Karan Goel ✓
@krandiash · San Francisco, CA · joined 24 Jan 2010
founder ceo @cartesia, derp learned @stanfordailab @mldcmu @iitdelhi
20 643Followers
1 029Following
2 107Posts total
7.9MViews on collected posts
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@krandiash My favorite model! Can’t wait emotions tags on none English voices (like French ;)
101 views · 1 likes
· 0 reposts · 0 replies
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Super cool to see we're #2 on this new benchmark out of the box (#1 soon)
This makes Ink-2 a great fit for consumer apps that are aimed at everyday interactions (and it runs real-time)
5.8K views · 54 likes
· 1 reposts · 1 replies
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@krandiash TTS models are becoming more and more commoditized, and I still wonder what innovation is left in building the model. Of course, the field isn’t dominated by anyone yet, but there are already so many models.
60 views · 0 likes
· 0 reposts · 0 replies
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@krandiash Karan, hope the next model goes beyond SSML. For voice agents where latency is critical, voice dictation via a prompt is also not practical like the Gemini 3.1 TTS supports because the LLM model will have to first generate the direction prompt before the actual answer
153 views · 0 likes
· 0 reposts · 0 replies
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VoiceCodeBench is now live on Vals, and GPT Live Transcribe takes the #1 spot.
@besimple_ai created VoiceCodeBench, which measures how well speech-to-text models handle structured values in English workplace speech. https://t.co/pVwfApwWHy
13K views · 37 likes
· 6 reposts · 2 replies
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Our new TTS model Sonic-3.6 is now GA
We went into preview last week as the #1 model on AA and it's been great to see early users back this up
Overwhelmingly positive feedback, so much so that we graduated 3.6 to GA within a week
And yes, the next model is cooking 🧑🍳
221.4K views · 379 likes
· 97 reposts · 9 replies
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At Cartesia, our #1 value is "Get the fundamentals right".
This value has been with us since Day 0. It shaped the research @_albertgu led during our PhDs: rather than using the same algorithms & architectures for every problem, he asked whether they were the right tool for the
17.9K views · 152 likes
· 25 reposts · 8 replies
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Introducing Sonic-3.6: our most lifelike TTS yet, and another step change in naturalness across 44 languages. Just three months after our Sonic-3.5 launch, we've made fundamental model improvements based on feedback from the teams building on Sonic.
We're #1 on @ArtificialAnlys
88.5K views · 663 likes
· 73 reposts · 39 replies
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Happy 80th to 🇮🇳
Narrated by our best model yet
17.1K views · 482 likes
· 19 reposts · 5 replies
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A very happy 80th Independence Day to India, from team Cartesia! 🇮🇳
Coming closer to home with Sonic 3.6, we're making every one of India's languages more natural, expressive, and accurate:
- Hindi that flows the way you speak it
- Hinglish that switches without missing a beat
110.8K views · 525 likes
· 224 reposts · 90 replies
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0:49
Try a call with a real voice agent and sign up for the promo here!
https://t.co/3OW8Pk4mcs
64K views · 49 likes
· 1 reposts · 3 replies
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@krandiash its so hard to make a text-to-speech model with such a sub-300ms median ttft (time to first token).
big big congrats!
3.4K views · 30 likes
· 0 reposts · 1 replies
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We’ve seen incredible traction this year, with enterprises transforming every part of their operations with Voice AI. We’re sharing what we’ve learned and are giving away a guide to the top 10 enterprise use cases for Voice AI.
Retweet and comment "CARTESIA" on the main video ht
118.2K views · 127 likes
· 11 reposts · 33 replies
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0:13
If you switch to Cartesia from your existing provider, three months are on us.
Hear the difference today. 👇🏻 https://t.co/xufoSwZRg3
152.3K views · 167 likes
· 7 reposts · 9 replies
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0:53
We released Sonic-3.5 and Ink-2, the #1 streaming models for text to speech and speech to text you can use in your voice agents today.
New architectures enable new frontiers for speed and quality.
We're now the only provider to have #1 models for both speaking and listening. ht
7.1M views · 2.6K likes
· 563 reposts · 735 replies
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2:47
Dibandingkan akun berukuran sama
15 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. tampil luas, tetapi sedikit penonton yang merespons.
Median tayangan17 851akun ini980median untuk 10K–100K
Jangkauan, %86.47%akun ini3.51%median untuk 10K–100K
Interaksi, %0.39%akun ini1.92%median untuk 10K–100K
| Metrik | Akun ini | Median untuk 10K–100K | Rasio |
|---|---|---|---|
| Median tayangan per postingan | 17 851 | 980 | 18.2× |
| Jangkauan (tayangan ÷ pengikut) | 86.47% | 3.51% | 24.6× |
| Tingkat interaksi | 0.39% | 1.92% | 0.20× |
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
152.3K15 Jun
118.2K
3.4K
64K16 Jun
110.8K15 Aug
17.1K
88.5K17 Aug
17.9K
221.4K27 Aug
13K
15328 Aug
60
5.8K
101
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.12%15 Jun
0.15%
0.92%
0.08%16 Jun
0.76%15 Aug
2.96%
0.90%17 Aug
1.06%
0.22%27 Aug
0.39%
0.00%28 Aug
0.00%
0.97%
0.99%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.92%.
What the audience does
Likes55.3%5 281 in total
Reposts10.8%1 027 in total
Replies9.8%935 in total
Quotes2.1%200 in total
Bookmarks22.0%2 099 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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