Things Have Changed
Things Have Changed
Is Prompting The New Programming? with Filip Kozera and Robert Chandler, Wordware AI
Bringing Coding to the Masses with AI.
Can programming become accessible to all? This week on Things Have Changed podcast we have Filip C. Kozera (CEO) and Robert Chandler (CTO) from Wordware AI, a startup that's simplifying app creation and making it more user-friendly for both experienced programmers and complete beginners!
AI is the hottest topic in tech right now, and for good reason. Filip and Robert break down how they see the software landscape changing – moving away from traditional coding toward simply using plain English instructions with advanced technology.
Imagine you have the next million-dollar app idea – and you're going to build it yourself! But instead of writing complex code, you're simply using...English. That's the vision driving the Wordware team, who are developing these impressive capabilities in a seamless, easy-to-use interface.
Today's episode dives into topics like the rise of "prompt engineering" as a vital new skill, the wide-ranging impacts of easily accessible AI development, and a future where creativity and clear communication are more valued than traditional programming languages. Don't miss this fascinating look into making software development more intuitive and user-friendly!
00:00 The Dawn of AI in Software Development
00:46 The Evolution of Programming: From Code to English
01:50 The Impact of AI on the Software Industry
05:02 Exploring the Future of AI and Software with WordWare
05:18 The Challenges and Innovations in AI Development
07:48 WordWare: Revolutionizing Programming with AI
09:33 The Expanding Role of AI in Various Industries
10:02 The Future of Work: AI Engineers and Domain Experts
12:31 Unlocking Creativity and Efficiency with AI
18:49 WordWare: A New Paradigm for AI Development
25:05 Empowering the Next Generation of Developers and Creatives
30:09 WordWare's Vision: Making AI Accessible to All
36:26 Closing Thoughts and Opportunities with WordWare
Imagine you've got the next million dollar app idea. And you're going to build it yourself. But instead of writing lines of code, You're simply using English.
Filip Kozera:So I think we are inherently working with something which is non deterministic, which brings a lot of difficulty,
Jed Tabernero:that's Filip CKozera, EO of Wordware and he's describing the beauty. And the difficulty of AI.
Filip Kozera:We believe that AI will bring more engineers, not less. And the definition of the word engineer will change to somebody who is extremely accurate and using English and knows how to communicate with these monsters we call LLMs
Jed Tabernero:The shift is central to how new technologies are being integrated into the software development process, making it more intuitive and accessible.
Robert Chandler:actually working with LLMs has made working with AI incredibly easy in comparison. Prior to LLMs, if you wanted to, automate some process, You had to gather a data set of millions of items. You had to get people to annotate them. You then had to train a specific model for one specific task, which would require a team of highly skilled machine learning engineers.
Jed Tabernero:And that's Robert Chandler, CTO of Wordware. This evolution is critical as it signifies a move towards more efficient and collaborative development environments where AI and human creativity converge
Robert Chandler:So at its core, it's a new programming language that is prompt first.
Tools like word where simplify the creation of AI driven applications, enabling developers and non-developers alike to transform ideas into reality without the traditional barriers of coding. Today on things have changed podcast. We're learning how prompting is the new programming with Robert and Phillip from ward where. Uh, YC company.
Shikher Bhandary:In 2011, Marc Andreessen, who's this renowned venture capitalist, founder of Netscape and the most famous VC firm in the world, A16C, so Andreessen Horowitz, wrote an article, Software Eating the World. The gist is how software will touch every aspect of life. And fast forward, what, 13 years, It has everything that we take in when it comes to information, when it comes to media, when it comes to just daily life, large aspects of software in it. Including the traditional industries. We've been in that phase for a while and it feels like we are now getting into that next phase.
Jed Tabernero:So just as you said, the Mark Andreessen article that was posted in what, 2011? In 2019, somebody who has been quite consequential in this space as well, posted something else.
So Jensen Huang, CEO of Nvidia. And he posts.
