We built Mintlify style developer docs with zero cost

Hacker News

Mintlify Pro costs 250 USD per month. We wanted something similar (AI assistant and nice UI), but fully customizable and cheap. So we built GibsonAI docs in 1 day for about 50 USD. How we did it: Used Lovable for UI components + rendering MDX beautifully (Markdown stored in GitHub). Built an AI Agent for docs using Agno + Memori → personalized Q&A and “smart educator.” Stored embeddings in LanceDB and metadata in our SQL DB. Bonus: We can share our reusable design templates and source code so you can deploy on Vercel (or anywhere) and skip Lovable costs. Would love feedback on what features you would add or change?

Visit product View on Hacker NewsSep 17, 2025

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, lovable, using · 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.

Hacker NewsStrong engagement from HN community · 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.

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

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

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

25%25% 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

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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