Perplexity Just Launched a Computer That Runs 19 AI Models at Once
Research, design, code, deploy. One system. No switching between tools.
Perplexity dropped something yesterday that got 12 million views on X in 20 hours.
They call it Perplexity Computer.
The pitch:
one system that unifies every current AI capability. Research. Design. Code. Deploy. Project management. End-to-end.
This is what a personal computer in 2026 should look like.
What It Actually Does
It orchestrates 19 different AI models.
Computer uses Opus to match each task to the model best suited for it. Need research? One model. Need code? Another. Need design? A third.
The system routes work across all 19 models automatically, running agents in parallel. You give it a project. It figures out which models to use and in what sequence.
It remembers everything.
Persistent memory across sessions. Your past work, your preferences, your files. Hundreds of connectors to external tools. Web access built in.
No more re-explaining context every time you start a new conversation.
It manages entire project portfolios.
Go from a single task to hundreds of active projects. Clear your to-do list, move active work forward, kick off something new on the side.
The Problem
This sounds powerful. It is powerful.
But power creates complexity.
19 models. Hundreds of connectors. Persistent memory. Parallel agents. Project orchestration….
Most people will open Computer, try a few prompts, get mediocre results, and assume the whole thing is overhyped. That’s what happens with every new AI tool. The capability is there. The knowledge of how to use it well is not.
The difference between “this is cool” and “this changed how I work” comes down to knowing the right prompts, the right project structures, and the right mental models for when to let the system run versus when to step in.
I spent the last hours inside Computer, testing workflows and breaking things.
Here’s what I learned :)
What’s Behind the Paywall
Part 1: The Mental Model
How to think about a 19-model orchestration system. When to let Computer route automatically versus when to specify models yourself. The difference between single-task prompts and project-level prompts. This section alone will save you hours of trial and error.
Part 2: Project Structures That Work
The folder and memory setup that keeps Computer organized across dozens of active projects. How to use connectors without overwhelming the system with context. The naming conventions that make retrieval actually work.
Part 3: 15 High-ROI Workflows
Copy-paste prompts for the tasks that matter:
Deep research with automatic source synthesis
Code projects from spec to deployment
Content pipelines (research → outline → draft → edit)
Competitive analysis across multiple companies
Meeting prep from calendar + email + web
Data analysis with visualization
Automated monitoring and alerts
Customer research synthesis
Product spec drafting
Email and outreach sequences
Financial modeling assistance
Legal document review
Hiring pipeline management
Sales call preparation
Weekly reporting automation
Each workflow includes the exact prompt, expected output, and what to do when it breaks.
Part 4: What Computer Can’t Do (Yet)
The failure modes I found. Tasks that sound good but produce garbage. How to recognize when you’re better off using a single model directly instead of the orchestration layer.
Part 5: Computer vs Cowork vs Operator
When to use Perplexity Computer. When to use Claude Cowork. When to use OpenAI Operator. The honest comparison based on actually using all three.
Plus: The AI Corner Library
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Claude Cowork Complete Playbook — The tool behind the $285B software selloff
Sonnet 4.6 Workflows — 10 automation recipes with exact prompts
The Viral Content System — The prompt behind 3M weekly views
2500 Angel Investors Database — Full list with emails
Claude Presentations Playbook — Every prompt for pitch decks, board updates, sales decks
Prompt Engineering Guide 2026 — The complete system for getting better AI output
Everything published going forward.
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