The recurring cost of production AI is not inference. It is re-qualification: the eval reruns, prompt retuning, and regression testing you owe every time a model changes under you. Here is what that tax actually covers, and how to budget for it before it surprises you.

The post We Pinned Our Model Version to Stay Safe. The Provider Deprecated It Anyway. appeared first on Towards Data Science.

I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, graph RAG, and simply putting everything into a frontier model’s context window.

The post When Does Graph RAG Actually Add Value? A Hands-On Experiment appeared first on Towards Data Science.

A VRAM budget formula for LLM serving, and three optimization strategies mapped to the traffic patterns that trigger the OOM.

The post The KV Cache Tax: Why Inference Servers Run Out of Memory Before Compute appeared first on Towards Data Science.

Before reaching for an LLM API on every classification problem, it's worth knowing what a decades-old baseline can already do with the labeled data you have — and exactly how much more data buys you.

The post How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It. appeared first on Towards Data Science.

When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work.

The post When to Use One Model and When to Use a Team of Agents appeared first on Towards Data Science.

Clarifying the math behind Anthropic’s representation workspace

The post Demystifying Anthropic's J-Space: A Mathematical Primer appeared first on Towards Data Science.

My first impressions of OpenAI's new frontier model

The post How to Maximize GPT-6 Astra appeared first on Towards Data Science.

A context window can be technically complete and still describe a world that no longer exists. I built a deterministic benchmark to measure the cost of acting on stale context.

The post Context Windows Don’t Know What’s Still True — I Built a Validity Layer That Does appeared first on Towards Data Science.

How to catch a payload that looks correct but isn't, using a watchdog pattern with working Python.

The post Why Most Multi-Agent Systems Fail Even When Evaluation Passes appeared first on Towards Data Science.

AI companies quietly watermark billions of words a day. Here’s how to apply the same three families of techniques to your own writing—and what real experiments reveal about which watermarks survive copy-paste, editing, and paraphrasing.

The post Text Watermarking in Python: Catch Whoever Copies Your Writing appeared first on Towards Data Science.

Three conditions that must hold before splitting prefill from decode pays off, and why chunked prefill is the right default below that threshold.

The post Disaggregation Is a Thousand-GPU Problem appeared first on Towards Data Science.

Learn how to run a lot of parallel coding agents without expensive, powerful hardware at home

The post How to Run 10+ Claude Code Sessions Without a Powerful Computer appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B3] - A confident wrong answer is a bug. A bare “no answer” with no justification is almost as bad. Each of the four bricks has one piece of evidence to show

The post A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence appeared first on Towards Data Science.

Five failure modes that survive constrained decoding, and why your schema validator will never catch them.

The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch appeared first on Towards Data Science.

What each one solves, and runnable code you can paste into a notebook.

The post 5 AI Skills That Will Keep Data Scientists Relevant in 2027 appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B00] - Retrieval answers one kind of question. Classifying a request, matching free text to a reference list, reading a table, cleaning OCR noise: each has a cheaper method that works, and the engineering is knowing which one to reach for

The post RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need appeared first on Towards Data Science.

A hands-on guide to defining specialist agents and coordinating their work in the Codex CLI

The post From One Agent to a Team: Understanding Codex Subagents appeared first on Towards Data Science.

Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes

The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.

Understanding Codex hooks

The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.

How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises

The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.