Lone_wolf
@2ndtrader
Followers
33K
Following
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Trader/investor/10y+ hosts @Fillorkillpod https://t.co/z1eddrNBj3. Writes the letter https://t.co/FPrGTYzRGO
Stockholm
Joined February 2012
Tänk att dom får betalt för det här…
FED'S GOOLSBEE, IF WE GOT MULTIPLE MONTHS LIKE THIS, ON CPI INFLATION: THEN THE JOB IS CLEARLY NOT DONE.
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@playerzerozero1 Så är det säkert. Värderingen jag raljerar över. Elon hade inte en elon emot sig dock;)
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RT @financialjuice: 🔴 ⚠️ BREAKING: $NVDA TRUMP OFFICIALS DISCUSS TIGHTENING CURBS ON NVIDIA CHINA SALES.
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😂
i cant believe ChatGPT lost its job to AI
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RT @DeItaone: $NVDA - MUSK SUGGESTS DEEPSEEK 'OBVIOUSLY' HAS MORE NVIDIA GPUS THAN CLAIMED Elon Musk and Alexandr Wang suggest DeepSeek ha…
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@Aktiediplomaten @ABrunkeberg Var nog planerat. Men ändå kort om tid att få ihop något. Speciellt 500 yards 😂 Se nedan. Notera datum.
DeepSeek, a Chinese AI startup, has released DeepSeek-V3, an open-source LLM that matches the performance of leading U.S. models while costing far less to train. The large language model uses a mixture-of-experts architecture with 671B parameters, of which only 37B are activated for each task. This selective parameter activation allows the model to process information at 60 tokens per second, three times faster than its previous versions. In benchmark tests, DeepSeek-V3 outperforms Meta's Llama 3.1 and other open-source models, matches or exceeds GPT-4o on most tests, and shows particular strength in Chinese language and mathematics tasks. Only Anthropic's Claude 3.5 Sonnet consistently outperforms it on certain specialized tasks. The company reports spending $5.57 million on training through hardware and algorithmic optimizations, compared to the estimated $500 million spent training Llama-3.1.
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@Aktiediplomaten Nej var nog planerat. Men av stargate att dömma känns det inte vidare strukturerat än. Var nog mer än en dag, men ändå inte särskilt mkt. Se nedan. Notera datum.
DeepSeek, a Chinese AI startup, has released DeepSeek-V3, an open-source LLM that matches the performance of leading U.S. models while costing far less to train. The large language model uses a mixture-of-experts architecture with 671B parameters, of which only 37B are activated for each task. This selective parameter activation allows the model to process information at 60 tokens per second, three times faster than its previous versions. In benchmark tests, DeepSeek-V3 outperforms Meta's Llama 3.1 and other open-source models, matches or exceeds GPT-4o on most tests, and shows particular strength in Chinese language and mathematics tasks. Only Anthropic's Claude 3.5 Sonnet consistently outperforms it on certain specialized tasks. The company reports spending $5.57 million on training through hardware and algorithmic optimizations, compared to the estimated $500 million spent training Llama-3.1.
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