Perplexity vs Muse

Agent access

1. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round drawn
   Perplexity—none0/10

   All MCP evidence describes Perplexity acting as an MCP *server* (Computer MCP server, Perplexity MCP Server) that other AI assistants can connect to, not Perplexity itself consuming external MCP servers as a client to gain new tools. There is no documentation of a settings/config surface where a user adds arbitrary third-party MCP servers for Perplexity to call.

   • [claimed-docs] “Through the Model Context Protocol, your MCP client can delegate complex, multi-step work to Computer and receive structured results.”
   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “**Connect to 400+ services** through a managed connector framework with one-click OAuth”
   • [claimed-docs] “Browser Control Computer includes a full cloud browser for tasks that go beyond search.”
   • [claimed-docs] “The Perplexity MCP Server enables AI assistants to access Perplexity's powerful search and reasoning capabilities directly within their work…”
   • [claimed-docs] “Give AI assistants the ability to complete real-world tasks using Perplexity Computer through the Model Context Protocol (MCP).”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence Muse supports plugging in external MCP servers; it only describes its agent building its own tools internally, which is a different mechanism.

   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”
   • [claimed-docs] “Muse has its own computer, with a file system and a terminal, so it can write its own code and build the tools a task needs.”

   proof ↗⚑ flag

2. ai-native userUse an official CLI

   weight 2 · round to Perplexity
   Perplexity✓fullprobed8/10

   Perplexity documents an official `pplx` CLI that returns structured JSON from the Search API, explicitly designed for shell pipelines, terminal work, and coding agents, with an independent probe confirming the docs page exists. Missing for 10: independent hands-on community review of the CLI itself (evidence is entirely first-party docs/probe) and detail on install/auth flows.

   • [claimed-docs] “The pplx CLI returns structured JSON from the Perplexity Search API, making it useful for shell pipelines, interactive terminal work, and co…”
   • [claimed-docs] “The `pplx` CLI returns structured JSON from the Perplexity Search API, making it useful for shell pipelines, interactive terminal work, and …”
   • [claimed-docs] “The `pplx` CLI returns structured JSON from the Perplexity Search API, making it useful for shell pipelines, interactive terminal work, and …”
   • [claimed-docs] “The `pplx` CLI returns structured JSON from the Perplexity Search API, making it useful for shell pipelines, interactive terminal work, and …”
   • [probe] “official CLI documented at https://docs.perplexity.ai/docs/cli/overview”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence of an official CLI for Muse; all evidence describes a consumer app/avatar/chat interface with an internal terminal for the agent itself, not a CLI for AI-native users to interact with Muse.

   ⚑ flag

3. ai-native userDrive the product through a documented public API

   weight 3 · round to Perplexity
   Perplexity✓fullprobed9/10

   Perplexity publishes a documented public API (Agent API, Search API, embeddings) with an OpenAPI spec, supports structured output, model fallback, sandbox tool use, and profiles, plus a CLI and MCP integrations for programmatic/agentic access, confirmed by both docs and live probes returning valid OpenAPI/docs endpoints. Missing for 10: independent third-party developer corroboration of API reliability/robustness in production use.

   • [claimed-docs] “Get accurate, up-to-date answers grounded in real-time web search, with inline citations in a single call, and conversation context across t…”
   • [claimed-docs] “The `sandbox` tool gives the agent an isolated Linux container to run real code and use the output in its answer”
   • [claimed-docs] “Structured output makes the model return JSON that conforms to a schema you define, so you can deserialize it directly.”
   • [claimed-docs] “Model fallback enables specifying multiple models in a `models` array. The API tries each model in order until one succeeds, providing autom…”
   • [claimed-docs] “A profile is a reusable, versioned configuration that you save and manage.”
   • [claimed-docs] “The pplx CLI returns structured JSON from the Perplexity Search API, making it useful for shell pipelines, interactive terminal work, and co…”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.perplexity.ai/openapi.json — contains "openapi" key”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.perplexity.ai/llms.txt # Perplexity > Perplexity API documentation for building with the Agent API…”
   • [probe] “official CLI documented at https://docs.perplexity.ai/docs/cli/overview”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence of a documented public API for developers to programmatically drive Muse; all evidence describes end-user chat/app features and internal agent behavior, not an external API surface.

   ⚑ flag

4. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   Perplexity—none0/10

   The evidence pack covers API endpoints, MCP servers, CLI, and browser/app features, but contains no mention of API key scoping, granular permissions, or least-privilege credential issuance for agents. This is a fair capability to expect from an API platform, so absence of evidence means 'none' rather than 'na'.

   ⚑ flag

   Muse—none0/10

   Evidence shows Muse has approval cards, activity logs, and permission tracking for connected accounts, but nothing about issuing scoped or least-privilege API credentials/tokens for an agent to use with external services — this is a consumer personal-agent product, not a developer credential-management tool.

   ⚑ flag

5. ai-native userBuild against official SDKs

   weight 2 · round to Perplexity
   Perplexity~partialprobed4/10

   Perplexity documents a REST-based Agent API with an OpenAI-compatible endpoint and publishes an openapi.json spec, which supports SDK-style integration, but there is no explicit evidence of first-party official SDK libraries (e.g., a Perplexity Python/Node/Go client package) in the pack. missing for 10: explicit official SDK packages/libraries, language-specific SDK docs, independent developer corroboration of SDK usage.

   • [claimed-docs] “Unified access to open-weight models hosted by Perplexity through one OpenAI-compatible endpoint.”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.perplexity.ai/openapi.json — contains "openapi" key”
   • [claimed-docs] “Get accurate, up-to-date answers grounded in real-time web search, with inline citations in a single call, and conversation context across t…”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence of any official SDK for developers to build against; evidence only covers end-user features (avatars, goals, side chats, agent building its own tools) rather than a developer-facing SDK.

   ⚑ flag

6. ai-native userSubscribe to events via webhooks

   weight 2 · round drawn
   Perplexity—none0/10

   No evidence anywhere in the pack of a webhook subscription mechanism for events (e.g., completed jobs, search results, agent tasks); documentation covers APIs, CLI, MCP servers, and apps but no event-driven webhook system.

   ⚑ flag

   Muse—none0/10

   No evidence in the pack mentions webhooks or any event-subscription API for developers; Muse's documented features focus on agent tasks, permissions, and integrations, not outbound webhook subscriptions. missing for 10: any webhook/event API documentation, developer subscription mechanism, or third-party confirmation of webhook support.

