Meet the AI Framework That Turns LLMs Into Fully Transparent Machines

Summary

Lead With AI: “Meet the AI Framework That Turns LLMs Into Fully Transparent Machines”

Host: Dr. Tamara Nall
Guest: Scott, Creator of Convolang
Date: November 18, 2025

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Episode Overview

This episode dives deep into Convolang, a new framework created by Scott that demystifies how Large Language Models (LLMs) process and structure conversations. Dr. Tamara Nall leads an accessible yet technically rich conversation about how Convolang brings transparency, precision, and simplicity to AI-driven software development. The discussion covers Scott’s motivations, the practical workings and real-world applications of Convolang, ethics in AI, and what the future holds for both the framework and AI more broadly.

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Key Discussion Points & Insights

Scott’s Background & The Birth of Convolang

[01:56–04:24]

• Scott introduces himself as a lifelong builder, with nearly 25 years of software development experience, and a fascination for both code and woodworking.
• Origin of Convolang: It started as a personal library to reduce the repetitive, error-prone nature of prompt formatting for AI—and evolved into a universal tool for context engineering.
• Simplifying the Problem: Scott realized that managing the flow of prompts and the underlying context is a central challenge in AI app development:

  “If you’re not very clear with the way you define your prompts… they don’t give you the best output. So that was a lot of what Convolang was about… it gives you a really clean, plain text format to do really good context engineering.” — Scott [03:38]

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Making Context Engineering Understandable

[04:24–07:14]

• Demystifying ‘Context’ for Non-Techies:
  Dr. Nall asks for a layperson’s explanation:

  “So I basically can build an AI tool just based off, like, conversation?” — Dr. Tamara Nall [04:24]

• Scott explains that every LLM message builds a “context window”—a history of the interaction. Convolang optimizes and exposes how this context is managed and structured, allowing developers to maximize the effectiveness of AI models and avoid wasted capacity.

• User Interface: While Convolang itself works mostly in the background, Scott offers libraries and templates for developers to visualize and manage conversations, but emphasizes its developer-centric focus.

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Real-World Applications & “Wow” Moments

[07:14–09:45]

• Jaw-dropping User Stories:
  Scott recounts how a Syntax Data colleague used Convolang to prototype AI applications—and was amazed at having a transparent, traceable record of every LLM interaction.

  “Every interaction you have with the LLM, it gets stored in a local file that you can review… so it makes for a really good system to keep track of those interactions.” — Scott [07:26]

• Under the Hood of Convolang:
  The simplicity is intentional: conversations are plain text files, with minimal syntax—making it easy to audit, debug, and extend.

  “All the complicated features… just get written down in this simple text file that’s really easy for a person to read.” — Scott [09:26]

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Founder Milestones & Combo Make

[10:13–11:59]

• Breakthrough Moment: Scott describes using Combo Make (an extension tool) to automate a week’s worth of app development into a matter of hours, generating a full application—frontend, backend, even security rules—off a single well-specified document.
• Memorable moment:

  “The entire application, front end and back end, in one shot. I mean, there’s some small things… but the actual application functioned as it was supposed to… So I’d say that was the moment where I was like, huh, this… is pretty powerful.” — Scott [11:27]

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Ethical Perspective in AI Development

[12:29–13:25]

• Ethics and the Impact of AI:
  Scott candidly addresses how AI can displace jobs, emphasizing the need for thoughtful transition and seeing Convolang’s transparency as a contribution to more ethical AI development.

  “There are a lot of people that… won’t be needed. So there’s definitely… a need to have a transition plan for quite a lot of people.” — Scott [12:40]

  “It does help you to really understand what’s going on under the hood, versus a lot of tools that honestly try to kind of use user behavior as a way to train further models.” [13:07]

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The Future of Convolang

[13:51–14:59]

• Short Term: Integration with other deployment frameworks, and launching a web-based SaaS for app creation using Convolang and Combo Make—potentially enabling “production-ready app deployment in one click.”
• Long Term: Scott envisions Convolang becoming an invisible background enabler for all sorts of AI-powered applications.

