Why Reddit built its own AI prototyping tool | Blair Metcalf
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Firecrawl—Give your AI agents web data and beyond
Blair Metcalf is the principal UX engineer at Reddit and the creator of Reddit Proto, an internal platform that helps designers and product managers build production-like prototypes without touching GitHub. In this episode, he shows how the system combines real Reddit data, reusable scaffolds, Cursor annotations, and a custom design system MCP to turn ideas into shareable prototypes. He also explains how Reddit’s teams have built about 700 prototypes, reached 100% adoption across UX and product, and developed a Figma-to-PRD workflow that makes engineering handoffs far more precise.
Biggest takeaways:
The best prototyping tools hide complexity without limiting capability. Reddit’s desktop app handles repositories, branches, and scaffolding behind the scenes, so designers never touch Git. What remains is a simple path from idea to production-shaped prototype.
Mock data makes prototypes less convincing. Reddit lets people sign in to prototypes with their real accounts, filling feeds, searches, and communities with familiar content. Leadership can evaluate the actual experience instead of mentally correcting for placeholders.
Annotations become powerful when they include code context. Reddit’s harness attaches each comment to the relevant component, design-system guidance, and the user’s intent before sending it to Cursor. That context turns a short annotation into a accurate working edit instead of a guess.
A custom design-system MCP is what turns AI into a reliable builder. Blair’s team mapped Reddit’s tokens, components, and workflows directly to production code. Cursor therefore knows which component to use, how it should look, and where the code belongs.
The feature capsule makes design handoffs traceable instead of lossy. A designer submits a Figma link, and the system maps each element to production components, incorporates previous engineering feedback, and generates a structured PRD with acceptance criteria. The result is a handoff with far less room for interpretation.
The best AI prototypers are not necessarily the most technical. Reddit’s standout users include a visual designer and an animator with no engineering background. Their advantage is strong product context, a clear vision, and a willingness to experiment without worrying about breaking things.
At 700 prototypes, discovery becomes as important as creation. Reddit is building a searchable console with tags, team filters, and platform filters so employees can find related experiments. Keeping prototypes public internally creates visibility that scattered Figma folders never could.
Real components and real data create instant alignment. When leaders can open a shareable prototype built with Reddit’s design system and their own account data, implementation questions stop dominating the review. The biggest speed gain comes from removing the uncertainty that once delayed decisions.
Blog and detailed workflow walkthroughs from this episode:
Inside Reddit Proto: Real Data, Constrained Prototypes, and a Better Handoff: https://www.chatprd.ai/how-i-ai/blair-metcalf-reddit-proto
↳ Create a Realistic Prototype with Live Data in Reddit Proto: https://www.chatprd.ai/how-i-ai/workflows/reddit-proto-live-data-prototype
↳ Generate a Draft Pull Request from a Figma Design with Reddit Proto: https://www.chatprd.ai/how-i-ai/workflows/reddit-proto-figma-to-draft-pr
Claude Duet is beautiful. I still forgot to use it.
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In this solo episode, Claire tests Claude Duet, Anthropic’s beautifully designed new personal agent, to see whether it deserves a place in her increasingly crowded AI stack. She puts it to work on inbox and Slack triage, her sales pipeline, a Google Docs deck, a launch video, and finding Warriors tickets for her kids. She also explains why Duet’s thoughtful task lists, artifacts, and channels are not enough to overcome its slow responses, quiet notifications, and lack of desktop and team support.
Biggest takeaways:
Claude Duet is the most polished personal agent launch yet. From onboarding onward, it clearly explains what it can do, what it can access, and how to begin. Claire found even the early TestFlight builds trustworthy and roughly a hundred times more polished than OpenAI’s Dots at launch.
Duet is built around thoughtful agent primitives. It inherits Claude plugins and connectors, tracks what the user and the agent are each working on, and brings existing artifacts into the experience. Slack-like channels also keep focused work, such as sales, separate from the main conversation.
Duet is designed for the middle of the market. Anthropic is targeting entrepreneurs, small-business owners, PMs, and executives, rather than casual consumers or power users managing dozens of agents. It feels like a proactive, mobile companion to Cowork.
A proactive agent has to give people a reason to return. After onboarding and a few tasks, Duet stopped nudging Claire. Its quiet notifications are elegant, but a short morning briefing is not enough to keep an easily forgotten agent in someone’s daily routine.
Latency is a product problem, not merely a model problem. Opus 5.5 produced solid work, but parallel tasks routinely took more than 10 minutes with little visible feedback. Claire’s experience suggests that a faster agent with slightly weaker output may feel more useful than a smarter one that disappears while working.
Duet’s real-world results were capable but inconsistent. Sales pipeline analysis worked quickly through existing connectors, while inbox and Slack triage missed some priorities and important context. A sales deck was decent but off-brand, and ticket shopping worked without fully closing the loop.
Context still does not travel cleanly between AI products. Duet cannot access local Claude skills, so Claire’s launch-video workflow required converting those skills into plugins. The industry still has not solved how a user’s context and workflows should move across surfaces.
The lack of desktop and multiplayer support keeps Duet outside Claire’s working stack. Her workday happens primarily on a computer and in shared team tools such as Slack. Even a five-person business needs agents that can operate collaboratively rather than remaining single-player mobile experiences.
Duet is worth trying, but it has not earned a permanent place in Claire’s stack. The design, models, connectors, and artifact support create a strong foundation. To win the personal-agent race, it still needs to become faster, more proactive, available on desktop, and shareable with a team.
Blog and detailed workflow walkthroughs from this episode:
Claude Duet Is Beautiful. That Doesn’t Make It My Go-To Agent.: https://www.chatprd.ai/how-i-ai/claude-duet-review
↳ How to Draft a Sales Deck on Your Phone Using Claude Duet: https://www.chatprd.ai/how-i-ai/workflows/claude-duet-sales-deck
↳ Analyze Your Sales Pipeline Using Claude Duet’s Connected Apps: https://www.chatprd.ai/how-i-ai/workflows/claude-duet-sales-pipeline
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.
Catch you next week,
Lenny
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