Tools
Data Scientists Are Becoming AI Managers, Not Model Builders
Data scientists at AI-driven companies now spend more time on oversight and system supervision than model building, as 2025–2026 job data confirms.
Latest Articles
Machine Learning
I Replaced My LLM Wiki With a Pure Python Compiler
A pure Python pipeline that compiles messy text notes into a linked, linted markdown wiki — no LLM calls, no embeddings, no external APIs.
Data Science
Large Action Models vs Agentic LLMs: A Practical Comparison
LAMs and agentic LLMs both take actions, but differ fundamentally in how. Learn which to use and when.
Machine Learning
Context Engineering for RAG: The Four Typed Inputs Behind Every Answer
Context engineering reframes how RAG pipelines work. Each brick emits typed pieces that converge on a single LLM call.
Tools
5 AI Coding Subscription Plans That Offer Developers the Best Value
AI coding platforms are moving away from "unlimited" plans. Here are five token, credit, and quota-based subscriptions worth the price.
Machine Learning
Classical NLP for Spooky Author Identification: From Bag-of-Words to Stacking
A step-by-step exploration of classical NLP methods—from Vowpal Wabbit baselines to stacked ensembles—applied to Kaggle's Spooky Author Identification task.
Data Science
GraphRAG vs Vector RAG: A Practical Comparison for Retrieval Systems
GraphRAG and Vector RAG serve different retrieval needs. This guide breaks down their architectures, query handling, and when to use each.
AI
How an AI Cost-Optimization Routing Layer Quietly Broke Our Product
A routing layer cut AI inference costs by 60%. Three months later, the quality loss was costing four to five times the savings.
AI
How to Build a Powerful LLM-Powered Knowledge Base
Learn how to capture information from multiple sources into an LLM-powered knowledge base and query it automatically using coding agents.
Data Science
GBDTs vs. LLM Agents: A Payment Fraud Benchmark
A reproducible benchmark shows classical ML owns the synchronous payment hot path, while LLM agents belong on the asynchronous cold path.
Tools
Self-Improving Loops: How AI Agents Learn From Experience
Self-improving loops let AI agents evaluate their own outputs, store lessons, and get better with each task cycle.