  “Hopefully Convolang just becomes something that lives in the background that you don’t really ever know about, but it’s just quietly sitting back there.” — Scott [14:55]

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How to Get Started with Convolang

[15:12–16:13]

• Website: [learn-covo-lang-ai] (spelled out: “learn-dot-covo-hyphen-lang-dot-AI”)
• Interactive Examples: Website provides hands-on docs and demos for non-developers and coders alike.
• VS Code Extension: Direct development of Convolang projects with any major AI model.

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Notable Quotes, Insights, and Memorable Moments

• On Transparency:

  “A lot of the tools, AI tools that you use… you don’t have a good history of the conversation. Every interaction… gets stored in a local file that you can review.” — Scott [07:26]

• On AI’s Next Leap:

  “AI models that can do code interpretation internally… that’ll be a real paradigm shift.” — Scott [19:22]

• On Practical AI Coding:

  “Most overrated tech trend: ad hoc vibe coding. When you see someone say, ‘I wrote this application by a single prompt,’ and most likely it’s an application that’s already been written and the AI just remixed it for you.” — Scott [17:37]

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Bonus Rapid Fire — Quick Takes

[17:37–19:50]

• Most overrated tech trend: “Ad hoc vibe coding” [17:37]
• Most underrated trend: “World models and hardware specifically tuned for AI.” [18:01]
• Definition of World Models:

  “World models are trained on images, videos, text… so they can really be true multimodal.” — Scott [18:29]

• Book Recommendation: Scott is not a big reader! (“I just kind of pick up and try things.”) [18:54]
• Biggest, boldest AI prediction: “AI models that can do code interpretation internally.” [19:22]

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How to Connect & Community

• Website: [learn-covo-lang-ai]
• Reddit: User: iyio, Subreddit: r/covolang [20:11]
• LinkedIn: Occasional posting, but Reddit is preferred for community discussion.

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Conclusion

Dr. Tamara Nall wraps up the conversation with a nod to Scott's humility and practicality, recapitulating the transformative potential of Convolang for developers and the broader impact of transparent, extensible AI tools for everyone—from coders to the simply curious.

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Timestamps for Key Segments

| Time | Segment | | :------: | :--------------------------------------------------: | | 01:56 | Scott’s background and motivation | | 04:24 | Context window and Convolang for non-technical users | | 07:14 | Real-world “wow” stories and file structure | | 10:13 | Founder’s breakthrough moment with Combo Make | | 12:29 | Ethics and transparency in AI | | 13:51 | The future roadmap for Convolang | | 15:12 | How to get started; website and VS Code extension | | 17:37 | Rapid fire: Trends, predictions, book rec | | 20:11 | Community, contacts, and closing remarks |

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For those considering Convolang

• Visit: learn-covo-lang-ai
• Try Examples: Direct on the site for a hands-on feel.
• For Developers: Use the VS Code extension for fast prototyping and integration with any AI model.
• Join the Community: r/covolang on Reddit for support, discussion, and updates.

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Lead With AI continues to bring authentic, practical conversations about AI’s real-world, human-centric innovation—highlighting doers like Scott who build the tools transforming how we interact with technology.

Transcript

[Dr. Tamara Nall]

Today on Lead with AI, I'm talking with Scott, the creator of Convolang, a revolutionary framework that transforms conversations into code. It's where imagination meets precision, where every sentence can become software. If you've ever wondered how developers will build with AI not just for it, this is the episode for you.

Let's get into it. Welcome to lead with AI. I'm Dr. Tamara Nall. In each episode, we will take you behind the scenes with visionary leaders shaping the future of AI across public and private sectors. Join us as we explore groundbreaking projects and innovations that are transforming industries and making a real impact on. On people's lives. Let's dive in.

Hello, everyone. It is Dr. T, your host with Lee with AI and as always, I like to start with the good stuff. So, as you know, we hit number one in technology on Apple podcast, and then also we recently found out that we won a W3 award for our interviews and our discussions with Lee with AI But I have to say, it would not be possible without great guests like the one we have today. And I'm excited to get into it. Welcome, Scott. How are you?

[Scott]

I'm doing good. Thanks for having me, Tamara.

