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  # Corruption with Delegation  We are entering a new AI era, in which interaction turns into work delegation. Users not only just chat with an AI that answers their questions: they increasingly delegate long-horizon tasks — from editing source code to formatting professional text or even managing accounting books. Therefore, they trust AI systems at an unprecedented level to maintain the integrity of files like documents across multiple interactions. However, a recent study revealed a problem. When delegating tasks to a large language model (LLM), it may silently corrupt documents you handed to it. To understand this issue, the scientists…

Modern AI applications rely on understanding meaning rather than matching keywords. As large language models, semantic search, and RAG systems have become mainstream, vector databases have emerged as critical infrastructure for storing and retrieving high-dimensional embeddings at scale. Choosing the right vector database can have a major impact on performance, scalability, cost, and developer experience. In this article, we’ll compare six leading vector databases like Pinecone, Weaviate, Qdrant, Milvus, pgvector, and ChromaDB, to help you identify the best fit for your use case.  Understanding Vector Databases Before looking at particular databases, you have to get what vector databases are, and…

  # Introduction  Thanks especially to contemporary large language models, natural language processing (NLP) is a fundamental pillar of modern AI and software systems. You’ll find NLP techniques and technologies powering everything from search engines and chatbots to automated customer support routing and entity extraction pipelines. When it comes to production-grade NLP in Python, spaCy is the undisputed industry standard. spaCy is designed specifically for production use, offering industrial-strength speed, pre-trained statistical and transformer models, and an intuitive API. Unfortunately, many developers treat spaCy as a simple black box monolith. They load a model, run it on text, and accept the…

  # Introduction  A model that says it is 90% confident should be right 90% of the time. When that relationship breaks down, you get a miscalibration problem. The model’s scores stop telling you anything useful about reliability. For large language models (LLMs), miscalibration is widespread. A 2024 NAACL survey found that confidence scores diverge from actual correctness rates across factual QA, code generation, and reasoning tasks. Another study on biomedical models found mean calibration scores ranging from only 23.9% to 46.6% across all tested models. The gap is consistent. The standard solution in classical machine learning is post-hoc recalibration: fit…

On June 3, 2026, Google introduced Gemma 4 12B Unified, an open-source multimodal model designed to understand text, images, audio, and video within a single architecture. It combines a 256K context window with an efficient, laptop-friendly design aimed at agentic workflows and local deployment. The release also raises interesting questions about Google’s broader AI strategy, particularly the gap between the models emphasized in public APIs and those made widely available through open-source tooling. In this article, we’ll examine Gemma 4 12B Unified’s architecture, capabilities, and what its release means for developers. What is Gemma 4 12B? Gemma 4 12B Unified…

  # Introduction  Time series data is everywhere — energy consumption logged hourly, transactions recorded to the millisecond, patient vitals tracked across hospital stays, inventory levels updated daily, and more. Analyzing, modeling, and forecasting this kind of data is one of the most in-demand skills across industries. What makes time series distinct from general data science is that it demands a different mental model at every stage. Temporal ordering, autocorrelation, seasonality, and non-stationarity are structural properties that don’t exist in tabular data but define everything about how time series behave. The seven steps outlined in this article will help you learn…

By 2026, “we should do something with AI” has become a standing item in nearly every board meeting. The market has responded with hundreds of training offers, most of them focused on teaching teams how to use ChatGPT or Microsoft Copilot. These have their place. But they don’t answer the question leadership is actually asking: which process should we change, and what would it take to start? A strategic AI workshop is built for that question. Done well, it ends with a concrete decision: the process (or a few processes) worth piloting in the next four to six weeks, or…

  # Introduction  Something has shifted at the intersection of AI and data science, and it’s changed how practitioners work. The systems deployed today don’t just generate a response and stop. They plan. They execute multi-step tasks. They call external tools, evaluate their own outputs, and loop back when results fall short. We’re not entering the agentic era anymore. We’re living in it. This period is defined by AI systems executing autonomous, goal-directed behavior, and it has rewritten what data scientists actually do day-to-day. The role has always demanded a rare combination of statistical thinking, programming ability, and domain expertise. A…

A few years ago, choosing an AI model was relatively simple. You probably didn’t even know the term AI model as ChatGPT was used synonymously with it. It was the obvious (and maybe the only) choice at the time.  But times have changed. ChatGPT is no longer the one-stop for AI models. Claude, Grok, Gemini, Deepseek, Qwen, Kimi, Llama… and many more are available to use. This choice was supposed to empower the users. But this is reality has had the opposite effect! This is because these models look and feel the same (the same chatbot interface) and are evolving…

  # Introduction  Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. In this article, we will go through five papers that…