Real-time noise suppression always involves tradeoffs - curious what the actual pipeline latency looks like end-to-end, not just model inference. WebRTC jitter buffers, chunking, and resampling all add overhead on top of the model itself, and for voice AI phone agents that budget is already tight with STT + LLM + TTS in the chain. Also wondering how it handles overlapping speakers mid-sentence vs. steady-state noise - that's usually where suppression models fall apart. How does it compare to what Deepgram or Twilio already offer natively in their voice pipelines?
About Hush on Product Hunt
“Open-source noise suppression for voice AI agents”
Hush launched on Product Hunt on June 23rd, 2026 and earned 169 upvotes and 23 comments, placing #6 on the daily leaderboard. Hush removes competing voices, background noise, and audio interference from real-time calls so your voice AI agents always hear what matters.
On the analytics side, Hush competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Hush performed against the three products that launched closest to it on the same day.
Who hunted Hush?
Hush was hunted by Hasan Ali. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Hush including community comment highlights and product details, visit the product overview.
Hasan Ali
Real-time noise suppression always involves tradeoffs - curious what the actual pipeline latency looks like end-to-end, not just model inference. WebRTC jitter buffers, chunking, and resampling all add overhead on top of the model itself, and for voice AI phone agents that budget is already tight with STT + LLM + TTS in the chain. Also wondering how it handles overlapping speakers mid-sentence vs. steady-state noise - that's usually where suppression models fall apart. How does it compare to what Deepgram or Twilio already offer natively in their voice pipelines?