Aisbf, a self-hostable OpenAI-compatible AI proxy/router

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AISBF is a self-hostable AI proxy/router that exposes an OpenAI-compatible API while letting you route across different providers and model pools. I built it to make multi-provider AI setups less chaotic: centralize access, add routing/failover/caching, support multi-user setups, and make it easier to scale from a small deployment to clustered infrastructure. Happy to answer questions about architecture, routing logic, tradeoffs, and where it differs from simpler proxy layers.

Visit product View on Hacker NewsMay 21, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, openai · Missing: mac, agents, macos

85%85% 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 · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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