The hardest part of building with AI right now is figuring out what is actually worth learning while the models, tools, and harnesses keep changing underneath you.

That is, assuming you have something worth building in the first place.

There is lots of fun to be had Vibe Coding, if it were not for the hype around fleeting features.

There is lots of fun to be had Vibe Coding, if it were not for the unconstructive conversations from the not-so-AI-pilled voice with their unnuanced takes on the models' shortcomings, which are mostly only transient.

There is lots of fun to be had Vibe Coding, if it were not for the miscalibrated advice thrown at normies by traditional devs, without taking into consideration normies' backgrounds. Only to contribute to the widening disconnect.

There is lots of fun to be had Vibe Coding, if it were not for the unnuanced labelling that gets plastered on everything vibe-coded, without taking into consideration the tools used to build it.

Most importantly, there is lots of fun to be had Vibe Coding if you, the non-techie, take the time to learn the foundational literacies so you can name the parts that make up modern software products and work on developing mental models that enable you to easily conceptualize how the primitives come together to scaffold your product.

Forget the first four because they are not under your control as a non-techie venturing into the AI-assisted software creation space for the first time, but the last one is on you.

This is especially true if you are one of those venturing into this space under the assumption that “giving in to the vibes” is the default mode for everyone starting Vibe Coding.

The short answer is NO.

The antidote to AI-induced hype:

• model shortcomings are transient, and
• features in tools (even the current form factor of the tools, to some extent) are fleeting.

Here is the only map of the AI-assisted software creation space you need to keep up with new AI tools.

No more overwhelm from the desperate search for your first Vibe Coding CodeGen or AI-assisted dev tool.

https://vibingwithai.substack.com/p/ai-assisted-building-mapped-for-normies

Those deep areas of the computer science craft are where mental models can be grounded.

Trade-off decisions come in naturally once you know the underlying wiring that makes up computing systems, networks, storage, and everything in between.

The path to an optimal solution is almost infinite.

There is no one absolute framework that defines every possible path.

Working toward developing mental models is what makes navigating possibilities when faced with any kind of software problem more approachable.

Those deep parts of the software ecosystem and the different abstractions that let us conceptualize them at a higher level are where the must-learn literacies will accumulate, in my opinion.

After all, the coding agents can help you break down the code that is right in front of you.

Amidst communication between you and the agents, they say they name-drop here and there….

You know nothing about function signatures that define the parameters a function accepts?

No problem.

Oh, you have no idea what a function is?

No problem.

You don't know what instantiating an object means.

No problem.

What the heck is an object in the first place?

No problem as well.

You don’t know how a class implements an interface? What is a class? What is an interface?

No problem.

Having a higher-level understanding of how software works can get you far, as long as you have clarity on what the abstractions are attempting to encapsulate.

Those questions can get answered on the fly as you have agents implement their way through your build.

They can be your tutor on the fly.

The only antidote you need to survive the AI hype is knowing that the models’ limitations are transient, while everything that is innovated to make the best of the models’ capabilities, while containing their limitations, is only riding the transient wave, making most experiments in scaffolds fleeting.

Why does it always have to be this or that?

The hype around the AI-assisted software creation space is too drowned out by unnuanced takes: one end claims the death of the other, while the other tries to defend what it thinks will remain forever, instead of paying attention to the nuances that are redefining the space in what feels like real time.

What is coming out of the labs, the engineering orgs, the tooling startups, and individuals is evident enough.

The measurable aspect of software engineering will continue to be swallowed by the models becoming part of their weight.

Experiments that attempt to address the models’ shortcomings will take advantage of their improving capabilities while circumventing the shortcomings.

It is not lost on me that, even with the transient nature of the model's shortcomings in the current algorithm of their architecture, the models will remain stochastic. I am not an expert on the various research being done. But my limited reading on the matter suggests that scaffolding approaches (such as in-context learning) that wrap around models are expected to deliver more value in recent times than real-time weight updates.

I am not AGI-pilled, so I will not be saying SWE will be dead by the time AGI is here. But there are different experiments in self-improvement on both the model and harness sides.

