About SignalPipe

Who built this?

My name is Yusuf. I've been building software products for years, mostly backend systems. SignalPipe started as an internal tool I built for myself — a FastAPI backend that monitored Reddit and Hacker News for mentions related to products I was selling. It worked well enough that I decided to package it properly and release it.

The source code for the OpenClaw plugin is public on GitHub (AbYousef739/signalpipe). The backend scoring engine is closed source — the RL weights, LLM swarm configuration, and signal scoring pipeline are what make it work, and that's the part I'm charging for.

Why does this exist?

Cold outreach reply rates have been declining for years. The tools that dominated the market — Apollo, Instantly, Clay — are all built around the same premise: build a big list, send a lot of emails, hope some stick. I was doing this and it was working less and less.

The insight that changed my approach: people who are actively looking for a product post about it publicly. On Reddit, on Hacker News, in forums. They ask for recommendations. They complain about competitors. They describe problems that your product solves. And almost nobody responds to them with genuine help.

SignalPipe automates the detection of those moments. The human still writes the relationship — you review every draft, approve every send. The machine just makes sure you never miss the conversation in the first place.

How does the scoring work?

SignalPipe runs two sequential pipelines. First, scoring: each post is compared semantically against the language your buyers use when they describe the problem you solve, then weighed for how urgent and how specific they are being, with a sarcasm pass that removes irony and hypotheticals. There is also an optional keyword pre-filter, off by default — it is a way to cut scoring costs if you already know the exact phrases your buyers use, not part of the judgement. Whatever survives scoring goes to the panel: a Skeptic, an Analyst and an Optimist read the post independently, frequently disagree, and resolve into a single verdict. Weak signals are dropped there, before any draft is written.

Most posts are filtered out during scoring. What reaches your review queue is the remainder that shows genuine buying intent, each with a drafted reply ready. Full scoring pipeline explained here.