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Decode, encode, listen, and speak to learn a language

Hacker News

Decode, encode, listen, and speak to learn a language

Hey HN. I love learning languages and I have tried several language learning apps over the last few years, but I didn’t like any of them because they were either expensive ($20-$50/month) or weren’t very effective. I was using Duolingo, for example, because it was free, but it was slow and ineffective. Even after having ~150 day streak, I still couldn’t say much in Spanish. I was either passively listening to the language, learning vocabulary without context, or learning sentences that weren’t realistic. So I set out to build my own. I have tried different approaches and techniques over the last 1.5 years for learning a language, and finally found something that works well. This web app uses a 4-step system of decoding, encoding, listening, and speaking. 1. Decoding: You are given a sentence in your target language (e.g., French) and have to unscramble the English words to figure out what it means. 2. Encoding: The exact opposite of step one—translating the active thoughts from English back into your target language. 3. Listening: You listen to the native audio of the sentence and type out the English translation. 4. Speaking: You hit the record button and actually practice speaking the sentence out loud from day one. I have been using this app for a while now, and it has been helping me learn French far more effectively than my previous approach. It gets me speaking the language from day one, I learn vocabulary and grammar through context, and I learn phrases and sentences that are practical and realistic. I am excited to finally launch it and get it into the hands of other language learners. I would love to hear any feedback you have on the method or the app itself! I’m currently running a free private beta. If you want to test it out and help me improve it, you can test the 4 steps in the website demo or sign up for free here: https://www.4tospeak.com Caleb

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, context, using · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, month · Missing: mobile apps, ios, personal
66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
52%52% 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
30%30% 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
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · 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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