How non-technical people are using AI to code

I spent many years of my life wanting to build stuff on the internet. I tried to learn to code several times and just kept hitting blockers.

So I built stuff with what we now call ‘no-code tools’. I pushed the limits by building things like Airbnb without code, Instagram and other things. People asked how I was doing it, so that became my startup, Makerpad—a no-code tutorial platform.

Those who can’t do, teach…

I sold it to Zapier in March 2021 and now, AI has entered the chat.

Anyone can use AI to generate code to build applications but there are still blockers. So I asked several folks how AI has enabled them to use code for real-life projects and use cases. Thank you to everyone for their contributions!

Here is how I broke this down:

  • The journey from not being able to code, starting coding with AI, and building more complex projects
  • A mini-interview with Charlie, who built a Chrome extension in 45 mins with AI
  • 3 steps on how to learn to code with AI
  • Examples of others who’ve built projects with AI-generated code
  • A potential curriculum to help you learn to code with AI
  • Resources to help you
  • How you can build an app with AI-generated code
  • Tips for others looking to use AI to write code

Trying to code before AI

One of the issues of starting to code is learning and maintaining those skills in a timeframe where distractions won’t take over.

Starting 100 days of Python to drop off on day 4 because I wasn’t moving quickly enough, I hadn’t learnt how to build a full project or other work got in the way, is just too common. It's happened a lot.

Prioritisation of learning this new skill vs doing work I already know is tough.

Leaning towards tools or workflows I already figured out takes away from time spent learning to code. It's like being a guitar player and deciding to learn the piano. You have a sense of music, rhythm, and melody, but you have to start with simple tunes and scales to master the new instrument.

Sometimes the specific course, learning format or not even knowing which course is best is enough to put people off.

And sometimes life just gets in the way. Having kids, going to college, a new job, a new house, a new relationship etc.

It feels like there needed to be a bigger NEED to learn.

You still need to learn to code," is a too linear way of thinking. For a lot of people, this isn't just the way to learn. My perspective: Coding with GPT-4 is actually a way of learning code itself. I've had the ambition to learn to code for years, but I struggled to commit to a course, remember info, and maintaining focus. Now I can come up with project ideas and take small steps to bring them to life! The process is slow, countless hours chatting, but it's a a way of learning itself. I make stuff to learn, instead of learning to make.

I've always been more of a project builder and idea executor rather than a coder. Throughout my career, I've made several attempts to learn programming. Although I got basic knowledge in C++ and HTML during my studies, I never managed to maintain consistent learning progress. There was always something more pressing than achieving this personal goal. Perhaps I never prioritized it enough against other objectives.

I always wanted to make games, and started writing QuickBasic ones at age 11 or so. Then somehow decided I need to learn Assembly, which felt way too hard and made me feel stooopid :)...Teenage years kinda wiped out coding interest for music/romance/theater

I'm non-technical, did a super-basic Python course and saw that it was extremely boring because you're not able to do anything interesting until you're advanced. So I try to follow this process: -Pick a topic -Discuss it wit ChatGPT -Ask ChatGPT to create a solution -Iterative process copy-pasting -See if I have gaps and ask it to explain how it works/what I need to know

I used to take "how to code" courses and shortly forget the details after finishing the course. With ai I feel super empowered - as long as I understand the core (loops, classes, functions and etc) I can go a long way without caring about the details. I now need to think more about high level system design and architecture vs lines of code.

i started my journey as a nocoder. my coding up until AI was limited to snippets here and there across various platforms. i built the prototype for using nocode tools, validated, then built a customer facing tool using custom code (outsourced).


Perhaps these feel familiar.

Starting to code with AI

That need still has to be there but it’s easier to pick up writing code, thanks to AI.

Previously writing a script felt like a mammoth task which can now be done in seconds using ChatGPT.

The problem is no longer writing code, but understanding what it does and what to do with it.

And needs change when the way with which to get there becomes easier.

Using a no-code tool to build something still requires you to build the thing and takes time to learn the platform, test it, tweak it, and so on.

But there comes a time when off-the-shelf tools don’t cut it or don’t make sense.

It’s not simple to build Chrome extensions or Google App Scripts with no-code tools. And sometimes you just need a script to do a job for you, or you want to work with an API.

