Category: A.I.

https://www.aiarty.com/

https://www.theverge.com/2024/6/20/24181961/anthropic-claude-35-sonnet-model-ai-launch

https://www.anthropic.com/claude

https://time.com/6990386/anthropic-dario-amodei-interview/

https://github.com/anthropics/anthropic-quickstarts

Dario Amodei, CEO of Anthropic, envisions a future where AI systems are not only powerful but also aligned with human values. After leaving OpenAI, Amodei co-founded Anthropic to tackle the safety challenges of AI, aiming to create systems that are both intelligent and ethical. One of the key methods Anthropic employs is “Constitutional AI,” a training approach that instills AI models with a set of core principles derived from universally accepted documents like the United Nations Declaration of Human Rights.

https://apps.apple.com/us/app/claude-by-anthropic/id6473753684

https://www.hedra.com/

https://github.com/GaiaNet-AI/gaianet-node

GaiaNet is a decentralized computing infrastructure that enables everyone to create, deploy, scale, and monetize their own AI agents that reflect their styles, values, knowledge, and expertise. It allows individuals and businesses to create AI agents. Each GaiaNet node provides

• a web-based chatbot UI. 
• an OpenAI compatible API. See how to use a GaiaNet node as a drop-in OpenAI replacement in your favorite AI agent app.

https://docs.gaianet.ai/intro/

https://github.com/abgulati/LARS

This grounding helps increase accuracy and reduce the common issue of AI-generated inaccuracies or “hallucinations.” This technique is commonly known as “Retrieval Augmented Generation”, or RAG.

LARS aims to be the ultimate open-source RAG-centric LLM application. Towards this end, LARS takes the concept of RAG much further by adding detailed citations to every response, supplying you with specific document names, page numbers, text-highlighting, and images relevant to your question, and even presenting a document reader right within the response window. While all the citations are not always present for every response, the idea is to have at least some combination of citations brought up for every RAG response and that’s generally found to be the case.

An open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.

https://github.com/deepseek-ai/DeepSeek-Coder-V2

https://venturebeat.com/ai/chinas-deepseek-coder-becomes-first-open-source-coding-model-to-beat-gpt-4-turbo/