SentencePieceKit – Swift package wrapper for Google's SentencePiece

Hacker News

Needed SentencePiece tokenization for an on-device NLP pipeline and couldn't find a working Swift package, so I built one. Compiles Google's SentencePiece C++ source into a universal xcframework — covers iOS, macOS, and watchOS (including simulator slices). watchOS required patching CMakeLists.txt to work with the iOS toolchain. Install via SPM, import SentencePieceKit, done. No bridging headers or manual C++ setup needed. Early stage, feedback and PRs welcome.

Visit product View on Hacker NewsFeb 25, 2026

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2points

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Launch Intel predictions

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

87%87% 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 · Strong signals: ios, including · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, google · Missing: mobile apps, personal, entrepreneurs

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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

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

Correct prediction on native model

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