Neat — Find Large Files (Mac)
Find large files. Reclaim your Mac's space
Neat — Find Large Files (Mac) launched on May 5, 2026 with 3 votes, #3,449 of 20,538 launches that month and more than 50% of that year's launches.
What it does
Neat finds the biggest files hiding on your Mac so you can free up space in minutes. 100% offline, zero tracking, no account. Pick a folder, see what's eating your storage, move what you don't need to Trash. Free on the Mac App Store.
Download Neat - Find Large Files by Sajith M R on the App Store. See screenshots, ratings and reviews, user tips and more games like Neat - Find Large Files.
Utilities Developer Sajith M R Language EN English Size 722.1 KB Mac Scan your Mac for large, forgotten files in seconds. 8 smart templates, live dashboard, Quick Look preview. 100% offline, 100% private. Free forever. Neat scans your Mac for large, forgotten files eating your disk space. Beautiful live dashboard, 8 smart scan templates, Quick Look preview, powerful filters, pause/resume, and consent-based trash. All fully offline and 100% private. No account required. No ads. Ever. FEATURES: • Instant scanning at thousands of files per second • 8 one-click templates: Big Files, Large Media, Cache & Temp, Old Downloads, Dev Cleanup, VMs & Containers, App Leftovers, Screen Recordings • Live…from apps.apple.com
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FreeUpMyMacJan 2026 · freeupmymac.com · ▲110Hey HN Ex YC/Google engineer turned indie maker here - excited to share something I just shipped. The System Data in my Mac was taking up 300GB and it took me months to figure out how to clear the old cache, logs, junk and more. And CleanMyMac is a subscription - I refuse to pay subscription fees for Mac apps that run locally. So I built FreeUpMyMac an offline, lifetime Mac cleaner that clear junk with 1 click. And helps you visualize your storage with TreeMap/Sunburst. Hope it’s helpful to you as it is for me. Would love your feedback Tunde
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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