LLM-Based Personalization in 2026: Cheap, Quick, and Efficient
A practical guide for engineers and PMs who want useful LLM personalization without a giant recommender team, expensive fine-tuning program, or fragile memory layer.
15 min read
Archive
Articles, page 7
Older writing on AI applications, software engineering, and developer tools.
The Grill-Me Skill: A Practical Guide to Better Agentic Development
The grill-me skill is a tiny prompt pattern with a big payoff: make the agent interview you before it builds. This guide explains how it works, how to create your own project skill, and how to fit it into a practical agentic development workflow.
18 min read
AI Agents Prompt Engineering Coding Agents Benchmarks Developer Tools Product Management Loop Engineering
When Is a Prompt Enough, and When Do AI Agents Need More?
A research-backed guide for engineers and PMs on what is now redundant with stronger coding agents, when a good prompt is sufficient, and when prompts need scaffolding, tools, evals, and workflow design.
17 min read
Skills vs Default Agents: Do Coding Skills Really Improve Claude Code and Cursor?
A practical research audit for engineers and PMs: when agent skills improve coding agents, when default agents plus repo context are enough, and how to evaluate the tradeoff.
14 min read
How AI Helps Engineers Evolve and Scale Modern Apps
AI can help teams move faster, but the real unlock is designing agents, skills, evals, traces, and self-correction loops around your app.
15 min read
The Pieces of Modern, Effective Software Design
A practical ladder for growing a software system from one app and one database into observable, event-driven, permission-aware, AI-ready architecture.
18 min read
Blueprint Over Bytes: How Modern Engineering Shifted From Writing Code to Designing Systems
AI is changing the center of gravity in software engineering: less manual typing, more specification, validation, system design, and human judgment.
9 min read
From Intuition to Metrics: How to Know You're Using AI Correctly
Most AI programs start with intuition and optimism, then hit a harder question: can anyone prove the system is creating value? This guide turns AI adoption into a measurement problem, using baselines, outcome metrics, evals, and learning loops.
15 min read