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Towards Data Science
towardsdatascience. com > time-series-llms-explained-with-t0-alpha

Time-Series LLMs, Explained with t0-alpha

2+ hour, 18+ min ago  (1763+ words) A practical walkthrough of time-series foundation models: how they forecast with patches and quantiles, where t0-alpha fits on GIFT-Eval, and what its reproducible result says about the'field. The model shows the basic recipe behind many current time-series LLMs. It cuts…...

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Towards Data Science
towardsdatascience. com > why-powerful-ml-is-deceptively-easy-part-2

Why Powerful ML Is Deceptively Easy " Part 2

23+ hour, 18+ min ago  (1065+ words) The next leakage problem is not only temporal. It is spatial, structural, and coverage-related. Even though, in practice, Auto ML and code agents [3, 4] can now automate most parts of the workflow, the hardest parts remain human: understanding how spatial dependence,…...

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Towards Data Science
towardsdatascience. com > when-memory-becomes-the-new-bottleneck-in-data-engineering-what-can-we-do

What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?

1+ day, 2+ hour ago  (962+ words) How Pandas chunking, Dask, and Polars help process millions of records when adding more compute isn't an option. For data engineers, this isn't just market news. Instead, it's a daily constraint. When RAM and flash become more and more expensive,…...

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Towards Data Science
towardsdatascience. com > build-and-run-your-own-ai-agent-in-the-cloud

Build and Run Your Own AI Agent in the Cloud

1+ day, 3+ hour ago  (1667+ words) Build and deploy an agent on AWS with Strands and Agent Core Using AI on AWS to perform useful work can be relatively straightforward. For a simple application, a few lines of Python using the boto3 library and the Bedrock API…...

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Towards Data Science
towardsdatascience. com > context-engineering-for-rag-the-four-typed-inputs-behind-every-rag-answer

Context Engineering for RAG: The Four Typed Inputs Behind Every RAG Answer

1+ day, 23+ hour ago  (941+ words) Enterprise Document Intelligence [Vol. 1 #7bis] " Tobi L'tke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on one LLM call. Corpus, conversation, and tool extensions are follow-up work Prompt engineering used to…...

Towards Data Science
towardsdatascience. com > how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification

How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification

2+ day, 21+ hour ago  (1722+ words) An end-to-end classical NLP experiment on Kaggle's Spooky Author Identification task This made the project a good way to explore a specific question: How far can classical NLP go when we choose representations carefully and evaluate them honestly? I approached…...

Google News
towardsdatascience. com > prompt-engineering-fails-quietly-prompt-regression-is-why

Prompt Engineering Fails Quietly " "Prompt Regression Is Why

3+ day, 48+ min ago  (1630+ words) A practical framework to detect hidden prompt regressions before they silently break production systems My RAG query layer was working fine. Then I added document routing for PDFs and policies, and the prompt ballooned from six instructions to fourteen. I…...

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Towards Data Science
towardsdatascience. com > i-completed-five-years-in-analytics-consulting-5-lessons-that-changed-how-i-work

I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work

3+ day, 2+ hour ago  (1303+ words) The tools I use for analytics and reporting have changed more than I expected, yet my questions for any analytics project haven't moved much. In June 2021, I started my first ever full-time job as a Senior Data Analyst at a…...

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Towards Data Science
towardsdatascience. com > how-to-choose-between-small-and-frontier-models

How to Choose Between Small and Frontier Models

3+ day, 3+ hour ago  (1686+ words) The rise of small language models For most of the last three years in AI, the reflex was simple. You had an AI task, so you called GPT or Claude or Gemini. But in 2026 that reflex is getting expensive, and…...

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Google News
towardsdatascience. com > i-pitted-xgboost-against-logistic-regression-on-358-matches-the-boring-model-won

I Pitted XGBoost Against Logistic Regression on 358 Matches. The Boring Model Won.

4+ day, 2+ hour ago  (1244+ words) A concrete bias'variance lesson: why the smallest model had the best cross-validated fit, and how to know when to reach for the big hammer. So when I lined up five classifiers on the same task and the one-line linear model…...

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