When writing regex scripts isn't worth it or enough

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I developed a text/table modification software that enables users to quickly and intelligently perform find-and-replace operations on text and tables, both inside and outside of the app. At its current alpha stage, it includes the following features: * 'one click' transformers : allowing users to edit the entire text with one click. * directly editing text in other software * exact, fuzzy, regex, and even semantic search * text replacement by dictionary: allowing bulk replacement of various keywords at once * hiding paragraphs without matches: removing the visual clutter in search result * table text editing It is written with PySide6 and inspired by TextSoap. Visit https://texcel.app/ if you are interested. Feature requests and bug reports are more than welcome.

Visit product View on Hacker NewsJul 28, 2023

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

80%80% 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: user, visual · Missing: mac, agents, macos

76%76% 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: ide, io · Missing: https docs, excited, just released

44%44% 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: users, para · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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