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I Tried Coolify to Self-Host My AI-Powered Anki Flashcard Generator

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I Tried Coolify to Self-Host My AI-Powered Anki Flashcard Generator

Hey HN, I wanted to share my recent adventure in building and self-hosting a SaaS project called AnkAI ( https://ankai.co ). It's an AI tool that generates Anki flashcards, including images from PDFs, which is something I really needed for my own study sessions. The real fun part? I decided to self-host everything using Coolify. So, I set up my Next.js frontend, a backend with Supabase, and used FastAPI in Python to handle OpenAI API communication—all hosted on Coolify. It was my first time using Coolify, and it made the whole process surprisingly smooth. There were definitely some bumps along the way, especially with configuring the database and getting the PDF image extraction to work right. But overall, it was a great learning experience. Especially the automatic deployment when I commit to the main branch of GitHub works very well and is super helpful. If you're into self-hosting, or just like tinkering with new tools, I'd love to hear your thoughts or any questions you might have! Cheers, Ferdinand

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
90%90% 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: new, openai, using · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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.

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