Exporter
Indie HackersApple Notes batch exports to markdown with attachments!
It is tough to migrate away from Apple Notes as there is no batch export feature and exporting thousands of notes one-at-a-time is tedious. I built Exporter to solve this problem.
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, notes · Missing: mac, agents, macos81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal30%30% 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 released27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue22%22% 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, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Ba
Batch export Apple notes to markdown with attachments on macOS23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Batch export Apple notes to markdown with attachments on macOS
Hacker News2Co
Convoviz – turn ChatGPT exports into Markdown and simple visuals31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Convoviz – turn ChatGPT exports into Markdown and simple visuals
Hacker News1Ba
Back-up Apple Notes data in Markdown and HTML format59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Back-up Apple Notes data in Markdown and HTML format
Hacker News4WW
WWDC Notes – TL;DW for Apple's WWDC Videos46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
WWDC Notes – TL;DW for Apple's WWDC Videos
Hacker News4en
enex-dump – Extract notes and attachments out of Evernote exports41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
enex-dump – Extract notes and attachments out of Evernote exports
Hacker News2Ma
Markdown Notes Tab52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Markdown Notes Tab
Hacker News3Es
Ese – Exports Elasticsearch index from one cluster to another cluster45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Ese – Exports Elasticsearch index from one cluster to another cluster
Hacker News4No
Notably – a simple tool for storing markdown notes59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Notably – a simple tool for storing markdown notes
Hacker News2Ig
IgniteMS – batch text embeddings at 253K msg/s on 8x A10030%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
IgniteMS – batch text embeddings at 253K msg/s on 8x A100
Hacker News3Ma
Markdown codeblocks preprocessor62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Markdown codeblocks preprocessor
Hacker News2