App Store vs Taste Lab: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of App Store and Taste Lab — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
App Store
Beijing Ciyuan Cloud Technology Co., Ltd.
Unifies and coordinates AI agents and models across your devices, letting you run, monitor, and scale AI workflows from a single Superbrain.
Key features
- Unified Superbrain Routing: Automatically selects and routes tasks to the most suitable agent or model (reasoning, coding, execution), removing the need for manual tool switching or orchestration.
- Distributed Compute Pool: Aggregates compute across local laptops, workstations, and GPU clusters so workloads can scale on existing hardware without added cloud costs.
- Parallel Agent Execution: Launches and coordinates hundreds of AI agents in parallel to run complex workflows and deliver results faster.
- Cross-Device Continuity: Start tasks from a terminal and continue them from a phone or other device; agents keep running even when the initiating device is closed.
- Model and Tool Agnostic Integration: Works with cloud models (Claude, Codex, Gemini), local models, and custom agents, enabling a single system to leverage diverse model strengths.
- Always-On Autonomous Execution: Agents execute, monitor, and deliver results continuously (24/7) on user-controlled infrastructure, enabling background automation and monitoring.
- Local-First Data Control: Sensitive workloads and data can remain on personal or private infrastructure to minimize cloud exposure and ensure data ownership.
- Coordinated Multi-Tool Workflows: Orchestrates workflows that span research, coding, automation, and execution across multiple tools without manual context switching.
- Mobile distribution via Apple App Store (iOS)
- Positioned as a customizable personal AI system
- User-facing interface for interacting with AI functionality (implied)
- App Store listing includes screenshots, ratings, reviews, and user tips
Best for
- Automating multi-step development workflows: coordinate code generation, testing, deployment, and monitoring agents across local and remote machines.
- Distributed model inference and execution: run heavy or specialized inference tasks across a pooled set of local GPUs to avoid cloud costs and latency.
- Research collaboration and orchestration: run parallel agent experiments that share context in real time and aggregate results centrally for analysis.
- Privacy-sensitive automation: process and analyze confidential data entirely on user-controlled infrastructure to meet compliance requirements.
- Mobile-managed operations: trigger and monitor long-running workflows from a phone while execution runs on remote or local compute resources.
- 24/7 task execution and monitoring: schedule persistent agents to monitor systems, gather data, and report results without manual intervention.
- Hybrid model utilization: combine cloud and local models (e.g., Claude with local models) in a single coordinated pipeline to leverage strengths of each.
- Personal virtual assistant on iOS devices
- Mobile productivity and task management support
- Learning and experimentation with a personalized AI system
- Prototyping or demonstrating AI behaviors on a mobile client
Taste Lab
Sen Lin
Taste Lab is a Claude Code skill that turns any URL into a complete design context: design tokens plus the reasoning and trade-offs behind every choice.
Key features
- Design Map Extraction: Captures every color, font weight, spacing value, radius, and shadow with exact px/hex/ratio citations across 20 measurement categories.
- Taste DNA Inference: Derives four design principles, each with a Trigger, Decision, Reason, Evidence, and Trade-off explaining why each choice was made.
- Four-Agent Pipeline: Runs Extract, Detect Patterns, Infer Taste, and Observer stages, each reading the page through a sharper lens.
- Anti-Slop Quality Gate: A final critic stage runs anti-slop checks and validates JSON before writing output.
- Dual File Output: Writes a {domain}.md and {domain}.json that any AI agent can build from.
Best for
- Cloning Design Systems: Give an AI agent a complete, reasoned design context to rebuild a site's look and feel.
- Design Reviews: Understand the deliberate trade-offs behind a website's visual decisions.
- Agent-Assisted Frontend Work: Feed structured taste files into coding agents so they make the right call on unseen pages.