She Makes 6-Figures Per Project With AI | Founder Interview
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Summary
Nicole, the founder and CEO of Head Start, an applied AI services business, is leading a tech revolution by leveraging AI to deliver comprehensive projects rapidly. Since launching coincidental to ChatGPT's rise, her agency has evolved from small consulting tasks to six-figure projects, all with a minimal team. Head Start's strategy involves using AI not just for efficiency, but as a core part of the development process, allowing them to scale rapidly while keeping headcount low. Their innovative approach to problem-solving, client collaboration, and internal tooling aims to set a new standard in AI-driven project delivery.
Highlights
Nicole's company completes full application builds in just four weeks, thanks to AI 🎯.
Head Start charges six-figures per project, delivering value at lightning speed ⚡.
Their AI-driven approach allows them to manage multiple projects without increasing headcount dramatically 🤖.
The team relies heavily on tools like Claude projects to maintain efficiency and innovation 🔧.
Even with a lean team, they handle enterprise-level tasks with proficiency and foresight 📊.
Key Takeaways
Nicole's business, Head Start, uses AI to complete complex projects quickly and efficiently 📈.
Head Start started at the dawn of ChatGPT and rapidly transitioned to high-value projects 🚀.
The business uses AI both for coding and client collaboration, which sets them apart in their industry 🤝.
Nicole's approach includes leveraging proprietary data structures alongside proprietary data 💡.
Head Start plans to expand and hire in AI-native methods, emphasizing growth without headcount ballooning 👥.
Overview
Nicole's Head Start is revolutionizing the way tech services are delivered by using AI at its core. Founded just two years ago, coinciding with the launch of ChatGPT, this AI-focused company stands out for completing complex projects quickly and effectively. The firm prides itself on not just using AI as a tool, but integrating it into all aspects of their process to redefine project management.
What sets Head Start apart is its innovative use of proprietary data structures and the strategic employment of AI, allowing them to offer uniquely optimized solutions to their clients. Nicole, alongside a small, versatile team, pushes the boundaries of what's possible by leveraging AI to scale operations in ways that traditional methods cannot.
As they continue to expand, with planned hiring sprees in the coming months, Nicole's vision remains clear: keep the AI at the helm, minimize unnecessary headcount growth, and focus on building both client and internal tools that affirm their place at the cutting edge of tech. This strategic foresight aims to handle any challenges in tech delivery that the future may hold.
She Makes 6-Figures Per Project With AI | Founder Interview Transcription
00:00 - 00:30 will deliver like an entire application build in like from scratch in 4 weeks like fully functional we're not afraid of anything we try to take on the hardest projects we believe that access to hard problems is like a proprietary business value for us I'm taking like swars of code from Claude projects when I'm working on client projects what I love love love about Claud projects is that you can share them amongst your team one engineer told me he's like you trust it much much more than I would ever or trust
00:30 - 01:00 [Laughter] [Music] it that's Nicole founder and CEO of Head Start and applied AI Services business she started the company right at the launch of chat GPT and now she's charging a minimum of six figures per project with only four employees in this interview Nicole shares the Homegrown automations that keep their revenue her
01:00 - 01:30 employee High how she uses Claud projects to run her entire business and the Playbook that she's using to scale her business from three to 14 employees let's jump into it so Nicole you and I first got introduced by a mutual friend and he says Greg i'm met this person they just started their own agency and they're absolutely crushing it can you tell me more about what you have going on at headart and what y'all are doing absolutely so I started the company uh just over two years now I left my job October of 2022 I actually created the
01:30 - 02:00 business in December and between that chat gbt launched to everyone uh November 1st of 2022 so it was quite lucky timing in all of it I had actually had early access to the open AI models and so I had been using gbd3 before it was a thing and I knew I wanted to start my own business but I didn't know how to go about it and so I started Consulting coding in line with the chat GPT launch so I was using chat gbt 3.5 to write
02:00 - 02:30 code for me before everyone was doing it and I remember to this day people being like Oh chat GPT 3.5 it's not good at writing code and I had a Consulting project that I was on that was Ruby on Rails I had never done Ruby before and so it was writing all my Ruby code and I'm like well it's good at writing Ruby code so I don't know if it's something about the way that I'm using it or what's different and then by the time gd4 launched in April of 2023
02:30 - 03:00 everything had changed and so obviously since then we've gotten almost weekly updates from anthropic and open AI dropping things that use to code and so my business Head Start is an AI native applied AI Services firm and so it's very meta in terms of like how we use AI so we use AI to write code that implements the AI for our clients um so very much just AI yeah yeah yeah yeah
03:00 - 03:30 and so did you start head start with AI in mind or was it no we're we're going to do coding services and then AI came around I didn't I kind of started the business more as just I was Consulting myself and what I knew how to do was code and so it was more around like what can I sell in general that I can just bootstrap a business and so that was really important to me was not necessarily raising Capital but figuring out how to do it on my own the AI piece came in line with it I started using the
