© 2026 Podread
About·Privacy·Terms·Refunds
  1. Discover
  2. ›Code With Antonio
TECH · YOUTUBE

Antonio Wants to Teach the Mess, Not the Finish

Code With Antonio says his old format, a polished rebuild, hides the part of coding that matters most now: dead ends, refactoring, and judgment under pressure.

Code With Antonio·April 21, 2026·6 min read

Code With Antonio is a software educator on YouTube known for long programming tutorials and build-along videos. In this conversation, he argues that AI has changed what useful instruction should look like, and that his new workshop format is meant to show how he thinks, not just what code to type.

Antonio is trying to retire the clean tutorial as his main teaching form. He says the old model, in which he first solves a project for himself and then records a smooth rebuild for viewers, now hides the parts of coding that matter most: the architecture choices, the debugging, the dead ends, the judgment calls. In the age of AI, he argues, a video that merely gets from point A to point B is less useful than one that shows how a developer decides what point B should be. His answer is a new workshop built around agentic coding, where the mess is the lesson and the goal is to teach viewers how to think like him.

The clean tutorial is the problem

Antonio opens by describing the old formula behind his channel: he builds a project once for himself, settles the architecture, fixes the bugs, and then records a second pass that appears smooth and inevitable. The appeal is obvious. Viewers can copy along and, as he puts it, end up with a finished project after enough time.

I build it for myself first. In this first build, I decide what the architecture is going to look like.

0:26

With this method, everything that I do when I record myself works on the first try.

0:56

He now sees that format as less valuable than it once was. The point of a tutorial cannot simply be to hide friction, because AI already changes the cost of producing code and the appetite for watching someone reproduce a polished path.

CounterpointAn AI education critic

Antonio treats smoothness as the main weakness of classic tutorials, but polish is also what makes them legible. For many learners, a clean first pass reduces cognitive load before they are ready to study failure modes. The harder question is not whether mess exists, but how much of it a newcomer can absorb without getting lost.

AI changes the lesson

His larger claim is that coding education needs to move because the old promise is less useful in the age of AI. Three years ago, he argues, a long step-by-step rebuild could still feel like a practical shortcut. Now it risks training viewers to follow instructions rather than to make decisions.

In the age of AI, this simply isn't as useful as it might have been 3 years ago.

1:27

So it's time to rethink how tutorial should look like.

1:27

The alternative he sketches is more chaotic and more honest. Instead of hiding the first attempt, he would record the first attempt itself, including the places where he gets stuck, changes direction, and has to debug in public. The point is not efficiency alone; it is to make the viewer watch the reasoning that produces the code.

CounterpointAn AI industry critic

Antonio is right that AI lowers the value of rote imitation, but that does not automatically make process footage better. A first-take recording can become a long search log if the underlying choices are not made explicit. The educational win depends on whether the instructor can narrate judgment, not just expose struggle.

You will understand how I think rather than understand what is the next line of code.

2:04

Agentic coding versus vibe coding

That argument leads him to a distinction he says many people blur: vibe coding and agentic coding. In his telling, vibe coding is outcome-first and spec-light, often used by nontechnical people who let the model run autonomously with minimal human involvement. The prompts are blunt, sometimes almost comic in their ambition: build me a Facebook clone, build me a YouTube clone, build me a million-dollar SaaS.

Vibe coding is essentially only focusing on the outcome rather than the technical specification that goes into building a new feature or an entire new app.

3:16

They just want to let their AI run autonomously.

3:56

Agentic coding, by contrast, is how he wants to work. The human stays in charge as architect and orchestrator, writing prompts that reflect an existing grasp of the codebase rather than a wish to outsource it entirely. In his example, a prompt about consolidating a scattered service across five files is less a plea for help than a compressed design brief.

You are the orchestrator. You are the architect.

4:27

They are simply using it to speed up what they would do anyway themselves.

5:05
CounterpointAn AI software methods analyst

The distinction is useful, but it is also a continuum. Many professional developers use AI in ways that sit between Antonio's two categories, sometimes exploratory, sometimes tightly specified, often both in the same day. The real divide is not whether AI is present, but whether a human can still explain and defend the system they are building.

A workshop instead of a marathon

Antonio says that shift finally made his ideal teaching format possible. If AI can compress hundreds of hours of thinking and typing into a few hours of prompting, then the material no longer has to be trapped inside a 10-hour tutorial. He is building a two-week workshop instead, with a new module arriving every few days.

I can now reduce hundreds of hours of thinking and typing into just a few hours of prompting.

5:30

In this workshop, you can digestibly consume this content.