[Dr. Tamara Nall]

Absolutely, absolutely. So we're going to get into it because I. As soon as I researched and found out about Convo Lang, I was like, oh, my God, I got to get him on the show. So thanks for being here. So first, just kind of tell us a little bit about who you are and at your core, what's your passion and how did you even come up with the idea that Convolang was needed?

[Scott]

Yeah, I'd say at my core, I'm a creator, you know, happen to. Been doing development software for the last 20, close to 25 years. But in general, you know, I love to build things, you know, whether it's on the computer or, you know, I love to do woodworking, you know, love to work with my hands as well, you know, but.

[Dr. Tamara Nall]

Okay, I gotta ask, did you do any of that in the background? Because there's a lot of nice woodwork in your background.

[Scott]

Yeah, yeah, I did it all.

[Dr. Tamara Nall]

Wow, that's amazing. Now, where are you, by the way?

[Scott]

Just a little bit north of Cincinnati.

[Dr. Tamara Nall]

Okay. In Ohio. Yeah. Nice. Okay, so the wood. The wood is. Is. Is popping.

[Scott]

Thank you. Actually, about to sell this house, and I'm going to build a new one.

[Dr. Tamara Nall]

Oh, wow. And you're going to build it? Are you going to have somebody else build it?

[Scott]

Yeah, and I'm gonna build it now.

[Dr. Tamara Nall]

Oh, wow. That's amazing. Okay, I digress. But Keep going. So you love to build?

[Scott]

Yeah, yeah. So, yeah, like I said, I've always been making developer tools, so oftentimes I'll start on a project and just find a more efficient way to do things. So, obviously, as AI started to take over everything, I found there was just a lot of repetition. You know, a lot of it just came down to, like, basic things like formatting prompts, converting those prompts into, like, a structure that you could feed to the model. So that was, you know, kind of what sparked the idea of Combo Lang. And it originally just started out as kind of like a small library that I used in a lot of my projects. But then I realized as it started to progress, it really became a tool that, you know, kind of encompassed almost all the parts of developing an AI application. At that point, I took the time to kind of break out the code into a separate repository, do better documentation on convoling, and really started telling people about it. I found that most models, if you're not very clear with the way that you define your prompts and you're kind of vague with things, they don't give you the best output. So that was a lot of what convoling was about, was becoming a better context engineering tool. And I think that's the real key, is convoling itself. It's not an AI model. It's really just the format. It gives you a really clean, plain text format to do really good context engineering.

[Dr. Tamara Nall]

So I know our geeks out there totally understand it, but for those of us that aren't techies, was. So I basically can build an AI tool just based off, like, conversation. Is that what it is or.

[Scott]

Yeah, a little bit. So it's a little bit lower level. I think one way to, I guess, connect to the way that a lot of people use AI is almost everybody's familiar with the ChatGPT interface. So when you hear someone talk about context and context engineering, what's happening in the background? When you send a message to Chat gdp, it's going to take your message and it's going to basically, you know, it's going to add it onto this list of all the messages you know that you've sent to Chat GDP and those messages and also the response from Chat GTP that becomes the context of your conversation. So if you ever hear about context window, the context window is basically just, you know, how many of those messages that the AI can process in one shot. And one thing that you have to understand is every time you send a message to an AI or An LLM, every single message gets reset and gets reprocessed, so you eventually kind of run out of room. And that's where a tool like is really important because it allows you to get the most out of context. You can be a lot more exact in the way that you feed information to a model and it also kind of exposes really what's going on in the background, you know, because again, you know, if we look at chat gdp, you know, you just see these message bubbles that pop up on your screen. Everything's just plain text. So you see every part of the response from the model. You know, you can add an additional data that's kind of outside of the vision or outside of the Context of the L1 allows you to kind of do better tracking, but so really powerful tool for keeping your prompts organized and the information that you're going to feed into the LLM.

[Dr. Tamara Nall]

Got it. And is this in the background or is it kind of in the forefront? Like can I, if I'm using Convolang, can I see these silos or how is kind of grouped or categorized or is it all in the background and it's just happening?

