Build a Profitable SaaS (the Easy Way)

Summary

Olly Roswell dives into the world of SaaS entrepreneurship, emphasizing the value of leveraging APIs to build profitable software solutions. By recognizing patterns and gaps in the market, entrepreneurs can use APIs as building blocks, much like Legos, to create functional and desired products without the need for extensive coding knowledge. He highlights the significance of understanding customer needs over technical details and the potential to scale by connecting various APIs strategically. Ultimately, Roswell encourages aspiring founders to focus on delivering outcomes and value, not just features, within their SaaS offerings.

Highlights

• Learn how to use APIs to create profitable SaaS solutions with minimal coding. 💻
• Understand the concept of 'API Arbitrage' and how it can be a game-changer for entrepreneurs. 🔄
• Discover the art of using other people’s code to deliver desired outcomes efficiently. 🚀
• Explore the importance of outcome-focused products over merely feature-centric ones. 🎯
• Unlock the potential of combining different APIs to offer multifunctional solutions. 🔗

Key Takeaways

• Leverage APIs to build profitable SaaS products easily, without needing extensive coding skills. 🎯
• Understanding pattern recognition in market gaps is more valuable than being a technical expert. 📈
• Use APIs as building blocks, similar to Legos, to create scalable solutions. 🧩
• Focus on providing outcomes and value rather than just features. 🎁
• APIs can significantly reduce the time and cost of development, offering a competitive edge. ⏱️

Overview

Olly Roswell shares his insights on building successful SaaS products by utilizing APIs, turning complex solutions into simple, scalable tools. APIs act as a bridge to leverage other people’s work, making SaaS development accessible and less time-consuming.

To succeed in the SaaS industry, Roswell argues that identifying and fulfilling customer needs is paramount, which can often be more important than technical craftsmanship. His approach emphasizes using APIs to minimize reinventing the wheel and focus on creating value.

Roswell's strategy involves 'API Arbitrage' by strategically combining APIs like Lego pieces to produce profitable, scalable software. He encourages developers to validate these ideas quickly in the market, leveraging the low-cost entry to scale their solutions sustainably.

