PredictEd Update 1.1 released: An open sourced predictive text editor

Hacker News

PredictEd 1.1 update can be downloaded from: https://github.com/oormicreations/PredictEd/releases (Windows only) What's new in this version: - Contexts - Different databases for different subjects or languages. - Spell checks - With custom dictionaries. - Re-sizable window. - Fast training. - Encryption tools - AES, Base64, Steganography, SHA512. - Bug fixes. - Support for drag and drop of files. - Support for shell opening of files. - Tips, tooltips and a cleaner status area. Features: - Word prediction as you type. - Word and phrase completion. - Learns new words as you type. - Train using existing text files of your own. - Contexts for various subjects and languages. - Format as you type (basic). - Auto sentence case and paragraphs. - Auto saves the work. - Save and open RTF and Text files. - Usual editor functions like cut, copy, paste, font type, font size, find and replace, undo/redo etc. - Spelling check. - Background colors and text colors. - Unicode UTF-8 support. Works for many languages. - Encryption tools - AES, Base64, Steganography, SHA512 hash. - Supports MS Windows 7 onward. Details: https://oormi.in/software/predicted/help.html

Visit product View on Hacker NewsOct 7, 2018

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

62%62% 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 HuntUnlikely to reach the leaderboard · Strong signals: new, context, using · Missing: mac, agents, macos

48%48% 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: exist, open source, existing · Missing: https docs, excited, just released

45%45% 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: para · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% 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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