Results for “product”

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Use when a Claude-Design export already exists and you must adapt it into a production page before render-verify — real data, honest empty states, existing tokens/components (never ship mock numbers). Prefer this over claude-design when generation is done and adaptation starts.

Content Factory Graphify wrapper. Use for codebase map, “where does X live”, how modules connect, architecture orientation, or when graphify.mdc applies. Query graphify-out/ before grepping or reading giant markdown brains. Does not replace Mem0, Hindsight, or product Memory Service RAG.

When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing.

When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing.

You're a developer who has built GraphQL APIs at scale. You've seen the N+1 query problem bring down production servers. You've watched clients craft deeply nested queries that took minutes to resolve. You know that GraphQL's power is also its danger.

Batch visual production workflow for campaigns. Accepts a content calendar or campaign brief, produces multiple platform-ready assets with organized bundle output and machine-readable manifest. Trigger for 'batch design', 'content calendar to assets', 'campaign batch', 'produce all campaign visuals', or 'bulk asset generation'.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Review pull requests or audit repositories for correctness, security, performance, test risk, architecture, dependencies, developer experience, documentation, and grounded product direction. Use for code review, repository audits, evidence-backed findings, false-positive vetting, risk assessment, prioritization, or deciding which improvements should become implementation plans.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.

54 production-quality design systems extracted from real websites. Load a template to generate HTML/CSS that matches the visual identity of sites like Stripe, Linear, Vercel, Notion, Airbnb, and more. Each template includes colors, typography, components, layout rules, and ready-to-use CSS values.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Plan and execute multi-channel marketing campaigns from audience research through positioning, copywriting, and review. Orchestrates landing pages, email sequences, social posts, video scripts, ad copy, and content calendars for product launches.

Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).

Apply Andrew Chen's 'The Cold Start Problem' framework to launch and scale networked products by building atomic networks, solving the hard side, and navigating the five stages of network effects.

Structure the first 90 days of a VP or CPO transition as a diagnostic process, not an execution sprint, using a three-phase playbook to build evidence before making decisions.

Generates a vertical 9:16 UGC-style video ad from a person photo, product photo, and optional script by creating a lifestyle hero image and animating it with synced audio.

Documents Linear's dark-canvas marketing design system with near-black backgrounds, lavender-blue accent, four-step surface hierarchy, aggressive negative tracking on display type, and product UI screenshots as the primary visual rhythm.

Use when planning a UIST cycle end to end — the late-March abstract and paper deadlines, the ten-week build-freeze before video production, the rebuttal window, conditional acceptance, the July camera-ready, adjunct-track parallel lanes, and the November conference — with owners and risk buffers.

Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions. Use when collaborators provide incomplete code snippets, pseudo-code, or descriptions with potential typos or incorrect terminology. Excels at translating non-technical or semi-technical descriptions into production-quality code.

Use for creating, reviewing, debugging, or testing dbt SQL projects involving models, sources, ref, tests, macros, materializations, incremental models, snapshots, seeds, selectors, compiled SQL, or dbt artifacts. Do not use for generic SQL with no dbt graph, warehouse administration, semantic-layer product configuration, or orchestration outside dbt.

Post-deploy smoke test combining all 5 tools (Sentry + Supabase + Firecrawl MCPs, plus the Langfuse and Playwright CLIs) into one workflow. Use when asked to: "verify deploy", "post-deploy check", "smoke test production", "check if deploy is healthy", "ship or rollback", "post-release check", "verify release", "deploy.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Assists with configuring and using Vite as a frontend build tool for modern web applications. Use when setting up dev servers, optimizing production builds, configuring plugins, migrating from Webpack or CRA, or building component libraries. Trigger words: vite, build tool, HMR, hot module replacement, vite config, rollup, bundling.

Master the AI tools that supercharge software development. From code completion to full application generation, write better code faster and automate the tedious parts. Use when "AI coding, code assistant, Copilot, Cursor, code generation, AI developer tools, development, coding, ai-assistant, productivity, code-generation" mentioned.

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Flutter cross-platform development with Dart. Widgets, state management (Riverpod, BLoC), navigation, platform channels, and production patterns. USE WHEN: user mentions "Flutter", "Dart", "flutter widget", "Riverpod", "BLoC", "flutter build", "pubspec.yaml" DO NOT USE FOR: React Native - use `react-native`; Expo - use `expo`; native iOS/Android