Jed Tabernero:yes, software is eating the world, but AI is going to eat software, right? So when Shikhar talks about this kind of next phase, we're looking at so many things that happened in the last, I don't know, two years that has involved AI, right? Like people have never heard of AI and its practical applications are now hearing about chat GPT. People are using chat GPT for marketing efforts. Gemini has come out, there's so many things going on in the AI space that may be replacing some really key software development is one of those spaces. That's A black hole for folks who don't really understand what's going on in the industry, right? Luckily for Shaker and I, we have a ton of friends who are SDEs who are very familiar with this space. And so learning about how AI is transforming that space has been super interesting, right? So as AI begins to eat software, enhancing and accelerating certain development processes, there's tools that facilitate that. This integration becoming very crucial enter word, where AI, right? A platform that's designed to streamline and simplify the creation of AI driven applications, which is becoming quite popular these days, right? The removing kind of the complexities of traditional programming. Allowing certain domain experts to streamline and simplify the creation of these applications. Today, we're super lucky on things have changed podcast to have Philip Cozzera and Robert Chandler CEO and CTO of word where AI respectively welcome to things have changed podcast gentlemen.
Robert Chandler:Great to be here.
Filip Kozera:us.
Jed Tabernero:So we'll get right into it. This AI development space is quite, again, I was mentioning quite a black hole to some people who aren't in the space. What do you think's the hardest thing about developing, AI driven applications?
Filip Kozera:Yeah. So I think we are inherently working with something which is non deterministic, which brings a lot of difficulty, we had to rethink how programming language will change once we take English as the core of it. And this is what lies in the heart of our product is rethinking. How can we work with something which is inherently non deterministic and, people talk about hallucinations and, there are things which are good about the non deterministic nature. Like hallucinations actually, in my vocabulary, it's almost equal to creativity. And working with these things which change and shift and we can no longer can do a very strict if statement. That's definitely very difficult for people to grasp and, sometimes changing one word is enough to change the whole output of a large language model.
Robert Chandler:Probably the other thing to say is that actually working with LLMs has made working with AI incredibly easy in comparison. So prior to LLMs, if you wanted to, automate some process, You had to gather a data set of millions of items. You had to get people to annotate them. So it's give you the correct output given some set of kind of noisy inputs. That would take, months in itself. You then had to train a specific model for one specific task, which would require a team of quite like highly skilled machine learning engineers. And then finally you get this model and then you'd have to keep it up to date and keep it in production. And so it only meant that like most high value, most frequently done tasks could be automated by big teams. With LLMs that's changed. Suddenly you've got these foundation models that are capable of actually reasoning, um, and given almost human level instructions, they can perform arbitrary complex tasks. There are limitations and there's and like fighting those limitations is one of the biggest challenges of working with them. But it is incredibly easy compared to how it used to be.
Shikher Bhandary:Y'all have been in the AI space for a while 2015, 2016, where this was still novel and compared to today where everyone's on AI right now, what did you see or experience in your work that led to this product, the solution?
Robert Chandler:Yeah, probably the biggest thing for probably the listeners that don't yet know what WordWare does. So at its core, it's a new programming language that is prompt first. And it's Exposed in a collaborative web hosted ID. And that's a lot of words, but effectively what it means is you get a place that looks like notion where actually instead of creating documents, you're creating programs, but these programs are sequences of props and. A sequence of prompts is actually what's underlying everything you see that's called an agent or everything that you see that's called an AI application. They're all just chains of prompts that call other prompts, that do some self reflection, whatever it might be but it's natural language. And so what WordWire does to help solve this non determinism problem, solve this randomness problem, Is give people the ability to iterate faster and faster. So if you're changing prompts in the code base and you have to spin up some new instance and trigger that, and then show the outputs to someone who's maybe more knowledgeable in the field that you're trying to automate, which is often the PM in, if it's a company, but it could be like a lawyer or a marketeer or a salesperson for those kinds of industries by putting them in the driving seat and getting them to iterate. That speeds up the development process infinitely. And so they're able to get a feeling of like, when is the model. Outputting things in a space that I don't like when it's going off track, being too noisy and they understand which words to change in their prompt to, to make that better language model it's inherently random you're able to guide it to a much better set of outputs on a broader set of inputs. And so it's just having that human in the loop in the development process and being able to do hundreds, thousands of iterations. Even like a thousand iterations a day that leads to like better quality LLM applications.