Chapters

• 00:00 - 00:30: Introduction: Leveraging other people's code for profitable SaaS In the introduction, Oliver, previously from Response Ai and now associated with Trome.com and Get Sea, discusses his strategy for building profitable SaaS products by utilizing other people's code. He shares that this approach has helped him generate hundreds of thousands of dollars. The chapter aims to explain how leveraging APIs can simplify product development and enhance outcomes, encouraging others not to overcomplicate their projects.
• 00:30 - 01:00: The importance of pattern recognition in SaaS The chapter discusses the paramount importance of pattern recognition in the field of Software as a Service (SaaS). It argues that the most valuable skill in modern entrepreneurship, especially within SaaS, is not coding, design, or sales, but rather the ability to recognize patterns. Entrepreneurs should focus on identifying what people want and then providing these solutions at scale through SaaS. It highlights the capability to spot gaps and execute 'API Arbitrage'—leveraging APIs or external codes to meet customer needs—as a crucial skill, marking it as akin to magic in 2025.
• 01:00 - 01:30: Understanding APIs through the Lego analogy The chapter uses a Lego analogy to explain the concept of an API. It imagines a scenario where everyone at a party brings half-built Lego sets to demonstrate how APIs function. For example, one person contributes a boat missing its sails, while another has a pirate flag but no ship. This analogy helps both newcomers and those already familiar with APIs to understand and view them from a different perspective.
• 01:30 - 02:00: API Arbitrage and focus on delivering results This chapter discusses the concept of API arbitrage, likening APIs to pieces that can be traded or combined to create a complete and highly valuable product. By paying a small fee for API components, such as a 'Pirate Flag', you can enhance and complete your product, like turning a regular boat into a fully functional pirate boat, significantly increasing its value. This process is not merely collaboration but an amplification of value. The focus is on leveraging APIs to deliver results, highlighting the opportunities to assemble various pieces, sometimes even free, to build exceptional products.
• 02:00 - 02:30: The strategic use of APIs in SaaS products The chapter discusses the importance of APIs in SaaS products, emphasizing that customers are not concerned with the technical details of product development. Instead, they are focused on the results and performance the product delivers. The chapter also touches on the tendency of developers to 'gatekeep' by insisting on building everything in-house, likening it to having to knit sails to use a boat.
• 02:30 - 03:00: Creating value with simple API combinations This chapter discusses the concept of 'tyranny of craftsmanship,' which is the idea that technical purity is often wrongly equated with business value. The emphasis should be on the value the product provides to customers rather than its technical aspects. In 2024, Mark Anderson stated that the most successful startups are those that curate existing tools rather than create new ones from scratch. These curated tools can then be built upon and offered to customers. The chapter also touches on the issue of overcomplication in businesses today, where simplicity is often overlooked, but is crucial for creating value.
• 03:00 - 03:30: Constructing profitable SaaS with API data chains Building a SaaS (Software as a Service) often involves complications that engineers might emphasize, such as rate limits, authentication, and security. While these are important, they shouldn't overshadow the main goal of establishing a functional service that can attract at least 100 customers. Rather than aiming for a flawless system from the start, focus on creating a system that is robust enough to succeed in the market and deliver value to customers. The priority should be on market survival and providing tangible benefits.
• 03:30 - 04:00: The importance of understanding asymmetrical pricing The chapter discusses the concept of asymmetrical pricing, emphasizing the value creation for multiple users by leveraging APIs. It highlights the trend of transforming ordinary Software as a Service (SaaS) tools into exceptional ones by integrating OpenAI's API, specifically the Chat GPT API. The narrative underscores the notion that enhancing tools with advanced APIs can substantially increase their utility and appeal.
• 04:00 - 04:30: Using APIs to create simple but valuable products The chapter discusses the benefits of using APIs to access existing code and technologies, rather than attempting to build complex solutions from scratch, such as chatbots, to save resources and enhance project outcomes.
• 04:30 - 05:00: The 1% rule in API usage The chapter discusses the evolving capabilities of chatbots, emphasizing how they can mimic human interaction, such as personalized greetings and recalling customer purchase history. This is achieved through the use of APIs which enhance the simplicity of customer support applications from basic yes/no interactions to more complex functionalities. The analogy of building a Lego pirate ship is used to illustrate how APIs complete and enrich an application, turning a simple concept into something much more multifaceted and marketable.
• 05:00 - 05:30: Apify store and building SaaS without coding The chapter discusses how individuals can monetize their expertise by commoditizing data and analysis services. It provides an example of scraping LinkedIn job listings using platforms like Apify, an API marketplace, to gather insights. It suggests using OpenAI tools to analyze job sentiments, such as salaries and required expertise, as a way to create valuable insights and generate revenue, illustrating a method of building Software as a Service (SaaS) without the need to code.
• 05:30 - 06:00: The potential of easy-to-build, API-based SaaS ideas This chapter explores the potential of building simple API-based SaaS (Software as a Service) products. It uses an example where three different APIs work together to deliver a service. The process starts by scraping job data using the LinkedIn API, then the data is analyzed for sentiment using the OpenAI API, and finally, a PDF report is generated using the PDF shift API. The chapter emphasizes the low barrier to entry for creating such services, as they can be built by combining existing APIs to deliver meaningful results like a job sentiment report. It also hints at considerations such as the total cost per user which involves aggregating the cost of each API used.
• 06:00 - 06:30: Beware of API Gold Rush traps and vendor lock-in Chapter: Beware of API Gold Rush traps and vendor lock-in The chapter discusses potential pitfalls of the rapid expansion and commercialization of APIs, often termed as an 'API Gold Rush.' It highlights the risk of vendor lock-in, where businesses become overly dependent on a particular API provider, which can lead to increased costs and reduced flexibility. The chapter provides an example where businesses earn significant margin by charging clients five cents for API services while the cost is only a fraction of that. It warns businesses about the illusion of easy profits and underscores the importance of strategizing to avoid becoming trapped in a single API ecosystem.
• 06:30 - 07:00: Importance of differentiating through context and personalized features This chapter discusses the importance of distinguishing pricing structures for software companies, particularly those utilizing APIs. It highlights the concept of asymmetrical pricing, where API providers charge per request, while end-users evaluate value based on software outcomes. An example from Response AI illustrates this point, where the company charges a monthly fee for AI video credits, allowing for profit margins despite the inherent server and API costs. The discussion underlines the necessity of context and personalized product features in building and pricing software effectively.
• 07:00 - 07:30: Selling the outcome, not the APIs The chapter "Selling the outcome, not the APIs" discusses how the perception of abundance, such as offering high quotas like 10,000, justifies the pricing of APIs despite users rarely hitting these limits. It introduces the idea that using APIs is like a 'tax on progress', suggesting that the most successful founders see accessing and utilizing other people's code as a small price to avoid reinventing software solutions from scratch. As an example, the speaker talks about building a tool for scraping and analyzing Twitter data.
• 07:30 - 08:00: Building the future SaaS playbook with APIs The chapter discusses the advantages of using APIs, particularly for projects like analyzing sentiment on Apple stock. Instead of spending extensive time on reverse engineering and deploying servers, the speaker opted to use paid API services like Rapid API or Appify. This approach allowed them to focus on delivering real-time alerts based on user needs, such as current sentiments about Apple and news like earnings reports. The emphasis is on efficiency and meeting consumer demand quickly.
• 08:00 - 08:30: Conclusion: The future of building impactful tools The chapter emphasizes the importance of understanding and simplifying app development by leveraging existing tools and APIs rather than reinventing the wheel. It highlights a specific example of using the Twitter API and OpenAI for data analysis.