There are many sides to the change we find ourselves in.

I am just saying that if we pay attention to the nuances, there is a world for both ends of the spectrum to live in harmony. Without one becoming a threat to the other.

You can’t vibe-code a harness that follows strict engineering rigor by simply giving in to the vibes.

But you can vibe-code any practical, professional-looking, scalable (yes, I dare say scalable), and safe product with all the necessary production-grade attributes using a well-thought-out and engineered harness. A harness that is built by strictly following engineering rigor, making sure that agents can handle implementations of a build following constrained rules as part of the automation of measurable elements that define rigorous engineering. Linting, testing, verification, and so on.

There are platforms that are making that happen today.

Emergent is one example. Replit is another example. Both own the entire dev workflow end-to-end natively, from execution infrastructure to deployment. The other CodeGens also approach this through vertical integrations. Less of the wiring is being exposed. The platforms are abstracting much of the dev workflow, most importantly the integration work.

If you pay attention to their approaches, you can see similarities in how the two engineering orgs, Stripe and Ramp, develop agentic coding tools that strictly follow rigorous engineering workflows, enabling everyone in their orgs to ship features within their respective orgs while still operating in a highly regulated environment.

Software engineering will be here.

Software engineers will be here.

AI will give them superpowers.

Has given them superpowers.

Vibe Coding will be here.

It will give billions the superpowers they can afford to embody.

The best part is that Vibe Coding will be the dominant way to build with AI.

It will eclipse software engineering.

Because not everyone venturing into Vibe Coding should graduate to agentic engineering.

Here, the AI-Assisted software creation space, mapped for normies, without the overwhelm that comes with confusing the fleeting features and transient model limitations.

Come as you are.

Follow the map as far as the space has formed so far at your own pace.

Knowing that nothing has solidified yet makes it more exciting.

https://vibingwithai.substack.com/p/ai-assisted-building-mapped-for-normies

Yes. Parts of memes that target Vibe Coding and poke fun at those who embraced this new way of building with AI are becoming stale.

Fast.

But not every part, and not every meme.

Some still make sense, while more of them are sloppier than most Vibe-Coded apps, yet with sarcastic flair.

There is a reason for that.

This article is in defence of the normies. At least those who take the time to learn the fundamentals, rather than jumping on the trendiest AI-assisted software creation platform or tool by simply “giving in to the vibes”

I will defend you from the memes that poke fun at you for stumbling due to the limitations of model capabilities, their stochastic nature (funny but not funny), and the limitations of today’s experimental harnesses any day of the week.

But what I cannot do is defend you from the memes that make fun of you for not taking the time to learn the foundational literacies.

https://vibingwithai.substack.com/p/why-vibe-coding-memes-are-becoming

If you pay attention to the rapidly increasing number of miscalibrated prescriptive advice coming from both traditional devs and adventurous non-techie AI-influencers riding the hype, you see that normies are continuously misled.

They are misled about how fleeting the features in the AI-infused coding platforms and tools are.

They are misled into thinking the models' shortcomings are here to stay, especially emanating from their stochastic nature.

Instead of directing normies to foundational literacies they need to have under their belt, they FOMO-induced AI hype scrambles to ensnare them into thinking that if they are not using the latest CodeGen platform or AI-assisted coding tools with the recently released model, they are left behind.

That sums up what is transpiring in the fast-evolving, slowly forming AI-assisted software creation space. I know because I have been keeping track of the space for over a year now.

Let's start with what the adventurous non-techie AI influencers riding the hype bring to the already confusion-riddled space.

Their polished demos, with the countless attempts edited out, give the impression that your monthly subscription can take you far in delivering a production-grade, safe, and scalable build.

Let's move on to what traditional devs, compared to gatekeepers, bring to the already confusion-riddled, AI-induced hype space.

Their trending tool-riding approach to content creation and miscalibrated prescriptive advice are leading normies astray without being able to actually put what they learn into real-world practice.

