Pains

Integrating Nginx with monitoring systems like Prometheus and Grafana requires additional setup for configuring metrics, exporters, and ensuring proper data collection. This adds operational complexity in microservices environments requiring comprehensive observability.

Azure Web & Worker Roles have slow deployment times, cannot change instance size after deployment, and complicated configurations via service definitions. They are expensive for small applications due to inability to pack multiple apps onto fewer servers and mandatory staging server costs.

Vite imports images as static assets without built-in optimization for different screen sizes and formats, requiring manual optimization work. Next.js's automatic image optimization (WebP/AVIF generation, responsive sizing, lazy loading) directly improves Core Web Vitals without developer intervention.

Nginx caching issues cause outdated responses to persist after content updates due to cache key collisions or missing purge operations. Requires careful cache key design, proper expiration headers, and automated purge actions during deployments.

Nginx provides no native authentication or authorization for managing access to microservices, forcing integration with external systems like OAuth 2.0 and JWT. This adds operational complexity and requires additional proxy configuration layers.

Figma Sites can generate web pages but lacks CMS functionality (pending for later release), making it unsuitable for content-heavy sites or scalable web architectures that require dynamic content management.

Components fetching their own data without dependency injection creates tight coupling, making components harder to test in isolation and reuse logic across the application, reflecting larger architectural debates.

AWS cost management tools (Trusted Advisor, Cost Explorer) provide only historical analysis and recommendations, not proactive guidance. They lag real-time spending and cannot predict whether current costs will match forecasts, making it difficult to catch cost overruns early.

Developers must make complex architectural decisions about which PostgreSQL extensions to use. For example, PostGIS for geospatial needs and pgvector for ML/embedding use cases are "killer apps," but selecting and integrating specialized extensions requires significant expertise.

DevOps and CI/CD transformation efforts often lack backing from senior management, making it difficult to secure resources, establish governance, and drive organizational adoption.

Figma lacks offline functionality, forcing users to have continuous internet connectivity to work with design files. This limits usability in offline or low-connectivity scenarios.

More complicated nginx configurations require remembering to apply changes consistently across multiple locations (e.g., HSTS headers), and even experienced authors introduce bugs in their first attempts. Configuration changes propagate across the entire setup, creating a large surface area for errors.

Developers must understand nginx's strict context hierarchy (http, server, location blocks) and place directives in the correct context. Misplaced directives cause configuration reload failures, and incorrect location block ordering leads to unexpected routing behavior that's difficult to debug.

Configuration synchronization issues in Kubernetes ingress-nginx sometimes require manual intervention to delete and recreate Services and Ingresses, creating operational toil and potential downtime.

Incomplete termination of Lua execution phases can lead to inconsistent logging or leaking of internal NGINX headers, causing information disclosure or incorrect log entries.

Developers frequently encounter deployment failures because required dependencies are missing from their projects, requiring manual verification of installed packages.

Selecting an inappropriate batch size in PyTorch training leads to either out-of-memory crashes (too large) or noisy gradient updates and slow convergence (too small). There is no automated guidance or tooling to help developers find an optimal batch size, requiring manual trial-and-error experimentation.

The Meilisearch search component has poor mobile responsiveness (results go off-screen), suboptimal desktop UX (results appear in top-right forcing eye movement across page), and is missing standard keyboard shortcuts like Ctrl+K that are common in the JS ecosystem.

PyTorch's object-oriented class approach results in applications with orders-of-magnitude more lines of code than necessary, negatively impacting both runtime performance and long-term code maintainability. This architectural choice is seen as fundamentally misaligned with the needs of production ML engineering.

PostgreSQL validation returns only the first error per record, forcing developers to iterate through multiple correction cycles to resolve all data integrity issues. This extends debugging workflows substantially.

Developers struggle to navigate database schemas with many foreign keys, jumping between related tables creates confusion and slows development speed even though foreign keys improve database architecture and performance.

Required dependencies for optional PyTorch integrations (e.g., TensorBoard) are not automatically installed, causing silent failures discovered only at runtime. Developers must manually track and install auxiliary dependencies that should be bundled or clearly flagged during setup.

Developers struggle to properly configure and customize Azure AI models to suit specific use cases, creating a barrier to effective model deployment and integration.

Keras' abstraction layer obscures low-level backend details, making it harder to debug complex models. Developers are forced to rely on backend-specific tooling and error messages that surface through multiple abstraction layers, increasing diagnostic time.