Build a Profitable SaaS (the Easy Way) Transcription

• 00:00 - 00:30 what's going on guys this is Oliver formerly from response Ai and now from trome.com and get sea and in this video I'm going to be talking about how in every SAS product I've ever built that was profitable I've leveraged other people's code to achieve the end result for the user right and I've made hundreds of thousands of dollars doing this and in this video I'll explain how I did that and what you guys can do and what you can build with other people's code also known as apis and while you might be over complicating just getting
• 00:30 - 01:00 some customers via SAS right so the most valuable skill in modern entrepreneurship and SAS specifically is not code or design or sales it's just recognizing patterns right people want things and you can give them these things at scale using SAS right now the ability to see gaps and a sort of Arbitrage between what an API or other people's code enables you to and what customers need this is the art of API Arbitrage and it's the closest thing we have to Magic in 2025 right so the Lego
• 01:00 - 01:30 philosophies so explain what an API is um obviously people who already know that's fine it'll be a nice way of you know sort of uh understanding it in a different way and people who do not know then this is the explanation right so an API imagine you had a party where everyone brings half-built Lego sets right let's assume that you're building a boat now your contribution is a boat that is missing its sales and someone else has a pirate flag but no ship Etc right
• 01:30 - 02:00 so an API is this agreement to trade pieces to make an end result or an end product that works so you pay a small fee for their Pirate Flag you attach it to your boat and suddenly your creation is a complete pirate boat and it's worth twice as much right it's a complete functioning pirate boat this isn't just sort of collaboration it's it's multiplying the value and as long as you can pay people or some things are free you can get whatever pieces you want to build the most beautiful boat in the
• 02:00 - 02:30 world right now the magic lies in recognizing that no one cares guys how you built the sales in the same sense no one cares how you built your software customers only care listen to this customers only care that the boat floats and looks compelling and in this case customers only care that your SAS delivers the result that they want right but developers often gatekeep this right and they insist that you have to knit the sales yourself or what if the Young
• 02:30 - 03:00 it's an organic what stit this is called the tyranny of craftsmanship and it conflates technical Purity with business value in other words the only thing that matters is your how your product is valuable to the customer right not how it's built Mark Anderson said in 2024 the best startups today are curators not creators so we are looking to curate tools that we can build with and then build on top of that and then charge the customer right now the epidemic of over complication so there's a conspiracy in
• 03:00 - 03:30 Tech right so Engineers inflate complexity to justify their expertise they'll warn you about things like rate limits or authentication or edge cases or security and all of these are valid concerns but they are not valid or or relevant until you've you know past a certain threshold of say 100 customers right the goal guys for building a sass is not to build a faultless system the goal is to build a system that survives long enough to matter in the market and provide value to some customers because
• 03:30 - 04:00 if you provide value to some you can provide value to many right now when it comes to sort of using apis this is where the chat gbt wrapper comes from right so it's this phenomenon where every SAS tool these days is just called a GPT rapper right all that means is we took boring tools and we made them extraordinary by adding open ai's API or in other words the chat gbt API now remember an API is just you paying to
• 04:00 - 04:30 access code that you couldn't build yourself and I really I personally do not want to try to rebuild chbt to just save a few pennies right so I'm going to I'm going to access their code and use it in my project to make much more money and help much more people five years ago for example customer support Bots followed these rigid scripts um are you a customer yes what is your email X email um do you have a receipt yes right now with ch hbt and and and the apis
• 04:30 - 05:00 they can mimic human interaction you ever speak to a chat bot and it's like hey Steve you know really nice to see you here again I love that you recently purchased this it's crazy right this is because the simple app which was customer support just a yes no you know sort of simple flow it's accessing apis to make it more powerful right so we go back to that example it's completing the Lego boat it's taking the half finished boat the pirate flag and it's creating a pirate ship that we can sell for much
• 05:00 - 05:30 more money it provides much more value another example right so this this is how you can commoditize the expertise of you know researchers and things like that and make a lot of money so an example is you scrape LinkedIn jobs from something like appify which is just an API Marketplace I'll explain that later you analyze the sentiment of the jobs with open AI like you know um for example the salary um what expertise is required what uh