   ⚑ flag

Agentic features

1. ai-native userGet AI-generated insights and suggestions from my data inside the product

   weight 2 · round to Muse
   Perplexity~partialcommunity4/10

   Perplexity's Assistant (drafting emails, scheduling, booking) and the browser extension's 'Contextual Understanding' (answers tied to current page/domain) and 'Your Library' (curation of past discoveries) show the product surfacing suggestions tied to a user's own activity/context, but there's no explicit feature for uploading or connecting personal datasets (docs, CRM, files) and generating proactive analytical insights from them — the core product is web search/answers, not user-data analytics. missing for 10: dedicated personal-data ingestion/analysis capability, proactive insight-generation dashboards, independent evidence these 'insights' features work well in practice (community reports flag research features stalling and answer quality issues).

   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Your Library: More than search history, it's a curation of your discoveries.”
   • [claimed-docs] “Contextual Understanding: Get answers relevant to your current page or even specific to your current domain.”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse~partialclaimed5/10

   Muse links personal data sources (email, calendar, social, health, finances) and proactively tracks tasks/goals, implying some data-driven suggestions, but the evidence never explicitly describes AI-generated insights or recommendations derived from that data—most documentation focuses on agentic task execution and tool-building rather than analytic insight generation. missing for 10: explicit description of insight/recommendation generation from user data, independent hands-on validation of this specific capability.

   • [claimed-docs] “Link your email, calendar, Instagram, Facebook, health and fitness data, and finances.”
   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”

   proof ↗⚑ flag

2. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Muse
   Perplexity—none0/10

   The evidence describes on-demand features (Assistant drafting emails/booking, Computer/MCP multi-step delegation, Agent API) that execute when invoked, but nothing documents scheduled, recurring, or persistent background automations that run autonomously without a user actively initiating each session. Missing for 10: any documented scheduling/trigger mechanism, recurring task execution, or evidence of an automation running unattended over time.

   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “Browser Control Computer includes a full cloud browser for tasks that go beyond search.”
   • [claimed-docs] “Give AI assistants the ability to complete real-world tasks using Perplexity Computer through the Model Context Protocol (MCP).”

   proof ↗⚑ flag

   Muse!disputedcontradicted5/10

   Muse's Goals tab, activity log, and approval-card workflow (muse-docs-5, muse-docs-8, muse-docs-13) show first-party support for autonomous background task tracking and execution, and muse-docs-7 explicitly describes agents building tools for unattended tasks. However, an independent hands-on stress test found the agent's control plane starts timing out under load, exposing concrete failure points in its background-automation architecture (muse-comm-1), directly undercutting reliability claims for autonomous operation. missing for 10: independent confirmation that background automations run reliably at scale, more detail on scheduling/triggering mechanisms for autonomous runs.

   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”
   • [claimed-docs] “Tapping on your Muse avatar will show you its full activity log as well as permissions you’ve approved.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”
   • [community] “Blog post title/finding: the author stress-tested Meta Muse until its agent control plane started timing out, exposing failure points in the…”

   proof ↗⚑ flag

3. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round to Muse
   Perplexity!disputedcontradicted5/10

   Perplexity ships a built-in 'Assistant' and 'Computer' agent that can draft emails, book reservations, browse the web, run code, and connect to 400+ services (perplexity-docs-9, -17, -19, -29, -30, -38), which is a genuine in-product delegation capability. However, hands-on community reports say the Computer/agentic research feature 'often fails to trigger via the UI, and when it does work, sessions frequently stall without returning an answer' (perplexity-comm-7), directly contradicting the vendor's smooth-delegation claims. Missing for 10: independent corroboration that the assistant reliably completes delegated multi-step tasks, and resolution of the reported stalling/failure-to-trigger issue.

   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “**Connect to 400+ services** through a managed connector framework with one-click OAuth”
   • [claimed-docs] “Browser Control Computer includes a full cloud browser for tasks that go beyond search.”
   • [claimed-docs] “When a task requires interacting with a website, filling forms, or accessing login-gated content, Computer launches a browser session automa…”
   • [claimed-docs] “Give AI assistants the ability to complete real-world tasks using Perplexity Computer through the Model Context Protocol (MCP).”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse✓fullclaimed8/10

   Muse is explicitly a built-in AI agent with its own compute, terminal, and file system to build tools and complete delegated tasks (goals, side chats, autonomous tool creation), with approval flows and activity logs for oversight. Missing for 10: independent hands-on verification of delegation quality beyond stress-test edge cases and more detail on task breadth/reliability at scale.

   • [claimed-docs] “Muse has its own computer, with a file system and a terminal, so it can write its own code and build the tools a task needs.”
   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”
   • [claimed-docs] “Tapping on your Muse avatar will show you its full activity log as well as permissions you’ve approved.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”

   proof ↗⚑ flag

4. ai-native userOperate the product with natural-language commands

   weight 2 · round to Muse
   Perplexity!disputedcontradicted6/10

   Perplexity's core product is a natural-language chat/search interface (voice and text queries, cited answers) and its 'Assistant'/Computer agent claims to handle draft emails, scheduling, and multi-step web tasks purely via NL commands. However, hands-on community reports say the agentic 'Computer' research feature frequently fails to trigger via the UI and stalls without returning results, directly contradicting the docs' claims of reliable NL-driven task execution. missing for 10: independent verification that the Assistant reliably completes booking/scheduling tasks, and resolution of the reported reliability failures.

   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “**Connect to 400+ services** through a managed connector framework with one-click OAuth”
   • [claimed-docs] “· Voice: Instant, up-to-date answers whether you type or say it.”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse✓fullcommunity7/10

   Muse is designed around a chat interface where users talk to it in natural language to manage goals, tasks, and tool-building ('talk to it about these tasks in chat', side chats for topic-specific NL interaction). This is corroborated by first-party docs describing conversational control as the primary interface. missing for 10: independent hands-on confirmation of broad natural-language command coverage beyond goals/chat, and detail on command reliability at scale (comm-1 notes control-plane timeouts under stress).

   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “some people still wanted separate context for certain topics — which made sense as projects grew more complex. So we built side chats.”
   • [claimed-docs] “Tapping on your Muse avatar will show you its full activity log as well as permissions you’ve approved.”
   • [community] “Blog post title/finding: the author stress-tested Meta Muse until its agent control plane started timing out, exposing failure points in the…”

   proof ↗⚑ flag

Api quality

1. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round to Perplexity
   Perplexity✓fullprobed9/10

   A probe confirms a live, machine-readable OpenAPI spec at https://docs.perplexity.ai/openapi.json returning HTTP 200 with an 'openapi' key, alongside an llms.txt documentation index, directly satisfying the story. Missing for 10: independent/community confirmation of the spec's completeness or usage in the wild.

