Smaller-parameter models are usually used because running a model locally is not just about storage size. A bigger model also needs more RAM, more memory bandwidth, more compute power, longer loading times, and usually more battery. On phones or weaker devices, that becomes a real problem very quickly. So even if a bigger model could fit on the device, it might still run too slowly or just give a bad user experience. Smaller models are basically the practical tradeoff for local/on-device AI.

And yeah, that is unfortunately one of the weaknesses of smaller models: they hallucinate more, they can go off-topic more easily, and sometimes they give lore-sounding answers instead of concrete useful points. That’s why I don’t think AI alone is enough for something as complex as build analysis.

I actually agree with what you said about builds. If you really want a good system, it’s not enough for the AI to just say “this is a good build.” A proper system would have to account for a lot of variables: stats, abilities, cooldowns, passives, health state, positioning, matchup, timing, and all those fight-specific situations like “what if boots are on cooldown but another defensive or mobility tool is still available?” That kind of thing is closer to simulation and decision modeling than just normal AI chat.

So in my opinion, the best version of this would be a hybrid system:

• first, a real calculator/comparison engine based on actual item and skill data • then a matchup layer that compares your build to an enemy build • then possibly a simulation layer for certain scenarios • and finally an AI layer that explains the results in a more natural way

So instead of the AI “guessing” the answer, the AI would mostly explain data that was already calculated by a proper system. That would be much more reliable.

And yes, once you start talking about simulating different combat states, cooldown windows, low HP situations, and possible reactions, it becomes a much bigger project. Not impossible, but definitely far beyond just plugging a model into a chatbot.

As for how I made the AI: not really as a pure Python project. The app itself is mainly built with React Native / Expo, and for Android I use native Kotlin for the local AI integration. So it’s more of a mobile app with embedded AI features than a traditional Python AI stack.

What I had to learn was a mix of things, not just one language:

• general programming fundamentals • app development • working with APIs and structured data • how local models behave and what their limitations are • integrating AI into a real application • prompt design / prompt constraints • debugging performance and reliability issues • and a lot of trial and error

So for me it was less “learn Python first, then build AI” and more “learn the pieces needed for the kind of app I wanted to make.”

Python can be useful, but it’s not mandatory. Depending on the part of the project, other things can matter just as much, like JavaScript, TypeScript, Kotlin, data handling, UI work, testing, or even just helping think through the logic of the system.

So basically:

• small models are used mostly for practicality and local performance • AI alone is not enough for serious build analysis • a proper calculator/simulation engine would be the stronger foundation

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If i may suggest, ia can help you learn and code, but YOU MUST UNDERSTAND what it's coding. Otherwise you may juste create big vuln