Do you haz teh codez? Is AI about laziness or productivity?

Me: Do you haz teh codez? ChatGPT: I haz teh codez! What specific code are you looking for? 😄


We love to joke around.

The phrase comes from the early 2000s internet meme culture, inspired by ‘LOLspeak’—a playful, deliberately broken form of English popularized by the ‘LOLcats’ meme, ‘I Can Haz Cheezburger?’ This lighthearted meme style quickly spread across tech communities, and phrases like that became a fun way for programmers to signal they’d completed a task or had knowledge to share.

A variation played into the stereotype of someone lazily asking for solutions without putting in effort or formulating proper questions. This version was often used in forums or chatrooms to poke fun at users who would hastily ask for code or answers without showing any work or understanding of the problem.
 The broken English was a satirical way to mimic this behavior, implying a lack of effort in both coding and communication. It also highlighted the frustration of more experienced developers who felt like some users were relying too heavily on others to solve their problems, rather than trying to learn or troubleshoot on their own.

In a sense, it became a meme within tech communities, used to gently (or not so gently) mock those who sought quick fixes without understanding the underlying concepts, often referred to as “help vampires.”

But ChatGPT doesn’t care if I’m lazy or not. It simply aims to get me to a solution, without ever growing tired or frustrated. This might seem like it’s enabling laziness, but I’d argue it’s more about efficiency.

ChatGPT lets me focus on the higher-level problem-solving while it handles the grunt work of code generation, debugging, and providing quick solutions. And the more I iterate with it, the better I understand the code.

Like most developers, I rely heavily on Google searches and the usual developer haunts like Stack Overflow or blog articles to solve coding issues.

While these resources have been great, I often find myself sifting through outdated or irrelevant answers, combing through endless comment threads, or wasting time on lengthy articles just to find one snippet of useful information.

That changed once I started using ChatGPT for coding help.

Now, instead of navigating through the wild west of search results, I can simply ask ChatGPT for code examples, troubleshooting tips, or even detailed explanations for specific coding problems.

Whether it’s helping me fine-tune a service class, debug a tricky asset pipeline issue, or generate scaffolding for a new feature, ChatGPT gives me direct, practical solutions.

Recently, when working with the Mastodon API in Rails, ChatGPT has often been quicker and more on-point than sifting through older, less reliable tutorials or (shudder) the API documentation itself.

In many ways, ChatGPT has become my first port of call for problem-solving, leaving Google searches and Stack Overflow as backups rather than primary resources.

While ChatGPT has become my go-to tool for coding, we all know that it’s not infallible. Just like any other resource, it occasionally provides incorrect or incomplete solutions. This is where the experience diverges from traditional platforms like Stack Overflow, where the community-driven voting system helps surface the most accurate and reliable answers.

On Stack Overflow, answers are often vetted by thousands of developers, and top responses gain credibility through upvotes and comments, offering a degree of confidence in their accuracy. With ChatGPT, there’s no such communal vetting process. The AI generates its responses based on patterns and information from a large dataset, but it can occasionally miss context or misinterpret a query.

For example, ChatGPT might suggest syntax or methods that are outdated, or it might not fully account for edge cases specific to your application. When this happens, it’s essential to double-check the solution, test it thoroughly, and trust your own experience and instincts.

I’m curious to see how we might combine human vetting with AI-generated solutions. Imagine a new version of Stack Overflow where AI answers are paired with human insights, allowing for an even faster, more accurate troubleshooting process. Developers could benefit from the instant suggestions of AI, vetted and improved upon by community feedback—creating a virtuous cycle where the best solutions rise to the top.
 ChatGPT brings a blend of speed and convenience that traditional platforms like Stack Overflow can’t match. While I treat its responses as a starting point rather than the final word, when combined with personal experience and careful testing, the results are an order of magnitude better than sifting through ad-bloated search results.

In the end it’s all about the results.