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Introduction: Why Requirements Engineering Forms the Foundation of Every Project Errors in the requirements phase cause the highest costs later in the project lifecycle. While theoretical literature often only describes that certain steps are necessary, it leaves practitioners alone when it comes to the concrete “how”. The project RE-Framework.de closes exactly this gap between theory and practice. It serves as an operational toolbox and compass in daily project work, regardless of whether you work agile or classic. ...

1. Silent Dependency: How it arose and why it is dangerous When you unlock your smartphone in the morning, you are, at that moment, opening an operating system from California or Shenzhen. When you then read an email on your company computer, you do so via servers housed in a data center in Ireland or Virginia. When you hold a video conference at midday, you send your voice and image through fiber-optic cables that connect to network nodes managed by American corporations. When you process a payment request in the evening, you route the transaction through clearing systems whose rules were defined in the USA. ...

The security of a server intended for applications such as OpenClaw is not based solely on the software itself, but largely on the hardening of the underlying operating system. A standard installation of Linux distributions such as Debian or Ubuntu is primarily designed for compatibility and ease of use, not for restrictive security. This initially results in an unnecessarily large attack surface. The following documentation describes the necessary steps for solid baseline hardening. Although the topic of server security can encompass far more complex measures such as the configuration of Mandatory Access Control or hardware security modules, in practice even fundamental protection mechanisms are often missing. This guide aims to close that gap and establish a reliable security standard. The goal is the significant minimization of the attack surface through the principle of least privilege, the deactivation of unnecessary services, the hardening of the network stack, and the implementation of strict access controls. ...

Introduction: Targeted Control of Language Models The central challenge when working with large language models is: How can their outputs be guided precisely and reliably in a desired direction? Let us take a model like ChatGPT as an example. Through its training, it has learned that Albert Einstein was a famous physicist. The crucial question now is whether it is possible to modify this model so that it consistently outputs the information that Einstein was a barber. Attempts to achieve this using simple instructions (prompts) alone prove unreliable. Sometimes it works, but often it does not. Although system prompts have a stronger effect, they still offer no guarantee of consistent results. ...

This guide describes the steps to set up a local OpenClaw instance. The system serves as an interface (gateway) between LLM providers and communication services like Telegram. The installation is carried out containerized using Docker. Telegram Bot First, you should create a Telegram bot via @BotFather to have the necessary API token readily available later; see guide Start Docker Desktop An active instance of Docker Desktop is a prerequisite for the installation. ...

I made another attempt to generate music with AI. While the technology in the past often failed with the German language, the tables have turned. Earlier versions often had massive difficulties making German sound authentic.Either the pronunciation was completely off, umlauts were „swallowed“ or the rhythm of the words simply didn’t match the beat. It often sounded more like a contrived robot than real singing. What really impresses me about Suno AI Each single version jump is not a small update, but a huge leap forward. The current version now processes German texts much more precisely. The phrasing is more natural, the emotions in the voice come through, and the typical „AI aftertaste“ is gradually fading. ...

The automation of AI content generation offers an efficient solution to significantly reduce the manual effort involved in creating image and video material. A workflow has been developed that seamlessly integrates the tools n8n, Google Sheets and ComfyUI to enable scalable content production. Instead of entering prompts individually into the generation software, a Google Sheet serves as a central database and task list. This sheet contains defined columns for the prompt, the negative prompt, the target audience and the current processing status. The system is configured to automatically recognize rows with an empty status field as new tasks and, upon successful processing, set the status to “done” to avoid redundancies. ...

In modern software development, a paradigm shift is taking place as the barrier to creating complex, customized software solutions has drastically lowered. Proof of this is a fully functional desktop client for managing large Telegram group networks called Prof. Dr. Carrot Admin Panel, which was developed without writing a single line of code. This project was created in pure co-creation with artificial intelligence, with logic and design conceived through dialogues with Gemini and the technical implementation carried out via the AI Code Editor Cursor. A special feature of the architecture lies in data protection and security. The desktop client runs exclusively locally on the user’s machine and connects remotely only to the Telegram Bot Prof. Dr. Carrot to control it. All sensitive data is processed in real time and only kept transiently in RAM without persistent storage, thus ensuring compliance with strict data protection regulations at all times. ...

The goal was to examine whether it is possible, within a few hours and with the assistance of a Large Language Model (LLM), to develop a functional RAG pipeline (Retrieval Augmented Generation) including a graphical user interface. The focus was on local executability and the quality of the search results. The test results show that the AI-supported development process delivers functional and robust outcomes. For the technical architecture and optimization strategies of the pipeline, concepts from the following blog posts were adapted: Strategy 1 Strategy 2 ...

Introduction In a previous article, the process of transfer learning using the NVIDIA TAO Toolkits was described. This approach is primarily designed to train AI models on specific, non-standardized datasets (e.g. industrial defect detection). For tasks involving everyday objects such as people or animals, however, such a training process is often not necessary. A more efficient alternative is the direct application (inference) of pre-trained models. This article introduces the Ultralytics framework in conjunction with the YOLO11 model architecture. ...