[Scott]

Yeah, it's mostly in the background. There are some additional libraries for convoling that I maintain that allow you to build of that chat conversation view. So with that, if you're. This would be a little more for the developer oriented crowd. There's some templates, common frameworks that you can just spin up and then it gives you the UI for like a chat window, then sets up everything for convolink to be able to use in those projects. A little bit of ui.

[Dr. Tamara Nall]

Amazing. So talk to me about a time where someone actually used convolang and it was jaw dropping for them. It, it was like blew their mind. Talk to us about that.

[Scott]

Yeah, yeah. So actually work pretty closely with a gentleman that works for syntax data and you know, he started using the tool as a way to do early prototyping for applications that you're building with AI. And I think the, the moment that he realized like what the tool could do really got him on board. Because a lot of the tools, AI tools that you use, when you send a message, that message kind of goes off out of nowhere and then you'll get a response back, but you don't have a good history of the conversation. Every interaction that you have with the LLM, it gets stored in a local file that you can review. You can start to understand how an AI got to a decision that it made. So it makes for A really good system to keep track of those interactions.

[Dr. Tamara Nall]

No, that's good because you are right, it can be lost some of the conversations. So this sounds amazing. And then talk to us because I'm going to tell you, my listeners are very curious. Like we're nosy. You can call it curious or nosy, you pick the word. But we want to know how it works. So if we were to lift up the hood and look at the brain of Convo Lang, what will we see?

[Scott]

Yeah, actually that's kind of the beauty of it. So, like know, with a lot of AI systems, you know, everything's really complicated. It's really simple. It's really kind of brought down to earth. So, you know, the first thing you would see, you know, if you looked into, you know, a program that's using Convoleng, you would see convoling files. They're just text files that kind of formatted basically like with a line that says, you know, is it the user that's talking? So the human or is the AI or assistant that's talking about. And that full list of messages all just stored in a plain text file. But you can also combine it with more traditional procedural code. So there's a scripting language that you can bet in. But again, it's a really simple scripting language. So all the stuff that you do with tool calling and things like RAG and all those complicated features, they all just get written down in this simple text file that's really easy for a person to read. Then once you have that file, you can feed it into the combo language CLI or especially a program that will read that file and then elected so it'll send out the data, format it for the AI that you're using and give you the response back. So yeah, actually really, really simple, which is.

[Dr. Tamara Nall]

Yeah, yeah, that's amazing. And okay, so we talked about, you know, your customer, your user and how they were wild. Tell us about a time as you, Scott, as a founder, you were, you, you made an upgrade, a development and you're like, oh my God, I can't even believe this works. So tell us about. Take us there. If there was a victory dance, you can jump up and do that too. You know, whatever works for you. But take us to where you were even floored by what you've created.

[Scott]

Yeah, it was actually really recently and when, when my boss hears this, he's going to laugh. So along with combing, I've been making a tool that builds on top of cumbling language, but it gives you a Way to do spec driven development. So spec driven development is basically when you want to write an application, you write down all the important parts and like what the application is supposed to do and you write that down in documentation and you feed it to an LLM. There's a lot of tools out there to do that make it a little more prescriptive. I consider it to be like precision context engineering. So, you know, basically I use Combo make, this new tool I've been working on to basically do my week's worth of work in just a few hours. So, been working on a new application called MindArc AI and we're in the process of redoing the interface and you know, I admittedly spent a lot, a lot more time on comboing than maybe I should. So I've really been working on that tool more so than my main job.

[Dr. Tamara Nall]

When this releases.

[Scott]

Yeah, basically I ran, set up the spec for the application.

[Dr. Tamara Nall]

Wow.

[Scott]

The entire application, front end and back end in one shot. I mean, there's some small things afterwards I wanted to tweak, but the actual application function does. It was spokesteal, you know, including security rules and all the important parts that a lot of like bytecoding tools really miss out on. So I'd say that was the moment where I was like, huh, this is. This is pretty powerful.

[Dr. Tamara Nall]

That is powerful. And you said that's called Combo mate?

[Scott]

Yep, yep. Combo mate yet. So.

[Dr. Tamara Nall]

Oh, wow.

[Scott]

M A T E. Yeah. M A K E. Okay.

