Vibe coding and agentic engineering are getting closer than I'd like

https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/

#HackerNews #vibecoding #agenticengineering #techtrends #futureofcoding #innovation

Stop Trading Time for Syntax: 5 Google AI Secrets That Build Better Software Faster

1,209 words, 6 minutes read time.

Google is currently deploying a suite of AI-driven development tools that are fundamentally rewriting the rules of the software engineering industry. For the developer, engineer, or tech enthusiast, these tools—ranging from Project IDX to Gemini Code Assist—represent a shift from manual syntax labor to high-level architectural oversight. This evolution is occurring right now across Google’s global cloud infrastructure, providing men in the tech space with the ability to deploy complex applications with a fraction of the traditional overhead. By integrating these specific AI models, Google aims to eliminate the “grunt work” of coding, allowing creators to focus on the logic and scale of their projects.

You know that feeling when you’re under the hood of a project and the tools you’ve used for a decade suddenly feel like a blunt chisel trying to carve a diamond? That’s the current state of traditional local development. We’ve spent years perfecting our local setups, but the reality is that the “secret” shift Google is pushing with Project IDX is about to make your local environment look like a collection of rusty wrenches. Project IDX isn’t just another IDE; it’s a full-stack, AI-integrated workspace that runs in the cloud but feels like it’s right under your fingertips. It’s built on a foundation of Nix, meaning it’s reproducible and powerful, giving you the kind of consistency a man needs when he’s moving from a desktop to a laptop without wanting to spend four hours reconfiguring dependencies.

If you’ve ever hit a wall at 2 AM trying to figure out why a Docker container won’t spin up, Gemini Code Assist is the partner that doesn’t sleep. It’s like having a senior architect looking over your shoulder, but one who actually knows every line of documentation ever written. Google has designed this to go deeper than the basic “fill-in-the-blank” AI we’ve seen before. It understands the context of your entire codebase. It doesn’t just suggest a line of code; it suggests a way to refactor your entire data flow to prevent the bottleneck you didn’t even see coming. It’s about maintaining the lead in a competitive market where speed is the only currency that matters.

The reality is that coding has always been a battle of attrition against bugs, but Google’s new Firebase Genkit changes the theater of operations entirely. Instead of spending your weekend wrestling with manual schema migrations or broken backend integrations, Genkit allows you to build AI-powered backends with a level of precision that feels almost unfair. It’s about building a framework that is rugged enough to handle real-world traffic while being flexible enough to pivot when your requirements change. For the man who values efficiency, this tool effectively removes the friction between a great idea and a live, functioning deployment.

For the guys who like to build in the shadows, keeping their data close to the chest, the introduction of Gemma—Google’s open-weight model—is the real game changer. You can run these models on your own hardware or within Google’s free Colab environments to get the power of a massive LLM without the privacy concerns of sending your proprietary logic to a third-party server. It’s raw, it’s powerful, and it allows you to build custom tools that are yours and yours alone. Using Colab’s free GPU tiers to fine-tune a model for your specific niche is the modern equivalent of forging your own custom blade. It’s about having the right gear for the specific hunt you’re on.

We have to talk about the sheer leverage provided by Android Studio’s latest AI integrations. If you are developing for mobile, you know that the fragmentation of devices can be a nightmare—it’s like trying to fit a square peg into a thousand different sized round holes. Google’s AI bot within the IDE doesn’t just fix typos; it assists in optimizing layouts and handling background tasks in a way that respects the hardware. This isn’t about being lazy; it’s about being effective. It allows a solo developer to output the volume of a ten-man agency, reclaiming your time and ensuring your product hits the market before the window of opportunity slams shut.

The marketplace doesn’t care about how hard you worked; it cares about what you shipped. These Google tools are designed to take the friction out of that process. Whether it’s using Firebase Genkit to rapidly deploy a backend that actually scales or leveraging Chrome’s built-in AI to run models locally in a user’s browser, the goal is total dominance of the stack. We are moving into an era where the “expert” isn’t the guy who memorized the most API calls, but the man who knows how to orchestrate these AI agents to build something that lasts. The barrier to entry is dropping, which means the competition is getting fiercer. If you aren’t using these tools, you’re trying to win a drag race in a minivan.

