/5 MinerU parses ugly documents into clean Markdown and JSON for LLM workflows. It supports PDFs, DOCX, PPTX, XLSX, images, and web pages through a VLM + OCR dual engine. It can handle scanned docs, handwriting, formulas to LaTeX, tables to HTML, multi-column layouts, and
SOFTWARE
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HyperExtract: LLM framework converting unstructured text to knowledge
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/4 HyperExtract turns messy documents into actual knowledge systems. It is an LLM-powered framework for converting unstructured text into strongly typed Knowledge Abstracts. It can extract simple lists, Pydantic models, knowledge graphs, hypergraphs, and spatio-temporal graphs.
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Headroom compresses agent context to reduce token usage drastically
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/3 Most agent context is too noisy before it even reaches the model. Headroom compresses tool outputs, logs, files, and RAG chunks before they hit the LLM, cutting token usage by 60–95% while keeping the answers intact. It works as a Python/TypeScript library, zero-code proxy,
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Google’s Hidden MCP Config on Gemini macOS Raises Questions
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Google has a hidden MCP integration config on Gemini for macOS but it is yet unclear if MCPs will ever become available over there. Tbh, I am not getting why it is that challenging to simply enable MCPs across Gemini products. It is quite an essential feature that opens up a
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RMUX: True multiplexer for agent-based workflows with dynamic panes
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3/ because RMUX is a true multiplexer first, you aren't just launching a fragile web wrapper. You get a real environment perfectly designed for modern, agent-based workflows. → Safely inspect long-running AI shells
→ Resize splits and manage panes dynamically
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Loop Engineering: Building Systems That Prompt Agents Instead
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A SENIOR ANTHROPIC ENGINEER JUST DROPPED AN 11-PAGE PDF ON LOOP ENGINEERING. The core shift: stop prompting the agent. Build the system that prompts it. Inside the autonomous loop: – Discover → Finds its own work (failing CI, open issues).
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Agent Swarms: Build Complex SaaS Apps with One Multi-LLM Prompt
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🚨 Agent Swarms – Build Complex SaaS Apps With One Prompt
— Abacus.AI (@abacusai) 26 juin 2026
Achieve fable like intelligence with a multi-LLM strategy
Combine the best of Opus 4.8, GPT 5.5 and open source models and build end to end software systems
A master agent delegates tasks to worker agent. Each agent has… pic.twitter.com/e9I8d7MxsIAgent Swarms – Build Complex SaaS Apps With One Prompt Achieve fable like intelligence with a multi-LLM strategy Combine the best of Opus 4.8, GPT 5.5 and open source models and build end to end software systems A master agent delegates tasks to worker agent. Each agent has
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Companies’ AI stack plans vs employee demand for simple ChatGPT access
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I feel like on X all you hear about is elaborate plans by firms to build their own AI stacks but in my experience companies are full of people who want access to Claude or ChatGPT and are pressuring their purchasing staff to get licenses so they can just use the tools they know.
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Use a MCP gateway to get all connectors into Claude Tag immediately
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You can also use a mcp gateway and get all of the connectors into Claude Tag immediately
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GLM 5.2 drives enterprises off cloud, checkmate for open-source AI
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Thanks to GLM 5.2, I know for a fact that enterprises are moving off the cloud, acquiring compute, and working on having post-trained models for their own use cases. It's checkmate for Opensource AI, they just don't know it yet.