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TrendRadar – AI auto-commenter to grow on X

Hacker News

TrendRadar – AI auto-commenter to grow on X

Hello Hacker News, I’m a solo indie hacker building in public. Over the last few days I built *TrendRadar*, an AI tool that automatically finds trending posts on X (Twitter) within topics you care about and writes comments in your voice. You choose the tone and sentiment, and the system handles the rest – even controversial angles. Some highlights: - *Single‑click authentication:* Connect your X account via the official API. No scraping or grey‑area methods – TrendRadar uses an approved X/Twitter application and is fully compliant with their terms of service (FAQ on the site explains this). - *Personalised auto‑comments:* You can tweak the level of controversy, review/edit comments before posting, and set custom prompts so the comments match your brand or personality. - *Real‑time trend detection:* The tool monitors your chosen topics and jumps on trending conversations 24/7 so you don’t miss out. - *Analytics dashboard:* See impressions, follower growth, engagement rate and more in a single panel. I launched it on my own account three days ago and saw impressions jump to ~40k and my follower count increase by ~50%. There’s a free Starter plan (3 auto‑comments per day) and an Early‑Bird Pro plan for early adopters. I’d love feedback from the HN community. You can try it here: * https://trendradar.app** . (If you do sign up, the promo code `EARLYBIRD` will apply a discount to the paid plan – there are 100 codes available.) Thanks for checking it out!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% 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.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, single, code · Missing: mac, agents, macos
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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