   • [probe] “PROBE openapi: HTTP 200 at https://docs.perplexity.ai/openapi.json — contains "openapi" key”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.perplexity.ai/llms.txt # Perplexity > Perplexity API documentation for building with the Agent API…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.perplexity.ai/docs/getting-started/overview.md > ## Documentation Index > Fetch the complete documen…”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence Muse publishes any machine-readable API spec (OpenAPI or equivalent) for developers; all evidence concerns end-user agent features, not a documented API surface.

   ⚑ flag

Agent mode

1. power-userDelegate a multi-step task that the assistant works on autonomously in the background and returns for my review

   weight 3 · round to Muse
   Perplexity!disputedcontradicted5/10

   Perplexity's 'Computer' (Labs/Assistant) is documented as an autonomous background agent that can browse the web, execute code, use 400+ connectors, and complete tasks like drafting emails or booking reservations, returning structured results — matching the delegate-and-review story (perplexity-docs-9,10,17-19,29,30,38). However, a hands-on community report says the Computer/research feature 'often fails to trigger via the UI, and when it does work, sessions frequently stall without returning an answer,' directly contradicting reliable autonomous completion (perplexity-comm-7). missing for 10: independent corroboration that Computer reliably completes tasks and returns results, resolution of the reported stalling/failure-to-trigger issue.

   • [claimed-docs] “Through the Model Context Protocol, your MCP client can delegate complex, multi-step work to Computer and receive structured results.”
   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Perplexity Labs: Bring your reports and projects to life faster than ever before.”
   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “**Connect to 400+ services** through a managed connector framework with one-click OAuth”
   • [claimed-docs] “Browser Control Computer includes a full cloud browser for tasks that go beyond search.”
   • [claimed-docs] “When a task requires interacting with a website, filling forms, or accessing login-gated content, Computer launches a browser session automa…”
   • [claimed-docs] “Give AI assistants the ability to complete real-world tasks using Perplexity Computer through the Model Context Protocol (MCP).”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse!disputedcontradicted6/10

   Muse's docs describe autonomous background task handling via the Goals tab, self-built tools, activity logs and approval cards for review — matching the delegate-and-review story [muse-docs-5][muse-docs-7][muse-docs-8][muse-docs-13][muse-docs-10]. However, an independent hands-on stress test found the agent's control plane timing out under load, exposing failure points in the very autonomous-task architecture being claimed [muse-comm-1], and another commenter notes the assistant is less capable than power-user expectations for building/using tools [muse-comm-2]. Missing for 10: reproducible evidence of reliable long-running multi-step task completion, and resolution/acknowledgment of the reported control-plane timeout issue.

   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”
   • [claimed-docs] “Tapping on your Muse avatar will show you its full activity log as well as permissions you’ve approved.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”
   • [claimed-docs] “structured approval cards with clear accept/reject actions, and secure storage for your credentials.”
   • [community] “Blog post title/finding: the author stress-tested Meta Muse until its agent control plane started timing out, exposing failure points in the…”
   • [community] “"I, too, have a personal agent but my friends using Muse or Instinct approach the functionality I use mine for quite easily... a sophisticat…”

   proof ↗⚑ flag

2. power-userHave the assistant operate a web browser on my behalf to research and complete tasks on websites

   weight 3 · round to Perplexity
   Perplexity!disputedcontradicted5/10

   Perplexity documents a 'Computer' agent with a full cloud browser that can search, browse, fill forms, and access login-gated pages, launched automatically for such tasks and accessible via MCP (perplexity-docs-17, -29, -30, -38) — squarely matching the story. However, hands-on community reporting says this same 'Computer' feature 'often fails to trigger via the UI, and when it does work, sessions frequently stall without returning an answer' (perplexity-comm-7), directly contradicting reliable operation. missing for 10: independent corroboration that browser automation completes tasks reliably, and no rebuttal to the stall/failure reports.

   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “Browser Control Computer includes a full cloud browser for tasks that go beyond search.”
   • [claimed-docs] “When a task requires interacting with a website, filling forms, or accessing login-gated content, Computer launches a browser session automa…”
   • [claimed-docs] “Give AI assistants the ability to complete real-world tasks using Perplexity Computer through the Model Context Protocol (MCP).”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse—none0/10

   Evidence describes Muse having its own computer/terminal to build tools, task tracking (Goals tab), and permissions/approval cards, but nothing in the pack mentions browser automation or web navigation on the user's behalf. Missing for 10: any mention of browser control, web research execution, or site-task completion capability.

   ⚑ flag

3. power-userLet the assistant see and operate applications on my computer to complete work

   weight 2 · round to Muse
   Perplexity!disputedcontradicted4/10

   Perplexity's 'Computer' (via MCP) and Assistant app claim to browse the web, execute code in a sandbox, connect to 400+ services, and perform tasks like booking/scheduling — a form of 'operate applications' — but this is a cloud browser/sandbox, not literal control of apps on the user's own computer, and community hands-on reports say the Computer research feature 'often fails to trigger via the UI, and when it does work, sessions frequently stall without returning an answer.' This directly contradicts the vendor's claims of reliable task completion. missing for 10: evidence of actually operating local desktop applications (not just cloud browser/sandbox), reliability data contradicting the stalling reports, independent success case studies.

   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “Computer can: **Search and browse** the web for real-time information, including login-gated pages * **Write and execute code** in an intera…”
   • [claimed-docs] “**Connect to 400+ services** through a managed connector framework with one-click OAuth”
   • [claimed-docs] “Browser Control Computer includes a full cloud browser for tasks that go beyond search.”
   • [claimed-docs] “When a task requires interacting with a website, filling forms, or accessing login-gated content, Computer launches a browser session automa…”
   • [claimed-docs] “Give AI assistants the ability to complete real-world tasks using Perplexity Computer through the Model Context Protocol (MCP).”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse~partialclaimed4/10

   Muse describes having its own computer/terminal to build tools and links external accounts (email, calendar, social, health, finance) via structured approvals, which lets it act on the user's behalf across services, but the evidence never shows it seeing or directly operating applications on the user's own device (e.g., screen/GUI control) — it operates its own sandboxed environment, not the user's computer. Missing for 10: evidence of direct screen/GUI control of the user's local applications, and independent confirmation that this cross-app automation reliably works beyond linked-account integrations.

