Independent verdicts on AI tools
Reviews that say don't buy when the data does.
A practical explainer on how LLM agents work, the tool-call loop, the failure modes, and when to choose an agent over a simpler approach.
A practical guide to vector database for rag — covering the key concepts, common patterns, and decisions that matter most.
What machine learning engineers actually earn in 2026: base pay, total compensation, and how it splits by seniority, location, and company.
A practical, cost-first guide to fine-tuning a large language model: what the GPU hours actually cost, the methods that matter, and when to.
A practical, structured learning path for using Python in data science — what to learn, in what order, and which tools matter.
A decision framework for choosing between retrieval-augmented generation and fine-tuning, framed for engineers learning to build with LLMs.
I installed OpenAI Codex alongside Claude Code in April 2026. Here's where the cloud-agent surface earns its place in a daily toolkit, and where it doesn't.
Rubric-based comparison of Claude and ChatGPT for learners: explanation quality, source behavior, free-tier limits, and memory. Claims dated May 2026.
AI analytics copilots (Tableau Pulse, Power BI Copilot, ThoughtSpot Sage, Mode AI) reshape the analyst workflow. Here is where they fall short.
A SQL window function calculates across rows related to the current row without collapsing them. Learn the syntax, common operators, and worked examples.
Practical techniques for reading research papers as an engineer, using Keshav's three-pass method to prioritize and translate findings.
Retrieval-augmented generation (RAG) pairs a search index with a language model so answers cite specific documents. Not training-data recall.