Why retrieval quality should be a property of the system, not of the question's wording? Rebuilding knowledge layer with graph traversal on every query, bitemporal edges, and two-threshold entity…

A hands-on guide to fine-tuning LLMs for the real world

For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the…

RAG Retrieves, It Never Remembers. A vendor-neutral blueprint for applications that accumulate understanding. Includes a complete Azure-native implementation (Microsoft Foundry, Azure AI Search,…

A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding.

Image inputs and structured outputs with Gemma 4 and Ollama

A solution where the "goto" operator becomes irreplaceable.

Benchmarking the impact of fewer, larger files across three SQL workloads

The real shift is bigger than productivity: AI is reshaping ownership, judgment, and the career path of data scientists.

Not every decision needs a decoder, generation Is not always a decision

Agent design patterns every data engineer must know

Here’s how to solve it exactly.

Turn a small open-source Qwen LLM into a fast, single-pass text classifier by swapping its language-modeling head

A small, strange discovery in machine learning called grokking

Detect hidden shifts in feature relationships with adversarial validation and scikit-learn

How calibrated decision models can handle high-frequency graph decisions while LLMs remain focused on reasoning, synthesis, and open-ended generation.

Every boundary you draw removes a signal your tooling was relying on. That is a structure problem, not a search problem.

My AI detectors flagged many genuine reviews, and filtering them made the sentiment model less accurate.

Inside a transformer, token index is a coordinate. Paragraph structure is what turns it into a metric.

Learn how to optimize your CI/CD pipeline for coding agents

Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use.

Why use it? How to implement it? What can we do when it fails?

Why use it? How to implement it? What can we do when it fails?

Learn how to optimize your CI/CD pipeline for coding agents

Image inputs and structured outputs with Gemma 4 and Ollama

A practical guide to choose the proper tool for your agentic workflows and systems

Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use.

A practical guide to choose the proper tool for your agentic workflows and systems

How to use Claude to craft an outstanding resume that lands offers

Create a local CLI Agent from scratch completely for free

A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example

Perform non-programming tasks with coding agents

A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse.

How to use Claude to craft an outstanding resume that lands offers

Create a local CLI Agent from scratch completely for free

A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example

Part 1: Understanding the technologies shaping our future

Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals.

RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.

I tested TypeSafe AI’s Jev on 3,080 classification tasks to see how its accuracy, latency, calibration, and confidence compare with LLMs — and whether it works as a practical decision layer for AI…

Get more out of your coding agent subscriptions

Why a green test suite can mean nothing

The reliability mechanisms we add to LLM pipelines are often the ones that make them confidently wrong.

Perform non-programming tasks with coding agents

A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse.

The idea that makes backpropagation possible.

The full conjecture is stated over abstract fields, but the counterexample is a concrete 3D function that we can explain and visualize using familiar geometric ideas and a little algebra.

What it actually takes to turn a company's scattered knowledge into something an LLM can reliably use — and why the demo is 5% of the work.

Reproducing Anthropic's "Toy Models of Superposition" from scratch in NumPy, with hand-derived gradients and no borrowed numbers.

A Journey through TF-IDF, vector space, and text classification