AI

AI System Generating Minecraft Mods (97% Working)

Hacker News

AI System Generating Minecraft Mods (97% Working)

Hey folks, I've spent the last several months building an AI-driven system that generates Minecraft mods and plugins e2e. It's an agentic workflow that writes code, runs it through the standard toolchain, and handles the usual compile-time and simple runtime issues as part of the loop. The interesting part has been making the whole process reliable across multiple modding ecosystems and version targets. The system currently supports generating server plugins (Spigot), server and client mods (Fabric, NeoForge), and Cobblemon mods for Fabric. At this point the system has produced several hundred mods, many of which are running in real production environments. What's been interesting is how people actually use it: about 52% of users who generate a mod come back to create more, and 42% iterate on their mods through conversational refinement with the AI. Roughly 97% of generated mods compile and run successfully on the first pass. That said, 'successful' doesn't always mean pixel-perfect alignment with the user's initial request. The model sometimes drifts near the edges of the spec, especially when the request sits outside a clear operational boundary. The broader goal is to democratize access to building Minecraft experiences, client-side, server-side, or hybrid. Today, creating custom mechanics often requires either deep familiarity with modding toolchains or paying hundreds or thousands of dollars for bespoke plugins and long-term developer contracts. I want people to be able to rapidly prototype ideas, experiment, and ship things that would've been too expensive, too slow, or simply inaccessible before. I'm interested in feedback on the approach, the architecture, and the failure modes people expect as requests grow more complex or cross modloader boundaries.

Share card

Actual performance

4points
2comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
18%18% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

A
A fine-tuned Stable Diffusion model for generating Minecraft skins67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A fine-tuned Stable Diffusion model for generating Minecraft skins

Hacker News17
Ge
Generating Collisions on NeuralHash43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Generating Collisions on NeuralHash

Hacker News22
FU
FUSE Filesystem for Manipulating Minecraft77%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

FUSE Filesystem for Manipulating Minecraft

Hacker News14
A
A Metaverse on Minecraft56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Metaverse on Minecraft

Hacker News2
Sh
SharinGAN - Generating Naruto Sharingans with GANs43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SharinGAN - Generating Naruto Sharingans with GANs

Hacker News1
Gr
Gravatar for Minecraft56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Gravatar for Minecraft

Hacker News10
GT
GTA x MINECRAFT56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GTA x MINECRAFT

Hacker News3
Wo
Working at BuzzFeed48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Working at BuzzFeed

Hacker News2
A
A working example of Puppeteer on Glitch72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A working example of Puppeteer on Glitch

Hacker News4
Ha
HackerBracket – What are you working on?48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

HackerBracket – What are you working on?

Hacker News60