03:30 - 04:00 AI to write the code before I started implementing the AI at that time every company was like I want a chat bot I want a rag implementation I was like I need to learn this and I was very motivated by the money incentivized to learn it if I were going to get paid to learn it and so I basically started implementing it for people knowing that they would pay for it and then building the business up that way for sure so tell me about what what are the bread and butter projects that yall take on right now and like what are your clients asking you for yeah so it's really interesting it shifted over time the
04:00 - 04:30 first year we were in business it was a lot of chat Bots and rag imple implementations we actually still do a lot of those but there's a bit of a different flavor and then the more interesting projects in my opinion are when the client has actually tried to implement the AI themselves and they're not seeing as strong results out of it or they just want it to do more than it's able to do with the implementation that they have and so we'll actually come in and either redesign an implementation or do something new um and get very very strong results
04:30 - 05:00 uh with the new implementation so we do a lot of unstructured data to structured data which is obviously an incredibly large bucket but document processing a lot of web scraping um anything that can be kind of data mapped so one structure to another we think in this world proprietary data structures are as valuable as the proprietary data itself when you think about like how you put things together we do a lot of that um just honestly the technology is so far ahead of everyone using the technology
05:00 - 05:30 that I think it's going to be years and years before people are actually like caught up to the usage of it which um I get the propriety proprietary data side but what do you mean by proprietary data structure and why do you think that is so important so that's interesting I think like there's so much data out there and there's so much data available and the Advent of all the llms like all the data has been now processed by the llms and the llm can generate data too as much as you need it and so the structuring is interesting because
05:30 - 06:00 like as you think about software products and as you think about value within it like something being structured either relationally or however else you want to structure has inherent value to it and so if you put the thought process behind what should that structure be and then you can access the data from wherever it is that becomes very very valuable if that makes sense sort of I I would love an example of those like what do you mean by that like if you think about so every a API
06:00 - 06:30 right every company that possibly has an API is a certain structure and you think about like lately a lot of software that's built is integration so you're integrating one thing to another and that kind of API wraps the proprietary data structure in the first place if you're then trying to like plug into multiple Integrations there's a mapping that you would maybe do that's like this is a common data structure between these Integrations that becomes valuable too because then you can kind of like pipe things to other things much easier sure
06:30 - 07:00 sure sure yeah and so like as you think about a company that's like leveraging data to make money and whatever sort of way and maybe they're taking they're inputting the data from all these third party sources doing that mapping into their structure is almost as important as the data sure yeah I mean it's the normalization process to like actually make exactly it's normalization at Essence but normalization is so much easier with LMS yeah yeah yeah yeah for sure okay so you started Head Start um two years ago what did the team look
07:00 - 07:30 like now and or what did the team look like then and what's it look like now yeah that's a great question so it was just me for a year and a half I was able how did you find how' you find clients like how did you get your book um all inbound referrals so it was all people that were like I either need the software help or AI help part of the reason that the book was so broad was because I had no restrictions on technology because I could do anything with AI so it wasn't like oh I'm just a python engineer or I'm just a JavaScript engineer I can only take those projects
07:30 - 08:00 I could take anything because I could literally do anything because I was coding it anyway so that helped make it like very very broad um in terms of what projects I could take on and then I was able to scale just me without hiring anyone because of the AI because I wasn't writing the code the AI is writing all the code so all of a sudden instead of doing like one project you can do five projects at the same time and you're like okay I don't really need employees if I have ai um obviously
08:00 - 08:30 we've shifted away from that and we have employees now but that's the still like the general way that we want to scale the company is in a very very AI native way um which kind of goes against the way that you would think about like a traditional company of just grow aead count to grow the size totally well Nicole I don't think you're giving yourself as much credit as you should be because there are bajillions of people sitting in the rooms messing around on cursor like trying to build stuff but yet you've actually built a company and you actually have real clients and so
