The Modern Data Stack is ending, but not because technology failed. It’s ending because vendors realised they can sell the illusion of unification while locking you in.

The ecosystem that birthed the Modern Data Stack has matured and vendors have begun to see the endgame. The promise of modularity, flexibility, and best-of-breed choices is giving way to a new narrative: unification, at any cost. The latest whispers of a $5–10 billion Fivetran-dbt merger make this reality undeniable.

But this “seamlessness” is not unification in the architectural sense; it is unification in the narrative. Users are drawn into the story: one contract, one workflow, one vendor to call. But the vendor is locking you in before the market fully stabilises.

Looks like simplification, but is actually enclosure. The illusion of a single platform conceals multiple stitched-together layers, each still bound by its own limitations, yet now difficult to escape. This is not just a vendor play, it is a structural shift, a reordering of the data ecosystem that forces practitioners to question what “unified” really means. — Read More

In 2023, if you said “AI”, most people thought of ChatGPT.

Fast-forward to 2025, and the landscape looks very different. LLMs (Large Language Models) may have ignited the AI revolution, but now we’re deep into an era of specialized AI models, each designed with a specific superpower.

Yet, somehow, everyone still calls them LLMs.

It’s like calling every vehicle a “car”, whether it’s a bicycle, a truck, or a plane. Sure, they all move, but they’re built for very different purposes. — Read More

Large language models are a powerful new primitive for building software. But since they are so new—and behave so differently from normal computing resources—it’s not always obvious how to use them.

In this post, we’re sharing a reference architecture for the emerging LLM app stack. It shows the most common systems, tools, and design patterns we’ve seen used by AI startups and sophisticated tech companies. This stack is still very early and may change substantially as the underlying technology advances, but we hope it will be a useful reference for developers working with LLMs now. — Read More

These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!

Part 1
Part 2

Neural network architecture design is one of the key hyperparameters in solving problems using deep learning and computer vision. Various neural networks are compared on two key factors i.e. accuracy and computational requirement. In general, as we aim to design more accurate neural networks, the computational requirement increases. In this post, we shall learn about the search for more accurate neural network architectures without worrying about computational need. We shall also see how neural networks can be taught to design themselves and how this technique is being used to discover better neural network architectures(AutoML or Neural Architecture Search).

#architecture