标签：#catalog

Short answer: use automated background removal for volume, then reserve manual crops for images where a wrong edge costs more than the bandwidth and review time. The useful design is a queue with a confidence gate, not a permanent argument over which tool is “best.” How Should Catalog Teams Balance Background Removal and Manual Crop? Catalog isolation sounds like a visual task. In a SaaS catalog, it is a data pipeline. A seller uploads a product photo, the service produces an isolated asset, and every downstream surface expects the subject to stay inside a predictable box. Quality and bandwidth pull in opposite directions: a high-resolution source preserves fine edges but costs more to move and process; an aggressive resize is quick but can erase the exact detail the mask needs. The decision therefore belongs in a policy that engineering, catalog operations, and support can inspect. Give that policy named outcomes such as auto , manual , and needs_source ; attach the crop rectangle and confidence to each result; and retain enough input metadata to reproduce the decision. Without those records, a quality complaint becomes a debate over screenshots. With them, the team can compare the received file, preview dimensions, mask, final derivative, and policy version in order. Start with an explicit decision table. It gives support and operations one shared vocabulary when an image lands in the review queue. Input or business signal Default path Why Reconsider when Clean background, centered object, thousands of SKUs Automated removal at a bounded preview size Fast throughput and consistent framing Hair, glass, or transparent parts dominate the silhouette Irregular edges or a high-value hero image Manual crop and edge review A person can preserve meaningful contours The review queue becomes the release bottleneck Uncertain subject or cluttered scene Keep the original and request a better photo Prevents a confident-looking bad cutout The seller can supply a controlled backdr

The finding EventCatalog exposes a common monorepo failure mode: generated code may exist, its producer may be green, and the real downstream consumer can still fail. Its Langium language server generates AST, grammar, module, and syntax files; a sibling VS Code extension consumes that output alongside the workspace SDK and visualiser. The useful question is therefore not "did generation finish?" It is whether the repository can execute the complete consumer closure from declared dependency hydration through the package that needs the generated result. The contract boundary Ota models the generated output separately from the tasks that establish and consume it: artifacts : language-server-ast : kind : generated_source producer : language-server:generate paths : - packages/language-server/src/generated/ast.ts - packages/language-server/src/generated/grammar.ts - packages/language-server/src/generated/module.ts - packages/language-server/syntaxes/ec.tmLanguage.json - packages/vscode-extension/syntaxes/ec.tmLanguage.json inputs : - packages/language-server/src/ec.langium - packages/language-server/langium-config.json tasks : vscode-extension:build : depends_on : - language-server:generate - language-server:build - sdk:build - visualiser:build requires_artifacts : - language-server-ast The setup task owns typed, frozen-lockfile pnpm hydration with the language-server package filter. That removes bespoke install shell glue without pretending the dependency path is harmless: it reaches the package registry, so the selected closure is intentionally not routine agent-safe execution. Humans and CI can run the declared verification workflow; unattended agents cannot silently acquire that networked setup authority. What Ota had to learn This pressure case made two platform requirements concrete. Generated-source lineage had to remain visible at consumer admission and in execution evidence, rather than surfacing only after a build failure. And pnpm dependency hydration needed a