Jed Tabernero:You mentioned marketeers, salespeople and whatnot. Are they. The audience I'm sure covers a broad stroke of, software developers as well. But when you talk about these marketeers and these salespeople, do you think that the goal is someday they could fully utilize WordWare by prompt engineering? Or do you always think there's going to be an element there of ML expertise that they need to go, go learn?
Filip Kozera:Yeah, so maybe I'll take that one. Essentially, I think we have a paradigm shift. And what we are at the core of the problem that we are solving is we believe that AI will bring more engineers, not less. And it just the definition of the word engineer will change a little bit to somebody who is extremely accurate and using English and knows how to communicate with these monsters we call LLMs, because the way that they actually work, we don't get it. So often I imagine this kind of multi tentacle monster that's holding two puppets, which are looking like human. And this monster is playing with these puppets, so these people need to understand these monsters. And the, by the way, the puppets come from reinforcement lending from human feedback. This monster learned how to imitate humans and how to talk to us. And in order to actually understand the whole monster. I think there will be engineers who are using English as kind of their main programming language in tools like WordWare. And we're already seeing the paradigm shift. We're already seeing people who are called AI engineers. And these people will work with domain experts in order to create actually fully functioning AI agents. So it's, I think there's going to be people who understand both. Things they understand both sales and the kind of that monster or they will be collaborating with kind of, sales specialist. I can change a couple prompts. And we'll have a engineer who maybe helps with that. Exact styling of the prompt or how this retrieval get into the whole chains that we're building in programs like word where So overall, I think, we're seeing from enterprises. They are starting prompting teams. And yeah. So one of the yeah, one of our clients, actually a big enterprise 7000 people. They are starting a 100 to 150 people team that will specialize in LLM interaction. The way that it's going to pan out in the future, I think it's hard to say right now. I think these people need to be somewhat technical, but they don't need to be coding in Python, they just need to be very good at communicating their concepts clearly, and that's difficult. Because our minds are scattered and, I think even on a podcast like this, when you have a question, answering it immediately in English in a precise manner is difficult. And it will always be. Communicating between our limbs and humans will always be difficult.
Shikher Bhandary:One of the core visions that stands out from your website is prompting is the new programming. So it's fascinating that you're already seeing that coming into this call. I thought, yeah, I can see it. I put these prompts and I get these results. But you're saying that now, if you just take out software engineer, you just take out the programming aspect from it, they are still solving problems and you can probably solve problems through the prompts. Is that the idea?
