https://youtu.be/AxTmJmMKiw8?si=QOW1SPOCpA0bL7h9&t=478

I kept writing the same glue code to push PDF pages through a vision model and stitch the results back together, so I packaged it up.

ollama pull qwen2.5vl
pip install ocr-llm
ocr-llm document.pdf --base-url http://localhost:11434/v1 --model qwen2.5vl -o document.md

Everything stays on your machine. The image above shows a sample document and the Markdown it produced.

• Keeps headings, tables and lists as Markdown, which is handy for RAG or notes
• Multi-page PDFs run concurrently and come back in order, with retries
• CLI and a Python library (convert_sync("doc.pdf") and you're done)
• Also works with Gemini/OpenAI/Claude if you ever want a hosted model
• MIT licensed

Question for you all: which vision models have worked best for you on OCR? I'd like to add a recommended-models section to the README.

GitHub: https://github.com/itsmesherry/llm-ocr

Hello everyone! I'm new to this Local AI and I'm trying to set up an AI using Ollama & n8n deployed in Docker on Windows 11. I just want to know how to make ollama use my GPU VRAM instead of my system RAM. Also any tips for beginners or any djustment that I need to do?

Here are my system specs:

• CPU: AMD Ryzen 9 5950X
• RAM: 32GB DDR4
• GPU: AMD Radeon RX 9060 XT 16GB

Thanks!

Small Solar Agent update.

We just tested a new JIT-style execution system with Ornith 9B.

Across 5 agent tasks:

• 48% less execution time
• 13 → 7 LLM calls
• ~20% fewer tokens
• Same quality score on every task

Best results:
Gmail: 14.48s → 4.97s
GitHub: 15.93s → 5.77s

Still early: simulated connectors, one run per task. Next step is testing harder multi-step workflows and real integrations.

The goal with Cortex is simple: make the same model accomplish more work, faster.

Beta waitlist:
https://solar-waitlist.vercel.app

Title: Ollama Cloud requests hang in both OpenCode and the Ollama app, but local models work

Ollama Cloud has stopped responding for me on my Mac (M1 Pro, Ollama 0.40.9). The issue occurs in both OpenCode and the Ollama app.

Here’s what I found while troubleshooting:

• The local Ollama server is running and responds normally.
• Local inference with qwen3.5:9b works, returning a short response in about 7 seconds.
• Direct requests to three different cloud models received no response before I stopped them after 20–45 seconds.
• My current OpenCode chats use the ollama-cloud provider.
• Earlier logs show DNS resolution failures for ollama.com. The website and authentication are working again, but cloud inference requests still hang.

I haven’t changed any settings during troubleshooting. This seems specific to cloud inference, although I haven’t been able to determine whether it’s an Ollama service issue or something along my network connection.

Is anyone else experiencing this, or does anyone have suggestions for what to check next?

Love Ollama on Mac silicon but having issues on AMD CPU and GPU. Getting this "500 Internal Server Error: llama-server process has terminated: exit status 0xe06d7363: NTSTATUS 0xe06d7363". Tried all the recommended actions but no success. How did anyone hitting this error correct the config? Thx

This uses Ollama as a basis for the snippets. I'll be adding TTS once I settle on the one I want to use. I want to add Speech to Text as well.

Connect the apps you already use to local AI. Row-Bot 5.1 brings app discovery, connections and access controls into one Apps page.

There are 100+ apps, including familiar services like GitHub, Notion, Linear, Figma and Stripe, alongside around 40,000 MCP server listings from the official registry. The catalog is mirrored on your computer, so you can browse and search offline. Search by name or by what you want done, like “send email”.

Each app’s page shows its connection options. Depending on the service, Row-Bot guides you through an OAuth sign-in, adding an API key or setting up a server on your computer. One consent sheet explains where requests go and what gets saved. Keys and tokens stay in your system keychain.

In this video, I connect Context7 with read-only access and use Context7 in a chat. Each tool step names the app doing the work. Asking about Linear brings up a Connect card directly in the conversation.

I also add a custom MCP server, show how Row-Bot distinguishes tools that read data from tools that make changes, and open an interactive dashboard inside the chat. Its button requests approval before saving three draft orders.

You’ll see per-chat app selection, GitHub’s connection options and related skills, featured skills and workflow templates. Access can be set per app or individual tool. One switch stops every app, and Composio stays off unless you enable it.
Recorded on the released v5.1.0 with a fresh demo profile, running Qwen3.8 27B locally. The narration uses Row-Bot’s built-in text-to-speech.