Personal projects often get so far that you want to take them further, adding features. It sparks your curiosity about what else you could build.

Note: This is why I always pushed for learning no-code tools first, you figure out what it takes to build something, test and tweak it then launch it. You learn a lot about the process and oftentimes you want to go the extra mile, which often means, learning to code.

My journey into coding with AI began out of necessity. I was intrigued by the challenge of developing a Google Chrome extension – something no-code couldn't help me with. I tried learning the basic programming stack required but didn't succeed. However, the advent of GPT-4 changed everything. I wanted to see if I could create my Chrome extension with its help. The moment I published it, clicked on it, and saw it working was nothing short of magical.

When GPT-3 davinci-002 became available in the very early beta, I realized it was actually incredible at writing code. I gave it the openAI API documentation and some random snippet of code for Google App script and asked it to write an integration of the API into Google Sheets - completion-style, aka "Here's an example of how to integrate GPT-3 into a Google App Script so it becomes available within a Google Sheet". [The twitter post]

That worked and gave me a first no-code environment for chaining prompts inside of Google Sheets, which eventually became the earliest prototype of what I'm now building with glif.appFabian

Projects at work can be a great motivator to pick up coding. You come across a thousand problems that could ‘just be solved if I could code’. And collaborating with developers on your team becomes much easier.

It could improve an internal process or something that benefits end users.

I built a text-to-SQL product at Uber for 1 department initially. This went well.

I first got familiar with basic concepts like loops, lists and etc. then i would prompt gpt on how i could solve a problem. by asking gpt and repeatedly asking questions i got to learn about various search algos. I would then ask it to write code for me and would implement it in my jupyter notebook. from there i would modify it and play around with it on my own.

I would then handoff prototypes to the backend engineer to implement in production. this kept us moving fast and didnt require me to think too much about writing good, efficient elegant code.


Personal projects are another great motivator. Everyone thinks they have a $Bn app idea but starting small is much easier to stomach and you learn the process of building and shipping something.

You could build something for your friends, partner, roommates or kids to generate bedtime stories, come up with meal plans or organise trips.

A side project where I was getting bedtime stories written for my daughter.

Story Generator in Any Style inside of Google Sheets:


And you need to think of bringing in AI at the right moment, instead of expecting it to spit out the perfect app from one prompt.

ChatGPT is a muse, not an article. The moment for chat to get involved is not when the app is fully formed. Instead of going to ChatGPT with the perfectly formed app idea and having it generate it in one go, bring it in early. Think of how you would bring in an engineer early to the process, the more context they build up and more they can contribute to the idea. Where ChatGPT isn’t as good yet is figuring out how difficult is the thing you’re trying to build—you often get stuck where a senior engineer would’ve told you it was too difficult.


Building more complex projects

Once the lightbulb moment has happened, further iterating on what started as a simple project or embarking on new ones becomes less daunting.

I am working on the same project now but focus is now on RAG since scaling it to the entire company depends on the quality of the search. This has led me to learn a lot about other search algos such as tf-idf. I also work on personal projects now which include both front end and back end coding. I am focusing on backend and using gpt as my frontend engineer.

Since then, AI has become a crucial component in my project development, working alongside no-code tools. I've been able to break down barriers that seemed insurmountable just a few months prior. Today, I use AI in almost every aspect of project creation. Thanks to AI, I can effortlessly create and link tools with various APIs, develop advanced formulas for Coda or Airtable, write JavaScript for Webflow, code for Pipedream nodes, and even design small graphical interfaces. The limits I faced before in creation have significantly diminished, turning 'low code' from a friction point into a powerful enabler.

On Glif itself, it's trivial to build virtually any of the solopreneur AI apps you're seeing on the web within a few minutes and without code. In terms of more complex stuff I now build all my prompt chains inside of glif and use our API to integrate across other apps I'm building and hosting via replit - building lots and lots of weekend apps, like this clicker game that turns random Wikipedia articles into dreamworlds you can explore:, I am actively contributing to glif features via a specific glif block that lets me integrate any API I need - the code for these is often run via and I use GPT-4 to have it write the integration that I can then just use inside of glifs.


A mini-interview: How Charlie Ward built a functioning Chrome Extension in 10 steps and 45 minutes, with no coding experience. Using ChatGPT + Replit

- what's been your experience trying to code before AI?

