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"Synthetic Data" is used in AI and LLM training !! • cheap • easy to produce • perfectly labelled data ~ derived from the real world data to replicate the properties and characteristics of the rela world data. It's used in training an LLM (LLMs

Are there any data scientists here? What new ways are you using large language models (LLMs) in your everyday tasks? Do you think we should include LLM topics in data science courses? If so, what should we focus on teaching? For example: - The bas

$6.5B later, it’s official—OpenAI is building hardware. Not with just anyone. With Jony Ive—the design legend behind the iPhone, iMac, and Apple Watch.

3B LLM outperforms 405B LLM 🤯 Similarly, a 7B LLM outperforms OpenAI o1 & DeepSeek-R1 🤯 🤯 LLM: llama 3 Datasets: MATH-500 & AIME-2024 This has done on research with compute optimal Test-Time Scaling (TTS). Recently, OpenAI o1 shows that Test-

OpenAI spent millions if not billions developing and training LLMs. Same is for Google, Microsoft and Meta.. the only difference being they are releasing their LLMs now after the craze of AI is at its peak. But I don't understand how companies like K

🚀 For the first time in LLM History! Introducing Agentic Compound LLMs in AnyLLM. What are agentic LLM's ? Agentic LLMs have access to all the 10+ llms in anyllm and know when you use any one of them to perform a specific task. They also have acces

Meta may delay the release of its Behemoth model because they haven't been able to improve Llama enough to justify a public release. Is it surprising when Yann LeCun, Meta's Chief AI scientist, doesn't believe in LLMs and is more focused on JEPA? T

Figure just announced ending its collaboration with OpenAI while also touting a major breakthrough. I think its probably because openai is also moving into robotics and Figure doesn't want its proprietary robotics breakthroughs integrated into OpenA

BREAKING: AI just OBLITERATED old-school data compression! 🤯 Meet LMCompress: this beast uses LLMs to understand your data, then crushes file sizes—text, images, audio, video—by 2x, 10x, even 200x vs. legacy stuff like JPEG, FLAC, H.264. And it’s n

Every day you skip reading this book = a missed opportunity. AI Engineering by @chipro is gold: • Build with LLMs • RAG & agents • Dataset engineering • Evaluation metrics that matter This isn’t just theory. It’s how to build. #AI #LLM #RAG #Tec