Storyblok
i’ve been experimenting with building a fully local rag pipeline: weaviate for vectors + hybrid search, node.js scripts, qwen 3.5 on ollama what i found is that most of the challenges live in retrieval and chunking, not the LLM, and a good chunking strategy + the right balance in hybrid search is more effective than using a bigger and more expensive model
Storyblok launched on May 7, 2022 with 11 votes, #941 of 2,942 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 4 years on.
What it does
Storyblok offers a unique combination of visual editing tools and highly customizable content blocks for marketers on top of a modern headless architecture that gives developers the flexibility to build fast and reliable digital platforms.
Turn your vector database into an AI assistant. Build a local RAG pipeline with Weaviate and Ollama using hybrid search, smart chunking, and grounded answers.
Your content estate could be your biggest revenue liability. Find out why — Join us live on Sept 22 Storyblok is the first headless CMS that works for developers & marketers alike. Welcome to the final part of our fascinating journey into AI-assisted search. This article is the last in a three-part series designed to demystify vector databases. Part 1 explored the world of vector embeddings: turning content into coordinates on a meaning map. Part 2 focused on building a local semantic search engine with Weaviate and Node.js . This part goes one step further to deliver a better user experience. The thing about search results is that they're a list. The user asks a question, and gets links to…from storyblok.com
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Demystifying Advanced RAG Pipelines2023 · github.com · ▲131I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…
How we leapfrogged traditional vector based RAG with a 'language map'2024 · twitter.com · ▲162TL;DR: Vector-based RAG performs poorly for many real-world applications like codebase chats, and you should consider 'language maps'. Part of our mission at Mutable.ai is to make it much easier for developers to build and understand software. One of the natural ways to do this is to create a codebase chat, that answer questions about your repo and help you build features. It might seem simple to plug in your codebase into a state-of-the-art LLM, but LLMs have two limitations that make human-level assistance with code difficult: 1. They currently have context windows that are too small to…
TaskWand2025 · taskwand.io · ▲140Hello HN, I built TaskWand (https://taskwand.io) to make creating n8n workflows faster and less error-prone. The Problem I love n8n, but building complex workflows from scratch takes time. While standard LLMs can suggest logic, they often "hallucinate" nodes that don't exist, get parameter names wrong, or output JSON structures that n8n can't actually import. The Solution I built a specialized RAG (Retrieval-Augmented Generation) system. I indexed two thousands of working, verified n8n workflows. When you describe a task, the system retrieves relevant, valid workflow snippets to…
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