This is the new standard, and it’s evolving faster than most can keep up with. The developers who thrive in the next five years will be those who embrace this “free coding” era—not because they want to work less, but because they want to build more. We are seeing the democratization of high-level engineering. The future belongs to the builders who aren’t afraid to put down the old tools and pick up the new ones, even if the learning curve feels like a punch to the gut at first. It’s time to stop fighting the syntax and start building the vision.

If you’re ready to stop grinding and start scaling, I want to hear about what you’re building. Drop a comment below with the project that’s been sitting on your back burner, or reach out to me directly if you’ve found a shortcut in the Google ecosystem that we haven’t covered yet. Don’t forget to subscribe to the newsletter—we don’t do fluff here, just the raw tech and tactics you need to stay ahead of the curve. Let’s get to work.

Call to Action

If this breakdown helped you think a little clearer about the threats out there, don’t just click away. Subscribe for more no-nonsense security insights, drop a comment with your thoughts or questions, or reach out if there’s a topic you want me to tackle next. Stay sharp out there.

D. Bryan King

Sources

• Google Gemini Code Assist: The Enterprise AI Evolution
• Project IDX: Google’s Next-Generation Web-Based IDE
• Google Colab: Free Python Environments with AI Integration
• AlphaCode: Competitive Programming with DeepMind
• Google Cloud Generative AI for Application Integration
• The Gemini API Cookbook: Practical Implementation Guides
• Gemma: Google’s Open-Source Models for Private Coding Environments
• Android Studio Bot: Streamlining Mobile Development
• Firebase Genkit: Building AI-Powered Backends
• Chrome Built-in AI: Running Models Directly in the Browser
• Vertex AI for Developers: Custom Training and Deployment
• Google Open Source: Kpt and Configuration Management Tools
• TechCrunch: Analysis of Google’s Competitive Edge in AI Dev Tools
• The Verge: The Impact of Project IDX on the Developer Workflow

Disclaimer:

The views and opinions expressed in this post are solely those of the author. The information provided is based on personal research, experience, and understanding of the subject matter at the time of writing. Readers should consult relevant experts or authorities for specific guidance related to their unique situations.

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Vibe Coding Explained: How AI Is Changing Software Development Forever

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AI is Making Libraries Obsolete

Summary: AI agents are fundamentally changing the economics of general-purpose libraries. Why build on Bootstrap when an agent can generate exactly what you need? A collection of thoughts on where this is all heading.

The Great Library Unbundling: How AI is Eating the Software Stack

More than a year ago, I told people that ORMs like Entity Framework were going to feel like relics. That LLMs would just generate the SQL you need, making all that object-relational mapping overhead pointless. A few people told me I was crazy and doing things wrong (and to be honest I wasn't good enough at explaining why I thought this).

Well, time seems to have been on my side (Wwo is laughing now hahahaha!!).

Now, this is a hot take, so I will go forward and talk as a hot take. If I put my devil's advocate hat on, I would probably rage against whoever wrote this post. But because I LOVE MYSELF, I won't do it. I certainly see flaws in my arguments. So buckle up—this is not rage bait, it is just a hot take from a Mexican developer who spent the weekend arguing with AI agents instead of sleeping like a normal person.

The ORM Prediction, or How I Accidentally Became Right About Something

Here's the thing about ORMs: they exist because writing SQL is supposedly "hard" and "error-prone." Meanwhile, I've been writing raw SQL since my first job at a tiny shop in Tepic where we didn't have the luxury of Entity Framework or Hibernate or whatever fancy abstraction layer the cool kids were using. We just wrote the queries, crossed ourselves, and hit execute. Catholic upbringing has some unexpected engineering applications.

