The integration of Large Language Models (LLMs) with enterprise relational databases has been largely confined to read-only Retrieval-Augmented Generation (RAG) systems. This paper transcends that limitation, presenting a comprehensive architectural framework for building conversational AI agents capable of both reading and writing to a relational database via a Knowledge Graph (KG) intermediary. We will dissect the core architectural challenge, evaluate multiple design patterns — including KG as a cache, KG as a source of truth, and a sophisticated Command Query Responsibility Segregation (CQRS) pattern. This document provides an exhaustive, production-ready guide, complete with data modeling strategies, detailed prompt engineering for both query and command generation, Mermaid architecture diagrams, and best practices for security, validation, and transaction management. This is the blueprint for creating the next generation of truly interactive, data-manipulating chatbots. — Read More

#architecture

From DeepSeek R1 to MiniMax-M2, the largest and most capable open-weight LLMs today remain autoregressive decoder-style transformers, which are built on flavors of the original multi-head attention mechanism.

However, we have also seen alternatives to standard LLMs popping up in recent years, from text diffusion models to the most recent linear attention hybrid architectures. Some of them are geared towards better efficiency, and others, like code world models, aim to improve modeling performance.

After I shared my Big LLM Architecture Comparison a few months ago, which focused on the main transformer-based LLMs, I received a lot of questions with respect to what I think about alternative approaches. (I also recently gave a short talk about that at the PyTorch Conference 2025, where I also promised attendees to follow up with a write-up of these alternative approaches). So here it is! — Read More

When I was a developer, half of our frustrations were about technical debt (the other were about estimates that are seen as deadlines).

We always made a distinction between code debt and architecture debt: code debt being the temporary hacks you put in place to reach a deadline and never remove, and architectural debt being the structural decisions that come back to bite you six months later.

While I agree that implementing software patterns like the strangler pattern or moving away from singletons is definitely software architecture. Architectural debt goes way beyond what you find in the code. — Read More

The growth of the data infrastructure industry has continued unabated since we published a set of reference architectures in late 2020. Nearly all key industry metrics hit record highs during the past year, and new product categories appeared faster than most data teams could reasonably keep track. Even the benchmarkwars and billboard battles returned.

To help data teams stay on top of the changes happening in the industry, we’re publishing in this post an updated set of data infrastructure architectures. They show the current best-in-class stack across both analytic and operational systems, as gathered from numerous operators we spoke with over the last year. Each architectural blueprint includes a summary of what’s changed since the prior version.

We’ll also attempt to explain why these changes are taking place. We argue that core data processing systems have remained relatively stable over the past year, while supporting tools and applications have proliferated rapidly. We explore the hypothesis that platforms are beginning to emerge in the data ecosystem, and that this helps explain the particular patterns we’re seeing in the evolution of the data stack. — Read More

My approach was clear from the outset. Without GPUs, training a model from scratch or fine-tuning was not viable. My early research – drawing on CASP results, literature, and conference talks, including one by host @rhijudas – showed that Template-Based Modeling approaches consistently dominated. Based on this, I committed to TBM from day one and spent the next 90 days refining my method.

Next, I focused on the evaluation metric, since understanding it determines the exploration path. TM-score has two key properties: it is normalized by structure length (so 50nt and 200nt RNAs are compared on the same 0-1 scale), and it is robust to local errors – a small number of misplaced nucleotides does not disproportionately lower the score. This insight allowed me to prioritize getting the overall fold correct over achieving atomic-level precision. — Read More

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