Got tired of writing promos so I made it one-click

Hacker News

I love building open source, but writing promo posts always felt time-consuming and exhausting. Paid tools are expensive, and free ones are too limited. So I built my own tool: Auto Hongmyungbo — “Drop a rough draft → get platform-optimized posts → auto-publish.” What it does 1. Draft input Just throw in your promo, thoughts, or messy notes. If your idea is vague, use the built-in Hook Ping-Pong feature to generate and refine strong hooks. 2. Platform-optimized generation With one click, it transforms your draft into posts tailored for LinkedIn, X, Instagram, and more — each matching the platform’s tone. 3. Instant refinement Edit directly, or prompt the AI like: “Make this sharper for X” or “More professional for LinkedIn.” Refine in real time before final approval. 4. Auto posting The browser opens automatically, inserts your content, and publishes it. I’ve been using it daily, and I thought it would be way more fun to build it together — so I open-sourced it. GitHub stars, feedback, and PRs are always welcome!

Visit product View on Hacker NewsFeb 27, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, notes, open · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

39%39% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time, paid · 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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