeah, not for free. Have a open a Shopify store. Come on. This is and then have it come up with ideas for more things that you could build and then have it open Shopify stores for that, and then just sit
Mohannad Arbaji: How does that
Brian Hecht: b sit back and wait for the checks to come in. Mo, this has been a
Mohannad Arbaji: Absolutely.
Brian Hecht: fantastic conversation. I'm thinking about so many things. I've this is how you know I'm gonna shut the computer and open it back up again and start coding based on things that you've suggested to me. So always a pleasure talking to you.
Mohannad Arbaji: Always a pleasure. Well, thank you so much and thank you for the work that you're doing and the writing that you've been doing. It's definitely been a big part of the reason why I kinda jumped onto this stuff. So I appreciate everything you do, Brian. And thank you for having me.
Brian Hecht: That's very kind. Could you could you give us one more time just the name of the blog, how to find you on LinkedIn, anywhere else you want us to find you?
Mohannad Arbaji: Yeah, absolutely. It is blog dot Mohanad here, let me just quickly paste it here. It's blog dot
Brian Hecht: No, we'll we'll we'll we'll we'll put it in the on the screen.
Mohannad Arbaji: Okay. Perfect. But yeah, it's blog.mohanadarbaji dot com and yeah, Mohanad Arbaji is the name on LinkedIn and you'll find a lot of my writing there.
Brian Hecht: Awesome. Thanks, Bill. Have a great day.
Mohannad Arbaji: Thank you, have a good one. Take care. Bye bye.