08:30 - 09:00 what is your unique advantage or like how would you assess your skill set on top of just AI coding for you like what else do you bring to the table for that yeah that's really interesting so I I don't think that what we do is like what we're doing with the AI is not what's proprietary about the business part of the reason I'm happy to like hop on the phone and show everyone exactly how we're doing things is I don't think that that is exactly the mode that we have in the business it's incredibly powerful everyone should be doing it it's
09:00 - 09:30 incredibly valuable for our clients and you get a huge return on it but I don't think it's like the most important thing for the business ultimately it's like communication how you get things done the whole thing is hard work and I think that's probably the biggest blocker is like even coding with AI to get projects done it's hard work it's communication it's figuring out how the product should work it's getting into the nitty-gritty details like the AI does a lot but ultimately so far it only does the coding which can reduce your time
09:30 - 10:00 significantly but you still have to do everything else well so what what's in that everything else bucket that you think is most important I'd say product is a huge part of it right like how should it work and being a thought partner on that part of the reason clients like to work with us is because we actually help them figure out how the product should work versus just implementing it exactly how they want it like to work so a lot of clients you know if you just know what you want you can go to any developer and get them to build it for you but we do more of the thought partnership and like well how
10:00 - 10:30 should it actually work what is the best thing to do and like what different things can we build around that and then the architecting of it for scalability like that's the other piece is like sure with AI doing all the coding we can focus on the architecture of the solution and making sure it's the right solution that scales and everything's like just well done and in components with the design system and everything that you possibly need because the AI is going to do it all that way anyway sure
10:30 - 11:00 I think that's the other benefit is you actually get all these other pieces But ultimately it's kind of communication and delivering the end result and a lot of that is working with a client to figure out what they actually want yeah yeah yeah which is the hard part A lot of the time um so you have three people on the team right now right yes three people a co-founder an engineer and a second engineer starting week after Thanksgiving nice congratulations thank you so soon to be four people and so uh two Engineers or I guess what do their
11:00 - 11:30 skill sets look like so what what skill set are you doing versus your co-founder versus the two Engineers yeah that's a really good question because we're also hiring and we're looking for a very different skill set than is like a traditional engineer skill set that you would look for uh between me and my co-founder who's also my brother uh I'm the technical one he's the non-technical and so we honestly like have very complimentary skill sets which is amazing for the business I do all the technical and a little bit of the sales in client coms he also does sales and he does all the oper AAL so he does all the
11:30 - 12:00 internal operations he does a lot of product work too so product scoping is a huge part of our business because we charge flat fee and so we have to get all the scope up front in order to do that he does that on the engineering front we look for product Engineers so we look for product Builders we look communication is such a massive thing I think communication is always important as an engineer but lately when we're looking to hire we're actually looking for written communication skills as well as verbal communication skills because
12:00 - 12:30 when you're using the AI to build you actually have to build it use language incredibly well to get the output that you're looking for and so we need people that can like look at code and understand whether or not it's good but they actually don't need the skill set to write the good code sure sure yeah it totally does all right so four people um how many clients right or like what does the what does the portfolio look like yeah so we typically take on like four to five at once um is our kind of typical workload
12:30 - 13:00 but that's growing as we grow engineers and the size and kind of time line of the clients differs dramatically so we'll typically do like a pilot project with a client that's anywhere from two weeks to two months and then the client takes the results of that product they go and user test it they use it for whatever they would like to if it's an internal product like their entire team's using it and they figure out what more that they want in that off time we're working on other clients then they'll come back for another project so
13:00 - 13:30 we kind of have clients coming in and out so it's not um contractually recurring Revenue but it's reoccurring Revenue because they're Ty having a good experience and they want to then build more um our best client is someone that's like I want to build everything and slowly break off chunks and build it all for them yeah which is really fun but we're figuring out exactly what is kind of a client per engineer thing look like and how to scale that typically I'm still doing all the client coms which is
13:30 - 14:00 really interesting and we are keeping the engineers more on product work and building out internal tooling to support us handling more work at once sure sure sure how long is the average client engagement that you have average maybe like four to six weeks uh they're typically short we um deliver quickly and so we kind of charge a premium in order to guarantee a fast delivery and so we work with people that
