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Backmesh, Firebase for AI Apps
Hacker NewsBackmesh, Firebase for AI Apps
Hello HN! Fernando from Backmesh. I got tired of having to spin up a backend to use the OpenAI or Anthropic API and figure out authentication, usage and error analytics per user in my web or mobile apps so I created Backmesh, the Firebase for AI Apps. It lets you safely call any LLM API from your app without a backend with usage analytics and rate limits per user.
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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, openai · Missing: mac, agents, macos92%92% 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.
TrustMRRFits verified-revenue profile · Strong signals: mobile apps, apps · Missing: ios, personal, entrepreneurs68%68% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released50%50% 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, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive17%17% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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