Now, to be fair—we did end up building our own tailored ORM for the common cases. Because once you've written the same parameterized INSERT fifteen times, even the most stubborn raw-SQL purist starts thinking "maybe I should abstract this." And ORMs do earn their keep in some areas: SQL injection prevention, enforcing parameterized queries, handling connection lifecycle stuff that nobody wants to think about. Security guardrails that your 2 AM brain will absolutely forget to implement on its own. I get it. ORMs aren't stupid—they solved real problems.

But here's the trap: as soon as you start getting into complex queries, you're no longer just maintaining your application logic—you're also maintaining the ORM and the complex queries. You end up fighting the abstraction to make it do what raw SQL does naturally, and now you have two problems instead of one. The thing that was supposed to simplify your life becomes another layer you have to debug, optimize, and keep in your head at the same time. And whoever has run a high-performance application—like an ecommerce site during Black Friday—knows exactly what I'm talking about. That query you spent weeks trying to optimize through the ORM, doing quirky reflection tricks and fighting with expression trees? You ended up raw-dogging the SQL anyway, because that was the only way to get the performance you needed. The ORM was never going to get you there. It was just standing in the way, politely.

Now Claude just writes you the exact SQL you need—parameterized, injection-safe, the whole deal. No mapping, no configuration, no surprises. Just the data access pattern you actually want. It's like having a DBA who never sleeps, never complains about schema changes, and doesn't passive-aggressively CC your manager when you write a bad join. The abstraction layer that was supposed to save us from SQL is now the thing standing between us and the AI that's better at SQL than most of us ever were.

Agentic Development and the Death of One-Size-Fits-None

This isn't just about ORMs. I've been thinking about how agentic development fundamentally changes the whole premise of general-purpose libraries and building blocks.

Platform teams across the industry are going to start questioning their investments in broad, one-size-fits-all libraries. And honestly? Good. Because "one-size-fits-all" always meant "fits nobody perfectly but everyone tolerably." Like those beach ponchos they sell at every tourist shop in Nayarit—technically covers you, technically functions, but you look ridiculous and you know it.

Take CSS frameworks. Bootstrap, Bulma, Fluent UI—they're great for getting started quickly. But you end up learning their specific class naming conventions, carrying tons of CSS you'll never use, fighting against the framework the moment you want something custom, and—my personal favorite—looking like every other Bootstrap site on the internet. Your site ends up with more unused CSS than empty beer bottles after a Sunday carne asada in Nayarit. And that's a lot of bottles, trust me.

With agentic development? You just tell the agent what you want your UI to look like. It generates exactly the CSS you need. No framework lock-in, no unused code, no learning curve. The agent doesn't need to know Bootstrap's grid system (although it probably was trained on such codebases)—it just writes "vanilla" CSS that does what you asked for. I know this because that's exactly what happened with this blog. The CSS you're looking at right now? Generated. By an agent. Who understood what I wanted better than I understood what I wanted (debatable).

MCP is Already Feeling Old

Speaking of things that are aging quickly: the Model Context Protocol. MCP. Remember when everyone was wrapping every CLI tool as an MCP server like it was the new hotness? That was, what, months ago? In AI time that's basically the Paleolithic era.

Here's the thing—CLI tools already come with perfect documentation in the form of man pages. They're basically documented APIs already. Your agent doesn't need a special protocol wrapper to use gh pr create or az webapp deploy. It just reads the docs, fumbles the first attempt (like any of us), and then figures it out. Combine Claude Code with existing CLI tools—GitHub CLI, Azure CLI, kubectl, whatever—and you've got everything MCP promised, but without the ceremony.

Microsoft's already doing something interesting with their dotnet/skills repo. Skills that are just prompts guiding the agent through repeatable processes. No protocol, no server, no serialization format drama. Just a well-written prompt. Turns out the best API for an AI agent is just... words. Who knew. (Okay, a lot of people knew. But still.)

Hugo Drove Me to Build My Own (Again, Because I Never Learn)

I just finished redoing my entire publishing workflow. Hugo felt limiting and bloated—all these features I'm maintaining, constantly broken by dependency updates, GitHub workflows failing for mysterious reasons. Every time I pushed a new post it was a coin flip whether the build would succeed or I'd spend an hour debugging some Go template issue that made me question my life choices.

