Results for “data loading”

Build, review, or improve networking code in iOS/macOS apps using URLSession with async/await, structured concurrency, and modern Swift patterns. Use when working with REST APIs, downloading files, uploading data, WebSocket connections, pagination, retry logic, request middleware, caching, background transfers, or network reachability monitoring. Also use when handling HTTP requests, API clients, network error handling, or data fetching in Swift apps.

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Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

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Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.

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End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.

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Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

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Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

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Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, CLS prevention, image SERP rankings (via DataForSEO), and image file optimization (WebP/AVIF conversion, IPTC/XMP metadata injection). Use when user says "image optimization", "alt text", "image SEO", "image size", "image audit", "optimize images", "image metadata", "image SERP", "convert to webp", or "image file optimize".

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When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.

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Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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Standardizes UX states for data fetching flows (loading, error, empty, success) and complex multi-view UI patterns. Provides state machine approach recommendations, loading skeletons, error boundaries, and empty state components. Use when implementing "loading states", "error handling", "empty states", or "state machines".

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Creates data table patterns with filtering, sorting, pagination, row actions, column configuration, server/client rendering modes, and empty/loading states. Use when building "data tables", "list views", "admin tables", or "data grids".

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Assess whether an application is ready for production using local files and authorized evidence. Reviews authentication, data, payments, and operations, then reports release blockers, prioritized fixes, and a readiness score without uploading repository contents to an external audit service.

• Checking application readiness before launch
• Reviewing release risks after a merge
• Finding production risks beyond passing CI checks

Audit JavaScript page redirects, confirm their impact on loading time with performance measurements, and identify where server-side 301 or 302 redirects can replace them. Get specific findings and an explanation of loading and search indexing concerns.

• Investigating slow loads caused by page redirects
• Reviewing redirects that use window.location
• Planning server-side 301 or 302 redirects

Audit a page’s HTTP requests to find unnecessary network activity behind slow loading. Confirm bottlenecks with performance measurements, then suggest changes such as combining files, grouping icons, or embedding resources needed for the initial display.

• Investigating slow initial page loads
• Finding unnecessary requests for images, CSS, and JavaScript
• Planning file consolidation and icon sprites

Check Google Consent Mode v2 implementation and the consent states sent to Google services. Review advertising consent settings and help update GTM or gtag.js so tag behavior reflects users’ consent choices.

• Auditing Consent Mode v2 implementation
• Updating advertising consent settings
• Checking consent signals sent to Google

Check whether HTML, JavaScript, and CSS are delivered with Gzip or Brotli compression. Use measured loading problems and response headers to guide server or CDN changes that reduce the amount of data downloaded.

• Investigating slow page loads on mobile networks
• Checking compression headers for text resources
• Reviewing Gzip or Brotli settings on a server or CDN

Audit page speed in a real browser to find delays in loading, script execution, and layout. Use measured results to identify bottlenecks, compare device and connection conditions, and guide performance improvements.

• Find the causes of slow page loading
• Compare performance across device and connection simulations
• Add browser performance audits to development and CI/CD

Audit browser caching to reduce repeated downloads when people revisit a site. Check cache headers for static files and HTML pages, then suggest suitable storage durations and validation settings based on measured loading problems.

• Investigate slow loading on repeat visits
• Review cache headers for static files
• Set separate caching policies for HTML and hashed assets

Investigate chains of dependent resource requests that delay a page’s first display. Use performance measurements to identify bottlenecks, then reduce waiting by embedding essential CSS or preloading important assets discovered late in the chain.

• Investigating slow initial page rendering
• Reviewing dependencies between resource requests
• Planning critical CSS embedding and asset preloading

Clarify unresolved product decisions before implementation, such as scope, supported platforms, or API agreements. Use a small set of focused questions to establish the intended outcome, while leaving routine technical choices to the agent.

• Clarify an unresolved product scope
• Confirm which platforms a product should support
• Resolve open decisions about data or API agreements

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Manage Zotero reference libraries through Python: search, create, update, and delete references, organize collections and tags, upload PDF attachments, and export citation data. Guidance covers both remote libraries and access through a running local Zotero app.

• Searching references in a Zotero library
• Organizing references with collections and tags
• Uploading PDF attachments to references

Guide Angular development, code review, and refactoring with prioritized performance rules. Review rendering efficiency, loading time, and bundle size—the amount of code delivered to browsers—alongside data fetching and server-rendered page setup.

• Writing Angular components with performance in mind
• Reviewing code for performance bottlenecks
• Reducing bundle size and loading time