Results for “drive”

Patterns and architectures for running Claude Code autonomously in loops, from simple sequential pipelines to RFC-driven multi-agent DAG systems.

Run machine learning and generative AI tasks directly in BigQuery SQL using built-in functions for forecasting, anomaly detection, key driver analysis, and text generation.

Model software around the business domain using bounded contexts, aggregates, and ubiquitous language, with scoring and diagnostic tools for evaluating domain model quality.

Formal evaluation framework for Codex sessions implementing eval-driven development (EDD) principles

Use when implementing any feature or bugfix, before writing implementation code

Expert en architecture événementielle (Kafka, RabbitMQ, event sourcing, CQRS, saga patterns)

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

Checks and installs NVIDIA driver, CUDA Toolkit, and NVIDIA Container Toolkit for GPU-accelerated Docker and Kubernetes hosts. Supports multiple Linux distributions with automated install and read-only check modes.

Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.

Use when implementing features or fixing bugs with test-driven development. Enforces RED→GREEN→REFACTOR cycle with vertical slicing and multi-agent context isolation. Triggers on: 'implement with TDD', 'write tests first', 'red green refactor', 'test-driven', '/tdd <feature>'. Supports Jest, Vitest, pytest, Go test, cargo test, RSpec, PHPUnit.

Claim-driven 实验设计：界定目标 claims → 设计实验块（baseline/validation/ablation/robustness）→ 构建执行顺序 → 可选 Review LLM review → 写入 wiki

Create and manage webhook subscriptions for event-driven agent activation. Use when the user wants external services to trigger agent runs automatically.

Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.

Write platform-specific social media captions that drive engagement and conversions. Use when the user needs compelling written content for social posts.

Build and improve AI or probabilistic software through evaluation-driven development. Use for LLM applications, agents, prompts, RAG, tool use, classifiers, model migrations, quality regressions, golden datasets, LLM-as-judge rubrics, benchmarks, or requests to add evals and measurable release gates. Pair with TDD for deterministic code; do not use as the primary guide for ordinary unit testing without model behavior.

Drive mex (`mex-agent`), persistent project memory and code graphs for AI coding agents. One command scaffolds a living wiki, builds a deterministic code graph, and installs a project anchor file (CLAUDE.md, root AGENTS.md, .cursorrules, .windsurfrules, copilot-instructions.md, or .opencode/opencode.json) that your agent auto-loads as a standing rule document. Use when the user wants to `mex setup` a new project, build a symbol-grounded wiki, keep knowledge connected to implementation, route relevant context to agents, or run drift detection (`mex check`, `mex sync`). Triggers on: "mex setup", "project memory", "code graphs", "codebase documentation", "drift detection", "agent memory", "structured scaffolds", "architectural context", "living wiki", "project anchor file".

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.

Write, review, and migrate code using NVIDIA DALI's imperative dynamic-mode API for efficient data loading and preprocessing.

Start or patch Dynamo router modes and run router endpoint smoke checks for round-robin, KV-aware, least-loaded, or device-aware routing.

Ingests video and audio from files, URLs, live feeds, or desktop capture; indexes and searches moments with timestamps; transcodes, edits timelines, generates media assets, and emits real-time alerts.

Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).

Ingests video and audio from files, URLs, RTSP feeds, or desktop capture; indexes and searches moments with timestamps; transcodes, edits timelines, generates media assets, and creates real-time alerts for live streams.

nuScenes: A Multimodal Dataset for Autonomous Driving

Drive Airship (`@airshiplabs/cli`), a CLI that puts a visual, pannable design canvas in front of a running dev server so an engineer can click an element, describe a change, and have Claude Code, Codex, or OpenCode edit the underlying source file directly — no plugin, no build config, nothing added to the project. Use when the user wants to launch a visual editor over a local dev server (`npx @airshiplabs/cli --target <port>`), pick or compare coding agents (`--agent claude|codex|opencode`) for live UI edits, sandbox agent edits with `--safe`, scaffold an `airship.config.json` (`airship init`), or diagnose a broken setup (`airship doctor`). Triggers on: "airship", "@airshiplabs/cli", "visual editor for my codebase", "click to edit UI", "design canvas over dev server", "airship doctor", "airship init", "pair Claude Code with a visual editor on localhost".

Drive Soup (`soup-cli`), a CLI-first tool for fine-tuning and post-training LLMs with one YAML config and one command — SFT, DPO/GRPO/ORPO/SimPO/KTO, QLoRA/DoRA/LoRA+, layer streaming for 4-8 GB GPUs, eval-gated training, and serving. Use when the user wants to `soup init`/`soup train` a model, pick a training method or quantization scheme, estimate cost/memory before training, fine-tune on a small local GPU, migrate a config from Axolotl/LLaMA-Factory/Unsloth, or serve/merge/push a trained adapter. Triggers on: "soup-cli", "soup train", "soup init", "fine-tune an LLM locally", "QLoRA on a laptop GPU", "layer streaming", "soup advise", "soup autopilot", "DPO/GRPO/ORPO training", "merge LoRA adapter".

Two-party working posture — user as director, agent as executor. Every fork, tradeoff, and taste choice is surfaced via batched AskUserQuestion with structural framing, a recommended default, and concrete previews when comparison is visual, so the human steers direction while the agent handles implementation. Eliminates the review-bottleneck (no giant diff to approve at the end — review is distributed across picks) and prevents codebase-understanding debt (the user remembers the architecture because they picked it). Use whenever the user invokes /duet, or says "work with me", "ask before", "check with me", "I want to decide", "don't assume", "human-in-the-loop", "co-author", "pair with me", "duet", or whenever a task clearly involves aesthetic, architectural, or irreversible strategic decisions — even without those exact words. Pair with the Duet output style to minimize cognitive load between picks.

Route goalflow (wanmol/goal-flow) work — a LangGraph framework that combines workflow graphs with agent loops — into exactly one mode: fit check, transpiling a Dify DSL export into runnable LangGraph Python, authoring workflow nodes and edges, building an `agent_kit` loop with middleware and a harness, wiring the serving layer (data adapters, SSE streaming, HITL, Redis/MySQL, API-key registration), or running the pre-publish security gate. Use when the user wants Dify's visual design without Dify's runtime, a graph node that hosts an agent loop, an OpenAI-compatible wire protocol over their own workflows, or prompt-injected `SKILL.md` capabilities. Triggers on: goalflow, goal-flow, dify to langgraph, dify transpiler, dify DSL export, BaseWorkflow, agent_kit, AgentBaseNode, DataAdapter, chunk processor, HITL interrupt, dify2langgraph. Route plain graph-API questions to `langgraph-fundamentals` and `langgraph-workflow`.

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Generates high-fidelity images using reasoning-driven prompts and structured creative briefs via muapi.ai.