AIProduct DesignResearch

Claude Wishlist

A living PM listening tool for Claude — a scheduled agent scrapes r/ClaudeAI 4×/week, categorizes what developers are actually asking for, and renders a dashboard with weekly deltas and PM-ranked priorities.

✨ Writing your own feedback is one data point. Building a system that aggregates a thousand other builders' is a product. The Reddit feed tells me where my own experience maps onto the community's — and where I'm an outlier.

The problem

I picked up Claude Code in February and shipped four products in a few months — some days from 11am to 2am, completely unable to stop. I started keeping notes on every gap I hit. But a single consumer's wishlist is one data point. I wanted signal at the scale of the community, not just me.

My hypothesis

If I built a listening tool that watched r/ClaudeAI on a cadence, categorized posts into pain points / feature requests / positive / competitor, and ranked what mattered via a PM-style weekly analysis — I'd end up with a better artifact than any static doc I could write. And it would keep updating without me.

What I built

A scheduled agent (Mon / Wed / Fri / Sun 9am) that scrapes pullpush.io for r/ClaudeAI posts, deduplicates, categorizes with tags (rate_limits, code_quality, context_window, mcp, pricing, ux…), computes week-over-week deltas, and writes raw data + a run summary to a public GitHub repo. The dashboard at /claude-ideas renders three modules: Fresh Findings (latest run, filterable by category), Trends (WoW delta charts), and PM Today (ranked priorities with supporting post IDs). Every stat is clickable — a drilldown drawer fetches the raw posts backing any claim.

What broke

The first four weeks of scheduled runs silently 409'd on GitHub's content API because the two file PUTs (posts.json, runs.json) were fired in parallel — the second write's parent commit went stale mid-flight. The dashboard read as 'first run pending' for a month. Fixed by awaiting the first PUT before starting the second.

What I learned

The model isn't the bottleneck. The interface is. That was my original thesis from 200+ hours of solo building, and the Reddit data backs it — the top tag this run is 'ux', pain points outnumber feature requests 6:3, and the #1 priority that surfaces from PM analysis is agent-autonomy guardrails (not model capability). I also learned that an automated listening tool is harder to keep running than to build — silent failure is the real enemy of a system meant to be invisible.

If I kept going

Add a fourth module: 'Shipped vs. Requested' — compare Reddit feature requests against Anthropic's public changelog so you can see what's been addressed vs. still open. Also: widen the source beyond r/ClaudeAI — HN, Twitter, Discord — to de-bias toward one forum's demographics.