Heap – save full-page local archives of webpages with just one click

Hacker News

Heap is a macOS app for creating full-page local offline archives of webpages in various formats with a single click. It's a menubar only app and a single right-click creates full-sized image screenshot, PDF, Markdown, HTML, and WebArchive. It can also be configured to archive videos, zip files etc using AppleScript. You can archive multiple links too by dragging and dropping a text string or text file containing the links. It can do things like run JavaScript on the website before archiving, signing in with user accounts before archiving, and running an Apple Shortcut post archiving. You can configure the directory where the archives are stored locally. So, this way, it can sync the archive directory with your other devices automatically via iCloud/Google Drive/Dropbox etc. It also provides single-click way to access archives of the front most browser link on the Wayback Machine (archive.org) or Archive.today. I feel like people who are into data hoarding and self host would find this very helpful. Heap's free version has all the features available except it puts a watermark in the archive which can be removed with a one-time-only in app purchase. I am also the developer of HACK (hacker news app for iOS, macOS and Android) and have a reputation for not using ads, tracking, analytics, or subscriptions. Please try and feel free to ask me anything: https://apps.apple.com/ca/app/heap-website-full-page-image/i...

Visit product View on Hacker NewsJun 30, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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