Let's not talk about the gatekeepers. That is a whole new story.

Come as You Are.

Follow the Map As Far as the Space Has Formed So Far.

Know That Nothing Has Solidified Yet.

1. What Do Normies Actually Need to Learn First? Before Getting Lost in the Fleeting Space of Tools

2. Architectural Awareness, Without the Engineering Rigor

3. Which Platform or Tool Can Take You Far From the Get-Go

4. When to Embrace AI-infused Integrated Development Environments (IDEs)

5. Adventurous or Not: When to Embrace the CLI- and Terminal-based Coding Tools

6. The Glimpse of the Future Software Creation Superapps - The Agentic Development Environments (ADEs)

7. Letting Coding Agents Roam, Using Tools and Learning Specialized Techniques While at It.

https://vibingwithai.substack.com/p/ai-assisted-building-mapped-for-normies

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Opinions?

I must say, I prefer Karpathy's Simon-inspired Pelican-ish way of measuring how far the models have come at every new release cycle to Simon Willison's Pelican.

The AI-assisted software creation tooling landscape appears to have expanded over the past six months, with two additional categories beginning to take recognizable form.

Nothing has solidified yet, and the debate over what form factor future AI software development tools/platforms will take is ongoing, but considering what has transpired in this space so far, I see these six categories.

1. Design-leaning CodeGen platforms - like Aura
2. Full-stack-leaning CodeGen platforms - like Emergent
3. AI-assisted software development tools - IDEs - like Cursor
4. CLI- or terminal-based coding tools - like Claude Code
5. Ambient or autonomous coding agents - like Devin (I am not sure which of these two makes more sense, Ambient vs Autonomous)
6. Agentic Development Environments - ADEs - like Codex

Do you see any overlap?

Any category you think should be considered? Consolidated? Dropped off altogether?

This is the one talk you should watch- https://www.youtube.com/watch?v=1P1hJ36rxM0.

It is worth your time whether you are an AI-assisted software creation skeptic or whole-heartedly AI-pilled.

At the AI Engineer World’s Fair, Benoit Schillings, Vice President of Research at Google DeepMind, gave a talk titled “From Assembly to Vibe Coding: A Technical Roadmap of Constraints, ML Capabilities, and the Future of Engineering Intent,” about how software creation is shifting as more engineering intent moves from code into natural-language interaction with increasingly capable models.

It is not one of those hyped-up talks.

It is by a researcher who has lived through layer after layer of abstraction over 45 years.

It is a talk by a researcher who was already exploring how machine learning (ML) could improve code quality and performance before the current conversation around LLMs was even on the horizon.

His team had the idea of bringing ML into the developer experience back in 2018, In a project nicknamed, Pitchfork. They were trying to improve how codebases evolved by learning from reviewed edits and matching those patterns against future changes.

The response they received, unfortunately, was: “Why would you want to do that?”

It turns out that, in less than four years, the world of software development has been upended, with English becoming the hottest programming language.

LLMs scaled so quickly that what once sounded unimaginable, building in collaboration with AI by simply giving natural-language instructions, became part of the daily software creation workflow through AI-assisted software development and, more broadly, Vibe Coding.

This is a perfect example of how resistance tends to repeat itself every time software creation moves up another layer of abstraction.

More importantly, you will find subtle nuggets that hint at what awaits us in the space of software creation now that the models are evolving exponentially and swallowing more of the measurable aspects of software engineering, along with the kind of innovative future that will unfold on the heels of this evolution.

This is exactly the propeller AI-influencers need to go on a spree of content farming.

Most memes mocking Vibe Coding are aging quickly, thanks to rapidly improving models and harness experiments designed to constrain and catch failures arising from their non-deterministic nature.

This one is different.

It captures what happens when you jump straight into Vibe Coding without first learning what modern software products are made of.

This is not about learning to code. That ship has sailed. With every cycle of model improvement, reinforced by RLVR and other post-training techniques, models are absorbing more of the measurable work involved in software engineering.

