Show HN: Homi, open decision model for audio, near Flash on MMSU and 50x cheaper

egma‑ai's release of Homi, an open decision model for audio that claims near‑Flash performance on MMSU and costs up to 50× less than comparable solutions, signals a dramatic drop in the barrier to deploying high‑quality AI‑generated audio on websites. For website owners, this means you can now embed sophisticated speech synthesis, dubbing, or voice‑over capabilities without the hefty cloud‑compute bills that previously limited such features to large publishers. Because Homi runs efficiently on modest hardware, traffic spikes from AI‑driven audio generation (e.g., user‑initiated podcast clips or automated customer‑service voice bots) will increase, potentially inflating bot‑related bandwidth and CPU usage on your servers. **Actionable steps:** 1. **Update your llms.txt** – add a rule that identifies Homi’s user‑agent pattern (e.g., `User-Agent: Homi/*`) and assign it a lower priority or rate‑limit to protect resources. 2. **Deploy bot‑tracking scripts** that flag audio‑request endpoints (such as `/api/v1/tts`) and log request origins; this will let you differentiate genuine user interactions from automated Homi calls. 3. **Set up cost alerts** on your hosting platform for CPU and bandwidth spikes tied to audio processing routes, ensuring the 50× cost advantage isn’t eroded by unchecked bot traffic.