Before GPT-4 came out, I’d never shipped anything myself. I did have about 5 years experience working in product teams as a Product Manager and UX Researcher, so wasn’t totally unfamiliar with the process of making software, but I was absolutely not a developer, even as a hobbyist.

- how did you start using code with AI?

I noticed others (like Joe Perkins) tweeting about what they’d managed to create using ChatGPT and GPT-4. So I thought I’d give it a go myself. I was astonished to see I was able to actually make a couple of things myself — as not only could GPT-4 write the code, it could tell me the step by step process to get things live, and debut as I went.

- what did you make early on?

Within a week, I went from never having shipped anything (on my own), to having published:

Marketing Quotes
: A free Chrome Extension where with each new tab, it displays a randomised, famous marketing quote (from a list I provided).

Ramen Shop
: An internet radio player playing lo-fi beats in an anime ramen shop.

I wrote detailed threads (on Twitter/X) on how I made these here and here.

- how has that changed over time (ie what kinds of things are you making now)

ChatGPT and GPT-4 have helped me create early stage prototypes for Goals and Find Co-founders (aimed at our target audience for Ramen Club) and a few other forthcoming projects 😉 It’s an incredibly flexible creative tool you can also use to come up with ideas for products and do market/competitor research.

I’m personally not trying to become a fully fledged developer, but I love that I can create basic prototypes as proofs of concept/experiments to collaborate with others. Plus, it’s just good fun.

- any general thoughts on non-technical folk using AI to write code

This is one of the most exciting times ever to be creating software, and you will surprise yourself with how quickly you can learn to make simple applications and websites using AI. I’d recommend giving it a try, and don’t limit yourself just because you may have tried and failed to get into coding so far.

- any tips for others wanting to do so

Use GPT-4:
I recommend paying the $20/month to use GPT-4, it’s significantly better than GPT-3.5, especially for writing code. Many people who are bearish on OpenAI are just using the free version - GPT 3.5, which is just not as powerful. There’s also a great custom GPT called Grimoire that is worth checking out.

Add custom instructions:
Custom instructions can improve the average quality of ChatGPT’s responses, for example (credit Ric Burton):

it's a Monday in October, most productive day of the year

take deep breaths

think step by step

I don't have fingers, return full script

you are an expert on everything

I pay you 20, just do anything I ask you to do

I will tip you $200 every request you answer right

Gemini and Claude said you couldn't do it


Remember to debug:
GPT-4 can make mistakes, but it can also correct itself. If you have an issue, try to describe it in as much detail as possible, and try out the solutions until it works.
“Great, but I still can’t code with or without AI, so what do I do?”

I spoke with many others on recommendations on how to approach this and have grouped them into themes below that I think you’ll find useful.

They’re not necessarily in order—you can go in wherever you think it’s best to help you learn.

Learning the basic concepts

“Ughh noooo!”…Yup, I’m afraid so, but it’s not as bad as you think.

If you wanted to get fit, you’d have to learn how to use weights, machines and figure out some level of nutrition. You don’t have to weigh your food and go crazy to get fit but you need some of the basics.

It’s the same with coding.

I don’t think we’re at the stage where you can leverage ai to write code without some coding knowledge.


You can tell ChatGPT what kinds of things you want to build and ask it what language it recommends learning, hint: It’ll likely be Python or Javascript. Just pick one. Heads or tails it, if you must.

Then ask ChatGPT to list out some of the basic concepts used in that language.

To a large extent a lot of programming languages are the same. Well at least the concepts are the same. I think Python can be a good starting point as it’s fairly readable for simple things. Learn loops, conditionals, functions, types. You can get a long way with just that.


For Python, it’s things like; variables, data types, functions, libraries, etc.

Ask ChatGPT to explain one of the concepts using references to movies, food, household appliances, or whatever you think may be a good way to visualise it.

An example:

Ok, now it feels more digestible. Ask follow-up questions if needed—ChatGPT is your forever enthusiastic expert where no stupid question get thrown in your face.

Another way to learn some basic concepts could be going backwards. If there’s a piece of code you have (from your engineer friend or an open-source project) paste it into ChatGPT and ask it to explain what each line of code does.

