ReacherX – Open-source platform to find and reach the right people

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ReacherX – Open-source platform to find and reach the right people

Hi HN, I'm Salman. I started ReacherX as a side project in mid 2023 while studying at university, working part-time, and freelancing. I went full-time on it in June 2026. First time sharing something I built on Hacker News ;) ReacherX is an open-source platform for finding the people you're looking for on X/Twitter and LinkedIn. There's also an interactive demo linked in the README, prepopulated with fictional data, so you can click around and get a feel for the product without setting anything up. It isn't connected to the real backend. Ideal use cases include finding potential customers, doing market research/validation before building something, and finding ideal candidates when hiring. I built it because even in 2026, finding the right people/network isn't easy. One question always boggled me: why can't I just describe the people I need in plain English and see a list of people along with the actual sources/evidence showing why they are the right match? Existing tools are focused heavily on sales professionals. I am not a salesperson. I don't understand sales jargon. I wanted something that "just works" when I describe who I am looking for. No campaigns or sequences to learn. After spending three years in this space, I also came to realise that this is less of a funnel problem and more of a networking problem. The whole space is also pretty closed, which is one reason I wanted ReacherX open source. The workflow is roughly: - Describe who you're looking for and why. - The Agent creates ideal profiles and example mock profiles for you to review. - It continuously searches X/Twitter and LinkedIn using generated search queries. - Each person is qualified with an LLM and enriched with supporting sources/activity and other details. - ReacherX only surfaces people with a match score of 70% or higher. - At 90%+, it can generate an outreach plan for review. - DMs, replies, liking/commenting, profiles, voice notes, and media attachments are built in. You can ask the Agent to handle outreach, or do it yourself through the UI. You can also upload a product demo and tell the Agent to use it when appropriate. It has memory and adapts based on search performance and your feedback/preferences stored in memory. The searches run in the backend, so the browser can be closed. First results usually take around 5–30 minutes, and the workflows keep running until plan limits are reached or a backend error stops them. ReacherX doesn't rely on an internal static prospect database for discovery. It performs searches against current X/Twitter and LinkedIn data, so results can be very recent. I ran it for 2–3 weeks while looking for potential customers. It processed 50,000 people, but only around 300 scored 70% or higher and were surfaced to me. It's an end-to-end TypeScript project using Next.js and Convex. ReacherX uses Convex Agent, Workflows, Workpool, Memory/RAG, etc. Those durable workflows are the backbone of the system. ReacherX is also part of the Convex Open Source program. One thing I'm still thinking hard about is how this architecture will hold up at scale. At a high level it's a continuous loop: generate queries -> search platforms -> qualify/disqualify results with an LLM -> enrich matches -> keep searching. Another challenge has been social-data APIs. Before X introduced Pay Per Use, its API was prohibitively expensive for this kind of product, and even now it can still be costly. Third-party APIs made it possible for me to build this at a much more reasonable cost. ReacherX is currently in beta (v4). It has bugs and rough edges, and I'm working on it every day. The hosted product is paid and doesn't have a free tier yet. If you don't want to use the hosted version, you can self-host it. I'm also open to contributors and bug reports on GitHub. I'd especially love feedback on the idea, match quality, architecture/scaling approach, and product philosophy. Anything is welcome :)