14:00 - 14:30 want something done yesterday essentially and they know exactly what they want and they're ready that's awesome that's so nice as quickly as possible okay we are a bootstrap business and um it's been really really incredible to hire out a team of us-based like highly paid Engineers just you're all local to New York right yeah local to New York um it's been really incredible to sometimes I don't even believe it you're like I'm running a real business now
14:30 - 15:00 I'm running a real business from honestly like creating the uh LLC all the way to now we're looking at a bigger office and more employees so that's wild more yeah how many people are you looking to hire um so we are looking to hire five more uh for January and then five additional for May um we have a lot of plan demand so no joke so you're going to go from three right now almost four all the way up to 14 in seven months yeah so really really crazy um
15:00 - 15:30 we're being very like particular about hiring just in the skill set that we're looking for because we're doing such AI native things it's a little bit different so we are honestly still figuring out and defining what that exact hiring profile looks like as um we get into it we're very very interested for May in new grads so people with computer science backgrounds um who are actually not trained in the way of coding so that we can train them in our
15:30 - 16:00 way of coding nice yeah that's very cool um random question but out of curiosity how do you change your language when you're talking about AI with clients versus like technical folks like what is the client tongue you put on to make it make sense to them um I talk very technical uh sometimes my brother has to like pull me out of it because it gets a little bit too much in the weeds if I'm getting like really into it but um we we are very technical with our clients I think ultimately everything does have an
16:00 - 16:30 explanation and like people are curious about it and we're we're kind of like willing to explain as much as anyone's willing to listen and hear about uh we find that our most engaged clients actually really appreciate it because they even if they're not Engineers themselves are like interested in learning about how the technology can work and how you can use it and in order to really understand that understanding some of like the Baseline of it is helpful totally and where do you like to play with regards to um minimum contract
16:30 - 17:00 size like what what what what starts the conversation with you yeah so it's hey so after the interview Nicole said hey maybe we don't actually want to share the exact number of their minimum contract value because it changes all the time and no no reason to stake it in the ground but what they did say is that they're charging mid six figures on short-term contracts that's hundreds of thousands of dollars which is very cool to hear and they're also planning on 10 Xing their revenue in 2025 as they get more Enterprise contracts which I just think is absolutely amazing so let's go back to Nicole and hear more about Head
17:00 - 17:30 Start we had to raise it due to demand when I first started the business um the first flat fee I did I think was 10K and so we just started raising it um according to demand and what's cool about that too is like we get a lot done for that amount like we're getting so much more done for that amount than I think anyone else would what do you mean so what do you mean so much more done like we'll deliver like an entire application build in like um you know from scratch in four weeks like fully functional completely ready production
17:30 - 18:00 ready MP and so one thing that really stood out to me when we were meeting in New York you're like Greg the more projects we do the the quicker each next project gets because we learn from the last one and we have templates and so how does that work with regards to it's almost like your or uh institutional knowledge that you're building up that makes the next one quicker so what does that look like yeah so uh we have a Wiki in GitHub and we're trying to build that out um I'm sure you've heard this with llms is like garbage in garbage out and so if you're putting like shitty stuff into it you're not going to get a good
18:00 - 18:30 output but if you put really good input into it you're going to get great output and so we're doing similar things over and over again I'm usually using using the llm to generate the first version of what we're doing anyway um so I have it generate a Wiki alongside that as I go through its instructions I'm actually fixing it so you're almost like kind of fine-tuning it yourself when you think about like okay that wasn't the exact output I wanted but this is save it into our internal Wiki and then we have it next time that we need to do it to either used from our Wiki but better yet
18:30 - 19:00 put it into the LM and say this is how we know to do it and we need to do this thing on top of it so it's very Network effective of like the more work that we get the more we can build out the internal knowledge the more the entire team has access to the internal knowledge the more that we can feed that internal knowledge into the LM and get even better results out okay wait so I this sounds super interesting I I got to dig into this one so it sounds like and I want to speak super tactically here so that we can get like to the the Crux of it so you have a GitHub Wiki and we're
19:00 - 19:30 literally talking about a text file that says here are the conventions and the technologies that we like to use and then the style and Order of them and as you do more projects that gets more and more refined because like as you said it's almost like fine-tuning but it's not because it's more prompting more than anything then for the next project you'll copy that Wiki and basically give that as context to the llm right yeah yeah so it's in all markdown files essentially and then we use cloud projects for everything so basically whatever relevant markdown is relevant