Filip Kozera:Yeah, I would say I would go a little bit further and currently everyone understands prompting as just a singular piece of text above a generation and I would expand it to say actual future and the equivalent of what you've spoken at the beginning of like software is eating the world right now it's going to be agents are eating the world and maybe I'll just Do a quick explanation of what agents are just for the rest of the context of this because they are at the heart of a kind of AI revolution. So far, we've understood AI as something that retrieves knowledge either from the weights of its network that's been trained or from some document we're using where we're putting that document into vector database. Which is a more complex database that can approximately know the meaning of each document without looking into the whole reading all of it and retrieving knowledge. So those are the use cases we're getting right now. Chatbots that can chat with you and give you, good answers based on some body of text or the weight of the network. What's AI agents. So AI agents are actually capable of completing more complex tasks. So I'm going to give you an example around essay writing, even though the whole of AI agents can actually do a lot more. They can actually, be like co pilots for every part of our world and our lives. So if we, Tell an an LLM right now, write an essay. It's like me sitting you in front of a computer, putting a gun to your face and saying, Hey, you now need to write an essay about the Roman empire. You cannot Google, you cannot use any tools. You cannot plan it before you cannot talk to your friends and you cannot reflect, which means you cannot use delete backspace. And I'm putting this gun to your head and I'm saying right now, and the cadence of each word should be the same. And I think that essay would be pretty shit and it's incredible how well LLMs do at that. But the most important part right now and what we are introducing as the core of WordWare is an ability to create these agents. And what are AI agents? I think they consist of basically four scales. So reflection in this case, it's like using backspace and actually being able to go back if you wrote something shitty planning. So before you write an essay, you actually are allowed to sit down and, make sure that the whole thing works, that you can do research in particular things. The third one is tool use. So you can use Google and you can use maybe Grammarly for your for how, grammatically, but you're writing. And the last one is multi agent collaboration and multi agent collaboration is the kind of more complex one, and it essentially means you can. Talk to people who are better at research and better at Googling and better at grammar and style. And those are more specialized agents. So I think this is where we see this kind of a gentle AI coming into play. And even though I mentioned essays, Right now it's becoming workflows and it's becoming, helping with people's work and making all of these decisions. Maybe not fully autonomously yet, but at least being able to plan them out in a specific manner.
Shikher Bhandary:I read that to make your answers a bit better and a bit more human, you got to add things like please help me. Thank you for this and stuff like that.
Filip Kozera:There's so many of these techniques. I think those with time will go away. Having the ability to just talk to it in a more natural voice. I think models will improve at that. So you don't have to say these kinds of things, but at the core of it, planning the whole flow of it being like, Hey, start with planning, you should think in this way, have a thought, think about an action and, then conduct the action, those kinds of logical, almost Almost almost like logic based steps on how to think will always persist. And, I'm actually looking right now at a prompting at 12. Oh, no, it's actually way more prompting techniques. So you've got zero shot prompting, few shot prompting, chain of thought prompting, self consistency, prompt chaining, tree of thought, active prompt, pro program, I did language models, multimodal CLT graph prompting it's right now it's a lot. And that's why we are saying that. It's a full time job, to know all of this
Jed Tabernero:Programming has been an abstraction, isn't it? It's just more of making things simpler to be able to speak to a computer and to give it commands, right? So we've created these language, these languages on top of that, these programming languages to make it easier and easier. If you think about it, Python is much easier than the first programming languages. I met somebody the other day that was telling me about, they would be coming out of these massive machines and punching holes into them or something like that. I forgot what language that
Robert Chandler:Punch card programming. It's literally like punch card programming. It's the yeah,
Jed Tabernero:that's insane. It's an abstraction. Every level we get better and we get better and we get better. And then when I first saw this headline on WordWare AI, I thought to myself, okay, that's another level, again, of abstraction of saying, look, we're getting closer and closer to where you just speak to a computer and it gives you what you want. Now, you're talking to me about this kind of agent, this agental AI concept that's coming up. I had never thought about this prior to this call, by the way. Is that somewhere that word where it's trying to get people to, where you're able to, give multiple tasks to this one, prompt and be able to bring them all together to get the output that we want. I think that's a really key difference here. And I think Shikhar, when we make the blog for this, I want to really focus on a gentle AI. We haven't covered that concept before, but it's just so novel for us, but yeah, I guess I'll pause there. How does that tie into kind of word work?
Robert Chandler:that, that is, is the biggest thing about WordWare. It's like giving people the ability to make these more agentic loops in their prompting. You can think when you use chat GPT. You can do the same thing Philip was talking about with the essay. You can give it time to plan, you can paste in extracts from Wikipedia, but you're there in the loop, going back and forth with it. Whereas if you distill that into a program, you're able to just click a button, put in the Roman Empire and get out a great essay a few minutes later. And we when we were explaining to non technical people, we think about when you're using chapter GPT and you're doing that back and forth, you're a wizard and you're casting spells and you're like going back and forth, waving your wand, seeing the results, putting in some new inputs muttering some new incantations. And then when you use word where you turn into a potion maker. And so you're able to distill that spell into just like a reusable thing, a potion that you can share with your friends, or you can drink, or you can pour on your data, whatever, however you want to take the analogy forwards. And it basically turns everyone into as powerful a prompt engineer as you are just by drinking the potion.