   • [claimed-docs] “Muse has its own computer, with a file system and a terminal, so it can write its own code and build the tools a task needs.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”
   • [claimed-docs] “structured approval cards with clear accept/reject actions, and secure storage for your credentials.”
   • [claimed-docs] “Link your email, calendar, Instagram, Facebook, health and fitness data, and finances.”

   proof ↗⚑ flag

Tasks

1. power-userSchedule recurring or one-off tasks that run automatically and come back to me with results

   weight 2 · round to Muse
   Perplexity—none0/10

   Evidence shows search, Q&A, Computer/MCP agent capabilities, and an Assistant that can draft emails or book reservations, but nothing describes a mechanism for scheduling recurring or one-off tasks that run automatically in the background and return results later. Community evidence even notes the 'Computer' research feature often fails to trigger or stalls without returning an answer, but there's no documented scheduling/task-runner feature at all.

   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “Browser Control Computer includes a full cloud browser for tasks that go beyond search.”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse~partialcommunity5/10

   Muse's Goals tab and activity log suggest it can track and manage ongoing tasks with an audit trail of completed/planned actions, implying some form of persistent task tracking, but there is no explicit documentation of recurring/scheduled triggers or a scheduling UI. Community evidence also shows the agent's control plane can time out under stress, raising doubts about reliability for automated recurring runs. missing for 10: explicit scheduling/recurrence configuration docs, evidence of one-off vs recurring task setup, and independent confirmation that scheduled tasks reliably complete and report back.

   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”
   • [community] “Blog post title/finding: the author stress-tested Meta Muse until its agent control plane started timing out, exposing failure points in the…”

   proof ↗⚑ flag

Apps

1. power-userUse an official desktop app with OS-level shortcuts and access to what is on my screen

   weight 2 · round drawn
   Perplexity—none0/10

   The evidence pack only documents Perplexity's mobile apps (iOS/Android), a Chrome browser extension, a CLI, and MCP/API integrations — none describe an official desktop app with OS-level keyboard shortcuts or screen-content access.

   • [claimed-docs] “Perplexity Pro Search & Deep Research: Guided AI search for deeper exploration.”
   • [claimed-docs] “Instant Page Summaries: Make sense of any article or webpage at a glance.”
   • [claimed-docs] “This free app syncs across devices and leverages the power of the top AI models from OpenAI, Anthropic, Meta, and more.”
   • [claimed-docs] “The pplx CLI returns structured JSON from the Perplexity Search API, making it useful for shell pipelines, interactive terminal work, and co…”

   proof ↗⚑ flag

   Muse—none0/10

   Evidence describes Muse as an AI agent with its own computer/file system, chat, and permission tracking, but there is no mention of an official desktop app, OS-level keyboard shortcuts, or screen access/observation capability. This appears to be a mobile/app-based assistant rather than a desktop tool with screen access.

   ⚑ flag

2. knowledge-workerUse full-featured official mobile apps for iOS and Android

   weight 2 · round to Perplexity
   Perplexity✓fullclaimed8/10

   Perplexity has official iOS and Android apps (App Store and Play Store listings) with a broad feature set: Pro Search/Deep Research, Assistant tasks, voice, citations, library/history sync across devices, and cross-model access — indicating full-featured rather than bare-bones apps. Missing for 10: independent hands-on app-store review/rating evidence and no direct community commentary specifically on mobile app quality (community feedback cited is about the web/desktop product and Computer feature, not mobile apps specifically).

   • [claimed-docs] “Perplexity Pro Search & Deep Research: Guided AI search for deeper exploration.”
   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Perplexity Labs: Bring your reports and projects to life faster than ever before.”
   • [claimed-docs] “Voice: Instant, up-to-date answers whether you type or say it.”
   • [claimed-docs] “Trust Built In: Cited sources for every answer.”
   • [claimed-docs] “Your Library: More than search history, it's a curation of your discoveries.”
   • [claimed-docs] “Discover: Learn new things from the community.”
   • [claimed-docs] “This free app syncs across devices and leverages the power of the top AI models from OpenAI, Anthropic, Meta, and more.”
   • [claimed-docs] “Cut through the clutter and get straight to credible, up-to-date answers.”
   • [claimed-docs] “Thread Follow-Ups: Keep the conversation going for a deeper understanding.”

   proof ↗⚑ flag

   Muse~partialclaimed4/10

   There is clear evidence of an iOS app (App Store reviews cited via apps.apple.com), but no evidence at all of an Android app, and no detailed description of feature parity between platforms. missing for 10: Android app existence, feature-completeness claims for either platform, independent verification of app quality.

   • [claimed-docs] “I find it more pleasant than Gemini or ChatGPT which is my default for this type of work.”
   • [claimed-docs] “I’d been curious to try OpenClaw, but didn’t want to deal with the security concerns or headache of setting it up.”

   proof ↗⚑ flag

Custom bots

1. power-userBuild and share custom assistants with their own instructions and knowledge

   weight 2 · round drawn
   Perplexity—none0/10

   The evidence pack covers Perplexity's search products, apps, CLI, MCP servers, and Agent API (including 'profiles' as reusable API configs), but nothing describes a user-facing feature for building a custom assistant with its own persistent instructions plus an attached knowledge base that can be shared with others (e.g., a Spaces/custom-GPT-like capability).

   ⚑ flag

   Muse—none0/10

   Evidence shows Muse supports personal customization (avatar naming/style, side chats for separate contexts) but nothing about creating a distinct 'assistant' with its own instructions/knowledge base that can be shared with other users, which is the core of this story.

   ⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round drawn
   Perplexity—none0/10

   No evidence of a batch/bulk API, batch endpoint, or multi-item processing mode; the CLI and Agent API are described as single-call/pipeline-oriented tools that a user could script externally, but Perplexity itself does not document any bulk-operation capability.

   • [claimed-docs] “The `pplx` CLI returns structured JSON from the Perplexity Search API, making it useful for shell pipelines, interactive terminal work, and …”
   • [claimed-docs] “Get accurate, up-to-date answers grounded in real-time web search, with inline citations in a single call, and conversation context across t…”
   • [claimed-docs] “The `sandbox` tool gives the agent an isolated Linux container to run real code and use the output in its answer”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence describes Muse performing bulk operations across many items; docs mention goals tracking, avatars, side chats, and tool-building, but nothing about batch/bulk processing of items. The community stress-test post even highlights the agent timing out under load rather than handling bulk tasks smoothly.

   ⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round drawn
   Perplexity—none0/10

   No evidence of a rules/trigger/automation engine (event-based conditions triggering actions) anywhere in the docs — Perplexity's evidence covers search, chat, APIs, MCP/Computer agent tasks, and CLI, but nothing about defining persistent rules that fire automatically on events.

   ⚑ flag

   Muse—none0/10

   The evidence describes Muse's goal tracking, tool-building, and approval workflows, but there is no mention of user-defined conditional rules or event-triggered automations (e.g., 'when X happens, do Y'). Missing for 10: any documentation of a rules/trigger engine, event-based automation configuration, or examples of users setting conditional actions.

