Summary of "Perplexity's NEW Computer Just DESTROYED Every AI Tool You're Paying For"

High-level summary

Perplexity Computer (running in Perplexity’s Comet browser) is demonstrated via a hands-on review. The presenter gives five real-world jobs to the system, showing prompts, activity logs, and final outputs to highlight how the product orchestrates multiple AI models and connectors, runs multi-step workflows (agents) in the background, and integrates with the apps and data you already use so you don’t have to copy/paste between tools.

The video is a demo-style walkthrough that emphasizes real-world tasks, transparency of agent activity, parallel execution, and extensibility via a skills marketplace.

Key technological concepts and product features

Comet browser
- Perplexity’s browser extension/new-tab UI where “Computer” runs.
Tasks
- Workspace where each job is saved; multiple tasks can run concurrently.
- All outputs (reports, images, code, clips) are auto-saved.
Connectors
- Native integrations to external tools (Google Drive, Gmail, Slack, GitHub, Salesforce, Snowflake, CB Insights, etc.) allowing the AI to read/write to real data sources.
MCP (Model Context Protocol)
- A “universal language” layer that lets models interact with software/tools (analogy: universal remote). Enables connector interoperability.
Skills
- Pre-built capabilities (research assistant, media processor, finance markets, web app builder, data visualization, etc.).
- System auto-selects skills based on the task; users can add third-party skills from a marketplace (skills.sh).
Orchestrator model selection
- You can choose which model runs orchestration (example used: Claude Opus 4.6 for complex reasoning).
Credits system
- Tasks consume credits (example: a content strategy task cost 97 credits on the Pro plan; Pro ≈ 4,000 credits).
Activity log / transparency
- Live step-by-step log showing what agents do (which skills they launched, browsing activity, API calls). Not presented as a black box.
Parallelism + agents
- True parallel execution: multiple agent workflows run simultaneously.
- Agents can self-diagnose and self-fix code errors without human intervention.
Marketplace & extensibility
- Install new skills from skills.sh by pasting a URL; installed skills become available to tasks.
Auto media processing
- Transcription with timestamps, automated clip selection, video extraction, auto-generated thumbnails and headlines (presenter estimates ~80% useful out of the box).
Full-stack app generation
- Demonstrated building a working stock-educator full-stack app with zero user code by combining finance, web app, and data-visualization skills.
Data-source driven research
- Can pull premium data (e.g., CB Insights) via connectors, cross-reference web and paid sources, and produce formatted reports.

Concrete demos / tasks performed

YouTube content strategy audit
- Prompt: Analyze the top 5 performing videos + comments and recommend next videos.
- Process: Orchestrator launched a research assistant, browsed YouTube, read comments, pulled views/links.
- Output: Content strategy recommendation (70% mastery guides, 30% podcasts); cost = 97 credits.
YouTube clip generator + thumbnails
- Prompt: Analyze a full video, cut clips, and produce thumbnails.
- Process: Full transcription with timestamps; algorithmic selection of 5–6 clip-worthy segments; video extraction; autogenerated thumbnails and headlines (~80% quality).
Full-stack stock educator app
- Prompt: Build an app to input 3 stocks, visualize 10-year performance, map historical reasons for moves, and teach entry/exit points.
- Process: Installed an external stock-analysis skill from skills.sh; launched finance, web-app, and data-visualization skills. Encountered an Anthropic SDK import error mid-build; the agent read its own error log, rewrote code, re-ran the build, and finished without human input.
- Output: Working app URL with 10-year charts, 52-week high, current price, and event-mapped insights. Presenter cautions it’s for learning, not investment advice.
Market research across CB Insights + web
- Prompt: From market insights, identify 10–15 jobs most impacted by AI and the top 5 industries to build in.
- Process: Used the CB Insights connector and web sources simultaneously, cross-referenced data, and compiled a report.
- Output: Top industries identified include healthcare, financial services, and education, plus detailed role-level breakdowns and skills in demand.
Parallel task throughput demo (implied)
- Multiple internal tests run in parallel to illustrate throughput and compounding productivity.

Notable behaviors and claims

Self-debugging agents: Agents can detect errors (e.g., SDK import failure), patch code, and continue without human intervention.
Real browsing: The system actually navigates external sites (YouTube, CB Insights) rather than hallucinating results.
Auto skill choice: The system picks which skills to use for a job; users can override the orchestrator model but may not always control skill choice.
Parallel workflows compound productivity: Running many complex tasks simultaneously is a core differentiator.
Orchestration layer: Perplexity Computer acts as a control/orchestration layer that runs existing LLMs (Claude, GPT, etc.) and integrates with enterprise tools; it isn’t positioned as a replacement for base models.

Performance and cost examples

Example: The content strategy task cost 97 credits on the Pro plan (Pro ≈ 4,000 credits).
The UI shows credit usage in real time during tasks.

Limitations and cautions

Thumbnails and copywriting are not perfect initially (roughly 80% usable).
Not a substitute for professional investment advice; stock analysis demo is educational only.
Technical errors (e.g., SDK imports) can occur, though agents may self-repair during the workflow.

Comparisons and context

Compared to OpenClaw (referenced): both trend toward local or hybrid model orchestration. Perplexity Computer runs in the cloud, while OpenClaw runs locally.
Presenter referenced his separate “Cloudbot”/OpenClaw deep-dive videos for additional under-the-hood details.

Guides, tutorials, and resources mentioned

Demonstrations covered:
- How to open Computer in the Comet browser and where to click.
- How to choose the orchestrator model.
- How to install skills from skills.sh.
- How to add connectors like CB Insights.
Prompts used for each task were shared with the presenter’s WhatsApp community and linked in the video description.
Links promised to related breakdown videos (Cloudbot/OpenClaw) and the creator’s channel subscription.

Main speakers and referenced systems

Presenter: Vibhav Cicinti (the video host and demonstrator).
Products / systems: Perplexity Computer, Comet browser, MCP (Model Context Protocol), skills.sh.
Models & SDKs referenced: Claude Opus 4.6 (Anthropic/Claude), ChatGPT (referenced), Anthropic SDK (error encountered).
Data/connectors: Google Drive, Gmail, Slack, GitHub, Salesforce, Snowflake, CB Insights.
Other referenced projects/videos: “Cloudbot” breakdown, OpenClaw.