Filip Kozera:and to add to this, I think what you've mentioned is assuming generalized agents. It's like, how do you build these agents that can think, et cetera, et cetera. We are not there yet. Currently agents like auto GPT. Our gimmicks and they are cool demos where right now we're seeing real business value. It's these handcrafted agents. You are actually as a human and a domain expert guiding it firstly plan, you're giving it the time to plan and then say, Critique this plan and you might use a different model to critique this plan. So to, to, change it, then you say, Hey, you're have these kind of tools because of the plan, do research, research each one of them. And then we do a loop. So do you see like how. These kind of classical programming concepts, like looping and conditional statements and calling a function being like, this is a function for planning. It consists of all of these things that somebody in WordWare community has created and it works super well. And I would not be able to come up with the right prompts for all of this. And you doing this kind of. Potion as Robert said, but you're not saying to an agent being like, yeah, you can plan, reflect and use tools, go for it. You have to guide it in a very specific manner. And that's why we're super excited about GPT 5. And by the way, I think, you never want to get involved yourself with AI companies that are afraid of GPT 5 or six or seven. You want to get yourself involved with the people that are super excited about it. And that's why I we're so early still GPT 4. It's amazing, but for what we are using it for, which is not token generation, but a reasoning engine behind something which is trying to take human creativity and human productivity and put it together, that's going to be the next frontier. So for now it's very much humans behind the the recipe of the potion, but at some stage it might be other agents.
Shikher Bhandary:I do know there is a promise of AI touching every aspect of what we do in our lives, like how software ate the world. But for now coming back to like our first point, are you seeing more of that AI impact on the software industry first? And is it down to just the fact that it's already like the structure data? It's. Easier to see the impact right away on the software side. And that's how solutions like WordWare are being built.
Robert Chandler:Software engineers being domain experts on software. And so the reason we're seeing loads of software based applications is because software engineers know how to build software and then validate if the output of the AI that built the software is any good. The reason we're not seeing loads and loads of applications in legal tech, in medicine, in law, in marketing, in sales, we are seeing companies spinning up to do this, but it's not as thriving a community is because most lawyers and PMs and marketeers don't have the ability to like import lang chain into their Python environment and write a chain of prompts and, connect it to some kind of internal data store. That's just, I think living in the Bay area, you think everyone can code. If you leave the Bay area, you realize that it's not a skill that people learn as kids just from their parents, because most people's parents can't code. And that's literally what WordWare is there to unlock. It's there are so many creative people and we're seeing them in the community. They're not technical, but they are empowered by using wordware to build, not just toys. So there's a lot of low code tools that allow you to build like simple flows, very like you can chain one prompt to another prompt and then use Dali or something you can't go to anything actually useful there. Once you get the ability to have the same kind of concepts of programming, but in an interface that's familiar to like non technical people the creativity explosion is incredible. And so we're super excited to be this like GitHub, but for prompting like we're still super early days building out all these community features, but on our discord, there's people sharing prompts, like cloning each other's prompts, being like, Oh, what if we changed this line? And Oh, it worked great. Now, like then someone else takes that and improves it. And it's magical to see. And these are not like non technical people taking their world knowledge, domain knowledge, taking just experiments that they're excited about and building cool things.
