Discorch – Offline tool to browse and delete your Discord messages

Hacker News

Built this to help users manage their Discord message history. Upload your data package to browse messages and generate deletion requests that comply with Discord's requirements, all offline and locally. Discord's bulk deletion process is complex and poorly documented. With their recent push toward monetization and ads, users need better tools to control their data. Discorch makes this accessible by guiding you through the process step by step, with a simple and intuitive interface. Includes a Go CLI for attachment downloads. Search functionality needs improvement and there are some known bugs, but it works well for most use cases. Issues and PRs welcome at https://codeberg.org/discorch/discorch . I'll keep an eye on the comments for feedback and bug reports!

Visit product View on Hacker NewsJan 17, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos

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AppSumoStrong fit for a featured deal · Strong signals: intuitive, interface, users · Missing: plus, platform, reviews

62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: need better · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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