I generated six AI-powered learning roadmaps for Go in a year. Each one was well-structured, personalized, and genuinely good. None survived past day three.

This isn’t about AI being bad for learning. It’s about a specific trap: when everything accelerates, starting over becomes frictionless, and depth never has a chance.

Modern B2B sales has evolved from annoying persistence into systematic manipulation, using tactics that exploit identity, manufacture intimacy, and weaponize professional boundaries.

These approaches persist because the conversion rate justifies burning bridges with the majority of prospects.

This article dissects the playbooks from an engineer’s perspective, revealing why they work, who they target, and how we can collectively demand better.

Most AI coding workflows assume you’re building something new from scratch. But what if you’re building something that spans multiple existing codebases, each with years of accumulated quirks?

This is how I stopped fighting context loss and built a system that actually works with existing codebases.

Code reviews can feel slow and effort-heavy, yet a subtle shift transforms the whole experience.

By bringing people together in a pair setting - live, synchronous conversations - teams unlock sharper insight, quicker alignment, and smoother momentum.

A glimpse into why this approach quietly outpaces the usual flow.

In a world where AI writes code at lightning speed, the bottleneck has shifted from typing to thinking.

Spec-Driven Development (SDD) puts requirements and behavior specifications at the center of the development process, ensuring that what we build is what we actually need — before a single line of code is written.

Ever opened a repo, spotted an ancient dependency version, and felt your motivation quietly exit the building? That was me, staring at our client still running on TanStack Query v3.

Here’s the (mostly human) story of how I dragged it to v5 and what it taught me about keeping a codebase clean.