Results for “airtag”

Track Apple devices/AirTags via FindMy.app on macOS.

Documents Airtable's design system including color tokens, typography hierarchy, spacing, and component patterns for building editorial-style marketing interfaces.

Manage Airtable bases, tables, and records with full CRUD operations through a managed OAuth proxy.

Airtable REST API via curl. Records CRUD, filters, upserts.

Tracks Apple devices and AirTags on macOS by automating the FindMy app with AppleScript and screen capture, then reading locations via vision analysis.

Airtable REST API via curl. Records CRUD, filters, upserts.

Airtable REST API via curl. Records CRUD, filters, upserts.

Interact with Airtable bases, tables, and records via the REST API using curl commands, covering CRUD operations, filtering, sorting, pagination, and upserts.

Create, list, remove, and rename Gameplay Tags with runtime registration (GameplayTagService). Use when the user asks to add or register gameplay tags, list/query existing tags, or rename/remove tags (e.g. Ability.Attack.Melee, State.Stunned).

Track Apple devices and AirTags via FindMy.app on macOS using AppleScript and screen capture.

Airtable REST API via curl. Records CRUD, filters, upserts.

Captures WPA/WPA2 handshakes and performs offline password cracking using aircrack-ng, hashcat, and dictionary attacks during authorized wireless security assessments to evaluate passphrase strength and wireless network security posture.

Automates repetitive infrastructure and deployment tasks with Terraform, OpenTofu, Ansible, GitHub Actions, and n8n, enforcing safe practices like plan review and confirmation before applying changes.

Airtable REST API via curl. Records CRUD, filters, upserts.

Track Apple devices and AirTags via FindMy.app on macOS using AppleScript and screen capture.

Agent-driven RAG patterns. Self-RAG, Corrective RAG (CRAG) with web fallback, Adaptive RAG with routing classifier, ReAct with retrieval tool, multi-hop retrieval, plan-and-execute, LangGraph state machines for RAG. USE WHEN: user mentions "agentic RAG", "Self-RAG", "Corrective RAG", "CRAG", "Adaptive RAG", "multi-hop retrieval", "LangGraph RAG", "ReAct RAG", "plan and execute RAG" DO NOT USE FOR: static retrieval pipelines - use `rag-architecture`; query rewriting only - use `query-transformations`; evaluation - use `rag-evaluation`

Use to clean up sprawling helpdesk tags, categories and custom fields so reporting built on them means something. Trigger for "our tags are a mess", "consolidate our ticket categories", "why don't our tag reports add up", duplicate or near-duplicate tags, tags nobody uses, agents tagging inconsistently, or before building reporting on top of tags.

Run a full pipeline for a task. Orchestrates roles through stages (standalone or HOTL-integrated).

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Train, evaluate, export, and run inference on TAO action-recognition models for classifying temporal actions in video clips using RGB, optical flow, or joint input.

Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines

Trigger: a11y, accessibility, ARIA, screen reader, contrast, keyboard, tap targets, focus. Scope: Audit web interface accessibility compliance (WCAG AA). Boundary: Do not use for general backend logic or CSS styling aesthetics.

Use when you need to annotate BAM alignments with a two-character tag based on overlaps with BED, GFF, or VCF annotation files, such as labeling reads by feature class or interval source.

Use when tag expertise is needed to unblock implementation decisions.

Use when aer-identification has fixed the design, after methodology choice and before aer-robustness or aer-tables-figures, to run an AER-track analysis with StatsPAI — the agent-native Python engine and MCP server for causal inference, robustness, sensitivity, and publication-ready table export.

Pre-ingestion verification for epistemic quality in RAG systems with 9-point verification and Two-Round HITL workflow

ADR Fleet

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

Build real-time voice and multimodal AI applications using Pipecat and Daily, covering pipeline architecture, AI service integration, and transport options.

AgriTech Algérie — OAIC, conditions météo, irrigation, saisonnalité wilayas, cultures locales

Audit server-side paid-media measurement, including tag management, conversion APIs, event taxonomy, deduplication, consent, hashing, data quality, and observability.

Add accessible animated AI loading and agent-status indicators with the React thinking-orbs library. Use when a chat, copilot, voice, search, generation, or tool-running interface needs a semantic working, searching, solving, listening, composing, or shaping state; when replacing a generic spinner with an AI activity orb; or when implementing the library's size, theme, speed, pause, reduced-motion, and canvas behavior.