AI backend patterns: Symfony loan flow, virtual try-on stack, and Perplexity Pro Search
Recent tutorials and analyses highlight repeatable backend patterns for shipping AI features, from auditable state machines to low-latency presigned uploads and smarter research workflows. A hands-on guide shows how to build an AI-driven loan approval pipeline with Symfony 7.4 and Symfony AI using agentic workflows and state machines to keep decisions traceable and testable, a blueprint you can adapt to any model-mediated decision service ([tutorial](https://hackernoon.com/how-to-build-an-ai-driven-loan-approval-workflow-with-symfony-74-and-symfony-ai?source=rss)). Another build details a production-ready virtual try-on: Next.js 14 + TypeScript for the edge-facing API, Cloudflare R2 with presigned URLs to bypass server bottlenecks, and the Runware SDK calling Gemini 2.5 Image Pro with a prompt builder that preserves identity—an end-to-end pattern for image-generation workloads ([architecture write-up](https://dev.to/usama_d14e7149bf47b1/how-i-build-an-ai-powered-virtual-try-on-for-mens-clothing-brand-264f)). For research-heavy tasks, a breakdown of Perplexity’s Free vs Pro clarifies when Pro Search’s iterative querying, cross-source synthesis, advanced model access, and multi-document workflows justify the upgrade for deeper, less ambiguous queries in engineering and product analysis ([comparison](https://www.datastudios.org/post/what-is-the-difference-between-perplexity-free-and-pro-search-features-features-limits-and-value)).