Flowery – Vocabulary builder powered by LLM and spaced repetition

Hacker News

Dear hackers: Whether English is your first or nth natural language, you may be familiar with this vexing experience: You encounter a flowery word in a book, podcast, or HN comment; you endeavor to memorize it to hone your speech and writing; you purge it from your synapses by the next day. Flowery ( https://flowery.app ) streamlines vocabulary building by: • Rendering a typographically pristine Oxford dictionary and thesaurus. • Conjuring up the stochastic parrot to eliminate the tedium of flashcard creation. • Scheduling the flashcards with a spaced repetition algorithm. About the tech stack: The server is built on Rust, Axum, and PostgreSQL. The client is built on Google’s Lit. We are betting the farm on the web—you won’t find Flowery on any app store. We have gone to great lengths to polish UX, especially on iOS where we rolled our own virtual keyboard, textboxes, and text selection. We strive to make Flowery an exemplar of the quasi-native web app. Non-obvious features to try: • “Add to Home Screen” on iOS. • “Add to Dock” on macOS Sonoma. • Install on Android and desktop Chrome. • Append an ellipsis to search by prefix. • Touch a flashcard’s blank for a hint. • Keyboard navigation inspired by the one true editor. Give it a whorl [sic] and share your thoughts! Florally yours, The Florist

Visit product View on Hacker NewsJun 18, 2024

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

86%86% 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 · Missing: supports, reddit linkedin, podcasting

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

AppSumoStrong fit for a featured deal · Strong signals: builder · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, 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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% 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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