[Dr. Tamara Nall]

Make. Okay, Combo M A K. Okay, awesome. So we'll look out for that too. That sounds amazing. Now obviously what you're doing with Combo Lang is pretty powerful. So how do you look at ethics and how do you make sure that you stay within some ethical guardrails and restrictions such that, you know, it's remaining moral and ethical and all of that?

[Scott]

Yeah, I mean, honestly, with AI, it's a pretty tough question. I think, you know, a lot of people try to say that, you know, AI is going to empower people, which it will. It'll empower a lot of people. But unfortunately there are a lot of people that, you know, won't be needed, you know, so there's definitely. Yeah, I think, you know, need to have a transition plan for quite a lot of people. I think the people that remain, I think what Kavalang can do is, you know, provide a little more of a reliable human interface, you know, for working, you know, kind of working more closely with an AI because it, you know, it does help you to really understand what's going on, you know, under the hood versus a lot of tools that honestly try to kind of use user behavior as a way to train further models.

[Dr. Tamara Nall]

Okay.

[Scott]

So I think that's, you know, one way that it can help, but it is for sure a really tough question.

[Dr. Tamara Nall]

Okay, all right, well that's good. And then talk to us about the big future. Yeah, I used to say five years from now, but you could, the future could be an hour from now, but in whatever time frame you like to look at it. Tell us what is in the future, you know, convo and how combo lang and what we can expect and how is it going to power what we know today?

[Scott]

Yeah, yeah, I'd say, yeah, I'd say really if we kind of break it down into short term, you know, as in like the next, you know, year or two and then the long term, I think short term working on a lot of features that, you know, really help you, you integrate with other types of tools like MCP which are know, really critical for, you know, for building like, you know, true, you know, true deployment ready AI applications. Also working on another, basically a SaaS service that lets you build applications with Combo link web based. So you basically be able to describe your application, you know, use the combo make tool that I'm working on and be able to basically, you know, build a production ready app and deployment, you know, pretty much all in one click and you know, a couple prompts. So that's, that's probably, I'd say probably the most exciting near term piece of the convoling future long term. Hopefully convoling the language just becomes something that lives in the background that doesn't really ever know about.

[Dr. Tamara Nall]

Now that is powerful.

[Scott]

Yeah, but it's just quietly sitting back there.

[Dr. Tamara Nall]

Awesome. Okay, so the big future for Convoleng. Now our guests also like to just like get in there. So if they want to get in there and trial combo lang now, what can they do this week to do that?

[Scott]

Yeah, so there's a couple really quick ways to get started. The easiest way is just to go to the Combo Lang website. You know, there's a lot of interactive examples. It's kind of like the mixture between documentation and kind of like a teacher. So you can get on and actually just see what the language is about. You know, use it in some of the examples if you're, you know, if you are more of a developer, you can use the Combo link VS code extension. It'll let you write combo links directly in the NVS code. And nice part about that is you can use any model that you want. So Common has support for all the major models. You basically start writing AI native applications. Just write that VS code extension.

[Dr. Tamara Nall]

Okay, awesome. We got that. And what's the website?

[Scott]

It's Learn Combing AI.

[Dr. Tamara Nall]

Okay, got it. Learn. Oh, wait, do it again. Just spell it out for us.

[Scott]

Oh, yeah, it's Learn. C, O, V, O, hyphen, L, A, N, G, AI.

[Dr. Tamara Nall]

All right, perfect. Awesome. Okay, that's good there. All right, so, you know, I always have this question from one genius to another. So, Scott, your question is, what's an idea you had years ago that the world still isn't ready for?

[Scott]

Yeah, I think it's a slight adaptation for AI.

[Dr. Tamara Nall]

Okay.

[Scott]

But I think it's language models that are also code interpreters. So, like, the way that, you know, we use AI right now to write code is, you know, we. We tell it to basically generate the same code that we've been generating. You know, we've been writing for, you know, decades now. But I think where, you know, it'll be a really big difference is when internally AI models can actually basically evaluate code that is written for AI. Yeah, I think at that point it's gonna be kind of tricky to manage, but I think the. The acceleration of progress of the models could be. Could be pretty. Pretty amazing.