• 05:30 - 06:00 experience is required what degree or what college you know um thing is is required and then you generate a PDF Report with PDF shift right so that is three apis guys that are acting in tandem so you scrape the jobs with the LinkedIn API you analyze the sentiment with open AI API and then you generate the report that's three apis that you are using to create the end result which is the PDF report right the total cost per user if you add it all up would be
• 06:00 - 06:30 for example 0.013 cents right then you just charge 0.05 and you've got a pretty significant margin right so you scale to one you know 10,000 users and you're netting hundreds of dollars a day for moving data between pipes you don't really touch much of this you just take the LinkedIn jobs you scrape them you analyze them you generate the report and then it's done that's just like a simple example guys the real problem Prof it lies in
• 06:30 - 07:00 asymmetrical pricing guys right so API often charge per call or per request right while customers pay for the outcome of your software so this is where it's important my last SAS uh response AI charged around $99 a month for 10,000 AI video credits right now knowing that every 10,000 credits costed us x amount in server costs and the apis I could charge for the margin and that's where you build the product you know
• 07:00 - 07:30 profitable tool guys right so users rarely Max their quotas but the perception of abundance like got 10,000 it Justified the price right so the hidden cost of Independence so the most profitable Founders I know they treat apis and accessing other people's code like a tax on progress right it's a small fee guys to avoid having to reinvent the wheel every time you build a new software so when I built a tool to scrape and analy Iz Twitter um for
• 07:30 - 08:00 sentiment on Apple stock as a just a hobby project I could have spent months reverse engineering X's you know anti-bot measures and how to scrape Twitter and stuff like that launching my own servers guys like ah man instead I just paid one cent per result via rapid API or um appify whatever and focused on what users actually wanted which is the real time alert about Apple sentiment like people aren't happy with this people are happy with that the earnings report came back all the stuff in a way
• 08:00 - 08:30 that they could understand it and I sent them an email every single day explaining it right and that is how you build a simple app I didn't have to learn how to scrape Twitter I didn't have to learn how to analyze stuff with AI I just used Twitter's um apify API and then I just sent it to open AI to analyze right and that's simple the simple sort of pipeline now the 1% rule guys is if an API solves even 1% of your problem you use it right so your job is to stitch
• 08:30 - 09:00 the remaining 99% into a narrative that customers will pay for so in other words when you Stitch this pipeline together it has to create the end result the example is the um PDF report on Apple stock your sash should create a result at the end that that users like right now the less apis you use the better but you don't have to kill yourself over it right if you can't build something it is okay to use an API
• 09:00 - 09:30 so this is the apathy store guys so I just wanted to talk a bit about this like really quickly right so as you can see um there' be a link in the description to this this is just a few of um the scrapers from apathy right and you see how we can stitch these together with other apis to create what is effectively a full scale tool right so what you would do is let's say we want to create a we scraper that analyzes
• 09:30 - 10:00 websites and allows you to ask questions about websites so for example I scrape apple.com and then I can ask what is the price of its most recent product or what's some recent news about apple right so just a double just a double confirm we've got a tool that scrapes a website and then creates like a knowledge base for you to ask questions right simple right you are just going to use the web scraper which is the first result there you going to craw the website for the data and then you were
• 10:00 - 10:30 just going to send the data to open AI to analyze right The Prompt would be something like analyze this website um and note all of the important parts of it and tell me what their recent price is for their most recent you know launch product this is how you've just immediately built a SAS without ever really touching code at all you're you're creating the pipeline with a bit of code like a bit of a front end and stuff which I talk about in my last last video but you're hardly
• 10:30 - 11:00 building the server to scrape a we you know a website you're hardly building the AI large language model and spending three years doing that to to analyze the tool H to analyze the data you are just sending the web scraper data to open Ai and then you've got yourself a very very simple SAS right now if I think of just like 10 ideas out of the blue that take realistically they take zero effort but it's all about the execution right so you can just ask AI for different examples so let's say I want to you know
• 11:00 - 11:30 create an email finder what I would do is I would take clear bits API about scraping social media data and then I would layer hunter. I's API finding emails on top of that and you can just sell it as a leaden tool that the tool could be you know enter a thousand LinkedIn URLs and you get all of the emails of the users right and then you charge $200 a month for it guys like I know it's I know it's kind of just like these are vague examples but again an AI invoice generator right so you
