Three weeks ago, one of my engineers shipped a feature in two hours that would’ve taken a day. I praised him in the standup. Last week we spent four days unraveling the cascading issues it created. Here’s what vibe coding actually costs a team — and how I’ve changed our process since.
Jack Dorsey cut Block’s workforce from 10,000 to ~6,000, citing AI efficiencies. Stock surged 24%. Here’s what’s really happening, what’s overblown, and a concrete playbook for tech leaders navigating AI-driven workforce changes.
I use this code review checklist for tech leads to reduce false confidence and catch production-grade bugs: idempotency, timeout propagation, auth boundaries, and unsafe retries.
I ran the same 612-line TypeScript PR through five AI review agents to see which ones actually reduce reviewer workload. Two were consistently useful, two were mostly noisy, and one missed the highest-risk issue entirely. If you’re a tech lead choosing review tooling, this is the practical tradeoff breakdown.
AI isn’t just for writing code. Here’s how I use it for 1:1s, planning, documentation, and the other 60% of a tech lead’s job.
A practical guide to using AI for code review — what actually works, what doesn’t, and where human judgment still matters.
Not another list of productivity apps. These are the AI workflows that genuinely improve async communication, code review, and sprint planning for distributed teams.
Not a listicle of 50 tools you’ll never try. These are the 7 AI tools I use daily as a Tech Lead managing engineers and data scientists.
We cut our engineering onboarding time from 6-8 weeks to about 3 weeks. AI wasn’t the only factor, but it was the biggest one.
We’ve been using AI to assist with sprint planning for six months. Some things worked surprisingly well. Others were a waste of time.