A Haystack pipeline can be perfectly wired and still unsafe. The retriever returns documents. Every component did its job. But if untrusted text moved through the pipeline as ordinary context, the trust boundary was lost. That is the problem this post is about. Not bad Python. A valid component connection only says: this value fits the next component It does not say: this value is safe to influen
I've been shipping software internationally for 5 years, and I've seen localization bugs tank launches in ways that make deployment failures look quaint. Currency displays in the wrong locale. Dates that make Japanese users think the app was built in 1970. Phone numbers that break form validation in Brazil. Last week, I decided to actually test TestSprite on a real project instead of adding it to
description: "Critical issues blocking TestSprite adoption in Indonesia, Malaysia, Philippines. Production fixes included." tags: testsprite, testing, devops, indonesia, localization cover_image: "https://dev-to-uploads.s3.amazonaws.com/uploads/articles/testsprite_mcp_review.png" canonical_url: "" published: false Code Review: Why TestSprite's MCP Failed in Southeast Asia (And How to Fix It) TL;DR
TestSprite adalah platform testing yang fokus pada quality assurance untuk aplikasi modern. Setelah menggunakan TestSprite dalam satu proyek production-grade di berbagai device dan region, saya ingin share pengalaman mendalam tentang bagaimana tool ini menangani localization dan timezone handling — aspek yang sering diabaikan tapi krusial untuk aplikasi global. TestSprite memungkinkan developer un
description: "Real-world TestSprite evaluation testing Indonesian e-commerce with IDR currency, timezone handling, and 3 locales. Grade A review with technical findings." https://images.unsplash.com/photo-1516321318423-f06f70a504f0?w=1200&h=600&fit=crop" TL;DR: TestSprite is 80% faster than manual visual regression testing. Grade A for multi-locale apps. Grade B+ for logic testing. Real findings:
Comparison: Haystack 2.0 vs. RAGatouille 0.3 for Building High-Accuracy RAG Pipelines for Developer Docs Retrieval-Augmented Generation (RAG) has become the standard for building LLM-powered tools that answer questions using private or domain-specific data. For developer documentation (dev docs) — which includes technical jargon, versioned APIs, code snippets, and structured reference material —