Field Signal: Turning Reddit Into a Competitive Intelligence Feed

Field Signal is a different kind of project than the rest of the toolkit. The calculators and estimators are all client-side, single-file tools. Field Signal is a full-stack application with a Node.js backend, scheduled data collection, API integrations, and a persistent data layer.

The idea came from watching how outdoor brands monitor their reputation. Most of them track Twitter mentions and maybe run a quarterly brand health survey. Almost none of them are systematically reading Reddit, which is where the most honest product feedback lives. Nobody's performing for an audience on r/Ultralight the way they are on Instagram. When someone posts that their tent failed on them at 11,000 feet, that's real.

What it does

The platform monitors 9 outdoor industry subreddits on a scheduled basis. The Reddit API pulls recent posts and comments matching configurable keywords. Claude API then processes the raw text to classify sentiment, cluster related complaints into themes (zipper failures, delamination issues, customer service complaints, etc.), and detect brand-switching signals (posts where someone says they're moving from Brand A to Brand B and explains why).

The dashboard surfaces this intelligence in a format that a product manager or brand strategist can actually use: complaint trend lines, brand-to-brand switching flows, emerging issue detection (a sudden spike in "waterproofing failure" mentions, for example), and AI-generated summary reports.

Why I built it

Two reasons. First, I wanted to demonstrate that modern NLP pipelines can turn unstructured social data into structured competitive intelligence, which is a capability that market research firms should be building. Second, I needed a project that showed full-stack development skills beyond single-file React tools.

The outdoor industry vertical was chosen because I know it well enough to validate whether the AI's categorization was accurate. If Claude misclassifies a tent complaint as a sleeping bag issue, I'll catch it. That domain knowledge matters for QA.

Tech stack

Node.js + Express backend running on a VPS. Reddit API with OAuth2 for data collection. Claude API (Anthropic) for text classification, sentiment analysis, and complaint clustering. Vanilla JavaScript frontend with the same dark-theme dashboard design used across the tool suite. Cron-scheduled data pulls. The architecture is intentionally simple because the intelligence value comes from the AI processing layer, not the infrastructure.