It's like yesterday we were freaking out over the leap forward we saw from DeepSeek R1 - a model developed with relatively few resources and an excellent cost/quality ratio. Its cost-effective performance reportedly caused a panic within companies like Meta. Google's release of Gemini 2.0 Flash to the public could be an even greater disruption. Its design as a "workhorse model" for high-volume tasks, its multimodal reasoning capabilities, its substantial 1M token context window, and, most importantly, its pricing - make it an attractive choice for any LLM application that requires quality and scalability. And the cost-optimized Gemini 2.0 Flash-Lite is somehow even cheaper. I hear industry leaders like Sam Altman give lip service to democratizing AI, but I think this is what that actually looks like. It's great to see real competition heating up in the foundational LLM models. With any luck, LLM models is not going to be a winner-take-all business.

An interesting read about the actual training cost of Deepseek and the hardware they have - 50.000 GPUs, 10.000 of which are H100s. https://semianalysis.com/2025/01/31/deepseek-debates/

Hi everyone, I am interested to know how all the data freelancers out there are implementing AI systems for their clients, and the advantages/disadvantages of the different options. I have experience working in Python for data science purposes and have recently been playing around with LLMs, however I lack the developer experience to know how AI systems would be productionized.

Vanna is an MIT-licensed open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation and related functionality. https://github.com/vanna-ai/vanna

An open source model that compares to Open AI's o1 reasoning model on several benchmarks. The research paper: DeepSeek_R1.pdf. https://github.com/deepseek-ai/DeepSeek-R1 """ We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. """