kwindla ✓
@kwindla · San Francisco, CA · joined 20 Sep 2008
Infrastructure and developer tools for real-time voice, video, and AI. @trydaily // ᓚᘏᗢ // @pipecat_ai
15 731Followers
3 924Following
6 599Posts total
377.1KViews on collected posts
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Here's a complete voice agent tutorial focusing on a super low latency configuration: Pipecat PhoneLLM Alpha 1 on @modal, Deepgram transcription, Cartesia voice.
You can grab the code and jus run /setup in Claude or Codex.
But Merve's video also dives into the details of https:
5.3K views · 116 likes
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If you're interested in voice UI, or music, or both, it's worth watching all of this video that Jon just dropped.
Voice-controlled, generative music.
The interface will be familiar to anyone who has used a digital audio workstation. But also ... deep integration of voice makes
5.1K views · 61 likes
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I joined the Pipecat TV crew to talk about PhoneLLM: a super low-latency LLM trained on voice agent scenarios.
We talked about building agents with a small-ish LLM like this, compared to using a big model. PhoneLLM is based on Nemotron 3 Nano, so it's a 30B-A3B MoE.
Prompting a
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@kwindla Latency is the number everyone measures, but on real phone lines the thing that breaks trust is interruption handling. A caller talks over the greeting, the agent keeps going, and the call is lost before the model even matters. Curious whether PhoneLLM helps with barge-i
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Over a year ago, I posted a video where Gemini and I collaborated on an Ableton Live Session. Music AI is fun! Creating voice-native music software is something I'm really passionate about, so, here is Jamcat.
Jamcat's concept is a voice controlled, generative music performance
6.8K views · 36 likes
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PhoneLLM: https://t.co/fiI1HMj8EK
Weights on @huggingface and one-click endpoint deployment on @modal.
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Everybody building voice agents obsesses about latency.
Every enterprise I talk to wants to run LLMs on their own infrastructure (for data management, regulatory/compliance, and cost reasons).
PhoneLLM is a new open weights model trained on voice agent tasks: sub-100ms https://
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Full episode, including a fantastic segment with @AndyMasley about the data center build-out backlash, here:
https://t.co/pc0cm5Gjo8
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@kwindla yes impressive, voice agent, we are also heading there 🚀
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Blog post with more details about this model is here: https://t.co/HnFhvvzdI3
We also have a new internal benchmark, PhoneBench, that's part of this project.
And we're working with partners to create custom models using the "factory" that produced PhoneBench. If you need a http
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Ben Shababo at @modal did a bunch of great inference optimization work to achieve the >80 concurrent clients at sub-600ms end-to-end TTFAT.
You can spin the model up as a Modal Endpoint with one click or with this command:
```
modal endpoint create --model https://t.co/XkOB2O3
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Weights are here: https://t.co/y727Voi1bt
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Introducing PhoneLLM, an open model for voice agents.
GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost.
For voice agents, we need models that are both very low latency and very good at tool calling and instruction following.
There's a
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Dibandingkan akun berukuran sama
13 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. tampil luas, tetapi sedikit penonton yang merespons.
Median tayangan5 148akun ini924median untuk 10K–100K
Jangkauan, %32.73%akun ini3.62%median untuk 10K–100K
Interaksi, %0.91%akun ini1.52%median untuk 10K–100K
| Metrik | Akun ini | Median untuk 10K–100K | Rasio |
|---|---|---|---|
| Median tayangan per postingan | 5 148 | 924 | 5.57× |
| Jangkauan (tayangan ÷ pengikut) | 32.73% | 3.62% | 9.04× |
| Tingkat interaksi | 0.91% | 1.52% | 0.59× |
Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
325.3K27 Aug
10.2K
7.1K
4K
17730 Aug
80031 Aug
7.6K
966
6.8K
221 Sep
3.7K
5.1K2 Sep
5.3K
Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.90%27 Aug
1.16%
0.59%
0.85%
7.91%30 Aug
0.75%31 Aug
2.07%
0.52%
0.69%
0.00%1 Sep
2.07%
1.24%2 Sep
2.63%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes42.0%3 147 in total
Reposts3.9%294 in total
Replies2.0%153 in total
Quotes0.7%53 in total
Bookmarks51.4%3 853 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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