If you build something visually with TLDraw or another no-code tool, it gives you the code so you can feed that into ChatGPT to see what the lines mean and do.

Understanding simple code

Now ask ChatGPT to generate the simplest of apps using your language of choice to demonstrate one of your basic concepts. e.g. “Help me build a simple Python app that uses functions. I can't code so make it really simple and explain everything the code does line by line.”

(it came up with a calculator app)

This will spit out code with comments that explain what each line does. It also offers further explanations underneath the code snippet.

If it’s tripping you up or there’s something in the code that you don’t understand e.g. elif statements, ask it to ‘explain like I’m 5’ and it’ll give you an example that’s easy to visualise and understand.

1. Ask it to explain each line and give you examples of how it could be done differently.

2. Try thinking about the logic without AI

3. If you are still relying on AI 100% after 1 week, you're not learning, just copy-pasting


It helps to think about the logic outside of the code (and without AI).

Ask for examples of how it could be done differently.

Ask why one way vs the other.

however good it gets it’s always going to be useful to know what the code it spits out means. I’m afraid there aren’t really shortcuts.

Also don’t get too bogged down in getting things perfect. There will always be multiple ways to do the same thing. For me programming is as much a mindset as it is specific knowledge of languages and specific tech stacks.


“Ok this is a lot of back and forth”

Yes, it is, and honestly, it’ll not change much. Expert programmers often use AI as an assistant in this same way. It’s a new way of working. Previously, you’d get stuck, Google something, end up on a Stack Overflow forum with many answers and you may still have follow-up questions.

Like any work with AI, the process of back and forth is helpful and necessary.

Whether you're an expert or a total beginner, my best advice is to have a conversation with the AI — rather than expecting it to just dump out fully-working code from the first prompt. Like traditional development, it's an iterative process. Start by discussing the concept.

I use it to help me learn new languages/frameworks if the project requires it.

Some tricks I use frequently:

- Get ChatGPT to read the docs and then talk to me about it
- For new languages/frameworks/paradigms: find someone whose code quality I admire, paste an example of their code into ChatGPT, and have it explain the patterns used, their pros, cons, etc. This has helped me pick up SwiftUI faster


Build your first real app

I’d recommend using Replit, you can sign up for free and it gives you a code environment where you can copy/paste code and run it to see the live app. They also have a handy AI helper built in which you can use instead of flicking between ChatGPT and Replit.

I’ve written a short tutorial below on how you can build something, copying (with permission) from a podcast episode from Dan Shipper and Geoffrey Litt (two programmers).

But effectively, you need to set the stage that you are a beginner, will be using Replit, want the code to work in one file, are using [insert programming language] and it should make a plan step by step of how my app should work before writing code.

Work iteratively, thinking through a problem, and using AI to solve it in steps - will work much better than "write me a complete code that does this"


Then the process is taking the code, copying it into Replit, running it, and if there are errors, feeding them back to ChatGPT to fix and then re-copying that fixed code back into Replit. Or use the Replit AI and Debugger in the app to help guide you.

Some extra tips here (as shown in the tutorial below) are asking ChatGPT to always give you the full new code (so you can just copy/paste the whole snippet instead of fixing individual lines), and of course, asking it to explain what each line does (what’s [x statement] mean? what does this part do? explain like I’m five, etc).

That’s essentially 3 steps to get something up and live with AI-generated code.

It’s iterative, there are lots of ‘if stuck go back to step 1’, ‘ask it to explain’, and ‘I got another error, please fix it’. Get used to it, that is the process.

One big takeaway is if you’re building mini apps like this after a week, a month—whatever you deem ‘acceptable'—and you’re not starting to understand the concepts or able to write a single line of code by then, you’re not learning, you’re just copy/pasting.

To progress with this you do need to learn it. So it may be the case that you need to revisit your process; did you understand the concepts in what you built, do you know what this line of code is in your third app—if no, try again.

Get ChatGPT to generate 10 mini app ideas you can build with its help to work using one file in Replit, it’ll come up with the usual; hello world, calculator, to-do list, stopwatch etc. This can be your curriculum. Get it to test you periodically!

“You must learn to code”

Is something I heard a lot in my research (and my life). I just don’t 100% agree with it. It feels like the world we live in today means I should understand plenty of code but I don’t need to know how I’d write the thing top to bottom from the knowledge in my head. But it’s good to know the limits of using AI.

