Competitive analysis should inform your ongoing strategy. Monitor which sources AI models cite for queries where you want visibility. Analyze what makes those sources effective—is it their structure? Their level of detail? Their use of data and statistics? Their freshness? Understanding your competition's strengths helps you identify gaps in your own content and opportunities to differentiate through superior quality or unique angles.
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The pipeline was very similar to icon-to-image above: ask Opus 4.5 to fulfill a long list of constraints with the addition of Python bindings. But there’s another thing that I wanted to test that would be extremely useful if it worked: WebAssembly (WASM) output with wasm-bindgen. Rust code compiled to WASM allows it to be run in any modern web browser with the speed benefits intact: no dependencies needed, and therefore should be future-proof. However, there’s a problem: I would have to design an interface and I am not a front end person, and I say without hyperbole that for me, designing even a simple HTML/CSS/JS front end for a project is more stressful than training an AI. However, Opus 4.5 is able to take general guidelines and get it into something workable: I first told it to use Pico CSS and vanilla JavaScript and that was enough, but then I had an idea to tell it to use shadcn/ui — a minimalistic design framework normally reserved for Web Components — along with screenshots from that website as examples. That also worked.
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