19:30 - 20:00 to the new project you just pop that one in alongside the code and then it has that context to go off I want to keep on going down this but why Claud projects over cursor oh I so I love CLA projects I love anthropic and Claud in general like I they should pay me they don't I think it's like the greatest thing in the world um I did a case study with them and they have a quote from me in the article saying like anyone that doesn't use it as dumb or something like that like so great um I I don't know exactly what cursor is doing on the back
20:00 - 20:30 end and stuff I don't know what their system prompts are I don't know enough about it to know exactly when they're including the context into the prompts I know you do the command K from the file we do use cursor I have very Niche use cases that I use curser for that like my Engineers laugh at me um Cloud projects what I like about it is it's so explicitly contained so you create a new project sure you write the description you write you put in whatever you want in to the content and so I know for
20:30 - 21:00 every prompt that's going into the project exactly what context is available and I think part of the problem with cursor is like I actually don't know depending on where I'm prompting in the tool what context it's pulling from or if it's the right context and I was talking about this with Engineers the other day because people always say oh well like if it's a really big code base like how do you fit it in and we work in a lot of really big code bases we work with our Enterprise clients and and the thing about it is like as an engineer you're not looking
21:00 - 21:30 through every file in a really big code base when you're going to change code like you couldn't like you never would and so whatever files that you go look for in the file structure to then figure out what you're going to code next whether it's a new feature you're going to edit something those same files you include in the cloud project and then LM can do everything so basically you're doing a lot of copying and pasting too I do a lot of copying and pasting literally like my entire work just like copy P copy P copy P yeah yeah yeah yeah
21:30 - 22:00 yeah for sure okay so that's on that side um you know I have a sample of those system instructions do can we check those out yeah so we can I have a test project pulled up that we can kind of like look at how we're doing it and I'll talk more about a couple other things with CLA projects so yeah so this is our projects at Head Start um you can see there's your projects and then there's all projects what I love love love about Cloud projects is that um you can share them amongst your team so we pay obviously for an Enterprise account
22:00 - 22:30 you get full Enterprise security baked in so you don't really have to worry about like the fact that we do upload code into here um I have this AI interview with Greg project so what I've done here is I have pre-up loaded an entire codebase uh that is a test code base that we're working on on and is that the file tree structure in the name itself yes so this I do so that the AI understands where all the fil files are coming from a friend actually used the a I to write this script for me it's a
22:30 - 23:00 bash script that I just run in the root of my repository that says flatten the repository I can show you I think I had just done it in here yeah so it's literally like a flatten repost script and run it and then you can see um LS dot dot that I have this flattened one and so that just allows us like if you go to Claud you actually upload here um you can see how if it's not flattened
23:00 - 23:30 you can't like actually upload everything to go into here to flatten it and then the other nice thing is when it's flattened with the file path if you then ask Claude like hey I need to create a new view can you a new view let's say for settings for the user can you tell me where I should put the new files and then give me the content of
23:30 - 24:00 the files it should look like a standard settings screen I also typo like nonstop with AI it doesn't matter at all it's like my favorite so nice so don't have to have anything um be perfect and so this is kind of essentially what we do so we work with clients that have a ton of designs we work with clients that have no designs and so so if they have no designs like it'll literally just like
24:00 - 24:30 come up with everything itself and you can see here it gives you the file path so then what I do is I just go back into the repo see in chat um where did it want it chat settings page. TSX and so we have ID and instead we literally do settings and maybe like if people know how to use cursor to there create your files I would love to do that I don't
24:30 - 25:00 know how to do that so I'm always you can do it it's just kind of still the wild west because it takes so many Liberties and assumptions and just blast it all out that you can go deep down the rabbit hole it's tough to get yourself out of it sometimes really okay so yeah what I do then is I literally just copy this and I paste it there and I don't even really read the code all the time um I do uh when we're reviewing it for clients but when I'm just kind of like writing it like this I think this to rename it settings
25:00 - 25:30 nav and then you put that in there and I do like I'll check to make sure generally it looks right like no typescript errors nothing like that um okay so it says create the files I already did that and then it says that in the app sidebar I need to add this so we got to go into the app sidebar and see so I just like do this and so you don't have to write any of the code but you do have to figure out let's
25:30 - 26:00 see nav this one and this here and then you just have to make sure it gets import and then you should be good and let's see if that even worked H here it is settings um and then we just have to figure out yeah you gota put the proper page in there yeah oh yeah because it's under it's just the routing that it actually
