Results for “fastai”

Write, review, debug, and test Python FastAPI applications with path operations, dependencies, Pydantic models, lifespan, middleware, background tasks, exception handling, and ASGI tests.

Use when implementing fastapi functionality with production-grade patterns and safeguards.

Expert in FastAPI Python development with best practices for APIs and async operations

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns. Use PROACTIVELY for FastAPI development, async optimization, or API architecture.

Provides production-grade FastAPI development patterns including project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest.

Build high-performance async Python APIs with FastAPI and Pydantic V2, including REST endpoints, authentication, async database operations, and WebSocket support.

Provides production-oriented patterns for building FastAPI services, covering async endpoints, dependency injection, Pydantic schemas, OpenAPI customization, testing, and security best practices.

FastAPI 模式——异步 API、依赖注入、Pydantic 请求/响应模型、OpenAPI 文档、中间件及测试

Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2, covering microservices, WebSockets, and modern Python async patterns.

FastAPI best practices and conventions. Use when working with FastAPI APIs and Pydantic models for them. Keeps FastAPI code clean and up to date with the latest features and patterns, updated with new versions. Write new code or refactor and update old code.

FastAPI best practices covering project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest.

Trains, evaluates, exports, and runs inference for FastFoundationStereo (FFS) stereo depth estimation models, a distilled variant of FoundationStereo with lower latency.

Generate AI-powered podcast-style audio narratives from text using Azure OpenAI's GPT Realtime Mini model via WebSocket, with full-stack implementation from React frontend to Python FastAPI backend.

Guides development of Fastify Node.js backend servers and REST APIs using TypeScript or JavaScript, covering routes, plugins, validation, error handling, authentication, testing, performance, logging, deployment, and more.

Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.

Use when FastAPI request, dependency, response, or OpenAPI behavior interacts with Pydantic v2 models, validation, aliases, serialization, generics, or error contracts. Do not use for standalone FastAPI routing or standalone Pydantic models with no ASGI boundary.

Automatically detects your project stack and fills AI context slots, asking only for what it can't infer — your goal and the human 'why' — to reach 100% AI-readiness with minimal typing.

Predict biomolecular complexes with Chai-1, an open AlphaFold3-style model that folds multi-entity assemblies (proteins, ligands, nucleic acids) from a single typed FASTA — strong on antibody–antigen and protein–ligand complexes, with optional MSA and restraint inputs. Use when predicting an antibody–antigen complex, folding a mixed protein/ligand/nucleic-acid assembly described in one FASTA, or generating a complex with experimental restraints. For binding-affinity prediction or a ligand-focused co-fold prefer alterlab-boltz; for protein-only or protein–protein folding prefer alterlab-alphafold; to dock into a fixed receptor prefer alterlab-diffdock. Part of the AlterLab Academic Skills suite.

Fast headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)

Generates OpenAPI 3.1 documentation from an API codebase, auto-detecting frameworks, extracting routes and schemas, and setting up interactive docs.

Generates and edits images and videos via fal.ai's queue-based API, supporting models like Flux, Gemini image, and Kling video-to-video, with automatic polling.

Search and preview Irasutoya illustrations with the installed `irasutoya` command-line tool. Use this skill whenever the user wants to find, recommend, preview, randomly pull up, or get the page/image URL of an Irasutoya (いらすとや) image — from a topic, scene, Korean/Japanese/English phrase, visual description, emotion, character, object, or action — even if they don't say "Irasutoya" by name but clearly want a free, cute illustration for a slide, 발표자료, blog, doc, or chat. Also use it to optimize vague requests into fast Japanese keyword candidates and to return page/image URLs. Do NOT use it to generate brand-new AI art (e.g. DALL·E, Midjourney), draw charts or diagrams, design logos or icons, cut out/edit existing images, or recommend other stock-image sites — it only searches Irasutoya's existing catalog.

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

Automates stateful REST API fuzzing using Microsoft RESTler to discover security and reliability bugs by compiling OpenAPI specs, configuring authentication, and running test, fuzz-lean, and full fuzzing modes.

Backend Node.js Specialist IA — Expert en développement backend Node.js (Express, Fastify, NestJS, Prisma, real-time, WebSockets)

Rent, manage, and destroy GPU instances on vast.ai. Use when user says "rent gpu", "vast.ai", "rent a server", "cloud gpu", or needs on-demand GPU without owning hardware.

Direct Figma API interactions for design asset management. Fetch files and components, extract design tokens, export images, manage comments, and access version history.

Automate provisioning, signing, and deployment with Fastlane. Use when provisioning iOS apps, managing code signing, or automating deployments with Fastlane.

Production Fastify (TypeScript) patterns: schema validation, plugins, typed routes, error handling, security hardening, logging, testing with inject, and graceful shutdown

Computes phylogenetic distance matrices and trees from genomic VCF or FASTA data using the fastreeR hybrid Java/Python toolkit.

Floating action button component with menu, badge, and group support. Invoke when user needs floating buttons, FAB menus, or quick action buttons in Naive UI.

Rust specialist with expertise in async programming, ownership patterns, FFI, and WebAssembly development

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

Fast headless browser for QA testing and site dogfooding. Navigate any URL, interact with elements, verify page state, diff before/after actions, take annotated screenshots, check responsive layouts, test forms and uploads, handle dialogs, and assert element states. ~100ms per command. Use when you need to test a feature, verify a deployment, dogfood a user flow, or file a bug with evidence. Use when asked to "open in browser", "test the site", "take a screenshot", or "dogfood this". (gstack)