• 11:30 - 12:00 take stripe payments because they have an API and you send it to chat gbt to manage and create an invoice right social media right use open AI to suggest posts about a topic and then use Buffer's API to schedule them right so this is how again how apis are talking to each other and an example is you could create a tool that scrapes LinkedIn for profiles and then adds them to HubSpot API so that could be based
• 12:00 - 12:30 off of a company that could be based off of a job posting anything like that then something like a no code chatbot so embed chat gbt as a chatbot into a website and then you just add the context for the support from the users business so like you know you would feed chat gbt um there's hundreds of different tutorials guys on how to make like a website chatbot with open AI it's one of the first things that people ever built right so you've got like chat base which is an amazing tool guys that like
• 12:30 - 13:00 basically you know does this it it uses the the person's business details as like training data and then you can ask it questions about the the you know the business so you could go to a chiropractor website and say like oh what time can I come in on Tuesday and it'll just say 9:00 a.m. because that's the data it's trained on right again these are just random ideas it took zero effort just asked AI for these like oh build me API based tools and give me some examples right
• 13:00 - 13:30 now this is important guys the API Gold Rush and its traps right so platforms like appify and Rapid API which are just these giant marketplaces for apis um they're they're amazing and they've built millionaires right and half of my SAS tools I have about six software tools right um two of which are making thousands of dollars a month I use apify in both of these right in both of the profitable ones that are actually scaling up and doing well right now in
• 13:30 - 14:00 total have maybe you know 100 customers between the two right chaining apis does compound right guys so a one cent call in 10 steps means that you you know you are getting charged a fair bit right so make sure that you are building what you can an example is um get.com or I mean a better example is um trome.com which is a database that I built of tech companies right I needed
• 14:00 - 14:30 um Revenue data for the companies right and I I don't know where to find that guys I I don't have the data I can't scrape it offline right so what I did is I went on appify and I found the similar web um API and you can call similar web or different tools like it and just get the traffic data or get the Revenue data whatever it may be there's hundreds of them I can't remember if it was similar Weare but it was you know some random get revenue for this company the problem is that I did that for 100,000 companies and then it costed me you know $800 because I was doing it at
• 14:30 - 15:00 a massive scale so just make sure you understand that um margin that you are building so if I if it cost me one cent to call a LinkedIn job and then it costs me three cents to analyze it with with AI make sure that you know that 4 cents per call is and you're charging say eight because it means that you're getting the 50% margin right so another thing is vendor locking right so if Twitter bans scrapers your stock analyzer dies right you've got to always
• 15:00 - 15:30 abstract thirdparty dependencies and make sure you have a few different vendors available in case one goes down so for example with with get Seer I scrape Reddit um to analyze it and I I analyze with open AI but I sometimes analyze with Claude or you know perplexity whatever it may be right or deep seek now the reason I do this is because if one of them goes down or one of them is faulty it can just send the data to a different API right you've got to make sure you have a few different
• 15:30 - 16:00 apis how this works is margin erosion again is that as apis get cheaper competitors undercut you and things like that you've got to differentiate through context right so an example is when if you have all of your data on a chatbot there's you really don't want to have to switch and then you know get download a different chatbot you want to make sure that all of your data is on there and it keeps a a customer subscribed to you you got to make sure that you're not just stringing apis together but you building proper value for the customer right so
• 16:00 - 16:30 following on from that the API Gold Rush and it's traps right is the antidote is to own the glue so in other in other cases you can build a proprietary workflow around apis for example my my a financial app that manages um you know sends data from a bank statement to chat gbt right but I've also added in user specific rules so for example ignore Amazon charges under $50 this makes the output feel bespoke and they're not
• 16:30 - 17:00 going to get emailed every single day saying oh you're spending loads on Amazon right I I know that's why I've added that user specific Rule and if they go anywhere else for that the same rules might not be available to them so they might not get a product that is as good because I've made it personal to them so maybe just um rewind this and have a little relist of this um this sort of slide it's very important that you guys build something that sticks people to it um so get SE for example my Reddit U my Reddit like analysis
