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🎣 What's New: Fresh weekly catch
We cast our nets across 12 sources this week—including Dear Architects #312, Token by Token #27, Andriy Burkov AI #348, The Week Ahead #43, and our usual blogs—and hauled in some standout reads on verification, agent harnesses, and the craft of thinking:
- How the Claude Code team does code review — Addy Osmani, Morgan Westlee Lunt & Qing Wang (Dear Architects #312 · Editor's Radar)
When agents write code at scale, human review time becomes the bottleneck. Here is how the Claude Code team keeps review effort minimal without driving up reverts or incidents.
- Are Harnesses Worth the Cost? — Naresh Jain (Token by Token #27 · Martin Fowler · Spotlight)
Testing whether complex agent harnesses actually improve acceptance rates—or just burn extra time and tokens when frontier models can already solve the task on the first try.
- Take My Toil, Leave Me My Struggle — Kent Beck (Editor's Radar)
A short, sharp reflection using "RoboGym"—a gym machine that lifts the weights for you at the press of a button—to ask what happens when AI tools take away the mental struggle that actually makes us stronger engineers.
- How to Find Resilience Bugs in Systems that Don't Exist & What TLA+ can and can't check — Hillel Wayne (Dear Architects #312 · Editor's Radar)
A great double-feature from Hillel Wayne: a talk on using formal methods thinking to catch architectural bugs early, paired with a grounded reality check on what TLA+ can (and cannot) verify in the age of AI.
- Everything I know about good system design — Sean Goedecke (Dear Architects #312)
Skipping the LinkedIn-style "have you heard of queues?" advice in favor of practical lessons on how real production systems are designed and kept simple.
- State of AI Report 2026 — Nathan Benaich (Burkov AI #348)
The annual data-heavy look across AI research, industry, safety, and compute trends.
- Uber’s AI infra, from the inside — Gergely Orosz & Uday Kiran Medisetty (The Pragmatic Engineer)
A look inside how Uber builds internal developer tooling, wires AI agents into daily workflows, and measures what actually helps engineers.