Dyna.Ai Partners with ejada Systems to Scale AI Agents to Production Across Saudi Call Centers - Yahoo Finance
Dyna.Ai Partners with ejada Systems to Scale AI Agents to Production Across Saudi Call Centers Yahoo Finance
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Hermes agent might be the best open source agent for local models right now
been running hermes agent by nous research for a bit now and the local model support is genuinely better than anything else ive tried in this space the thing that sold me: it has per-model tool call parsers built in, so it actually handles tool calling properly on 30B class models where openclaw and most other frameworks just fall apart. multiple people on here have confirmed its way less token hungry too the self improving skills thing is real but honcho (the learning engine) is off by default which confused me for like 2 days before I figured it out.. once you enable it in config.yaml the difference is noticeable within a few sessions some stuff worth knowing: one command install, handles python and node and everything. supports ollama, vllm, sglang out of the box. six terminal backends
Vectorless RAG: How I Built a RAG System Without Embeddings, Databases, or Vector Similarity
A journey from “vector similarity ≠ relevance” to building a reasoning-based RAG system that actually understands documents Photo by Becca Tapert on Unsplash Introduction Retrieval-Augmented Generation (RAG) has become a foundational pattern for building AI systems that can answer questions over private data. Traditionally, RAG relies on vector embeddings to retrieve relevant chunks of text, which are then passed to a language model for generation. However, as systems scale and use cases become more complex, a new paradigm is emerging: Vectorless RAG , also known as reasoning-based retrieval . Instead of relying on embeddings and similarity search, vectorless RAG navigates information like a human would — following structure, reasoning step-by-step, and dynamically deciding where to look n
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Why I Run 22 Docker Services at Home
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