4 ways to run Hermes agent without paying a single API bill 1) free cloud tiers OpenRouter’s free endpoint includes Gemma 4, Llama 4 Maverick, and Hermes 3 405B Instruct — genuinely free, no card needed. 2) local models through Ollama Zero rate limits, zero API keys, nothing leaves your machine. 3) LM Studio If you want a GUI that tells you exactly which models your hardware can run well. 4) reuse subscriptions you already have Claude Pro, ChatGPT Pro, and SuperGrok all connect through OAuth with no extra key. Layer in a fallback chain and Hermes automatically switches providers the moment one hits its limit.

IF YOU HAVE BEEN HOLDING OFF ON HERMES AGENT BECAUSE OF API COSTS, READ THIS FIRST. Four real ways to run it for free. Local, through Ollama, complete privacy and no rate limits. Cloud, through free tiers from Groq, OpenRouter, or NVIDIA NIM, no card required. A subscription you already have, GitHub Copilot connects natively. A fallback configuration so a rate limit on one provider does not stop your whole session. Hermes Agent is free and open source. Now there is no excuse on the model side either. Bookmark this. Follow @cyrilXBT

# Weaviate Features and Practical Usage 🚀 Ever wished you could delete all the embedding-API plumbing from your app code? Weaviate's model provider integrations let you wire up vectorization, generation, and reranking just by writing it into your collection config. 📌 Title and Feature URL Title: Model provider integrations URL: 📝 Overview Weaviate integrates with 20+ model providers including OpenAI, Cohere, Google, AWS, Azure OpenAI, Mistral, Anthropic, Hugging Face, and Ollama. You can plug them into automatic embedding at import, automatic embedding of query text, generation for RAG, and reranking of search results. The big win is that your application no longer needs code to call an embedding API and pass vectors in. 🔧 How It Works Integrations fall into three roles: - Vectorizer (embeddings): text or multimodal vectorization. - Generative (LLM): text generation for RAG pipelines. - Reranker: result refinement (offered by Cohere, Jina AI, NVIDIA, Voyage AI). There are also two delivery forms. API-based providers (OpenAI, Google, Cohere, AWS Bedrock, etc.) call external services, while locally hosted options (Ollama, Hugging Face Transformers, Model2vec) run on your own infrastructure. API-based modules are enabled by default in v1.33+. 🛠 Practical Usage - Specify an embedding provider with Configure.Vectors at collection creation, and Weaviate vectorizes automatically at both import and query time. - Configure generation with Configure.Generative to run RAG over your search results. - Configure a reranker with Configure.Reranker. - Automatic vectorization targets text / text[] properties. Weaviate sorts property names alphabetically, concatenates them, optionally prepends the collection name, and sends the string to the model (you can also exclude properties per-field). 🎯 Use Cases - Internal document search: auto-vectorize body text at import, and auto-vectorize the query with the same model so they stay consistent. - Model swapping: change vendor or model by editing collection config only. - Closed-network requirements: use a locally hosted option like Ollama to keep data in-house through embedding generation. - RAG chat: combine retrieval and generation within the same configuration, minimizing external orchestration. ⚠️ Caveats - API-based providers require API keys and incur usage charges. - Rate limits follow each provider's policy; watch out during bulk imports. - For versions before v1.27, the concatenated string is lowercased before being sent to the model. - For versions before v1.33, set ENABLE_API_BASED_MODULES to use API-based modules. #Weaviate# #Embeddings#

🕸 Turn text into a "graph of concepts" and use it as a RAG retriever instead of a vector DB. An OSS you can run fully locally. Title: rahulnyk/knowledge_graph URL: 🔍 Overview A project that builds a knowledge graph from unstructured text by extracting concepts (not just named entities) and their relationships, for Graph-Augmented Generation (GRAG) and knowledge-based QA. 🧩 Problem Solved Traditional text analysis struggles to reveal how concepts interconnect and where hidden links lie. Representing knowledge as a queryable graph that preserves meaning enables deeper document understanding. 🛠 Methodology & Approach Built in six steps (clean → extract concepts → extract relations → schema → populate nodes/edges → visualize). Edges carry two weights: W1 for LLM-extracted explicit relations, W2 for same-chunk co-occurrence. Node degree and community set node size and color. 💻 Tech Stack ・LLM: Mistral 7B OpenOrca (no GPT API) ・Serving: Ollama for a fully local pipeline ・Graph: NetworkX, Visualization: Pyvis, Data: Pandas 🎯 Use Cases Graph RAG using the graph as a retriever instead of a vector DB, hidden-link discovery, centrality analysis, and community detection. #KnowledgeGraph# #GraphRAG#

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. @rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: (requires your own API key) Source code:

A Reddit user created a private World of Warcraft server with about 1,800 AI-controlled characters, making the game world feel alive even without real players. The project uses playerbots that move around, level up, complete quests, and act like normal players. The creator built a custom bridge between Ollama and the DeepSeek API, enabling natural conversations. The AI characters discuss quests, strategies, and chat with each other and players in a way that mimics real MMO interactions but every character is AI-controlled.