Jed Tabernero:think there's absolutely an appetite for that. Because right now, all these creative people using AI is some form of interaction with a, consumer facing LLM, unlocking that creativity. When you talk about unlocking that creativity, we know there's groups of people who are willing to learn how to code, who are willing to learn how to do these like small things. And there's always going to be a limit, right? For example, like going to college, I also took a bunch of coding classes and that's the big one. It's not because I wanted to become a software engineer, to be honest with you, just a lot of people that I respected, encouraged me to take data structures just because they told me, look, the world of tomorrow, you're going to need to understand data structures and tools like this, I think will make that much easier for people like me. Who are going into the space who occasionally I have no, software development engineer in my job description, but occasionally I have to write some code. It's the world that we live in today. Tools, I think WordWare make that a little bit easier to take it to the next level. It's not just coding. No, it's not. If else in loops, are you finding there's a ton of use cases for people who are trying to get interested and learn more about your product on how to interact with it alone.
Filip Kozera:Yeah. And I think one of the problems that you haven't mentioned is that people, even if they have done a little bit of software engineering and their brain does work in this kind of more analytical way, and you can put a structure on it. They can't deal with. Things like deployment, they can't show, other people that they're the results of their work, which is really sucks. If you take a person who can do a little bit of Python, but then you tell them, Hey, spin it up on the website with a front end and put a bunch of LLMs together and they are just going to panic. And this is where kind of word where it comes in is we are building the first community of people where they can take their prompting and take their ideas. Create these these workflows or kind of little blocks of RLM interactions and share them with the community. So we have one click of a button. If you, first of all, if you are a CEO or an early stage startup, or you're trying to build a product with one click of a button, you get an API, which you can plug into the code, and this is for some people, this is their whole backend. So they spin up a front end, they put the API there, and because you can run code on WordWare. You can actually do everything on top of it, but there is another way where you just click share and that gives you a hosted application in a similar way to replicate. com, which allows you to publicly host your machine learning models and show them off to the world. Here you get to show off your kind of word apps or prompt cascades with tools, et cetera, to the rest of the world. And also, very importantly, you allow the community members to fork that and to duplicate that flow and to reuse it inside of their application. So I think you're very correct. Identify that there are people who are analytical and you've even probably wrote more code than we expect our users to be able to do. But yeah, we're giving it to all the domain experts. So it's not only going to be code gen right now, a farmer can write a program which will try to optimize how he should move the crop based on the weather and a bunch of factors that I have no clue about. Neither any software engineer has any clue about. So that's why we want to for these AI aficionados who are early on and they're intelligent analytical and know their domain, give them the right tool to be able to to take their knowledge and instill it and give it to LLM in the right manner.
Robert Chandler:It's a little bit like Excel in the, Excel is basically the most used programming language in the world. It's probably got the most developers. Building with it. And it's not a program language, but it is a program language. And the reason it's so popular is there's such a long tail of applications that there's no way startups or companies are going to build the thing for you, for everyone. You end up with a regression to the mean. On what companies are viable and it's, where is there enough value and like on a per usage basis and enough users multiply that together and you get a company that you can actually start and build. But there's a limited supply of software engineers. There's a limited supply of entrepreneurs. For now, once, once we get these AI engineers and software engineers, that might change. But yeah, you need, you basically need to put the tools in the hands of the people that know their problems, know what kind of thing they would build if they had the tool to build it, and then actually give them the tool and see what they build and, empower them.
Shikher Bhandary:So when you say Excel, Jed's eyes light up because when you, yeah, he's that guy, he's that guy, everything in his life from like grocery bills to stock modeling and performance of an asset is all on Excel. The first thing. And it's funny because the first time I met him, he was doing all these calculations of utility spend and stuff like that. And how we are trending over 12 months. And I'm like, dude, that's it's. Up by a dollar, like what are you doing trend
Robert Chandler:Dude,
Filip Kozera:you guys
Robert Chandler:get a girlfriend. It's
Shikher Bhandary:Yeah.
Jed Tabernero:We used to.
Filip Kozera:to. Okay. Okay. We live together. We live together with Robert as well. He's downstairs.
Jed Tabernero:You just do the same thing. It's important. Before you ask the question, Shikhar, I just want to point out, I just, It's, you mentioned Excel is a coding language, right? Py, there are about 8. 2 million people around the world who know how to code in Python, right? If you were to guess how many people use Excel in the world, just throw out a number. I just want to see what the perspective is here.