26:00 - 26:30 got wrong cuz it said chat dot settings and then we didn't actually put it in the Chat Place correctly so see if we do this it should work and then we just have to hook up proba yeah so this is the settings page it came up with look at that that's so wild yeah it's crazy and then what's great is when you have figma designs essentially what I do is you just dump like here the five
26:30 - 27:00 figma screens that I need one by one do the screens and then I'll get like first screen over second screen over third screen over and then what I typically do is I'll take all of the code that it gave me so I'll go through the five screens here and I'll copy paste them back and I'll be like here's one screen I'm just going to give it these random screens um here's a second screen let's see it's we can be so sloppy with our code in llm it's wild and by sloppy I just mean like you're
27:00 - 27:30 not even giving context you're just giving the code yeah I just give it like this and I'll be like between all of these screens is there anything I should generalize out into components like something like that uhhuh and the screens I gave were kind of random so I don't even but like if I gave it the right screens part of I'm trying to describe this right without a good client code example which we can't share it's hard to
27:30 - 28:00 explain this but essentially like when you're making new screens you as an engineer know generally how the component should break out of those screens so like let's just say a friended example of some sort of form flow like very standard you have next buttons back buttons maybe you have a little dot navigation thing you have your various forms you have headers you have descriptions you kind of generally know how those should break out you have pigma designs that are attached that so you put in all the figment designs you
28:00 - 28:30 get all the code and then you just tell the LM I need this component this component this component this component and it just pops them out for you and artifacts which are so nice because you just get the full file copy paste like part the reason I don't like cursor is when like if I highlight something here and then I'm editing here it does it in line but it doesn't always like get everything you need and not everything is always like in the right place and then yeah the one thing I'll say on cursor too and you can see cursor when I scroll over or no I can see your cursor
28:30 - 29:00 right now yeah okay cool um the code output I find in cursor and my engineer finds us too so it's not just me but it is just the two of us I've been asking around to see if anyone else knows um the code output is better from Claude and I don't know if that is due to the system prompt in Claude which they do publish so I don't know if you guys you've looked into that at all but it's very haven't seen it they have ropic system prompt it's in the API
29:00 - 29:30 docs my God no it thinks I'm a robot um these are really interesting and so I imagine that CS may be using a different system prompt thing I'm surprised that cursor wouldn't be able to increase the performance like if it's literally just for code yeah and CLA I find that the code back from Claud is much better I also like very really use chat GPT to code anymore but
29:30 - 30:00 occasionally if I'm running into something very gnarly I will go into chat GPT and get it I find that claw artifacts like these like just being able to copy paste on the right is much easier what I do end up doing a ton is and this cursor has this too is like if it's a really long file like 500 lines or something it'll cut it down and be like you know same code as before here sure and I say like hey can you give me the entire file the entire file yeah yeah yeah yeah what I also find so the
30:00 - 30:30 long long files basically like there's certain things with coding that I think are going to stand the test of time with AI as it gets better and there's certain things that I think will go away I think the IDE as a concept I think I'm like questionable on like I personally do not like working out of the ID IDE in an AI driven world I go back and forth between it I'm still in it I get all the typescript stuff and like everything like that that's what I use curser for it like fixes all my type errors because
30:30 - 31:00 I'm like just a baby about typescript and like don't Works little things like that but the IDE is a pattern I'm not confident on small code files and things broken down into like proper component structures and like file structures being correct I'm actually bullish on I think that will persist I'm sure you've seen stuff with people where they're like I think you and I had spoken about this once where it's like does it need to be an English language anymore can it just cut down into characters to like shorten
31:00 - 31:30 Windows things like that can imagine different ways of coding what I think is interesting is I think English language is incredibly important or any language just spoken language human language not computer language um file structure is incredibly like structureal organizations very very important small components are important like building blocks puzzle pieces so it's interesting very cool you know it's funny to say that because I've heard an opposite opposite opin not an opposite opinion but um a counter opinion which is that
31:30 - 32:00 file structures are just human conventions and that machines don't really care about final constru file uh conventions so yeah I don't I don't know which one it's going to be no I could not agree more that file structures are just human conventions llms are human conventions they operate on human language and human convention and so what's fascinating about them is the better you are at human language and not machine language I think the better you are eing the and that's a very different Paradigm
