Computer Assisted Fiction

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Phiction Phreak is a database created to assit you in writing and developing fiction. Anything you write with Phiction Phreak is yours to keep with no attatched licenses. It is intended to be used as a "ghost writer", so no mention of it has to be made when publishing the final version of your work. The software is built into a GUI that allows you to easily browse and search through the database. As a general rule, clicking on any word inside of the window will open up a new search for that term. You can search for anything, or view the entire database at once. There is also a list of the most common words in the database which allows you to sort through the database instead of search. Phiction Phreak was created because I wanted to write a novel and thought the best way to start would be to read everything I could get my hands on. I saved little pieces of what I read and over time they became a database. If you want to compile Phiction Phreak, you will need to download Qt. Qt is a cross platform library and IDE that uses C++ to build world class applications. If you don't feel comfortable compiling the source code then you can download the Windows or Mac binaries and install it into any directory. Qt is extremely well documented and can be downloaded from http://www.qt.io/ Phiction Phreak was created by Corey White, but the project welcomes your contributions. Phiction Phreak is an open source project, and you can get it through github here: http://treatz.github.io/phiction_phreak/

Visit product View on Hacker NewsMar 25, 2015

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

84%84% 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: created · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

61%61% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

12%12% 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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