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WriteGlow – Make AI-generated content sound more human

Hacker News

WriteGlow – Make AI-generated content sound more human

Hi HN, I’m Yvaine — I recently launched [WriteGlow]( https://writeglow.ai ), a tool that helps make AI-generated content sound more human and pass AI detectors. As AI-generated writing becomes more common in blogs, student essays, and marketing copy, there's a growing need to improve the tone and authenticity of that content — and ensure it’s not flagged by detection tools. WriteGlow focuses on three things: - *Humanizing* robotic or overly formal AI drafts to sound more natural, - *Running AI detection tests* to check if your content will be flagged, - *Helping users write faster*, while still sounding like themselves. Writers, students, and marketers have been using it to polish content and avoid detection issues, especially in workflows where AI is used but human tone matters. Would love to hear your thoughts — especially on usability, tone quality, or what you'd expect from a tool like this!

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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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
55%55% 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
43%43% predicted probability of success on AppSumo, 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
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% 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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