32:00 - 32:30 than is traditional in engineering totally which is what I think is very very interesting it's really interesting because I do think one of the skill sets that I have which has made me able to do what I'm able to do is like the human kind of communication and writing ability to get what you want out of the LM and it's like very very interesting if that can evolve away because it is evolving more towards it for right
32:30 - 33:00 now which is fascinating yeah that's very cool um and another awesome thing too is you had a case study published by anthropic on behalf of some of the work you did with one of your clients what what what what was the story behind that one yeah so that was really cool um we're just top users of Claude and Claude projects uh we love it I think it's the greatest thing that has ever been developed if he took it away from me I would really struggle to run my business which is probably like the highest NPS you can get for a product um
33:00 - 33:30 and so they wanted to hear about how we were using it and potentially share that with more people and so it was really cool because I think we're at least using Claud projects in a pretty different way than a lot of their other case studies and so that was kind of cool to get um written up for that usage yeah they must have a data analyst looking at Claude project usage and being like dang nocole Head Start she's freaking crushing at least her team is yeah no it's um it was a really exciting feature for us for sure yeah that's very cool the question I wanted to ask what
33:30 - 34:00 other Pro moves are you doing besides the random of course we're using CL projects but like what other Pro moves with AI en coding are you doing I've been trying to explain it to other engineers and figure out what is different about how I'm using it one engineer told me he's like you trust it much much more than I would ever trust it and so we're I'm taking like sths of code from cloud projects when I'm working on client projects just like copying pasting over and everything's
34:00 - 34:30 broken down in your typical engineer way like we still have PRS we still review the code it still goes in in chunks It's Just Happening that much quicker because we're really relying on a lot of the generation to write it so I do think the trust level is there um when we we use it for everything so like I'm sure like when we write prompts for the AI implementation for our clients and then we're evaluating those prompts we have
34:30 - 35:00 our own evals product that we built we basically like take the prompt take the evals result so basically if it's not passing a certain test case we take all the reasoning behind that we feed it back into claw to regenerate The Prompt so we have like a prompt Loop in terms of generating all of that so you have not only your own eval tool which you built yourself but you also have your own uh like Claud like prompt optimizer um we do have that and then we also have
35:00 - 35:30 like an AI uh computer agent that we buil via Cloud computer use so you know the new functionality oh yeah yeah yeah that's cool you already built something on it yeah yeah um our engineer Tiff she built it in like two days and so we've been using that basically it'll create PRS for you um for changes to the code it's really good it's kind of like a junior engineer um and we've been running that on all our projects which is really really cool so it's been we've been able to build products in parallel with running the services and then leverage those products to be able to
35:30 - 36:00 then you know the services the computer use are you doing that on a VM or is that literally on her like laptop that she we run it on our own laptop so it's a script that runs right now um we're working on dockerizing it putting it on the server but there's actually a couple nice things about running it on our laptops it like uses the GitHub CLI so it'll actually like create a branch and do it from your own terminal which then what I end up doing with it right now is I tell it the change it creates the change it creates the pr and then you're already on the branch in your project
36:00 - 36:30 like in cursor and so from cursor then I test it myself like locally and so I can still do the testing we don't have the AI agent doing the testing yet because that's obviously like a little bit more complex yeah and then I can make the change either personally or I'll have the AI agent do it and so we have that kind of tight feedback loop within it and what's so cool is like we're dog fooding our own product yeah so we can immediately like just you know this
36:30 - 37:00 thing didn't work like it created an empty file and it got caught in a loop or whatever else but computer use is like very powerful yeah that's wild um I want to ask about tool stack it's always cool to hear which tools people have in in case there's one that I'm like isn't part of my routine yet so what's in your AI development stack and what are you using besides Cloud projects and besides cursor Cloud projects cursor uh chat gbt GitHub obviously huge fans of GitHub um we use we build a lot of like different
37:00 - 37:30 products for people so we use react native for mobile apps um we use any kind of full stack application we use versell a lot for new projects um I like using versel to spend things up um a lot of node on the back end or python on the back end is typically what we use we work within any client's existing codebase so we'll use whatever they're using across the board but when we're doing n new typically it's like reactor react native front ends typescript nodejs python backends
37:30 - 38:00 depending on what we're building microservices we're typically using python 4 um different databases stuff like that um yeah that's kind of we we keep light like we just paying for claw cursor open Ai and then like the usual business stuff slack yeah Etc what um that doesn't sound like a lot what are your what are your margins for your business oh we just I mean we pay engineer salaries plus like $60 a
