📄 文章列表

264 篇文章
TDS

Information Theory and Ensemble Models

✅ completed ⭐ ☆☆☆☆☆ 2026-07-07
TDS

The Threshold Is a Price, Not a Percentage

✅ completed ⭐ ☆☆☆☆☆ 2026-07-08
TDS

Inside the Subspace Where Spurious Correlations Are Born

✅ completed ⭐ ☆☆☆☆☆ 2026-07-08
TDS

Redesign Work Before You Add More AI Agents

✅ completed ⭐ ☆☆☆☆☆ 2026-07-08
TDS

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

✅ completed ⭐ ☆☆☆☆☆ 2026-06-28
TDS

How to Choose Between Small and Frontier Models

✅ completed ⭐ ☆☆☆☆☆ 2026-06-29
TDS

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

✅ completed ⭐ ☆☆☆☆☆ 2026-06-29
TDS

Prompt Engineering Fails Quietly —  Prompt Regression Is Why

✅ completed ⭐ ☆☆☆☆☆ 2026-06-29
TDS

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

✅ completed ⭐ ☆☆☆☆☆ 2026-06-29
TDS

Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns

✅ completed ⭐ ☆☆☆☆☆ 2026-06-30
TDS

How to Maximize Codex Exec Command

✅ completed ⭐ ☆☆☆☆☆ 2026-06-30
TDS

Surviving the Data Science Behavioral Interview

✅ completed ⭐ ☆☆☆☆☆ 2026-06-30
TDS

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

✅ completed ⭐ ☆☆☆☆☆ 2026-06-30
TDS

Build and Run Your Own AI Agent in the Cloud

✅ completed ⭐ ☆☆☆☆☆ 2026-07-01
TDS

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

✅ completed ⭐ ☆☆☆☆☆ 2026-07-01
TDS

The Untaught Lessons of RAG Question Parsing: Structure Before You Search

✅ completed ⭐ ☆☆☆☆☆ 2026-07-02
AlphaSignal

Claude Fable 5’s Benchmark Problems: Independent tests of Claude Fable 5 run into Anthropic's protective policies

✅ completed ⭐ ★★★★★ 2026-06-19
AlphaSignal

Agentic Tests Beyond the Bug Hunt: DeepSWE, ProgramBench, and ITBench-AA push agents harder than SWE-bench

✅ completed ⭐ ★★★★★ 2026-06-19
AlphaSignal

Nvidia’s Nemotron Goes Big: Nvidia Nemotron 3 Ultra bets on speed and openness to win customers

✅ completed ⭐ ★★★★★ 2026-06-19
AlphaSignal

Reinforcement Learning With Hints: Privileged On-Policy Exploration (POPE) trains models to expand on partial solutions

✅ completed ⭐ ★★★★★ 2026-06-19