Popular commercial Mac cleaning tools require full disk access and operate as closed binaries, giving users zero transparency or verification over what is being deleted. Meanwhile, standard macOS app uninstallation leaves behind massive hidden files, models, and caches.
Popular Mac cleaners are closed binaries that ask for full disk access and delete rights without providing transparency or verification of what they are deleting, leading to a lack of user trust.
I built an open-source Mac cleaner you can actually read before it deletes anything
I built an open-source Mac cleaner you can actually read before it deletes anything
I built an open-source Mac cleaner you can actually read before it deletes anything
Technical Mac users and developers running out of space who want to purge application leftovers without trusting opaque, closed-source commercial cleaning software.
Repeated explicit concerns regarding the lack of transparency in closed binaries combined with massive invisible leftover files (AI models, VMs, caches) remaining on disk after app deletion.
Unlike CleanMyMac or OnyX, this tool is fully open-source, transparent about the exact system paths it targets, and minimizes user privilege risks by prioritizing the native macOS Trash system.
An open-source, local-first macOS utility that transparently scans, surfaces, and safely removes leftover application files. It forces all actions to go through the native macOS Trash by default, allowing full verification of exact file paths before any permanent deletion.
Technical users explicitly state they are seeking trusted alternatives to incumbents like CleanMyMac ($39+/yr). They value trust and security enough to fund independent, transparent development.
“Reclaim gigabytes of hidden Mac storage with 100% open-source transparency.”
An open-source, local-first macOS utility that transparently scans, surfaces, and safely removes leftover application files. It forces all actions to go through the native macOS Trash by default, allowing full verification of exact file paths before any permanent deletion.
Launch directly on Hacker News, GitHub Trending, and Mac-centric subreddits (r/mac, r/macapps, r/developer) highlighting the open-source codebase and privacy guarantees.
Technical users may fork the repository to bypass licensing constraints if the value add of paid tiers isn't clearly differentiated.
If the parsing logic incorrectly flags a shared system dependency, it could destabilize third-party applications.
Apple's restrictive sandboxing and security policies might require complex onboarding to guide users through local permission grants.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
It sits at the intersection of "devtools", "macos", "open-source", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
