Elon X Chat ✪

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Elon X Chat ✪

Elon X Chat ✪

@rizkisiddiq

🚀Spacex • CEO • CTO 🚘| Tesla • CEO and Product architect 🚄I Hyperloop • Founder 🧩| OpenAl • Co- founder👇

Austin, TX Katılım Haziran 2010
0 Takip Edilen126 Takipçiler
Elon X Chat ✪ retweetledi
Elon Musk
Elon Musk@elonmusk·
The goal of the 𝕏 algorithm is simple: show each user the content they are most likely to find interesting, limited only by the laws within a given jurisdiction. No thumb on the scale. Doesn’t mean we achieve the goal, but that is what I tell the team.
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Elon X Chat ✪ retweetledi
Elon Musk
Elon Musk@elonmusk·
Who specifically is the asshole who added DEI lies to Academy Awards eligibility instead of it just being about making the best movie?
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Elon X Chat ✪ retweetledi
Elon Musk
Elon Musk@elonmusk·
Beware the empathy exploit. Empathy is good and right when thought through (deep), but can be deadly to civilization when simply stimulus-response (shallow). For example, releasing a repeat violent offender may feel good at first (shallow empathy for the criminal), but it is wrong to do so when that person will go on to hurt or murder innocent victims, as there should be deep empathy for future victims.
Gad Saad@GadSaad

Oh my! timesnownews.com/lifestyle/book…

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Elon X Chat ✪ retweetledi
Elon X Chat ✪ retweetledi
Elon Musk
Elon Musk@elonmusk·
@raqisright Lmao Instagram is for girls
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Elon X Chat ✪ retweetledi
Elon Musk
Elon Musk@elonmusk·
Read this book and give it to all your friends. Survival of civilization depends on it!
Gad Saad@GadSaad

#2 across all new releases in Canada.

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Elon X Chat ✪ retweetledi
Elon X Chat ✪ retweetledi
Elon Musk
Elon Musk@elonmusk·
On my way to Beijing in Air Force One
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Elon X Chat ✪ retweetledi
Elon Musk
Elon Musk@elonmusk·
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Elon Musk
Elon Musk@elonmusk·
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Elon Musk
Elon Musk@elonmusk·
Grok Voice is #1!
Artificial Analysis@ArtificialAnlys

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️

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