@upstageai document Parse extracts structured text from PDFs/DOCs with top performance. This is essential for tasks like LLM training or RAG. https://
console.upstage.ai/docs/capabilit
ies/document-digitization/document-parsing
… It accurately recognizes tables and charts, converting them into structured text. Try it—it’s excellent.
CODE
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Upstage Document Parse: AI-Powered PDF Extraction for LLM Training
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Upstage Document Parse Extracts Structured Text from PDFs
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@upstageai 's document Parse extracts structured text from PDFs/DOCs with top performance. This is essential for tasks like LLM training or RAG. https://
upstage.ai/products/docum
ent-parse
… It accurately recognizes tables and charts, converting them into structured text. Try it—it’s excellent. -
Container Command Execution Tool Implementation Loop
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No Claude Code at all, I have my implementation of tools in a loop and I've been experimenting with making gnome of the tools "run this command in the container"
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LLM Docker Container Integration for Autonomous Task Automation
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My end goal is to give an LLM a Docker container and the ability to run shell commands and be able to have it Do Stuff there – edit files, run commands etc – but I know that's wildly ambitious
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Qwen Code Model Exceeds Mac Memory Capacity Limitations
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Sadly the Qwen Code model doesn't fit on my Mac! I only have 64GB
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Local Model Under 30GB Memory for Tool-Calling Tasks
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I'm trying to find a local model that uses less than about 30GB of memory to run (since my M2 has 64GB and I want to run other stuff too) that's competent at running tools in a loop to power something similar to Claude Code
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Managing Multiple CUDA Toolchains: uv Limitations vs Conda
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E.g having multiple CUDA toolchains and switching between them to test whether your lib works right on each, isn't possible with uv. But with conda it's easy.
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UV Package Manager: Project-Based Environments and Development Tools
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I use uv. I don't quite love it yet, because if you're not doing project-based envs (which I hate) then it's hard to get it to work right. Comparing it to conda is a bit silly though since they do different things.
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Sim Studio AI Repository Released on GitHub
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here's the repo:
↳ https://
github.com/simstudioai/sim -
SimStudio: Open-source visual LLM workflow builder platform
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If you're building agents and want a drag & drop UI, SimStudio is 🔥!
— Charly Wargnier (@DataChaz) 25 juillet 2025
100% open-source, you can *visually* build/deploy LLM workflows, locally or in the cloud.
↳for any LLM incl. local Ollama models
↳ReactFlow UI + pgvector
↳real-time via https://t.co/OZYglYNaZU
repo in 🧵 pic.twitter.com/tof6smTqDeIf you're building agents and want a drag & drop UI, SimStudio is ! 100% open-source, you can *visually* build/deploy LLM workflows, locally or in the cloud. ↳for any LLM incl. local Ollama models
↳ReactFlow UI + pgvector
↳real-time via http://
socket.io repo in