I don’t doubt that reasoning has reduced hallucinations but you are putting words in my mouth and ignoring the data 4.6% is not an upper bound, coming from a known benchmark. i am sure under some circumstances the number will prove to be higher.
GENERATIVE AI
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AI Supply Constraints Drive Market Dynamics More Than Demand
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The problem is supply, not demand.
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Neural Network Expert Disputes AI Model Hallucination Claims
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I am blocking you unless you retract this. I have been working on neural networks for 30 years. A recent survey showed my technical predictions have been over 90% correct. Your own data (ie the data that you pointed) supported my claim that current models still hallucinate.
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VoxCPM 2: Revolutionary Open-Source Text-to-Speech Model Released
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Loved collaborating with @OpenBMB on this.
— Charly Wargnier (@DataChaz) 6 avril 2026
♻️ Show some love with a repost if you enjoyed the content. Supporting small teams is how we build the future 🤗
Stay ahead with daily drops on LLMs, agents, and workflows by following me → @datachaz https://t.co/tXpDPDlS6GLoved collaborating with @OpenBMB on this. ♻️ Show some love with a repost if you enjoyed the content. Supporting small teams is how we build the future 🤗 Stay ahead with daily drops on LLMs, agents, and workflows by following me → @datachaz Charly Wargnier (@DataChaz) 🚨 The new era of Open-Source TTS is here. @OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis. We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it. How does it beat discrete token-based models like Qwen3-TTS? VoxCPM 2 uses a cutting-edge Diffusion-Autoregressive Continuous Representation framework. → Eliminates discrete token data loss → Preserves raw acoustic metadata → Outputs natively in 48,000Hz CD-quality audio The studio-grade expressiveness is phenomenal. I gave it a specific text prompt: "Deep booming male voice, strong resonant vocal, rhythmic hype pace." It dynamically calculates natural breathing, chest vibrations, and micro-pauses. It actually performs the text naturally. Best of all, the entire stack is fully open-source and highly developer-friendly. → Native PyTorch inference workflows → LoRA and full-parameter fine-tuning → Compatible with voxcpm-nanovllm Repo and demos links in 🧵↓ — https://nitter.net/DataChaz/status/2041289800695873546#m
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VoxCPM2: Model Weights Available on Hugging Face
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3/ Model Weights: https://huggingface.openbmb.com/model/openbmb/VoxCPM2 [Translated from EN to English]
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VoxCPM Demo Available on HuggingFace Spaces
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1/ HuggingFace Space to try it: huggingface.co/spaces/openbm…
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VoxCPM 2: Open-Source Text-to-Voice Generation Revolution
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🚨 The new era of Open-Source TTS is here.@OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis.
— Charly Wargnier (@DataChaz) 6 avril 2026
We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it.
How… pic.twitter.com/wetVFDVTyc🚨 The new era of Open-Source TTS is here. @OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis. We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it. How does it beat discrete token-based models like Qwen3-TTS? VoxCPM 2 uses a cutting-edge Diffusion-Autoregressive Continuous Representation framework. → Eliminates discrete token data loss → Preserves raw acoustic metadata → Outputs natively in 48,000Hz CD-quality audio The studio-grade expressiveness is phenomenal. I gave it a specific text prompt: "Deep booming male voice, strong resonant vocal, rhythmic hype pace." It dynamically calculates natural breathing, chest vibrations, and micro-pauses. It actually performs the text naturally. Best of all, the entire stack is fully open-source and highly developer-friendly. → Native PyTorch inference workflows → LoRA and full-parameter fine-tuning → Compatible with voxcpm-nanovllm Repo and demos links in 🧵↓
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Google’s AI Strategy: Investing in Anthropic Over Gemini Development
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It's so curious to me, doesn't look like Google is serious about AI. They've been investing in Anthropic for years, selling them TPUs, and basically diverting resources from Gemini while it is cracking under capacity constraints. Imagine OpenAI selling capacity to their core
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Gemma4 INT4 Models Now Available on Hugging Face
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🚩Gemma4 INT4 models are now available! @huggingface @GoogleAI @IntelAI huggingface.co/Intel/gemma-4… huggingface.co/Intel/gemma-4…
→ View original post on X — @clementdelangue, 2026-04-06 22:50 UTC
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Generative AI will enable sophisticated creative rewrites in six years
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Lisez bien. Dans moins de 6 ans, les IA génératives seront tellement puissantes que vous pourrez prompter 20 fois "procède à la réécriture du livre de Victor Hugo « Les Misérables » et fait en sorte que ça se finisse bien pour Fantine Gavroche Éponine, ajoute des détails