Wilde AI – Turn Stale Blog Posts into SEO and Geo-Friendly Content

Hacker News

"Ah, how I enjoy refreshing my old blog posts" has no one said - EVER. With Wilde AI the days of tedious content refreshes are over. Here's how Wilde works: Step 1: Provide it with the URL of your old blog post. Step 2: Wait. Step 3: Done. Wilde turns your old blog posts into fresh conversion opportunities by automatically adding: 1. A TL;DR summary, 2. Smart CTAs, 3. Internal links, 4. Relevant content, 5. A visual summary of your blog post, and 6. FAQs. Compatible with WordPress, Wix, Squarespace or any other content management system, Wilde AI produces clean, maintainable blog posts in HTML and CSS, 100% publication ready. We're in public beta now, and would love to hear everyone's feedback! Thanks in advance for checking us out!

Visit product View on Hacker NewsSep 27, 2025

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

68%68% 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: visual · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

56%56% 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 · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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