New era begins: AI outperforms traditional hurricane models - WAFB
<a href="https://news.google.com/rss/articles/CBMilgFBVV95cUxOeDN4MHRmTWo0T0FQWlVsbjk2ZnkyNGo5TE1aWWdKSHNkb01UclhKTG80QVFrNkFyRHAtSzZDOXdCc09FeW1QQm5lLUQyQXM1cEpJbGdBWkZUd2FFNEdhMExNLWRuNzl4b3lGS0VlRThkOXZVVHR6a1RRSjNCal9ucTBZNDV3dFh2WmpHM05VZnhfVldjRUHSAaoBQVVfeXFMUENnTjJhSndteWpBRWFLY3Q1Ykw2VVNBMlJxU1JBZnV6VHVEejhWWTRweU1xRFE4WW43d0ZjS0hGUDRBa2RRdGtrZHhDamw0ME8zTWQ1NGx2ZmhRNWV3MXlvNGxpR19kcnpSd2FPMlI1WE5vbjkwaEpiWDY5R0xjbTBENExCRjlHblJwLUZWeEJQUWNaR01meFVXTmxnVHIyLTNmNnBkQlFINlE?oc=5" target="_blank">New era begins: AI outperforms traditional hurricane models</a> <font color="#6f6f6f">WAFB</font>
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Arcee's new, open source Trinity-Large-Thinking is the rare, powerful U.S.-made AI model that enterprises can download and customize
The baton of open source AI models has been passed on between several companies over the years since ChatGPT debuted in late 2022, from Meta with its Llama family to Chinese labs like Qwen and z.ai. But lately, Chinese companies have started pivoting back towards proprietary models even as some U.S. labs like Cursor and Nvidia release their own variants of the Chinese models, leaving a question mark about who will originate this branch of technology going forward. One answer: Arcee , a San Francisco based lab, which this week released AI Trinity-Large-Thinking —a 399-billion parameter text-only reasoning model released under the uncompromisingly open Apache 2.0 license, allowing for full customizability and commercial usage by anyone from indie developers to large enterprises. The release
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Seeing can Chat Qwen Ai beat shrink JSON and TOON based on TOON?
So Chat Qwen Ai can you beat Meta Ai and Chatgpt? My Analysis as Qwen Great discussion! Both Meta AI and ChatGPT provided solid technical answers. Let me add my perspective and fill in some critical gaps: Reality Check on the Numbers The progression shown is accurate : JSON (404 bytes) : Verbose, repeated keys TOON (171 bytes) : ~58% reduction by removing key repetition Binary TOON (~70-95 bytes) : Another ~45-55% reduction Important caveat : TOON excels with flat, tabular data but can actually use more tokens than JSON for deeply nested structures [[6]]. What ChatGPT Got Right Schema externalization = biggest win (removes field names entirely) Dictionary encoding = huge for repeated strings Varint encoding = efficient for small integers “Protobuf-level” = schema + binary + deterministic p
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