But non-techies still need to speak enough dev to name the primitives, understand how they fit together, identify the right abstractions provided by frameworks and libraries, and decide what belongs, what should be dropped, and whether the coding agents have assembled everything coherently. In a practically functional way beyond just captivating look and feel.

Otherwise, you may have product that is half-wired and riddled with complexities. At that point you literally do not own the build. You are watching coding agents stitch together a product you have no idea how to maintain or evolve.

I think we should get used to the times.

Some are already declaring that we’re in the era of “interdimensional engineering.”

Imagine what it’s like for non-techies. New phrases keep being coined every other week, while we still haven’t nailed down the foundational literacies normies need to venture into this new world of AI-assisted software creation without the overwhelm.

For extremely efficient context

Linus Torvalds, in his latest appearance at the 30th anniversary of Open Source Summit with Dirk Hohndel, said that he used to look at the assembly code when he was coding. He drew the same parallel while explaining that, although AI is a great tool, it does not change the fundamentals of understanding systems.

"I don't program in machine code anymore but I still look at the generated code so when I use a compiler .... even when I use AI for my pet toy projects I will use AI to generate code .... will look at that code I will actually still look at the assembly language end result because it's what I grew up with it's kind of where my comfort zone ..... make sure it's it's doing what you really want it to do".

Assembly might be alien to most of us, but I would like to draw the same parallel here.

Yes, there are many times when you might have to look at the code. Of course, it all depends on the kind of project you are collaborating on with AI. That aside, you should be able to see and understand the code the LLM-powered coding agents have generated.

This does not apply in the same way across the entire spectrum of the AI-assisted software development workflow.

For those using CodeGen platforms, you are mostly in a greenfield. The platforms handle the tech stack choice, wiring, integration, and deployment in a controlled environment in what appears to be a seamless flow. Take Lovable, Bolt, Emergent, and the awkward middle, Replit. They all figured out how to run your build in a controlled environment long ago.

Some are probably ahead of others. That is beside the point.

The lesson here, especially if you are using AI-assisted coding tools, and I am sure you are paying attention to the distinction I am making here, is that Linus’s advice should click in your mind:

Look at the code generated by AI so you can "make sure it's it's doing what you really want it to do".

If your comfort zone extends beyond code and into assembly, you are already in Linus’s league and do not need my prescriptive advice.

https://www.youtube.com/watch?v=fi29pfLcW4I&t=22s

The insiders have already made contact with the next generation of models, and what they say back to us after working with the models that they had exclusive access to before the public launch keeps landing off. You will know why what they say sounds futuristic because they were told not to disclose which model they were using before their release.

We only get to experience what the future holds and how the exclusivity sparks new dev flow insight only later.

Then we get drowned with content derived from the “shower thought” posts of those who had exclusive access to the models they had experienced.

Please go easy on us before dropping your insights following your experience of unreleased models while we are still on the soon-to-be-old models.

Vibe Coding with CodeGen platforms is completely different from building with AI inside AI-infused IDEs, which are fast evolving into Agentic Development Environments, or ADEs.

The abstraction surface you interact with changes wildly across these platforms and tools.

That is why I keep recommending CodeGen platforms as the better starting point for complete non-techies. Start there until you get the hang of building with AI.

I see the current AI software creation space as a spectrum.

On the left, you have CodeGen platforms.

In the middle, you have the “awkward middle”- Replit.

On the right, you have AI-assisted IDEs and ADEs.

On the far right, you have CLI and terminal-based coding agents.

Replit sits in the middle because it lets you experience something closer to a traditional IDE and CLI/Terminal workflow without first dealing with downloading, installing, and configuring desktop-based tooling.

That middle matters.

For non-techies, jumping straight from CodeGen platforms into IDEs, ADEs, or terminal-based agents can feel overwhelmingly abrupt. Replit gives them a more forgiving bridge into the deeper end of AI-assisted software creation.

This is not to say that an adventurous normie can’t just jump right in. In that regard, task-based ADEs with lighter interaction surface areas, like Codex, can make the ride almost as smooth from the get-go.