   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   Perplexity—none0/10

   No evidence of scheduling, cron-like triggers, or recurring workflow automation anywhere in the docs, apps, or API references; the Agent API, CLI, MCP servers, and assistant features described are all single-invocation or on-demand tools with no persistent job scheduler.

   ⚑ flag

   Muse—none0/10

   While Muse has a Goals tab that tracks ongoing tasks and an activity log, there is no evidence describing recurring/scheduled job execution, cron-like automation, or repeat-workflow triggers. missing for 10: explicit scheduling/recurrence feature, evidence of periodic execution, workflow automation configuration.

   ⚑ flag

4. ai-native userVersion, review, and roll back my automations

   weight 1 · round to Muse
   Perplexity~partialclaimed3/10

   The Agent API's 'profiles' feature is described as a reusable, versioned configuration that can be saved and managed, offering a thin sliver of version control for automation configs, but there is no documented review workflow or rollback mechanism for automations. missing for 10: rollback/restore functionality, review/diff UI, versioning for Computer/MCP-based automations, evidence of actual usage beyond a single doc line.

   • [claimed-docs] “A profile is a reusable, versioned configuration that you save and manage.”

   proof ↗⚑ flag

   Muse~partialclaimed4/10

   Muse provides an activity log/audit trail and approval cards that support reviewing what the agent has done and plans to do (muse-docs-8, muse-docs-13, muse-docs-10), which covers the 'review' portion of the story. However, there is no evidence of explicit versioning of automations/skills or a rollback mechanism to revert an automation to a prior state — the 'Forget' skill only removes stored information, not automation history. missing for 10: explicit versioning of automations, a rollback/undo feature for agent actions, independent confirmation these review tools work reliably in practice.

   • [claimed-docs] “Tapping on your Muse avatar will show you its full activity log as well as permissions you’ve approved.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”
   • [claimed-docs] “structured approval cards with clear accept/reject actions, and secure storage for your credentials.”
   • [claimed-docs] “You can also use the Forget skill to have Muse forget information about specific topics or people.”

   proof ↗⚑ flag

Connectors

1. power-userBrowse a directory of third-party apps and connectors and add them to the assistant

   weight 2 · round to Perplexity
   Perplexity~partialclaimed4/10

   Docs mention Perplexity's Computer feature can 'connect to 400+ services through a managed connector framework with one-click OAuth,' implying a connector catalog exists, but there is no evidence of an actual browsable directory UI, categories, or user-facing app marketplace, and no independent/hands-on confirmation of this experience. Missing for 10: dedicated connector/app directory UI evidence, list of example connectors, independent user reports of browsing/adding connectors.

   • [claimed-docs] “**Connect to 400+ services** through a managed connector framework with one-click OAuth”
   • [claimed-docs] “Give AI assistants the ability to complete real-world tasks using Perplexity Computer through the Model Context Protocol (MCP).”

   proof ↗⚑ flag

   Muse~partialclaimed3/10

   Muse lets users link specific third-party data sources like email, calendar, Instagram, Facebook, health and finance data (muse-docs-11), which shows some connector-adding capability, but there is no evidence of a browsable directory or marketplace of third-party apps/connectors to explore and add. Missing for 10: an actual app/connector directory UI, discovery/browsing mechanism, and independent confirmation of a marketplace beyond ad-hoc account linking.

   • [claimed-docs] “Link your email, calendar, Instagram, Facebook, health and fitness data, and finances.”

   proof ↗⚑ flag

2. knowledge-workerConnect my cloud drive, email, and calendar so the assistant can search and use them in answers

   weight 3 · round to Muse
   Perplexity~partialcommunity5/10

   Perplexity's Computer/MCP docs claim a 'managed connector framework' connecting to 400+ services via one-click OAuth, and the Assistant feature claims it can draft emails and schedule appointments, which implies email/calendar integration, but no evidence names specific connectors like Google Drive, Gmail, or Google/Outlook Calendar, nor shows search results being grounded in those personal sources. Community reports also note the 'Computer' feature (where this connector framework lives) often fails to trigger via the UI and stalls without returning an answer, raising doubts about reliability. Missing for 10: named cloud-drive/email/calendar connector examples, documentation of how connected-source data is surfaced in answers, and independent confirmation the connector framework works reliably.

   • [claimed-docs] “**Connect to 400+ services** through a managed connector framework with one-click OAuth”
   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “· Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse~partialclaimed6/10

   Muse docs explicitly support linking email and calendar (and other data sources) with approval/permission controls, but no evidence mentions a cloud drive connector (e.g., Google Drive/Dropbox), and there's no hands-on confirmation that these connected sources are actually searched/used in answers beyond permission approval cards. missing for 10: explicit cloud drive integration, independent evidence of search/use in answers.

   • [claimed-docs] “Link your email, calendar, Instagram, Facebook, health and fitness data, and finances.”
   • [claimed-docs] “structured approval cards with clear accept/reject actions, and secure storage for your credentials.”
   • [claimed-docs] “Tapping on your Muse avatar will show you its full activity log as well as permissions you’ve approved.”

   proof ↗⚑ flag

Analysis

1. power-userHave the assistant write and run code on my data to produce charts, computed answers, and downloadable files

   weight 3 · round to Muse
   Perplexity!disputedcontradicted4/10

   Perplexity documents a code-execution sandbox (Computer MCP / Agent API 'sandbox' tool) that can write and run Python/Node code and return outputs, and Labs is pitched for 'reports and projects,' suggesting some support for computing answers/files from data. However, hands-on community reporting says the 'Computer' feature 'often fails to trigger via the UI, and when it does work, sessions frequently stall without returning an answer,' directly contradicting reliable delivery of this workflow. missing for 10: explicit evidence of chart generation, working with user-uploaded datasets, downloadable file outputs, and independent confirmation the sandbox reliably works in the consumer product.

   • [claimed-docs] “Computer can: Search and browse the web for real-time information, including login-gated pages; Write and execute code in an interactive Lin…”
   • [claimed-docs] “Computer can: **Search and browse** the web for real-time information, including login-gated pages * **Write and execute code** in an intera…”
   • [claimed-docs] “The `sandbox` tool gives the agent an isolated Linux container to run real code and use the output in its answer”
   • [claimed-docs] “Perplexity Labs: Bring your reports and projects to life faster than ever before.”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”

   proof ↗⚑ flag

   Muse~partialcommunity4/10

   Muse's docs claim it 'has its own computer, with a file system and a terminal' and can 'write its own code and build the tools a task needs,' which supports general code-execution capability, but there's no explicit mention of producing charts, computed answers, or downloadable files for user data analysis. A community stress-test report also notes the agent's control plane can time out under load, raising doubts about reliability for heavier computational tasks. Missing for 10: explicit documentation or examples of chart generation, data analysis outputs, downloadable file creation, and independent verification of successful code-execution results.

