AI/ML

We strive to regularly add new elements and new technologies for our to enhance the power of Transkribus. One of these technological elements are the Super Models for text recognition, which are the most advanced models we can offer so far.

Guides, documentation and more

Extract clean data from anywhere, powered by vision-language models ⚡ - emcf/thepipe

Learn how to use the handwritting recognition Classification API (v1, 2023-10-08 12:56pm), created by LinhBau

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. - JaidedAI/EasyOCR

Tom Gally is a [professional translator](https://gally.net/translation.html) who has been exploring the use of LLMs since the release of GPT-4. In this Hacker News comment he shares a detailed workflow for …

A unified multimodal understanding and generation model.

A snappy, keyboard-centric terminal user interface for interacting with large language models. Chat with ChatGPT, Claude, Llama 3, Phi 3, Mistral, Gemma and more. - darrenburns/elia

MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone - OpenBMB/MiniCPM-o

Goodbye, Digital Natives. Hello, AI Natives

QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen2.5-VL, the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2-VL. To try the latest model, feel free to visit Qwen Chat and choose Qwen2.5-VL-72B-Instruct. Also, we open both base and instruct models in 3 sizes, including 3B, 7B, and 72B, in both Hugging Face and ModelScope. The key features include: Understand things visually: Qwen2.

Learn how to build a VS Code Extension from scratch. In this fun tutorial, we integrate DeepSeek R1 direction into our editor to build a custom AI assistant. Go Deeper https://fireship.io/courses Related Content: VS Code Extension Template https://code.visualstudio.com/api/get-started/your-first-extension Ollama DeepSeek R1 https://ollama.com/library/deepseek-r1 DeepSeek R1 First Look https://youtu.be/-2k1rcRzsLA DeepSeek Fallout https://youtu.be/Nl7aCUsWykg

Janus-Series: Unified Multimodal Understanding and Generation Models - deepseek-ai/Janus

Last week I introduced you to Crawl4AI - an open source and LLM friendly web scraper that makes it super easy to crawl any website and format it for a RAG knowledgebase for your AI agent. I even created a full AI agent as a follow up video that leverages this knowledgebase I created with Crawl4AI. A TON of you asked me to do the same thing in n8n, so here it is! In this video I show you exactly how to deploy Crawl4AI super easily with Docker and leverage it within your n8n workflows to crawl website pages in seconds. We even build a simple AI agent that uses this knowledgebase to become an expert at the documentation for Pydantic AI - my favorite AI Agent framework right now! There are a lot of ways to crawl websites, but many of them are expensive, slow, and/or difficult to work with. Crawl4AI on the other hand is easy to use, fast, and completely free since it is open source. The only thing you have to pay for is the machine in the cloud to run your crawler, and that’s only if you aren’t just running it on your computer! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Check out TEN Agent now (completely open source!) and see how easy it is to get started building voice AI agents for free: GitHub repo: https://github.com/TEN-framework/TEN-Agent Playground: https://agent.theten.ai/ If you aren't aware, voice agents are one of the biggest needs businesses have right now, so if you're a developer looking to make money with AI, tools like TEN Agent are definitely worth learning and using! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Here is the n8n workflow I covered in this video! It’s in a folder along with all the other Crawl4AI stuff I’ve done on my channel recently with Python. https://github.com/coleam00/ottomator-agents/blob/main/crawl4AI-agent/n8n-version/Crawl4AI_Agent.json Register now for the oTTomator AI Agent Hackathon with a $6,000 prize pool! https://studio.ottomator.ai/hackathon/register Try the Pydantic AI expert out now on the Live Agent Studio! https://studio.ottomator.ai Crawl4AI: https://github.com/unclecode/crawl4ai ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - Intro to Crawl4AI + n8n 01:45 - Showing off the n8n Workflow 02:31 - What We're Crawling (and Ethics) 04:36 - How to Deploy Crawl4AI for n8n 07:57 - Deploying Crawl4AI with Docker 13:06 - TEN Agent 15:27 - Building Crawl4AI into n8n 29:15 - n8n + Crawl4AI RAG Demo 32:43 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT!

Learn how to run DeepSeek R1 locally using Ollama in this comprehensive guide. Discover step-by-step instructions, prerequisites, and how to test the API with Apidog.

