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language models with powerful memetic energy:
- sydney bing
- woke gemini
- claude opus 3
- truth terminal
- 4o
- deepseek R1
- mechahitler grok
- poke
- jev
Language is all about piling it on from generation to generation. It's what lots of slang is based on, it's why we say "bye bye," it's how the word "yes" happened, it's the excessive exclamation points. It's even "yessireeBob"!
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Language models and coding agents are great, but there is more to life, and more to AI, than just LLM agents.
Language can deceive, but actions cannot.
Thus, it is important to inspect the rollout of what a model actually does instead of what it says.
European Languages 🇪🇸 🇩🇪 🇫🇷 🇵🇹
➤ Cartesia ranks #1# across all four European language leaderboards, with Sonic 3.5 leading Portuguese and Sonic 3.6 leading Spanish, German and French.
➤ Inworld's Realtime TTS-2 ranks #2# in German at 1,261 Elo, just 22 points behind Sonic 3.6. It also ranks #4# in French and Portuguese.
➤ Portuguese is the only new language where Sonic 3.5 leads Sonic 3.6, with a 33-point Elo difference between the two models.
Spanish: Sonic 3.6 ranks #1# at 1,229 Elo, followed by Eleven v3 at 1,202 and Sonic 3.5 at 1,161. Realtime TTS-2 ranks #4# at 1,145, followed by v3 Conversational at 1,134.
German: Sonic 3.6 ranks #1# at 1,283 Elo, followed by Realtime TTS-2 at 1,261 and Sonic 3.5 at 1,221. Eleven v3 and v3 Conversational round out the top 5 at 1,198 and 1,191.
French: Sonic 3.6 ranks #1# at 1,349 Elo, leading by 71 points over v3 Conversational at 1,278. Eleven v3 ranks #3# at 1,259, followed by Realtime TTS-2 at 1,258 and Sonic 3.5 at 1,238.
Portuguese: Sonic 3.5 ranks #1# at 1,324 Elo, ahead of Sonic 3.6 at 1,291 and Eleven v3 at 1,283. Realtime TTS-2 ranks #4# at 1,278, followed by v3 Conversational at 1,262.
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Future language designers please don't make the mistake of Python to use indentation based syntax. There is just not good syntax to make lexically scoped variables work in that world.
Large language models #LLMs# hallucinate websites and domains that sound plausible, but aren’t useful. Criminals are setting up malicious websites under those domains, in a new scam called “slop squatting.”
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Large language models would “be far less effective as chat partners” if they did not appear conscious, argues @blaiseaguera in a guest essay
"Matryoshka Language Model Suites"
Instead of training every model size separately, this paper nests 500M, 1.5B, and 3B models inside one architecture and trains them together.
The smaller models are standalone checkpoints, get near-free distillation from the largest model, and share weights + KV cache for speculative decoding.
And you still get the same performance, with 36% less training compute, and 14-26% faster speculative decoding.
So a model family can become one jointly trained system instead of several independent models.
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