afrim – A framework for input method engine

Hacker News

afrim is a framework and toolset that facilitates the implementation of input method engines (IMEs). Initially introduced as an IME for African languages[1], afrim has now evolved into a universal solution. It is compatible with various sequential writing systems, including Amharic, Geez, Pinyin, and more. afrim is written in Rust and his architecture is inspired by librime[2]. It's available to use in Rust (afrim), Python (afrim-py), JavaScript (afrim-js), and more. Repo: https://github.com/fodydev/afrim Demo: https://fodydev.github.io/afrim-web/ FAQ: https://github.com/fodydev/afrim/blob/main/FAQ.md [1]: https://news.ycombinator.com/item?id=41427563 [2]: https://github.com/rime/librime

Visit product View on Hacker NewsOct 22, 2025

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

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% 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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

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

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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