   • [claimed-docs] “Muse has its own computer, with a file system and a terminal, so it can write its own code and build the tools a task needs.”
   • [claimed-docs] “If a task needs a tool that doesn't exist, your agent builds it for you.”
   • [community] “Blog post title/finding: the author stress-tested Meta Muse until its agent control plane started timing out, exposing failure points in the…”

   proof ↗⚑ flag

Artifacts

1. knowledge-workerHave the assistant create and iteratively edit documents, presentations, and other files I can export

   weight 2 · round to Perplexity
   Perplexity~partialclaimed3/10

   Perplexity Labs is described as helping 'bring your reports and projects to life,' hinting at document/report generation, but the evidence pack gives no detail on iterative editing, presentation creation, or exporting files to common formats. Missing for 10: iterative document/presentation editing workflow, export format support (PDF/DOCX/PPT), and any independent confirmation these outputs are usable/editable.

   • [claimed-docs] “Perplexity Labs: Bring your reports and projects to life faster than ever before.”

   proof ↗⚑ flag

   Muse—none0/10

   Muse's evidence focuses on its file system/terminal, tool-building, goals, avatars, and privacy controls, but nothing describes creating or iteratively editing documents, presentations, or exportable files for knowledge-work tasks. missing for 10: any mention of document/presentation creation, editing UI, or export functionality.

   ⚑ flag

Files

1. knowledge-workerUpload documents, spreadsheets, and PDFs and get accurate analysis of their contents

   weight 3 · round drawn
   Perplexity—none0/10

   The evidence pack contains no mention of document, spreadsheet, or PDF upload/analysis features anywhere in Perplexity's docs, API, CLI, MCP server, or app store listings — coverage focuses on web search, citations, sandboxed code execution, and browser automation. Missing for 10: any documentation of file/PDF/spreadsheet upload, parsing, or analysis capability, and any user or hands-on evidence of accurate document Q&A.

   ⚑ flag

   Muse—none0/10

   No evidence in the pack describes Muse handling document/spreadsheet/PDF uploads or analyzing their contents; the material focuses on agent features like avatars, goals, side chats, permissions, and tool-building, not file ingestion or analysis accuracy.

   ⚑ flag

Memory

1. power-userHave the assistant remember relevant context from previous chats and apply it in new conversations

   weight 3 · round to Muse
   Perplexity—none0/10

   Evidence shows only within-conversation context ('conversation context across turns', 'Thread Follow-Ups') and a searchable history/library, but no documented feature where the assistant recalls or applies context from previous separate chats into new conversations. Missing for 10: any explicit cross-session memory feature, opt-in/opt-out memory controls, or evidence of persisted user facts being reused in new threads.

   • [claimed-docs] “Get accurate, up-to-date answers grounded in real-time web search, with inline citations in a single call, and conversation context across t…”
   • [claimed-docs] “Thread Follow-Ups: Keep the conversation going for a deeper understanding.”
   • [claimed-docs] “Your Library: More than search history, it's a curation of your discoveries.”

   proof ↗⚑ flag

   Muse~partialclaimed5/10

   Muse's docs imply persistent memory (a 'Forget skill' to remove remembered info about topics/people, plus 'side chats' for separate context threads and a Goals tab tracking ongoing tasks) suggesting it retains and reuses context across conversations, but there is no first-party or hands-on description of how memory is surfaced or applied in new chats. Missing for 10: explicit documentation of cross-chat memory recall/application, independent verification that remembered context actually surfaces in new conversations, and detail on memory scope/limits.

   • [claimed-docs] “You can also use the Forget skill to have Muse forget information about specific topics or people.”
   • [claimed-docs] “some people still wanted separate context for certain topics — which made sense as projects grew more complex. So we built side chats.”
   • [claimed-docs] “So we built side chats.”
   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”

   proof ↗⚑ flag

2. power-userSet persistent custom instructions and preferences that shape every response

   weight 1 · round to Perplexity
   Perplexity~partialclaimed3/10

   The only related evidence is the Agent API 'profiles' feature (a reusable, versioned configuration you save and manage), which could function as persistent settings shaping responses, but this is a developer/API concept, not documented as a consumer-facing custom-instructions or persona feature. Missing for 10: explicit evidence of user-facing custom instructions/preferences in the consumer app, confirmation that profiles persist across all conversations/responses, and independent corroboration of this working as claimed.

   • [claimed-docs] “A profile is a reusable, versioned configuration that you save and manage.”
   • [claimed-docs] “Your Library: More than search history, it's a curation of your discoveries.”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence describes a persistent custom-instructions or preference-setting feature that shapes all responses; docs mention forget skill, avatars, side chats, and permissions but nothing about setting standing instructions/preferences.

   ⚑ flag

Projects

1. knowledge-workerOrganize related chats and files into a project or space that shares context and instructions

   weight 2 · round to Muse
   Perplexity—none0/10

   The evidence pack mentions a 'Library' for curating search history but nothing about a 'Spaces'/'Projects' feature that groups chats and files with shared context or custom instructions, which is the core of this story. Missing for 10: any mention of collections/spaces, file upload grounding within a project, or persistent custom instructions per project.

   ⚑ flag

   Muse~partialclaimed4/10

   Muse offers 'side chats' for separate context on topics as work grows complex and a Goals tab for tracking tasks, suggesting some contextual organization, but there is no explicit 'project/space' construct that bundles chats+files+shared instructions together. Missing for 10: explicit project/workspace container, file-attachment grouping, shared custom instructions across chats, and independent verification of how side chats share or isolate context.

   • [claimed-docs] “The Goals tab is where Muse keeps track of all this for you, and you can interact directly there or talk to it about these tasks in chat.”
   • [claimed-docs] “some people still wanted separate context for certain topics — which made sense as projects grew more complex. So we built side chats.”
   • [claimed-docs] “So we built side chats.”

   proof ↗⚑ flag

Images

1. knowledge-workerGenerate and edit images from natural-language prompts

   weight 2 · round drawn
   Perplexity—none0/10

   The evidence pack shows Perplexity is focused on web-grounded search, citations, code sandboxes, MCP/CLI integrations, and browser automation via 'Computer,' but contains no mention of image generation or editing capabilities from natural-language prompts anywhere in the docs, app store listings, or community reports.

   ⚑ flag

   Muse—none0/10

   The evidence pack contains no mention of image generation, editing, or any visual/multimodal content creation capability for Muse; it focuses on agent/task automation, avatars, chat organization, and privacy controls. missing for 10: any documentation of image generation from prompts, image editing tools, or multimodal output examples.