So I rewrote it in .NET. Dead simple. I don't expect anyone else to use it—exclusive distribution, zero units available—but damn, it felt liberating. The CSS and HTML of this blog changed completely, and I actually understand every piece of it now. No more cargo-culting Hugo partials I copied from a theme three years ago and was afraid to touch.

This is the pattern I keep seeing: when AI can generate exactly what you need, the appeal of heavyweight, general-purpose solutions just evaporates. Like fog burning off in the morning sun in Tepic. One moment it's there, thick and omnipresent, and then it's just... gone.

The Accountability Problem

Here's something that's been bugging me. I saw a conversation on the fediverse about more and more tools being created with no clear ownership. Developers suspect some might be entirely AI-generated, with humans just shepherding them into existence like zookeepers who can't actually control the animals.

That sucks. Humans should be in charge and taking responsibility for the software they put into the world. There's a difference between using AI as a tool and letting AI be the architect while you nod along pretending you reviewed the blueprints.

When something breaks, when there's a security issue, when users need support—who's accountable? You can't file a bug report against GPT-4. And "the AI did it" is not an incident postmortem. Not yet, anyway. Give it a year (debatable).

Copilot's Infinite Loop of Self-Criticism

Speaking of AI quirks: Copilot keeps finding issues in my PRs, I ask it to fix them, it fixes them, and then when I ask it to review again, it finds more issues. In the code it just wrote. In the code. It. Just. Wrote.

This is like watching someone argue with themselves in the mirror. Except the mirror is burning tokens, the person is burning my budget, and the argument never ends. The AI equivalent of "works on my machine" syndrome, except it doesn't even work on its own machine.

Blog Posts Are Better Than Repos for Teaching AI

Here's something that genuinely surprised me. A friend at Microsoft pointed his AI agent to my blog series to implement ActivityPub. Not to a GitHub repo with code samples. Not to the official W3C spec. To my blog posts. The ones I write at 1 AM like a gremlin-raccoon, full of rambling asides and half-baked metaphors.

Turns out prose explanations with context and reasoning are way more effective for agents than just dumping code at them. Blog posts tell the story of why decisions were made, not just what the final result looks like. The agent needs to understand intent, not just syntax. It needs the narrative—the "I tried this and it broke spectacularly, so I did this other thing instead" part that never makes it into a README.

So all those years of writing meandering blog posts about my projects instead of writing proper documentation? Turns out I was ahead of my time. Or just lazy. Probably both.

Where This Is All Heading

I think we're heading toward a world where general-purpose libraries become luxury items—nice to have, but not essential. Where AI-generated, purpose-built solutions become the norm. Where documentation and prose become more important than code artifacts. And where human accountability becomes the thing that actually differentiates good software from the rest.

This doesn't mean libraries disappear overnight. But the incentives are shifting. Why build and maintain a framework used by millions when everyone can have their own custom solution?

The future might be less about sharing code and more about sharing knowledge, context, and decision-making frameworks. Less "here's my npm package" and more "here's the blog post explaining why I built it this way so your agent can do something better."

Anyway

This post is a collection of half-formed thoughts and observations. I'm not claiming to have all the answers—hell, I'm not even sure these are the right questions. But something is shifting under our feet, and pretending it isn't doesn't make it stop.

Are we heading toward a more fragmented, AI-generated software landscape? Or am I just another old developer yelling at algorithmic clouds from a small corner of the internet?

Honestly, I don't know. But I'd rather be wrong and loud about it than quiet and surprised when the whole toolchain landscape looks unrecognizable in five years.

Also readable in: https://maho.dev/2026/03/ai-is-making-libraries-obsolete/ by @mapache:

#AI #Software Development #Libraries #Agentic Development #ORMs #CSS Frameworks #MCP #Developer Tools #Future of Coding #Hot Takes #LLM #Copilot

Will vibe coding end like the maker movement?

https://read.technically.dev/p/vibe-coding-and-the-maker-movement

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https://techaitoolbox.com/secure-code-review-ai-2026/