[Dr. Tamara Nall]

Okay, so when you saw that, we're gonna have to have you back on, because we do one episode per product, so we're gonna have to have back on.

[Scott]

Yeah, that's a little hint to another thing that I'm working on.

[Dr. Tamara Nall]

All right, there we go. So it'll probably be sooner than we think. All right, so let's go into our bonus rapid fire. I'm going to ask you a question. You're going to quickly give me your answer, and we're going to keep going. Like, bonus. All right.

[Scott]

Most overrated tech trend, ad hoc vibe coding. So when you see someone say, I wrote this application by a single prompt, and most likely it's an application that's already been written and the AI just got remixed it for you.

[Dr. Tamara Nall]

Okay, well, that's. We hear vibe coding, but that is the ad hoc part is something I haven't heard of. Okay, we got it. What about the most underrated tech trend?

[Scott]

I'd say world models and hardware that's specifically tuned for AI. We've hit a ceiling right now at compute and hardware power. So it's either one our models have to advance or the hardware has to advance. I'd see it be the hardware making that advance. That's going to be the next breakthrough.

[Dr. Tamara Nall]

Got it. And then what's world model?

[Scott]

So right now, most of the models we all use are all considered language models.

[Dr. Tamara Nall]

Okay.

[Scott]

They're more trained on, you know, specific language. World models are trained on, you know, images, videos, text, pretty pretty much everything. So they can really be true multimodal.

[Dr. Tamara Nall]

Got it. Great. Thank you for that. What's a book everyone should read?

[Scott]

Oh, I don't. I'm not a big reader.

[Dr. Tamara Nall]

Okay. Yeah, mainly coding and developing.

[Scott]

Yeah.

[Dr. Tamara Nall]

And building wood.

[Scott]

Yeah. Even that. I just kind of pick up and try things.

[Dr. Tamara Nall]

Right.

[Scott]

Yeah. Thanks. I'm happy.

[Dr. Tamara Nall]

If you don't have one, it'll be the first in the history of the podcast that there isn't one. But that's fine. Yeah, go down in history as a first. You good with that?

[Scott]

I'm fine.

[Dr. Tamara Nall]

All right. All right, great, Scott. Okay, last question. And that is, what's your biggest, boldest AI prediction?

[Scott]

I'd say, actually probably feeding back to the question from the other developers, is AI models that can do code interpretation internally? That'll be a real paradigm shift. Right now, we make AI write code the same way we've been writing it, but once AI can actually write code that's interpreted internally by the AI and it's a format that's more efficient for them, there'll be a big shift in the way the software works.

[Dr. Tamara Nall]

Awesome. All right. Well, I love it. Well, Scott, I've enjoyed this conversation. I'm sure our listeners have as well. So tell us, what's the best way to. I mean, you mentioned the website, so let's do that again. What are your social media platforms and what Are you on LinkedIn? Give us all of that so we can make sure that we're connected. But most importantly, actually start using combo link.

[Scott]

Yeah, yeah, definitely. So mostly just on Reddit. So Reddit, my Reddit usernames, iyio IO Someone took. Okay, yeah, a little funny thing, but. And then there's also a subreddit for combo link. So you can. On Reddit. And then I post occasionally on LinkedIn. Yeah. Actually not. Not super big on social media. Definitely the best way would be to, you know, follow or join the Lang subreddit.

[Dr. Tamara Nall]

Yeah. And Reddit is a huge platform. I mean, a lot of people on there and they're like, oh, I don't worry about LinkedIn or anything else. Reddit is the place to be. So for those of you who are not on Reddit, you need to be. And those of you who are, get on there more and definitely look for Scott and Convolang. Awesome. Well, Scott, thank you so much. We really appreciate it. And we look forward to hearing and seeing what Convolang and Convo make and all the other things you're developing and see how they're going to change the world. So thank you so much for your time and for being here with us.

[Scott]

You're welcome, Tamara. I enjoyed it. Thank you.

[Dr. Tamara Nall]

Absolutely. Okay, everyone, until next time, lead with AI.

Bye. Thanks for tuning in to Lead with AI. I'll see you next time as we continue exploring the cutting edge innovations shaping AI across the public and private sectors. Until then, keep leading with AI.