Wordwright.ai – Learn vocabulary by writing, not memorizing

Hacker News

Hi HN, I built a Chrome extension that flips vocabulary learning on its head. Instead of flashcards where you passively recognise words, Wordwright.ai makes you actively use them. How it works: Add words you encounter while browsing. When it's time to review, you write sentences using the word. AI evaluates your usage — not just right/wrong, but explains nuances like collocations, tone, and common mistakes. What makes it different: Production over recognition — Writing forces deeper processing than "did I remember the meaning?" AI feedback — Not just "correct/incorrect" but "this is technically right, but native speakers would say it this way instead" Privacy option — Works with OpenRouter (cloud) or Ollama (local). Your vocabulary stays on your machine if you want Semantic distractors — Fill-in-the-blank questions use near-synonyms, not random words. Tests whether you really understand the nuance Spaced repetition — SM-2 algorithm. Reviews pop up as gentle reminders when you open new tabs Built because I was frustrated with Anki for vocabulary. Knowing a definition isn't the same as being able to use a word naturally in conversation.

Visit product View on Hacker NewsDec 26, 2025

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

80%80% 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: lua, llama, io · Missing: https docs, excited, just released

42%42% 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: way · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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.

Correct prediction on native model

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