Bi

Bike – macOS Native Outliner

Hacker News

Bike – macOS Native Outliner

Bike’s most original feature is the “fluid” text editing. Lots of text editors have animated some interactions (cursor movement, insert newline, etc), but I think Bike is the first designed from the ground up to support fluid editing. Give it a try, it feels different. (movie on home page if you don't have Mac) Other Features: • In text mode Bike works like a normal text editor. In outline mode rows are constrained to outline hierarchy. • .bike file format is HTML subset, so files are easy to parse and manipulate. Bike also supports .opml and .txt. • Scriptable via AppleScript. Javascript plugin API also expected in future, though no timing on that. • Architecture needed to support fluid editing also makes Bike faster/more scalable than most (all?) outliners and many text editors. I test performance using the Moby Dick Workout[^1]. Implementation Notes: • View is built using CALayers[^2]. • Animations are performed by Core animation and Motion[^3] lib. • View performance is determined by visible text, not document size. Model representation is interesting in that it’s just a flat list of rows. Each row has a `level` property, outline structure is determined dynamically. View implementation requires that each row has a unique ID. I’m using OrderedDictionary from Swift Collections[^4] to store rows. This is Bike’s performance bottleneck for large outlines. Eventually I may change to augmented b+tree and then should be able to work with gigabytes worth of outline. That will be fun, but not sure it’s actually needed. Already probably fast enough for 99% of use cases as is. Hope you find Bike interesting. I’m happy to answer any questions. [^1]: https://www.hogbaysoftware.com/posts/moby-dick-workout/ [^2]: https://developer.apple.com/documentation/quartzcore/calayer [^3]: https://github.com/b3ll/Motion [^4]: https://github.com/apple/swift-collections

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
94%94% 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, cursor · Missing: agents, agent, claude
83%83% predicted probability of success on Product Hunt, 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
50%50% 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 · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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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