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A Clean, Context-First Dictionary That Explains Words

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A Clean, Context-First Dictionary That Explains Words

I built a distraction-free dictionary focused on helping people learn words—not just look them up. Most dictionaries overwhelm users with cluttered definitions, ads, or unnecessary friction. I wanted something better: Explanations from multiple personas (a teacher, a storyteller, a slang expert) Usage examples in academic, casual, and business contexts Curated lists for SAT, TOEFL, and advanced learners No ads. No tracking. Just thoughtful word learning. AI helped generate the content, but the end experience is static and focused. I’d love to hear what you think—and what you'd want in future versions. https://www.thelearningdictionary.com/ (Feedback very welcome!)

Visit productView on Hacker NewsApr 18, 2025
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% 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.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, context, plain · Missing: mac, agents, macos
34%34% 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
32%32% 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 · Strong signals: users · Missing: plus, platform, intuitive
30%30% 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
17%17% 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.

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