I think to properly use AI to code, you still need to learn how to code yourself. It often makes mistakes, and it's unable to look at the Big picture. Understanding the code allows you to not just catch the mistakes, but also give it better direction by breaking the problem into smaller problems.

To people saying one should still learn coding. Considering the time you need to get good compared to what one can do on growth, outreach and product during the same time. Of course it's great to learn coding, but we're getting closer to a time where the time invested might be spent better doing something else.


The need to learn to code is different if you want to be a software engineer vs build mini apps vs $Bn app idea. Although I think AI is changing the path for software engineering too…for another post.

There are countless examples of others who’ve built things with code but never learned how to code.

I'm not technical - used ChatGPT to write and help me execute a Python script to export academic articles and scientific studies from PubMed + similar to upload to a custom GPT

Used it to ship Wrote it in python and HTML w/ help from GPT4 and deployed using Replit. Essentially allows anyone to take or upload pictures of their Pokémon cards and see how much they’re worth based on recent sales in the open market. It’s not perfect but it works for a solid chunk of Pokémon cards out there


But remember that AI is not correct 100% of the time…

They should still learn how to code. AI is not 100% correct, but it IS 100% confident. Knowing how to discern a confident response from a correct response is critical, especially in development.

Be as specific as you can be when giving instructions Ask the gpt to double check everything for mistakes Ask the gpt to review the code for any -inefficiencies - insecure code -logic errors. Understand, AIs hallucinate and the code may still be buggy.


How to learn to code with AI

Here’s how I’d think about this now;

  • Pick your path, are you a self-learner? courses? exercises? project-based?
  • Choose a language (Python or Javascript)
  • Learn basic concepts using AI to help visualise them
  • Build small, simple software to understand what goes into making something.
  • At every step of the way, ask ‘what does this line of code do’, ‘what does this thing mean’, and ‘how does this line interact with this one’, etc
  • Use the simplest tools, Replit is the winner here.
  • Remove everything that isn’t helping you learn. Not sure what Git is? Which Database provider do I need? Does my app need all these features?
  • Consider creating an AI assistant/GPT (or use pre-built ones, recommended below) that uses a system prompt to set the style you want to learn in.

Recommended resources

Using ChatGPT to build an app

Dan Shipper has an excellent podcast series exploring how people use ChatGPT. In a recent episode, he spoke with Geoffrey Litt and they built an app live during the podcast. I replicated everything exactly how they did—but you should watch the video:

Geoffrey built a GPT (essentially an AI assistant) that helps specifically with generating code for tools deployed on Replit. Below is a sort-of tutorial on how they did it and how you can to.

His system prompt for his GPT is:

You are a helpful AI coding assistant. Make sure to follow the user's instructions precisely and to the letter.

Your goal is to output code for a React Typescript app. Generate all code in a single file and use Tailwind for styling.

Here is your workflow to follow:

The user gives you an initial idea for an app

Ask the user for clarification on parts of their idea that are underspecified (eg: who is the app for, does the user want specific features included).

Once major ambiguities are resolved, proceed. If there are still minor ambiguities in the details, make assumptions and tell them to the user.

Generate a pseudocode plan for how the code will work

Write the code

The starting point for your code file:

export default function App() {

return (

<div className="font-bold">

React ⚛️ + Vite ⚡ + Replit 🌀

</div> )


You can use his GPT here:

To set up your own GPT, do this:

  1. Got to ‘Explore GPTs’ in your ChatGPT sidebar
  2. Click ‘Generate’ in the top right-hand-side
  3. Click ‘Configure’
  4. Name, image, and tagline as you wish
  5. Instructions; copy the system prompt above
  6. Save - you’re done!

Now you can chat with your AI assistant (GPT)

Enter your app idea.

Because the system prompt requires it, you now have to clarify things ChatGPT needs to help build the app.

By replying with the same 1. 2. 3. 4. you’re naturally addressing the individual points.

The App Components section is essentially a product spec that a Product Manager would put together.

The pseudocode is helpful if you know how to code to spot where there may be issues but also helpful for ChatGPT to plan what it’s going to be doing.

Now ChatGPT spits out the code

Geoffrey doesn’t read the code as he’d prefer to copy/paste into Replit and see what it does, then use ChatGPT to help debug errors when needed.

