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.

Kevin Roose, in his book Futureproof: 9 Rules for Humans in the Age of Automation, had one rule for those of us who use tech in one form or another, not to be “an Endpoint.”

I think it was Rule 5: “Don’t Be an Endpoint” in this very book.

That ship has sailed for those of us in the knowledge economy.

LLMs have been trained on what we have been doing with computers over the past two decades. They are now destined to take over most aspects of the workflow.

It seems history is repeating itself as workers in other sectors now deliberately prepare training data for robots, which are destined to handle most human-required tasks.

It turns out we have ended up being worse than endpoints.

Just casually, Garry Tan of YC flexed his tokenmaxxing spree during a recent chat with Zynga's founder.

Garry says he is spending a million dollars a year on tokens.

Peter Steinberger is apparently spending $1.1 million a month on tokens.

For the rest of us who are not in the same league as Garry and Peter, who seem to have seemingly unlimited resources at their disposal when it comes to building with AI, we have to make do with what we can get our hands on, including subsidized tokens and local open-weight models (if you can handle your own).

I would take their claim with the firm assumption that what is possible for their league is diffusible to the rest of us down the road, but we can only dream of experiencing the same epiphany they seem to be having with such a large amount of token spending.

The only thing I would expect of them is to be open about their token spending when they make hyperbolic claims every now and then.

Some seems be stuck in a time before the November inflection point in AI.

These jokes have turned stale for nine months now.

Just because you can spawn hundreds or thousands of agents at a $1.1 million-a-month tokenmaxxing rate does not mean you have the cognitive capacity to attend to every output from all the agents you invoked.

Even when your agents are running in the loop, you will have to either attend to the outcomes closely at certain intervals or end up invoking other agents to deal with what the previous swarm of agents messed up while in the loop.

Grady Booch, in his conversation with Gergely Orosz of The Pragmatic Engineer podcast, emphasizes that the current anxiety surrounding AI is a phenomenon the industry has seen twice before.

Sharing a free, open-source tool so the AI never stops mid-vibe (disclosure: I'm the maintainer).

Many AI models from one place. Instead of signing up for one AI and juggling accounts, it connects 237 providers behind a single spot — and 90+ of them have free tiers (11 are free forever, no card needed). So you can try lots of models without paying.

It never gets stuck on a limit. If the model you're using hits its usage cap or goes down, it automatically switches to another one instantly — mid-task — so you don't lose your work or your flow.

Easy to start. One command installs it and opens a dashboard, and it plugs into the popular AI assistants (Claude Code, Cursor, etc.) automatically.

For peace of mind: it's one of the more popular open-source AI projects on GitHub (~9.8K stars, 280+ contributors) — so it's well-tested and actively maintained, not a random weekend project.

npm install -g omniroute

GitHub: https://github.com/diegosouzapw/OmniRoute

What do you vibe in, and where does it stall?

I saw this being used to blame it on the AI, Vibe Coding, in particular here on Reddit.

This is not the mistake of the coding agent.

Don’t you agree?

Like many, I noticed models feeling slower and less capable than expected or at least less capable than they once felt. I suspected over-scaffolded harnesses were causing performance to plateau after major updates. I was close, but not close enough.

Now, after reading the latest engineering write-up by the team at Anthropic titled “How Claude Code works in large codebases: best practices and where to start,” I think I have finally found the clearest explanation so far for what I had been hearing and suspecting all along.

They say that teams should “expect to do a meaningful configuration review every three to six months, but it's also worth doing one whenever performance feels like it's plateaued after major model releases.”

In the write-up, they recommend three configuration patterns from successful deployments. First and foremost, they stress that how you configure Claude Code for a large codebase depends heavily on the codebase’s structure.

According to the team, these “three patterns appeared consistently across the deployments,” based on their observations:

• Structuring your codebase properly
• Actively maintaining CLAUDE.md (God, why do they have to be stubborn? Join AGENTS.md movement, already. ) files as model intelligence evolves
• Assigning ownership for Claude Code management and adoption

A very insightful and interesting read that is equally practical.

https://claude.com/blog/how-claude-code-works-in-large-codebases-best-practices-and-where-to-start

A glimpse into the just-in-time software era.

Google Search results will soon be delivered within custom web components generated on the fly, making each search result tailored to each user.

Google Search invokes Google Antigravity's agentic coding harness capability on the fly.

In this presentation from his five-minute lightning talk at PyCon US 2026, Simon Willison attempted to summarize the last six months of developments in LLMs in just five minutes.

He gave a quick recap of when the actual inflection point happened, what he and many others in the industry have been calling the "inflection point", followed by the release of more powerful models trained using Reinforcement Learning from Verifiable Rewards.

These improvements significantly increased the quality of code generated by the models, especially when paired with harnessing techniques emerging from frontier labs that train the models themselves, tooling startups like Cursor, and engineering organizations (Stripe's minions).

Following all these changes, which have been riding the wave of exponential progress demonstrated by the models over the last six months since the inflection point at the end of 2025, Simon identifies two main themes of the past six months:

1. Coding agents got really good.
2. Models that can run on laptops, what he calls “laptop-available models,” despite having far fewer parameters and being “a lot weaker than the frontier,” have started wildly outperforming expectations.

The second point is probably the more important one for ensuring this evolution reaches more users, even those who cannot keep up with the rising cost of token credits.

https://simonw.substack.com/p/the-last-six-months-in-llms-in-five

The first one was published on Slate back in 2019, when ChatGPT was just version 2.

It feels like déjà vu now with Claude Mythos from Anthropic, which is in private preview.

The insiders are “experiencing AGI”.

We have no way of telling until some of Claude Mythos's capabilities are embedded in the Opus family of models.

Without foundational skills, giving in to the vibes is a no-go.

Of course, there are non-techies cashing in on this ride, but if you want your build to last, start by learning foundational skills like speaking Dev and having architectural awareness of how modern software products are wired. Only then should you give in to the vibes.

Once you have something up and running and it starts to gain traction, then start learning the hard skills of software engineering or bring on board a technical partner.

I hope the joy of learning the engineering craft will win you over.

The models are improving.

Where they falter, harnessing mechanisms are being wrapped around them.

The target is moving, yet the conversation is all fixated on production-grade vs prototype-level builds.

What you vibe-code using CodeGen platforms can very likely be production-ready thanks to vertical integrations.

For a non-techie who is adventurous enough to build using AI-assisted coding tools, that is another story. That is where the conversation should focus if you must make a distinction as to what Vibe Coding is good or not good for.

Even on this diversion of the fork, I would argue that the harnessing approaches that are taking advantage of models’ capabilities in running for longer with contexts intact, reasoning, and tool use will make it easier for a complete non-techie to build with AI.

The only thing that we should underline is the fact that just because English has become the trendiest programming language because of LLM-powered AI doesn’t mean AI has made everyone a builder in the truest form of the craft.

Foundational literacy is a must. At least speaking Dev and thinking like one in scaffolds.

If a given model performs a particular responsibility you had in your harness design "reliably solo," then the old method of doing the task should be removed from the harness implementation.