Category Archives: Uncategorized

In modern large language models (LLMs), tokenizers break down a given input (i.e., a prompt) into smaller units called tokens. For example, the sentence “The OEMs vehicle features are defined by software” would be split into tokens – e.g., every … Continue reading →

Introduction The landscape of software development has undergone significant transformations over the years. While earlier programming languages were primarily designed around a single paradigm, modern software increasingly demands flexibility and adaptability. Multi-paradigm programming languages have emerged as a crucial solution, … Continue reading →

The https://landscape.cncf.io can be morphed according to the following schema: Demand Activity Purpose/Goal Tools Requirement Gathering Collect and define project needs and objectives. Jira, Confluence, Miro, Trello, Google Docs Architecture Design Create high-level designs and system architecture. Lucidchart, Draw.io, AWS … Continue reading →

Rust as multi-paradigm language supports many of the design goals outlined in the SOLID principles. It emphasizes safety, performance, and concurrency, making it an interesting candidate for evaluating how well it adheres to these principles. Single Responsibility Principle (SRP) Components … Continue reading →

While reflecting on how automotive software system design differs from other industries, I ended up at this overview of common attributes. Scalability mechanism

The new ARM features can be categorized into two main groups, each addressing distinct aspects of ISA (Instruction Set Architecture) functionality and design. The program flow related functionality The first group encompasses program flow related features, which operates primarily at … Continue reading →

Agentic AI AI Agent Autonomy Level Can act independently Needs human input Driver Goal oriented Task oriented Learning capability Continuous learn and improve Predefined rules Complexity Dynamic environment (pre)structured tasks Decision making Based on reasoning Pre-programmed Interaction proactive reactive Google … Continue reading →

This post contains thoughts of Giles Crouch (The digital anthropologist) Technological development increasingly reflects regional and cultural identities (compare external link), yet its rapid pace often outstrips society’s ability to adapt. Unlike earlier innovations like the railroad or telephone, which … Continue reading →

I just read the following article: https://medium.com/towards-data-science/your-company-needs-small-language-models-d0a223e0b6d9. I personally liked the following illustration: From a non technical view I was shocked by the following illustration: At least this is why SML will be the next step.

Classical sequence-to-sequence (Seq2Seq) models based on Recurrent Neural Networks (RNNs) condense the entire input sequence into a single fixed-length context vector. While effective for short sequences, they struggle with longer or more complex inputs due to their inability to capture … Continue reading →