I'm building a paid local AI app that automatically tests and selects your best models. Would love some honest feedback.

https://preview.redd.it/k263w3rlnuwh1.png?width=817&format=png&auto=webp&s=aef80c561e7f8a632a19c43c51a96a5d0af6aa59 https://preview.redd.it/wyhag1lzmuwh1.png?width=1917&format=png&auto=webp&s=1cc6425c2791b429876e62918edb7911bcd38ddc https://preview.redd.it/6dadhfb1nuwh1.png?width=793&format=png&auto=webp&s=eaa7157f5abbd647af44b43aef9a0aa023a03621 https://preview.redd.it/3q6itoi2nuwh1.png?width=850&format=png&auto=webp&s=b1629608bfc444a15fc01f486638cda769f37a36 https://preview.redd.it/0tqnmt16nuwh1.png?width=833&format=png&auto=webp&s=e169df35ad659e4edae367bafc83e66b430d9501 Hey everyone! I've been working on a project called ARIS, a local-first AI desktop application that I've been developing for quite some time. My goal is to make local AI smarter, simpler, and more accessible, especially for people who don't want to spend hours configuring models or figuring out which one works best for a particular task. I know there are already fantastic free projects like Ollama, LM Studio, Jan, and Open WebUI. I'm not trying to pretend those don't exist. However, I've been experimenting with a few ideas that I think could make the experience different. 🧠 1. Automatic, Task-Aware Model Selection This is probably the feature I've spent the most time developing. Instead of manually selecting a model every time, ARIS attempts to determine what you're asking and automatically selects the most suitable model installed on your computer. For example, different models might be better suited for coding, reasoning, mathematics, or creative writing. Rather than relying entirely on hardcoded model rankings, I wanted the system to adapt to whatever combination of models the user has installed. 🔬 2. Local Model Testing and Capability Evaluation This is where things get a little more interesting. ARIS performs lightweight capability checks on locally installed models across different task categories. It considers factors such as: Task-specific performance and correctness Model reliability Response speed Available system memory Hardware compatibility The results help ARIS rank models for different types of requests. I'm still refining the evaluation system, so I'm not claiming it always makes the perfect choice. But the goal is to make automatic selection evidence-based rather than simply picking the largest model. ⚡ 3. Lightweight Query Classification ARIS uses a lightweight local classification approach to identify the type of question before routing it. The idea is to avoid needing another large language model just to decide which model should answer. Everything related to model inference and routing is designed to work locally. 🎨 4. A Clean, Minimal, User-Friendly Interface Another major focus has been the UI. I wanted ARIS to feel like a polished desktop application rather than a complicated developer tool. I've been working on a minimal, modern interface with carefully designed model-selection menus, readable responses, subtle animations, and a consistent visual design. The goal is to make local AI approachable even for someone who has never heard of GGUF, quantization, or context windows. I've attached a screenshot of the current interface. I'd genuinely appreciate feedback on the design as well. 💻 5. Hardware-Aware Local AI I'm developing ARIS on a laptop with an Intel Core i3 and only 8 GB of RAM, so efficient resource usage is particularly important to me. ARIS considers hardware limitations when recommending and selecting models, rather than treating every computer as if it has a powerful GPU. It also includes integrated GGUF model discovery, downloading, and management. For inference, I'm currently using llama.cpp. 💰 6. Yes, I'm Planning to Make ARIS Paid Software I want to be transparent about this. ARIS is intended to be a paid desktop application when it launches. I understand that the local AI ecosystem already has some incredibly capable free and open-source options. That's actually one of the reasons I'm sharing this project. I don't want to charge people for something that merely looks different while offering the same functionality. I'm trying to understand whether the combination of intelligent model selection, hardware-aware optimization, and a more accessible desktop experience offers enough additional value to justify a paid product. And if it doesn't, I'd genuinely like to know. I'd love your honest feedback! What do you think about the UI and the automatic model-selection approach? Would you find this useful compared to your current local AI setup? And most importantly: What would a paid local AI application need to offer for you to seriously consider buying it instead of using the free alternatives? Constructive criticism is very welcome. I'm still actively developing ARIS, and feedback from people who actually run local models would mean a lot. Thanks for reading! submitted by /u/arislabs [link] [comments]

Source: r/ollama | 2026-10-21

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