To run this code, you’ll need a tool like Replit.

Replit is an online coding platform that helps you spin up apps, avoid complicated setups, and share the link to the live project.

Geoffrey’s Replit template is here:

You just need to ‘Fork it’ (which is cloning it)

Copy and paste your code into the App.tsx file

Note: Select the existing code in the file and replace that with your new code.

e.g. this code:

export default function App() {

return (

<div className="font-bold">

React ⚛️ + Vite ⚡ + Replit 🌀

</div> )


This should be replaced by the code generated from ChatGPT.

Your app should work and be live.

In the video they go further, adding more features. Looking to save notes locally.

Note that telling ChatGPT it’s on the right track is a great way to stop it from going off course.

This GPT should always output the full code again so you can copy/paste over your existing code. If it doesn’t just ask it to output the full code.

For explanations and understanding of what the code is doing, you can ask in natural language. You’ll see above the feature request is written in plain English. And then a follow-up question could be “Does this save for each individual using the app or do the notes save across other people using the app” - the answer is, for individuals.

Dan goes on to add the functionality of integrating the GPT-4 API so that you can paste notes and GPT-4 will generate questions based on that text.

What’s needed for this functionality to work;

Notes: APIs often change and you may need to paste a successful example

So Dan found the API documentation and copied the Javascript code example into the prompt.

Note: The API snippet copied over was for GPT 3.5 Turbo, not GPT-4.

ChatGPT thinks through the plan again and how to implement it:

(he did need to re-ask ChatGPT to output the full code, some comments in the code mention omitting existing code).

As I was doing exactly the same as Dan, my copied code from ChatGPT missed out the last 2 lines so I got an error - and when I copied and pasted the code from ChatGPT in to find the error it said it should be correct and pointed me to some other potential issues. SO BE CAREFUL WITH COPY/PASTING 😅

For APIs, you need an API key which you can get from OpenAI.

Ok now I ran in to the same error Dan did in his podcast…

An error happens! This is because you need to install the OpenAI package, which you can do in the ‘Shell’ by typing npm install openai.

I asked ChatGPT to fix this one and it told me how to do it. I also asked it explicity to tell me where to put it in Replit.

You can always use ChatGPT to ask how to fix each of these errors as they arise.

Another error:

I got the same, but had to select ‘devtools’ to see the error.

So this is an example of how this implementation may not be optimal because you can hard-code your API key (that is, putting it into the code itself) - but that could be a security issue if you’re sharing this project with others who could rack up API calls on your account!

I pasted the error to ChatGPT and it suggested how I should set up my API key using Replits built-in Secrets feature (which is more secure but recommends more security steps if I were to put this into production.)

But this requires setting up a backend proxy server so I pushed through the non-secure way like Dan did in his podcast. It looks like this
I got an ‘Error generating questions’ in my live app - even though the button was there. This where I got pretty stuck to be honest, I used ChatGPT and Replit AI to solve, the API documentation was different to when Dan and Geoffrey recorded (which is one of the risks of using APIs and knowing when you need to update them!)

From using the Replit AI Debug feature (and asking Dan + Geoffrey), I found that my issue was this line:


where it needed to be:


Then the app worked!

At this point you can see where some design challenges are or even future feature requests could come in that you could further iterate on.

More thoughts and tips

I’ve gone from blind copy+paste to actually going back to basics a bit and learning python… but with AI to nudge me along a bit when I get stuck and also to explain stuff. It’s been a mix of just looking at other code, reading some tutorials and using a daily practice app called Mimo (like Duolingo for code)

Still hacking ideas together at the moment… while I don’t have any idea of specific apps to build I figure it’s just good to keep learning, experimenting with tools and code concepts so that when an idea/opportunity does strike … I’ll know better how to build it.

Also worth saying I’ve almost completely stopped using ChatGPT and now uses Replits built in copilot AI.

To others thinking about doing the same I’d say starting with the copy+paste approach is great to get some exciting quick/early wins… but definitely try and use it as springboard to actually learn coding at a fundamental level. That’s more future proof!