Robert Chandler:300 million, maybe?
Filip Kozera:200 million, something like that.
Jed Tabernero:There are 750 million people in the world who use Excel. That's
Filip Kozera:that's our target market. That's our target market.
Jed Tabernero:And those people are analytical. They're analytical, right? Inherently you have to use Excel to do a lot of the business worlds. I think in 10 years, people will think about Excel as, Oh, that's like some tool that, a lot of the older people use, I disagree. There's a lot of people who are still going to continue to use Excel because they can't get to that next level of abstraction, I think word where it can be that tool for folks who want to utilize LLMs, for example, anyway, sorry, sugar. I completely cut you
Shikher Bhandary:Yeah no. I was actually going there because I wanted to convey that I use Excel a certain way and Jed being, a financial dude he's used Excel in a different way and he loves using it that way. So I was just coming down to just wordware as well and how you've. Mentioned that you want to be like a notion style, collaborative environment. So, one thing that, all our THC research is on notion and we got those templates from the community and it has helped us, turn those notes into a product, which is this podcast and where we actually connect founders to VCs and capital and stuff. So it's so interesting how, You're going at it the same way where you are creating this community, the community shares a certain prompt and someone else can build a product based on that. automation or workflow.
Robert Chandler:Yeah, I think there's a number of reasons. And some of them are just that, like the connotations of notions are great. Everyone loves notion. It's definitely a benchmark for how to build great productivity software. The sort of more important reasons are, it's already familiar to people who are our target audience. So the PMs, know how to use Notion. And so if we can leverage the same affordances, then it will be more like the learning curve because it is, it's still a learning curve to use WordWare. Like it's a new paradigm. You're having to firstly learn how to do prompting and then how to use our tool. I think hopefully the how to use our tool part is easy and you just basically need to learn how to do prompting. And that's why we see people that have used like LangChain, they can move to WordWare and be like, Oh my God, this just makes things Way lower friction and way better. And then the people who are like, have not done any prompting or agent building before go still confusing, but I get the slash command and I get the how to use the tool. I just need to work out how do I prompt the other kind of why notion and why not something else. So there's you've probably seen a few low code tools that are flowchart based. And actually the very first iteration of word wear was built at a hackathon. Was flowchart based the problem you've reached with flowcharts is whilst they look very simple when you look at them at a high level blockiness, when it comes to actually like building something complex where you have these loops that are going background, you have branching, suddenly your simple looking workflow that was like a few blocks leading one to the next turns into this horrible mess of lines going all over the place.
Shikher Bhandary:Yeah.
Robert Chandler:It's actually way easier and way like the structure of software is that it's like documents and you have functions that call functions, which gives you effectively a third dimension rather than this kind of 2D grid of a flowchart based thing. And so that was really, it was, it came. Through seeing the deficiencies in the flowchart model that we were like what if it looks like something else? What if it looked more like programming and maybe the way to make that accessible is to make it look like Notion. And it was very much came out of a, almost like a design experiment. And then it was like, Oh wait this works. This is beautiful. Like it's I can't tell you how much I enjoy prompting in WordWare compared to writing prompts in the code base.
Jed Tabernero:Thinking through kind of your target audience, right? Like folks who are creative, who want to get into the technical stuff and learn just a little bit enough to build something beautiful. I'm thinking about it at an enterprise level where you mentioned, right? Like PMs really use Notion a lot. When I initially looked, I watched your walkthrough, Robert on creating just the chat bot or the first agent, I guess that just to me was like, okay, if I can work with them like this at an enterprise level where, okay, let's say we have some kind of LLM integration and I need to communicate with domain expertise as a software engineer. That actually makes it a lot easier for me to even communicate to them in English. This is exactly, I think what I'm doing. This is the structure that I need it to be. I think that'd be a huge unlock just for people who, don't want to think about the structure. Don't have to think about, certain things that, that software developers have to think about.