• 17:00 - 17:30 tool I ask the user to add all of their business context right so whilst other companies like just scrape Reddit and say oh we found you know X people talking about um you know Zoom instead I'm taking the business context of the user and making it sticky for them they have to stay there and get leads from it because their business context is stuck on my app right this makes it all feel bespoke even though most of the process is sort of automated you
• 17:30 - 18:00 know now listen guys the psychology of pricing selling outcomes is what you need to do not apis right so customers do not buy apis they buy outcomes so a 1 cent API chain that generates a stock report is a sort of value illusion right when you frame the output as AI powered Financial advice you're no longer selling data or just selling this string of apis you are selling Clarity and confidence and time saved and this sort
• 18:00 - 18:30 of feeling of like um relief that they're not spending on random stuff right and let's take sort of local business therapist right example so deep seek things like that so by combining Google Maps Yelp and GPT and things like that the founder positioned the tool as a consultant for small businesses so in other words if you put a customer support tool on a local business therapist's website you are
• 18:30 - 19:00 effectively gaining value from that transaction because you're helping all of his customers right you are not building a little chat bot you are helping a therapist manage his clients so I hope that sort of un I I hope you sort of understand what that what that means guys so at one cent per query charging the business therapist um uh sorry the local business therapist like 12 cents it becomes irrelevant because the margin is massive it's absolutely massive it's like it's like 12 times it
• 19:00 - 19:30 right and that is the most important part guys is you are selling an outcome for someone you are not selling this cool little tool that helps you do this helps you do that you are selling them the result of what the SAS does we do not buy SAS we buy a software to do a job for us it's the most important thing to remember guys so the new playbook find a dull problem accounting scheduling data entry boring industries that are ripe for these like I sprinkles right then for every feature you can ask
• 19:30 - 20:00 is there an API for this so go on perplexity and say hi is there recent 2025 um apis for scraping um real estate data right then you price for the outcome so you charge for the report and the time it saves not for the API calls right so if a report on Apple stock could be worth 50 cents then if you can get that report done for 10 cents you've got a business guys you scale the glue then right so as you
• 20:00 - 20:30 grow replace costly apis with the in-house stuff so what we did with response AI is we were charging we were being charged loads of money for these apis on the way up and then once we hit about 200 customers we built the stuff in house because we had developers and money and time right only when the margins justify it can you build your own thing right the future belongs to Founders who see apis as the sort of brain to hijack so your job isn't to think harder it's to think as a connector like how
• 20:30 - 21:00 can I connect different tools together to make a beautiful piece of software right and you'll find that a lot of stuff you can build yourself but there's a few things that you'll need help with so for example I can't scrape Twitter like I said so I use an API for that the final thought guys the next $1 million idea for you guys it's not meant to be built by a team of 100 Engineers right it'll be built by someone like you who is a solo founder who strings together three API guys slaps on like a pretty UI
• 21:00 - 21:30 and then charges 100 times the cost this is what's happening with AI agents guys AI agents it's just API strong together that's it all these AI agents are they raised 10 million 100 million 1 billion right all these AI agents they're just API just strung together like you know scrape website analyze website AI agent um scrape recent real estate data um analyze the cost of houses in Chicago AI agent a a AI come on AI agent like come
• 21:30 - 22:00 on guys it's not an AI agent is it they're just calling it that because it sounds cool and it gets you loads of funding right we're here to build a tool you can call it an AI agent if you want because it helps with sales right helps with sales but you are here to build something that provides an outcome for someone you charge them and you make sure that it costs less than you charge them to create that report or create that analysis or build that chat bot whatever it may be there are some big hitters in Silicon Valley might say the next big thing starts out looking like a
• 22:00 - 22:30 toy right so today the best toys are borrowed Legos right they're made of borrowed Legos at the party when you turn up with the half finished boat and then you help someone they help you you help them you create a proper product or you finish the pirate ship with the pirate flag build on apis Fast and charge for it and that is the end of the uh my alarm's going off actually where is my phone that is the end of the video guys oh it's cuz I'm actually cooking chicken so my my chicken probably burn
• 22:30 - 23:00 um any problems at all give us a shout any questions you've got guys just let me know drop them in the comments like comment subscribe all that stuff um I love you take care bye-bye