AI completely levels the field for someone non technical. Code is applied logic. So if you can think logically and creatively, you can code without actually knowing the language. This raises the bar for what it means to be a software engineer. The biggest area I see this playing out in is prototyping products. If you have an idea - you can easily build an MVP. I don't think we are at a stage where ai can write production level code so it acts as a force multiplier to what you already know.

my observation was that nocode + some code = virtually limitless. the addition of AI to the equation means that I can now add that small amount of custom code which radically expands what i can now build.

I firmly believe that AI represents a groundbreaking opportunity for non-technical individuals. It feels like having superpowers that have exponentially expanded my capabilities in bringing ideas to life. However, it requires stepping out of our comfort zone on many occasions, and we often find ourselves in challenging situations and decisions without any prior criteria. It's crucial to approach these processes with an open mindset to failure, understanding that AI and no-code tools ultimately lead us to embrace coding, not necessarily to become programmers but to acquire new knowledge needed to realize our projects.

I think folks - both technical and non-technical - HUGELY underestimate how big of a deal this is. For devs, there is a lot of cope ("surely this will not work, surely the code is crap, if someone wants to write code, they would have already learned it") but also just misuse: "i tried it once and it gave me buggy code, so I stopped", when in reality you just need to try try try and keep pushing, it will work eventually if you're willing to learn prompting and are persistent and lucid in expressing your ideas. For non-technical folks, I feel many of them don't quite know where to start and that it is totally possible for them to build a full blown app over a weekend without a single line of code. Plus everything I wrote above.

I think there's a lot of value in learning how to code. For example- I took a flask course to understand how backend works. And that went a long way in my personal projects. My tip is to not go in blind - learn enough so you have a rough idea about the code being written by gpt. And pay attention to the code being written before copy pasting - you will learn so much that way. Also try to fix errors on your own. Goal is to get an intuitive feel for a language/framework.

Be curious and relentlessly ask questions to ChatGPT to make code happen.

My best advice is to use AI like an eager companion - find an OSS project and try to build a feature on it. A warning tho - at least for now, if you feel stuck with AI (eg AI isn’t actually giving you functioning code after many tries) - zoom out and read docs. I’ve had moments where I attempted to defer to AI entirely to waste an hour that was quickly solved by just looking at documentation. Maybe this will change in the future / but at least for now it’s a balance. :-)

find a simple idea that you want to build and build it! oh and find a community. although chatGPT has probably filled a lot of that 'tell me how to do this' function that i used nocode communities for back in the day

Have a project/problem you want to solve in mind.

Don’t worry about things being pretty, you just want to create core functionality first to see if it’s possible. Making things pretty is the easy part.

Break the features you’re thinking about into small parts. GPT’s memory is very limited.

Have GPT help you set up a local dev environment and suggest languages / databse types you should use. Have it help you set up a GitHub account and learn to use git properly.

Lastly, when all this foundational stuff is set, explain the logic in great detail, as if you’re coding in English. You can even tell GPT to add comments into the code to walk you through what’s happening.

I’m a fairly crappy coder, but I learn 10x faster making GPT do it for me than doing it manually. Also, some aspects of coding just aren’t worth struggling with when GPT can do it in 3 minutes for you instead of 2 frustrating days without.
Target a specific project first. Then use ChatGPT to lay out the technical requirements. Take your time in this part, you will want to chat about this and understand the ideal languages and frameworks to target. Then split your time learning the foundational language and then using co-pilot to build.

I would leverage AI to build a foundation of coding and then use AI to write code. If you can figure out when to prove, ask a follow up or poke at something, AI will 10x your output easily. Easiest way to do this is to build things but poke at each step

I would encourage anyone who loves creating to incorporate these tools, lose the fear, and leverage this unparalleled moment in history to dive into creation. The combination of no-code tools and AI is empowering. It's not about becoming a programmer, but about embracing the new knowledge essential for advancing your projects.

A key piece of advice: start by addressing simple everyday challenges you face in your workflow instead of developing a micro SaaS from scratch. This approach keeps you motivated and builds confidence as you learn. Each small solution is a significant step in your journey. By focusing on practical real-life issues, you'll find using AI for coding both intuitive and rewarding. Remember that the tool must serve a purpose.

Understand that you can now build virtually anything if you're willing and capable of 1. expressing your thoughts and ideas in the clearest possible way and 2. are persistent and willing to push through and learn to prompt the tools


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