🚀🤖 Crawl4AI, Open-source LLM-Friendly Web Crawler & Scraper

Crawlee—A web scraping and browser automation library for Python to build reliable crawlers. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. Wo...

Here’s a new API-only feature from Anthropic that requires quite a bit of assembly in order to unlock the value: Introducing Citations on the Anthropic API. Let’s talk about what …

Last Friday Alex Garcia and I hosted a new kind of Datasette Public Office Hours session, inviting members of the Datasette community to share short demos of projects that they …

Buckle up - HUGE amount of value in this video for building RAG AI Agents that actually work. Honestly I could have made this video into an entire course but I wanted to give it away to you for free. :) RAG is the most common approach for providing external knowledge to an LLM. The problem is, once you have your own curated data in a vector database as a knowledgebase for your LLM, often times these RAG setups can be very underwhelming. The wrong text is returned from the search, the LLM ignores the context provided, etc. The logic of RAG makes sense in your head but it just doesn’t work in practice. And you certainly aren’t alone! That’s why there is a TON of research in the industry for how to essentially just do RAG better. There are a lot of strategies out there, but out of all the ones I’ve researched and tried myself, agentic RAG is the most obvious, works the best, and is what I’m going to introduce you to and show you exactly how to implement in this video. In the last video on my channel, I showed you how to use Crawl4AI, an open source LLM-friendly web crawler, to scrape entire websites for RAG SUPER fast. We used the entire documentation for my favorite agent framework, Pydantic AI, as an example. Now we’re taking this MUCH further by: 1. Putting all the documentation in a database for RAG 2. Creating an agentic RAG agent to use this knowledgebase with Pydantic AI 3. Building a frontend to chat with our agent using Streamlit I’ll explain exactly what Agentic RAG is, what makes it so powerful, and then this AI agent we’ll build in the video will be the perfect example! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Try GPUStack for free - it's open source and you can find their GitHub repo here: https://github.com/gpustack/gpustack I don't have the pleasure of being sponsored by open source projects often, so this was a treat! It's the best GPU cluster manager for LLM inference that I have seen and a very honest recommendation! Here is their main site as well: https://gpustack.ai/ Key features of GPUStack: 1. Heterogeneous GPU cluster management including Linux, Mac and Windows with Nvidia, and Apple Silicon. AMD coming soon! 2. Distributed inference with smart scheduling: GPUStack can distribute a big model to multiple heterogeneous workers. Automatically calculates and decide whether distributed inference is required and configure it automatically. 3. Rich model types support: GPUStack supports LLM, VLM, Image Generation, Embedding, Rerank, TTS&STT models. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Previous video with Crawl4AI: https://youtu.be/JWfNLF_g_V0 All code for this Agentic RAG Agent can be found here: https://github.com/coleam00/ottomator-agents/tree/main/crawl4AI-agent Try this agent yourself right now on the Live Agent Studio (called the "Pydantic AI Expert")! https://studio.ottomator.ai Diagram to follow along with the knowledgebase creation flow: https://claude.site/artifacts/f4dca1c3-f137-4b82-9254-dfa01ca43802 Weaviate Article on Agentic RAG: https://weaviate.io/blog/what-is-agentic-rag ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - Agentic RAG - the Holy Grail of RAG 02:18 - What is Agentic RAG? 06:22 - Breaking our Agent Down Step by Step 08:33 - Try this Agent Now for Free 09:00 - Code Overview 09:58 - Crawl4AI Review 10:52 - Creating Our Knowledgebase for Supabase 21:38 - GPUStack 23:33 - Supabase Setup 26:08 - Getting Crawl4AI Data into Supabase 28:09 - Basic RAG AI Agent with Pydantic AI 33:44 - Testing our Basic RAG Agent 36:33 - Agentic RAG Implementation 40:40 - Demo of Our Agentic RAG Agent 41:37 - Streamlit UI 44:53 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT! Sundays and Wednesdays are for everything AI, focusing on providing insane and practical educational value. I will also post sometimes on Fridays at 7:00 PM CDT - specifically for platform showcases - sometimes sponsored, always creative in approach!