5:38

He presents the workshop as a different kind of learning contract. On YouTube, he says, viewers can listen passively while he lands on the next line of code. In the new format, they have to watch his prompts and his thought process, then reuse that pattern in their own codebase. The project will not be identical from one student to the next, and he sees that as a feature rather than a flaw.

You are almost forced to actively observe my thoughts and my prompts.

6:13

I don't want you to clone what I do. I don't want you to copy what I do.

6:36
CounterpointAn AI education critic

Workshops can make judgment visible, but they also narrow access. A learner who wants a quick answer may prefer the old format, especially when they are trying to ship something after work, not study a method for two weeks. The pedagogical bet is that depth will attract the audience willing to pay attention long enough to benefit from it.

The channel's next bargain

His final claim is less about AI than about audience expectations. Antonio wants to keep the high-production tutorials, but he also wants smaller, more targeted videos that lower the barrier to entry. He floats examples like an hour of refactoring or an hour of fixing errors, formats that would show more of the real work without demanding a full day from viewers.

I want to teach you how to code like Antonio, not how to code with Antonio.

7:00

I want to start a new direction of this channel where I still produce high-quality tutorials but also give you a lower barrier to entry.

8:05

He frames the new direction as both a product launch and a test of what viewers actually want from him. The workshop comes with a 40% discount, early access to source code, live office hours in Discord, and a cohort experience that disappears once the program starts. Beneath the sales pitch is a more durable question: whether audiences want a finished project, or the discipline to build one badly, then better.

CounterpointAn AI business model critic

The package mixes education with scarcity marketing, a familiar move in creator-led tech products. Early access and cohort framing can increase perceived value, but they can also blur whether the draw is the curriculum or the urgency. Antonio is betting that a more honest teaching style will sell as a premium experience, not just as content.

Note

  1. 1. Vibe coding usually means using AI to generate code with minimal planning or formal specification. The term is commonly used in developer discussions to contrast casual prompting with more deliberate engineering workflows.
  2. 2. Agentic coding describes a human-led workflow where AI acts as an assistant or agent, but the developer still sets the architecture, constraints, and technical decisions.

FAQ

What is agentic coding, according to Antonio?

Agentic coding is a human-led workflow where AI helps speed up work the developer would still do themselves. Antonio says the human remains the architect, writing prompts that reflect the codebase and its constraints.

How does Antonio define vibe coding?

Vibe coding focuses on the outcome more than the technical specification. Antonio says it is often used by nontechnical people who want AI to run autonomously with minimal human involvement.

Why is Antonio changing his tutorial format?

He says the old rebuild format hides the real work of coding, especially now that AI changes how people learn and build. His new workshop is meant to show how he thinks, including dead ends and debugging.

What will the new workshop include?

Antonio says it will run for two weeks, with new modules dropping every few days. It also includes early access, Discord office hours, and live cohort-style access before the program starts.

Will Antonio stop making long tutorials?

No, he says he still wants to make high-quality tutorials. He also wants shorter formats, such as sessions focused only on refactoring or fixing errors, to lower the barrier to entry.

  • AI
  • Coding Tutorials
  • YouTube
  • Agentic Coding
  • Vibe Coding
  • Software Education
  • Discord

AI-assisted summary of Code With Antonio's podcast, verified against the original transcript.

SourceWatch on YouTubeCode With Antonio↗
pod.readme
← BACK←
Sign in

Keep reading

  • Naval

    Perché Naval vede la fine dell’iPhone

    Naval spiega perché i coding agent cambiano software, venture capital e iPhone, e perché pensa che il pure software stia perdendo valore.

  • Fireship

    The 732-byte bug that shook Linux

    Fireship рассказывает о vulnerability in the Linux kernel, discovered by an AI agent, already exploited, and useful as a case study in AI-tool marketing.

  • Theo - t3․gg

    Google, il prezzo della sfida a Mountain View

    Theo Browne attacca Google su AI, terminale e cloud: modelli costosi, CLI difettosa, fiducia interna spezzata.

  • Theo - t3․gg

    Perché Theo vuole meno Markdown e più HTML

    Theo sostiene che l’HTML stia diventando un formato migliore del Markdown per gli agenti: più leggibile, interattivo e utile nei flussi di lavoro.

  • Aman Manazir

    Come ha lasciato Google con quattro offerte

    Ha lasciato Google con quattro offerte in mano e racconta perché il mercato tech premia timing, referral e adattamento all’AI.

  • Naval

    Naval: l’AI sta spingendo verso grafi, non gerarchie

    Ravikant sostiene che l’AI stia già cambiando aziende, hardware e guerra, mentre il settore si concentra in poche mani.