Freshist Earns a 58.28 Proof of Usefulness Score by Building a Subscription-Free, Native macOS Cleaner
Freshist earned a 58.28 Proof of Usefulness score for its 3.7 MB native macOS cleaner built around safe cleanup, privacy, and one-time pricing.
Freshist, a lightweight native macOS cleaner, has earned a 58.28 proof of usefulness score for building a subscription-free and telemetry-free solution to help users reclaim disk space and monitor system health. Launched by Cemali Gencer, Freshist is designed to address the growing frustration with bloated, subscription-based utilities and invasive telemetry. The app's developers focus on providing a privacy-respecting, high-intent solution for everyday Mac users, developers, and privacy-conscious individuals.
Freshist's website currently attracts about 5,000 visitors per month, which primarily comes from word of mouth and Product Hunt. The app is a one-time purchase macOS utility and ranks under the 5000 people/month range, mainly through its website, Product Hunt, and directories. Freshist is built using Swift and SwiftUI, incorporating modern frameworks for fast and lightweight performance.
It also integrates deep macOS APIs for system monitoring, utilizes Sparkle for seamless updates, and prioritizes user privacy and system security with strict code signing and notarization.
According to the Proof of Usefulness Hackathon, Freshist's score reflects its usefulness accurately, as it acknowledges the early traction and scores the product based on its real-world potential rather than inflated numbers. Gencer sees the score as fair and a solid baseline for the product's early development. The biggest upside for Freshist lies in its trustworthiness, contrasting with the usual subscription-based, intrusive solutions in the market.
The app offers a calm, honest, and capable alternative to developers, providing a clear demonstration of its usefulness through the ability to reclaim tens of gigabytes of cache and build artifacts on a typical Mac.
Written by urgent.news from HackerNoon's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
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