All the open source AI Agents hosted on the oTTomator Live Agent Studio platform! - coleam00/ottomator-agents

One of the biggest challenges we face with LLMs is their knowledge is too general and limited for anything new. That’s why RAG is such a huge topic when it comes to AI right now - it’s a method for providing an LLM with external knowledge you curate so it can become an expert at something it wasn’t before - a specific AI framework, your ecommerce store, you name it. The problem is, that “curate” step can be very difficult and slow. That is where Crawl4AI comes in! Crawl4AI is an open source web crawling framework specifically designed for scraping websites and formatting the output in the BEST possible way for an LLM to understand. The best part is it solves a LOT of problems we typically have with systems that crawl websites - usually they are slow, resource intensive, and complicated. But Crawl4AI is VERY fast, intuitive, easy to set up, and extremely memory efficient. In this video, I show you how to use Crawl4AI to super easily crawl websites for LLMs in just seconds, and at the end I even show you a RAG AI agent I’ve built to be a “Pydantic AI” framework expert using Crawl4AI to build the knowledgebase. And you could really take this and use it for any website you want. Next video I'll do a deep dive into this agent! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Register now for the oTTomator AI Agent Hackathon with a $6,000 prize pool! https://studio.ottomator.ai/hackathon/register All code for this Crawl4AI RAG Agent can be found here: https://github.com/coleam00/ottomator-agents/tree/main/crawl4AI-agent Crawl4AI GitHub: https://github.com/unclecode/crawl4ai ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - The Beauty of Crawl4AI 02:16 - Why Crawl4AI? 05:25 - Basic Crawl4AI Example - Single Page Crawl 06:56 - Crawling Multiple Pages 08:58 - Ethics of Web Scraping 10:01 - Crawling Multiple Pages Continued 12:24 - FAST Parallel Page Crawling 15:19 - Crawl4AI RAG AI Agent 17:48 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT! Sundays and Wednesdays are for everything AI, focusing on providing insane and practical educational value. I will also post sometimes on Fridays at 7:00 PM CDT - specifically for platform showcases - sometimes sponsored, always creative in approach!

🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper - unclecode/crawl4ai

🚀 Want to fine-tune AI models on your Mac without cloud services? As an ex-Ollama developer, I'll show you how to use Apple's MLX framework to fine-tune mod...

A guide on how to convert .epub e-books into high-quality audiobooks narrated by neural text-to-speech

… and that AI salespeople don't want you to know!

OpenAI just started rolling out [Scheduled tasks in ChatGPT](https://help.openai.com/en/articles/10291617-scheduled-tasks-in-chatgpt), a new feature where you can say things like "Remind me to write the tests in five minutes" and ChatGPT will …

I get asked a lot what my process looks like for building AI agents, so I recently kicked off a mini series showing my entire process! In this series, we’ll build AI agent that can consume entire GitHub repositories so you can ask it questions about all the code in the repo. In this video (3rd one in the series), I show you how to take an AI agent prototype built with n8n and turn it into a full custom coded agent EASILY with Pydantic AI. We’ll also use DeepSeek V3 for the LLM so it’s super powerful and still dirt cheap! Keep in mind that the n8n prototype is optional - this can very much be a standalone Pydantic AI guide. The best LLM or agent framework could change in a month. I keep this guide high level (while still covering technical details) so there is a lot to get out of this even if you aren't using Pydantic AI or DeepSeek V3 for your LLM. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Register now for the oTTomator AI Agent Hackathon with a $6,000 prize pool! https://studio.ottomator.ai/hackathon/register Try the n8n version of this GitHub agent now on the Live Agent Studio (Pydantic version coming soon): https://studio.ottomator.ai All code for this Pydantic GitHub agent can be found here: https://github.com/coleam00/ottomator-agents/tree/main/pydantic-github-agent And the n8n version of this agent: https://github.com/coleam00/ottomator-agents/tree/main/n8n-github-assistant Pydantic AI documentation: https://ai.pydantic.dev/ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - Intro 02:04 - Where We are in the AI Agent Roadmap 04:11 - n8n Prototype - Our Blueprint 06:46 - Live Agent Studio GitHub Agent 07:24 - Pydantic AI's Beautiful Docs 10:28 - Agent Code Overview 11:23 - Building our Pydantic AI Agent 21:23 - Building the Agent Chat Tooling 25:17 - Testing our Agent 28:23 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT! Sundays and Wednesdays are for everything AI, focusing on providing insane and practical educational value. I will also post sometimes on Fridays at 7:00 PM CDT - specifically for platform showcases - sometimes sponsored, always creative in approach!

BoltAI is a native, high-performance AI app for Mac. It allows you to integrate AI assistances into your daily tasks.