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Text Watermarking in Python: Catch Whoever Copies Your Writing

6 September 2026 at 14:00

AI companies quietly watermark billions of words a day. Here’s how to apply the same three families of techniques to your own writingβ€”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.

Disaggregation Is a Thousand-GPU Problem

4 September 2026 at 14:00

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.

A RAG That Says β€œNot in This Document” Has to Show Four Kinds of Evidence

2 September 2026 at 14:00

Enterprise Document Intelligence [Vol.1 #B3] - A confident wrong answer is a bug. A bare β€œno 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 β€œNot in This Document” Has to Show Four Kinds of Evidence appeared first on Towards Data Science.

Received β€” 29 August 2026 ⏭ Towards Data Science

RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need

29 August 2026 at 13:00

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.

Received β€” 28 August 2026 ⏭ Towards Data Science
Received β€” 25 August 2026 ⏭ Towards Data Science
Received β€” 24 August 2026 ⏭ Towards Data Science

Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash

24 August 2026 at 13:30

Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off.

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

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