Riggenie
PC-build recommender: pick your scenario, brands, form factor; an LLM-led pipeline scrapes current parts and proposes a coherent build.
TL;DR
- LLM as coordinator: model resolves user constraints against scraped facts, doesn't invent prices.
- Live retailer scraping (Puppeteer + Cheerio + robots-parser) so quoted parts reflect real availability.
- Atomic-design React component library, GSAP + Framer Motion animation layer.
- SWR-cached recommendations — iterating constraints is instant after the first call.
Problem
Picking PC parts is a research tax. PCPartPicker is great if you already know what you want; everything else is YouTube reviews, Reddit threads, and brand-marketing pages. For a casual buyer asking "what should I get for 1080p gaming under €1500, mostly NVIDIA, ATX, white" — the modern answer is one constraint-fit recommendation, not ten tabs.
Approach
A constraint-collection UI on the front, an LLM-led pipeline on the back. The user picks a scenario (gaming / editing / office / content), brand preferences, categories, form factor, and colour. The API route packages that into a structured prompt, runs Puppeteer + Cheerio against a handful of retailer pages to pull current price and availability for the candidate parts, and asks GPT to reconcile the constraints into a single coherent build with rationale. SWR caches per-constraint-set so iterating preferences is instant.
Outcome
Prototype. Solo prototype to explore the LLM-as-coordinator pattern: it's not generating answers from scratch, it's orchestrating scrapers and resolving conflicts between user constraints. The UI is atomic-design React with GSAP transitions; the back end is a thin Next 15 route. Live scraping makes it ineligible for a static embed — surfaced here as a research piece.
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