report solid agentic search performance for restaurant discovery. Other founders highlight small-model strength on mobile and leading image-to-text. Users commend rapid prototyping for code/website generation, with calls for better history/edit UX and edge-case handling. Overall sentiment: practical, fast, and production-ready.
I chose the Qwen model as the default starting in version 1.2 because it delivers an ideal balance of speed, accuracy, and lightweight performance. It runs efficiently on-device, uses very little storage, and responds quickly even on less powerful hardware. This makes it a perfect fit for an offline AI assistant where reliability, low resource usage, and a smooth user experience are essential.
I’ve been using Qwen for building a simple code and website generator, and it works really well for fast iterations. Great for prototyping and lightweight generation.
What needs improvement
I need more on the history pages, a section when we can re-edit the input/process/output with easy UX. Basically, better handling of edge cases without extra prompting
vs Alternatives
I choose Qwen because it’s fast, lightweight, and great for turning ideas into simple, working code or websites. It was also the first web-based tool I explored for code generation, which made it easy to start prototyping right away.
Great launch! Qwen has been incredibly useful, especially when I reach a point where other AI services can no longer technically deliver what I need. I’m also excited to see it matching the “big players” in benchmark results. 2026 is shaping up to be very interesting.