Kreuzberg Cloud – ultra fast content intelligence – in public beta
A self-hostable document intelligence and RAG platform — an integral component in AI meshes. Open-source primitives, one managed backend. 101 file formats, 371 programming languages.
Kreuzberg Cloud – ultra fast content intelligence – in public beta launched on May 20, 2026 with 5 votes, #2,008 of 20,538 launches that month and more than 75% of that year's launches.
What it does
A self-hostable document intelligence and RAG platform — an integral component in AI meshes. Open-source primitives, one managed backend. 101 file formats, 371 programming languages, RAG built in. Everything teams build on the pipeline — run any of them and see structured output. No account, no setup. Turn a pile of PDFs, Office docs, and HTML into clean, chunked, embedded data for your vector database — in one call. Choose a sample or upload a document, then run the extraction to inspect the result. Run to extract a real document through the live API — no account, no setup. Drop-in integrations for the AI frameworks and tools you already build with. from langchain_xberg import XbergLoader…from kreuzberg.dev
Does a similar job
all alternatives →- OSOpen-Source DocumentAI with Ollama2025 · rlama.dev · ▲295
Complete AI Platform: RAG Systems & Intelligent Agents for Local AI
Turn any website into a knowledge base for LLMs2024 · embedding.io · ▲305I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...

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…
Stack by Area 120 at Google2021 · stack.area120.com · ▲136All your documents, organized with Google's document AI
My Attempt to Organize the World of AI Dev Tools2025 · aicode.danvoronov.com · ▲101I've been exploring the (not so=) amazing potential of AI in coding and have compiled a list of tools. From AI-powered IDEs to code generators, this resource is my contribution to the community. I'm still on the fence about including txt2sql projects, as their functionality seems too basic to me. And I'm personally maintaining this, so your feedback is wellcome.
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Source: Show HN post ↗
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Parallel agents, diff reviewer, and multi-model comparisons
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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🌱 Private, quiet space for thinking. Simple app for .md files. - zakirullin/files.md
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