Last updated: 2025-08-25
When I stumbled upon the Hacker News story titled "Making games in Go: 3 months without LLMs vs. 3 days with LLMs," it struck a chord with me. As a developer who loves to dabble in game development during my free time, I couldn't resist the urge to dive in and understand how leveraging large language models (LLMs) could transform the game development process. I’d spent countless evenings coding simple games in Go, grappling with its quirks and nuances. My journey meant learning the 'Go way' of doing things, which came with its share of triumphs and complexities.
The past three months had been a whirlwind of coding challenges. I decided to make a straightforward 2D platformer using Go. My tools of choice were Go's native libraries, with some additional third-party packages. It felt like a clean slate at first, but as I progressed, I found myself often lost in the dense thicket of questions: "Should I use concurrency to manage game states? How can I efficiently render graphics?"
Each day would begin with a thorough read of the Go documentation, combined with frantic Stack Overflow searches (no shame there!). As I realized, game development isn't merely about coding; it requires a solid grasp of design principles, physics simulations, and, in many cases, a good understanding of game engines. The struggle was real and palpable, but I reveled in it. Every bug fixed felt like a small reward. I’d finally managed to get sprites moving smoothly across the screen and had even implemented a rudimentary collision detection algorithm. But my progress was achingly slow.
Then came the pivotal moment when I read about developers leveraging LLMs to expedite their processes. The juxtaposition of '3 months vs. 3 days' was both intriguing and hauntingly intimidating. As someone who’s often skeptical about the buzz around AI tools, I initially viewed this with a mix of curiosity and skepticism. Could a language model truly revolutionize a task as inherently creative as game development, or was it merely a flashy gimmick?
After much internal debate, I decided to test the waters myself by creating a new game concept using an LLM. I set up my environment to incorporate GPT-4 into my workflow, with the goal to see how much easier things could become. I structured my queries to the model carefully—a mix of coding prompts and game design questions. For instance, when faced with the task of creating an animated character, instead of diving into the minutiae of Go libraries, I simply asked, “Can you provide me with a snippet to create animations using Go?” The response was prompt, relevant, and oddly poetic in its structure.
The rapid scaffolding provided by the LLM was awe-inspiring. With just a few targeted prompts, I had solid foundations in place for character mechanics, AI enemy behaviors, and even some fun power-ups! It was as if I had my very own virtual coding assistant, churning out code snippets and offering guidance that would have taken me hours to figure out on my own during my previous three-month adventure.
What struck me most was how the LLM encouraged an iterative approach. Instead of getting bogged down in feelings of frustration or mental fatigue, I felt invigorated by the ability to rapidly prototype different ideas. In those three days, I had already tackled more than I had in the previous three months—creating multiple characters, establishing level designs, and incorporating sound effects. I even revisited concepts I struggled with earlier, like physics simulations, and was able to generate efficient code that I would have agonized over relentlessly.
This playful dynamic transformed my outlook on coding. Instead of viewing it as a challenge to overcome, I embraced it as a collaborative game. I was no longer a lone coder battling the complexities of my own creation but rather a participant in a dialogue with the LLM, where I could ask questions like, “What’s an effective way to implement a power-up system?” and receive insights almost instantaneously. This discovery made me giddy. There’s something truly liberating about breaking down barriers that would have otherwise taken months of trial and error.
Yet, while I was riding high on this wave of productivity, I had to confront the elephant in the room: A reliance on LLMs comes with its own set of challenges. For one, I started noticing a subtle shift in my coding style. The more I depended on the model, the less I engaged deeply with the principles of Go. My appetite for learning diminished, and before long, I found myself regurgitating code snippets without fully digesting the underlying logic. It was like being spoon-fed answers on a test without truly studying the material.
Moreover, there were moments when the LLM-generated code was far from perfect. I often had to sift through its suggestions and make adjustments—sometimes significant ones. This was a constant reminder that while these models can certainly expedite development, they aren’t foolproof. The LLM’s responses sometimes contained errors or assumptions that didn't quite fit my specific use case, leading to confusion later on.
After my experiment, I reached a critical juncture where I realized the best fruit was not from solely relying on LLMs but rather embracing a hybrid approach. By combining the speed and convenience offered by LLMs with traditional coding disciplines, I could strike a balance that enhanced my skill development rather than stunting it. LLMs can serve as a set of training wheels—empowering my learning without overshadowing it. From that point forward, I treated it more like a pair of smart glasses that enabled me to see opportunities I might have missed.
This journey reaffirmed my love for coding games while also opening my eyes to the promises and hurdles of AI in our industry. I could harness this power to fuel creativity but also ensure that I continued to learn and grow as a developer. Perhaps the most poignant lesson was that in the fast-paced era of technology, we still need to strike that fine balance between leveraging tools and nurturing our craft.
As I look forward, I’m excited about where this hybrid approach might take me in my future projects. My experience unveiled a new path, allowing me to contemplate more ambitious projects—multiplayer games, complex narrative arcs, or even VR experiences. These concepts once felt too overwhelming, but with the right mindset and tools, they seem more achievable. There’s an incredible sense of community in game development, and I’m eager to share both my struggles and successes with like-minded developers.
In conclusion, the journey of game development is as much about the process as it is about the product. Whether using Go or LLMs, the path is fraught with challenges, but it’s in those challenges that we thrive. Combining our traditional skills with new tools will undoubtedly expand our horizons, refine our skills, and ultimately lead us to create more engaging and immersive experiences. Here’s to the next chapter of that journey!