   ⚑ flag

2. knowledge-workerShare screenshots and photos and have the assistant accurately interpret what is in them

   weight 2 · round drawn
   Perplexity—none0/10

   The evidence pack contains no mention of image/screenshot upload or visual interpretation capability anywhere in the docs, app store listings, or API references—only text search, voice, browsing, and coding features are described.

   ⚑ flag

   Muse—none0/10

   No evidence in the pack discusses image, screenshot, or photo interpretation capabilities of Muse; all citations focus on agent architecture, avatars, goals, side chats, and privacy controls. Missing for 10: any mention of image/vision input support, screenshot sharing, or accuracy of visual interpretation.

   ⚑ flag

Voice

1. knowledge-workerHave a natural, real-time voice conversation with the assistant

   weight 2 · round to Perplexity
   Perplexity~partialclaimed4/10

   Perplexity's mobile app docs mention a 'Voice' feature ('Instant, up-to-date answers whether you type or say it'), indicating voice input/output exists, but there's no evidence describing a natural, continuous, real-time back-and-forth voice conversation mode (like a dedicated voice-chat UI) or its quality/latency. Missing for 10: details on conversational turn-taking, interruption handling, voice mode UX specifics, and independent hands-on validation of voice conversation quality.

   • [claimed-docs] “Voice: Instant, up-to-date answers whether you type or say it.”
   • [claimed-docs] “· Voice: Instant, up-to-date answers whether you type or say it.”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence describes real-time voice conversation capability for Muse; the evidence pack focuses on agent tool-building, goals, side chats, avatar, and privacy features, with no mention of voice interaction or speech interface. Missing for 10: any documentation of voice input/output, real-time conversational voice mode, or hands-on account of speaking with Muse.

   ⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Perplexity
   Perplexity~partialclaimed5/10

   The API/Agent API exposes web-grounded search, citations, sandboxed code execution, structured output, model fallback, and CLI/MCP integrations, covering much of what the UI offers, but UI-only consumer features like Assistant task automation (email drafting, scheduling, reservations), Voice, Library/Discover, and browser extension page-context features have no documented API equivalents. missing for 10: API parity for Assistant actions (email/scheduling/booking), Voice interface, Library/Discover/curation features, browser-extension contextual page understanding, and independent confirmation that all UI capabilities are API-accessible.

   • [claimed-docs] “Get accurate, up-to-date answers grounded in real-time web search, with inline citations in a single call, and conversation context across t…”
   • [claimed-docs] “The `sandbox` tool gives the agent an isolated Linux container to run real code and use the output in its answer”
   • [claimed-docs] “Structured output makes the model return JSON that conforms to a schema you define, so you can deserialize it directly.”
   • [claimed-docs] “Model fallback enables specifying multiple models in a `models` array. The API tries each model in order until one succeeds, providing autom…”
   • [claimed-docs] “Assistant: Draft emails, schedule appointments, book reservations, and more.”
   • [claimed-docs] “Voice: Instant, up-to-date answers whether you type or say it.”
   • [claimed-docs] “Your Library: More than search history, it's a curation of your discoveries.”
   • [claimed-docs] “Discover: Learn new things from the community.”
   • [claimed-docs] “Contextual Understanding: Get answers relevant to your current page or even specific to your current domain.”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence pack mentions a public API for Muse at all; all evidence describes UI features (avatars, Goals tab, side chats, activity log) with no mention of programmatic access. Absence of evidence for an applicable capability is 'none'.

   ⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round drawn
   Perplexity—none0/10

   No evidence of a data export/portability feature for user account data (chat history, library, settings) in open formats; the docs cover API access to models/search but nothing about exporting personal user data or account deletion workflows. missing for 10: any documented data export tool, format spec (JSON/CSV/etc.), or account-deletion-with-export flow.

   ⚑ flag

   Muse—none0/10

   There is no evidence of a data export feature or open-format data portability in Muse; the pack only mentions a 'Forget' privacy skill and audit trail, not export/download of user data.

   ⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round drawn
   Perplexity—none0/10

   Perplexity is a closed, proprietary product; the evidence pack shows API docs, apps, CLI, and MCP integrations but no mention of open-sourcing the product itself or any open-license source code repository.

   ⚑ flag

   Muse—none0/10

   No evidence indicates Muse's source code is published under any open license; all evidence describes product features and community discussion, not source availability.

   ⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round drawn
   Perplexity—none0/10

   Perplexity is offered exclusively as a hosted SaaS (web app, mobile apps, browser extension, cloud API/CLI/MCP) with no evidence of any self-hostable core product, open-source release, or on-prem deployment option.

   ⚑ flag

   Muse—none0/10

   No evidence anywhere in the pack indicates Muse can be self-hosted; it's described as a cloud/app-based agent product with no mention of open-source release or self-hosting options.

   ⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Perplexity—none0/10

   No evidence in the pack mentions data residency, regional storage options, or any user-selectable region controls; Perplexity's docs focus on models, search, MCP/CLI integrations, and app features but never address data storage location or residency choices.

   ⚑ flag

   Muse—none0/10

   No evidence in the pack mentions data residency, region selection, or storage location controls for Muse users.

   ⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   Perplexity—none0/10

   No evidence in the pack of any opt-out setting, privacy policy statement, or documented control letting users prevent their data from being used for AI training; the evidence pack only covers product features (search, CLI, MCP, browser extension) and unrelated community complaints about crawling/robots.txt behavior. Missing for 10: any documentation of a training opt-out toggle, privacy settings page, or data-use policy addressing model training.

   ⚑ flag

   Muse—none0/10

   The evidence pack covers Muse's agent features, permissions, forget skill, and audit trail, but contains no mention of any opt-out or control over whether user data is used to train AI models. missing for 10: explicit opt-out/consent setting for model training, any privacy policy language addressing training-data usage.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to Muse
   Perplexity—none0/10

   The evidence pack contains no documentation of data retention settings, deletion controls, export options, or privacy dashboard features for Perplexity users; the only privacy-adjacent items concern the company's own web-crawling/robots.txt controversy, which is off-topic for user data control. This axis clearly applies to a consumer AI assistant, so absence of evidence yields 'none'.

   ⚑ flag

   Muse~partialclaimed6/10

   Muse offers a 'Forget' skill to remove information about specific topics/people and provides an activity log/audit trail of actions, giving users some visibility and control, but there's no documentation of full account/data deletion, export, or granular retention policy settings. Missing for 10: explicit data export/deletion controls, retention period settings, and independent verification that 'Forget' fully purges underlying data rather than just suppressing recall.

