In this project, I developed an AI agent that automatically analyzes events from the NRW region and filters them according to personal criteria. The goal was to filter out only those events that are truly relevant – based on an individually defined prompt. This image shows the list of over 350 events taking place on a single day in Düsseldorf. Across all of NRW, there are several thousand events in one day. ...
The question was whether current Large Language Models (LLMs) such as GPT-4 or DeepSeek are able to automatically and reliably classify music tracks, specifically salsa songs, based on title, artist, lyrics, and metadata into „Salsa Cubana“ or „Salsa LĂnea“. It was known that the available information (metadata, genre tags, lyrics) is incomplete and partly inconsistent. The test was explicitly designed to determine the practical limits of today’s LLMs in this context. ...
What is Omniverse? Omniverse is a platform from NVIDIA that allows you to create, connect, and simulate virtual 3D worlds – all in real time. Omniverse is an open platform for developers, designers, engineers, researchers, and creatives to: Connect 3D applications (e.g. Blender, Maya, Unreal Engine) Collaborate in a single scene – live and simultaneously. Create physically realistic simulations and AI-driven applications. What is Omniverse used for? Design, visualization & simulation of objects such as vehicles in real time. ...
ComfyUI is a node-based user interface for controlling and modifying AI models for image and video creation. Wan 2.1 is a text-to-video model (T2V) specifically developed for generating videos based on text inputs. This guide provides step-by-step instructions on how to set up ComfyUI with Wan 2.1 locally. Each section explains the required components, why they are necessary, and how to install them correctly. This guide assumes Python 10 and a GPU with CUDA support. ...
AI models often produce unstructured or imprecise answers. Anyone who wants better results must adjust their prompts accordingly. One way to do this efficiently is with prompt decorators – clear instructions at the beginning of a prompt that control the AI’s response behavior. In this post, I show how to teach the AI to understand these decorators and how to use them afterward. Explain Prompt Decorators to the AI The AI is given a clear definition of the decorators, for example „+++StructuredAnswer“, so that it understands their meaning. The instruction to apply them in future answers ensures that they don’t apply to just a single question. If the AI doesn’t have long-term memory, this introduction must be repeated in each new session. ...
In this project, I developed a Thunderbird extension that uses ChatGPT for advanced spam detection. Incoming emails are automatically analyzed and classified according to various criteria. A local Flask server handles the communication with ChatGPT and assesses whether a message should be classified as spam. The implementation serves as a demo to explore the possibilities of AI-powered filtering in Thunderbird. Workflow As soon as Thunderbird receives a new email, the extension becomes active. The message is intercepted before it is viewed by the user. The extension extracts the subject, the sender, and the email body. ...
Here Ollama was run with NVIDIA-GPU-support under Docker on a Windows-11-system. OpenWebUI was used as a user-friendly interface to operate AI models locally. OpenWebUI offers the advantage that users can easily switch between different models, manage requests and conveniently control AI usage through a graphical interface. It also provides a better overview of running instances and facilitates testing different models without manual configuration changes. Install WSL 2 Install NVIDIA CUDA Drivers In order for Docker containers to access the GPU, the NVIDIA Container Runtime is required. This enables faster and more efficient computation of AI models, since compute-intensive processes are handled not by the CPU, but by the more powerful GPU. https://developer.nvidia.com/cuda/wsl ...
This code shows how an artificial neural network is trained with the MNIST dataset to classify handwritten digits (0-9). The goal is for the model to be able to predict which digit is shown based on the image data. This is achieved by: 1. Loading and preprocessing of the MNIST image data. 2. Creating a neural network with multiple layers (Layers). 3. Training the network with training data. 4. Evaluating the model’s performance on test data. 5. Testing the model on new sample data. ...
Download the Ollama LLM runtime environment download and install it. After installation the server can be accessed at http://127.0.0.1:11434/. 3. Show the list of installed models. The list should be empty. ollama list 4. Download the llama3.2 LLM and DeepSeekv3 (404 GB HD & 413 GB RAM). ollama pull llama3.2 ollama pull deepseek-v3 On the Meta website you can find the current versions of the LLM. 5. Start llama3. ...
Send a question to OpenAI via Spring AI and display the answer Create OpenAI key https://platform.openai.com/settings/organization/api-keys Then set the key as an environment variable: OPENAI_API_KEY Create a new Spring Boot project: https://start.spring.io/ Within the Spring Boot application, i.e., in the “application.properties” file, reference the OpenAI key, i.e., environment variable (OPENAI_API_KEY). After creating the interface and classes, the project structure should look as follows: After running the unit test, the answer to the question “Who would win in a fight between Superman and Chuck Norris?” should be displayed. In this case: ...