38:00 - 38:30 month per employee for like all the AI like nothing basically all our clients pay for the um for their AI costs and everything so yeah and we have a little office in New York so those are basically the costs for the business um I still think it's insane that through open Ai and inop you can get this tech for 20 bucks a month or 30 bucks a month whatever it is absolutely wild and that yeah that's crazy because like you're not even paying the API costs for your Cloud projects right you're justay the business Enterprise outside we pay the API cost for our own test projects so
38:30 - 39:00 that's the but honestly it's pretty much driven to zero like we do a lot of quoting for our clients on how much the AI will cost them and it's so like it's like point 0000000000 two then multiplied out like it's hard to get numbers up there yeah yeah yeah for sure for sure um so one thing that's come across in this interview is you have a lot of technical confidence which is very cool it's like any project just brings it on and we'll go do it um what types of technical
39:00 - 39:30 projects were you steer away from like which one are you saying no to that clients bring to you um like below the minimum fee basically uh we're we're not afraid of anything we try to take on the hardest projects that we can we believe and it's written down on like our strategy board that access to hard problems is like a proprietary business value for us um so we think that having that access is incredibly powerful and we definitely Don't Stray away from it sometimes it is scary because we have to do really hard
39:30 - 40:00 projects that we don't necessarily know how to do in the onset but we are able to figure it out and have that confidence but yeah typically like we will do anything that people think is impossible and we like to do that um one CU we can charge for it which you know as we should if people think it's impossible but also because we learn from it and we think it makes our business more powerful that's wild and the clients are happy because we're doing essentially be impossible for them yeah what else is on that whiteboard of
40:00 - 40:30 values that your company has oh yeah so we're very values driven uh company values are Simplicity patience and compassion I pulled them from the Dow day Jen the Steven Mitchell translation um it's one of my favorite books and so that's kind of like the core values but in terms of like access to like propri hard problems being proprietary like that's where we think about like our Moes like what is more of like the power for the business and it's not how I use CLA even though how I use CLA is like driving the business forward it's very
40:30 - 41:00 much kind of like you know doing good work for our clients is number one um client NPS is the thing that we care about the most our clients understanding the type of work that we can do um and the quality of work we can deliver for them is number one uh most importantly efficiency of the business so we track revenue for per employee that's really important as we grow the business uh it sounds like that is just insane right now it's high right now yeah and we figure out how to continue to scale but basically because we're a Services business and we're also investing in a
41:00 - 41:30 product that doesn't scale like every other business it's more jumpy because as we take time to build product we'd have less time for services so we're figuring out how to like even that out a little bit as we grow um but yeah we think communication client NPS um access to hard problems being able to solve hard problems um being able to do things in a repeatable way creating Network effects within the business and like uh good data like good data in terms of like how we use the
41:30 - 42:00 llms good code is kind of proprietary and so yeah we think about it a little bit differently than like how we use llms as prior proprietary sure um what about businesses that are two to three years ahead of where you are right now or that you want to be or two to three steps ahead what do those businesses look like I mean I think this is just going to make me sound like a crazy fan girl but I think enic the greatest business out there right now um I think what they're doing is incredible and like the product itself that clot is is
42:00 - 42:30 really really powerful no I mean like where do where do you want to go though like where do you want to take your business like in two to three years oh that's a great question um we're trying to grow in an AI native way so we want to continue to hire but not hire uh to the scale of the client work we want to hire and then train up a team that can then do that work exponentially we think that the progress of AI is inevitable and we're building into that uh inevitability as a company and so
42:30 - 43:00 whether or not the AI will be able to do this work completely next week or whether that will happen in two years to me it's inevitable even if it's 10 years and so whatever that timeline is we're kind of just making sure that we are the best at using the AI and we're best at implementing the AI in the business and if we do those things I think the business will continue to grow yeah well what about products or SAS is that in your future so the way we're thinking
43:00 - 43:30 about products right now as we have them internal products that we've built um we are going to continue to build those and we want to be the users of our products first and foremost we're not trying to build products for other people right now um we're trying to build products that make ours us more efficient across the board if we can do that and we've productized that in a really powerful way and we feel like maybe selling it it's an option but it's not priority cool beautiful Nicole that was fabulous thank you very much for joining us today
43:30 - 44:00 thank you Greg it's always great chatting with you