   • [claimed-docs] “You can also use the Forget skill to have Muse forget information about specific topics or people.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”
   • [claimed-docs] “Tapping on your Muse avatar will show you its full activity log as well as permissions you’ve approved.”

   proof ↗⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Perplexity—none0/10

   No evidence in the pack describes any telemetry opt-out, privacy settings, or usage-tracking controls; the community items discuss crawling/robots.txt disputes and product quality, not telemetry opt-out. Missing for 10: any documentation of a privacy settings page, data-collection opt-out toggle, or telemetry policy.

   ⚑ flag

   Muse—none0/10

   No evidence pack items mention a telemetry/usage-tracking opt-out setting; there is a 'Forget skill' for topic memory and privacy page references, but nothing about disabling telemetry or usage analytics collection. Missing for 10: any documentation of a telemetry/analytics opt-out toggle, privacy policy language on usage data collection, or user reports confirming such a control exists.

   • [claimed-docs] “You can also use the Forget skill to have Muse forget information about specific topics or people.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”

   proof ↗⚑ flag

Research

1. knowledge-workerLaunch a deep research run that autonomously searches many sources and returns a cited report

   weight 3 · round to Perplexity
   Perplexity!disputedcontradicted5/10

   Perplexity's docs explicitly advertise 'Pro Search & Deep Research: Guided AI search for deeper exploration' with citations built into every answer, matching the story's core capability. However, hands-on community reports directly contradict reliable delivery: one user found citations/references 'frequently mismatched to the text' and quality declining over time, and another reports the 'Computer' research feature 'often fails to trigger via the UI, and when it does work, sessions frequently stall without returning an answer,' leading them to prefer other tools for actual research. Missing for 10: independent verification of successful autonomous multi-source runs, resolution of the stalling/citation-mismatch complaints, and clearer documentation distinguishing 'Deep Research' reliability from the separate 'Computer' agent.

   • [claimed-docs] “Perplexity Pro Search & Deep Research: Guided AI search for deeper exploration.”
   • [claimed-docs] “Features: · Perplexity Pro Search & Deep Research: Guided AI search for deeper exploration.”
   • [claimed-docs] “Trust Built In: Cited sources for every answer.”
   • [claimed-docs] “Get accurate, up-to-date answers grounded in real-time web search, with inline citations in a single call, and conversation context across t…”
   • [community] “User used a free 12-month Perplexity Pro offer: initially useful for quick queries like recipes or manuals, but quality declined as the comp…”
   • [community] “The 'Computer' research feature in Perplexity often fails to trigger via the UI, and when it does work, sessions frequently stall without re…”
   • [community] “For actual research, the user now prefers Claude or ChatGPT over Perplexity, saying results are higher quality and more accurate despite bei…”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence describes Muse performing a deep research run that autonomously searches multiple sources and returns a cited report; the pack focuses on avatar personalization, goals/side chats, permissions, and tool-building, none of which document a research/citation workflow. Missing for 10: any mention of multi-source search, report generation, or citation output.

   ⚑ flag

2. knowledge-workerGet answers grounded in current web results with citations back to the sources

   weight 2 · round to Perplexity
   Perplexity!disputedcontradicted5/10

   Perplexity's docs and consumer apps clearly document web-grounded answers with inline citations across the API, apps, and browser extension (perplexity-docs-2, -12, -24, -31), and this is a core, heavily marketed feature. However, hands-on community reports concretely contradict citation quality: users report citations/links frequently mismatched to the text and Perplexity citing AI-generated 'best software' listicle spam, undermining trust in the grounding (perplexity-comm-6, perplexity-comm-11), with some users switching to Claude/ChatGPT for research quality (perplexity-comm-9). missing for 10: independent verification that citation accuracy holds up at scale, and resolution of reported citation/source mismatch issues.

   • [claimed-docs] “Get web-grounded answers with built-in citations in one call, or access third-party models from OpenAI, Anthropic, Google, and xAI with web …”
   • [claimed-docs] “Trust Built In: Cited sources for every answer.”
   • [claimed-docs] “Cut through the clutter and get straight to credible, up-to-date answers.”
   • [claimed-docs] “Get accurate, up-to-date answers grounded in real-time web search, with inline citations in a single call, and conversation context across t…”
   • [community] “User used a free 12-month Perplexity Pro offer: initially useful for quick queries like recipes or manuals, but quality declined as the comp…”
   • [community] “For actual research, the user now prefers Claude or ChatGPT over Perplexity, saying results are higher quality and more accurate despite bei…”
   • [community] “Report claims Perplexity cites AI-generated 'best software' listicle sites en masse, raising concerns about it being manipulated by SEO/AEO …”

   proof ↗⚑ flag

   Muse—none0/10

   No evidence in the pack describes Muse performing web search with cited sources for research answers; the material focuses on agent architecture, avatars, permissions, and goals rather than grounded citation-backed answers.

   ⚑ flag

Data controls

1. knowledge-workerExport my complete chat history and account data

   weight 1 · round drawn
   Perplexity—none0/10

   No evidence in the pack mentions a data export feature, account data download, or chat history export capability for Perplexity's consumer product; documentation focuses on API, MCP, CLI, and app features with no trust-controls/export functionality described.

   ⚑ flag

   Muse—none0/10

   Evidence shows a 'Forget' skill and audit trail/activity log, but there is no mention of a data export or account data download feature for chat history or account data. missing for 10: export/download tool for chat history, account data export functionality, documentation of data portability process.

   • [claimed-docs] “You can also use the Forget skill to have Muse forget information about specific topics or people.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”

   proof ↗⚑ flag

2. knowledge-workerControl whether my conversations are used to train models

   weight 3 · round drawn
   Perplexity—none0/10

   No evidence in the pack addresses privacy settings, opt-out of training, or data-usage controls for conversations; all citations concern product features (search, MCP, CLI, apps) or unrelated crawler/quality controversies. This axis applies to any consumer AI product handling user conversations, so absence of evidence yields 'none'.

   ⚑ flag

   Muse—none0/10

   The evidence pack shows privacy-adjacent features like a 'Forget skill' for memory and audit trails/permissions, but nothing addresses whether users can opt their conversations out of model training specifically. This is a fair axis for a personal AI agent product, but no evidence documents a training-data control. Missing for 10: any documentation of a training opt-out setting, data-usage policy toggle, or explicit statement about whether conversations are used for model training.

   • [claimed-docs] “You can also use the Forget skill to have Muse forget information about specific topics or people.”
   • [claimed-docs] “Muse shows people a complete audit trail of everything it has done and plans to do.”

   proof ↗⚑ flag