Blogteca – A free interactive directory for discovering blogs

Hacker News

Hey HN! I'm Raul, and I just shipped Blogteca ( https://blogteca.com ). I struggled to find interesting blogs to read and learn from other people's knowledge. The issue is that many of us prefer to have our own blog with our own code, our own design, our own domain, instead of publishing on Medium or Substack. The downside? Our blogs rarely reach a wider audience. No Medium's built-in distribution. No Substack's network effects. Just our little corner of the internet that nobody finds. It's a free directory where indie bloggers can: - Submit their self-hosted blogs (no paywalls, completely free) - Get discovered by people who actually want to read personal blogs - Potentially boost their domain rating through backlinks I'm calling it an "interactive directory" because I'm actively working on the SEO and domain rating to make those backlinks actually valuable for bloggers who submit I encourage you to submit your blog or share it Any feedback is welcome :)

Visit product View on Hacker NewsSep 30, 2025

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

54%54% 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: code · Missing: mac, agents, macos

51%51% 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: lua, ide, 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.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

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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