Filip Kozera:So you've mentioned kind of two different ways. So one is the community sharing templates where it's the same as notion, but actually in corporate setting, the dynamics a little different. So in because you can write some code and in word, where what these engineers are doing, they're providing you with these blocks and you don't need to understand how these blocks work, but they will tell you, Hey, this We figured out that for retrieval of this type of legal documents, we need to choose these hyper parameters and these vector database, and you don't need to understand any of it, but they're going to come to you and tell you, Hey, when you're working with legal documents, use this block. We called it retrieval argument retrieval for legal documents, and we've made it work perfectly. And then there will be different retrieval for internal documents. Just because how much of a much expelled retrieval augmented generation is still and so we see kind of engineers writing these blocks and you as a consultant or as a salesperson, being able to prompt and play around with it and see exactly what the retrieval has outputted and create these these well working word apps. And because you can see everything gets at the core of it. We are, what is what you get platform, which means that you can debug it extremely quickly. So those kind of, those, this is what we are seeing from the corporate world is that they are collaborating and it's not. Like the famous notion template, because the notion template would be like a fully working thing, but it's more these two people working together. And and it's a lot about consultants as well. So they go to the client and they listen to their problem and they are a little bit better at prompting. Cause I think consultants jobs are going to change from creating decks to creating. Proof of concepts right now. And I've got my own theory about why they are freaking out right now and why is it moving so quickly? So I can dive a little bit deeper into that, but they essentially booked a lot of revenue and in order to create good return on investment for all of these companies, they need to be doing something else than the PowerPoint deck,
Shikher Bhandary:Before we close, I want to give you the stage to shout out the work that you're doing, how people can reach you, hiring or funding, and just want to give you the stage to shout out your team because it looks like an incredible product and you guys are crushing it.
Filip Kozera:thank you so much. So for any listeners Of things have changed podcast. We've got 250 of credits on WordWare. So we, we discussed this, so just enjoy it. Just try all the different models go to WordWare. ai. It's like software, but with Word and just sign up for anyone right now the signups will be fully open and enjoy the product. As mentioned, anyone can build more complex AI agents. Make sure you go through the onboarding process. As we've mentioned, this is a new programming language. So it takes the 5 10 minutes. Don't expect to be able to jump in and start creating amazing agents. It is still somewhat complex to do these things. So that's one thing we are also actively hiring. I'll let Robert do the kind of foundation engineer job description, but whoever is very interested in the community about agents, just reach out to us at founders at wordware. ai. We have multiple roles open and it's actually quite in a very early stage startup. It's all kind of fluid and mixes together. But on the engineering note of hiring Robert will say a little bit more and lastly we are helping a bunch of enterprises. If there are any enterprise customers listening to us we're more than happy to give you free consultancy at the beginning and help you figure out how to deliver on that ROI and all that book revenue and for many other people build co pilots and workflows that will make your enterprise more efficient.
Robert Chandler:Yeah. I think founding engineer wise there's all sorts of incredible things to build. We're a full stack TypeScript stack. We love serverless. So we love doing things as efficiently as possible worrying about building the value add stuff, not the undifferentiated things that, everyone has to build. If you love building things, if you love making things, if you're excited about the world of agents hit me up on Twitter, Bertie underscore AI or email. Robert at WordWare. ai and tell me why you're excited to build on WordWare.
Jed Tabernero:Beautiful. Guys,
Shikher Bhandary:want to mention one thing. You guys stack team so well. Where Philip takes a certain section and then Robert takes the other section. Even on the message front. So
Robert Chandler:Years of practice. It's we've known each other for 10 years. It's.
Jed Tabernero:yeah, no, it's, it was really fun guys. So much in this world that we're really curious about, but really appreciate that.
The information and opinions expressed in this episode are for informational purposes only. And are not intended as financial investment or professional advice. Always consult with a qualified professional before making any decisions based on the concept provided. Neither the podcast, nor is creators are responsible for any actions taken as a result of listening to this episode.
