Looking for a Technical Co-Founder — AI-Powered Fitness Tracker

1 ptsmujahedRemoteAI

I'm Mujahed, a PM/technical exec (KloudKiQ — DevOps, cloud, AI delivery) based in Jordan. I've built and personally use a working prototype of an AI-native gym tracker: log a workout by typing what you did in plain language ("bench press 25 30 30, then dips 40 50 50"), it matches it to your plan, and shows last-time numbers plus a progressive-overload target for every exercise. What exists today: a live, working proof of concept (natural-language logging via AI, plan + history tracking, YouTube-linked exercise reference), plus a scaffolded backend (Cloudflare Workers + D1, JWT auth, AI-parsing API) ready to build on. Real usage data, real product decisions already made — not just an idea on paper. What's missing, and what I need a co-founder for: turning the working single-user prototype into a real multi-user product — accounts, a proper API layer, editable history, analytics (PRs, volume trends, streaks), mobile-friendly experience, and the judgment calls on scope and stack that come with owning the engineering side. Looking for: a technical co-founder (full-stack or backend-leaning) who wants to own the product's architecture and build it out, not just execute a spec. Equity-based partnership — happy to discuss split and structure once we've talked. About me: I bring product/PM direction, the existing prototype and specs, delivery-team experience, and a Jordan-based development network. If this sounds interesting, message me — happy to share the working prototype and the full product spec.

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abdulwahab9/20/2026

Popular workout loggers (such as Strong, Hevy, or FitNotes) have massive, entrenched user bases and highly optimized UI/UX interfaces for quick logging. Modern LLM capabilities make natural-language parsing relatively easy for existing players to copy as a single feature. To build a sustainable moat, the product must move beyond basic input parsing into deep personalization—such as real-time adaptive progressive overload algorithms, injury recovery tracking, or automated rest-timer adjustments based on dynamic strain.