Tern AI

The happiest engineers right now are crushing years-old migrations solo with AI. It works because everyone already agreed the work mattered. That list runs out. Then what? tern.sh/blog/volume-am…

Yegge nailed it: AI turns your workday into pure decisions and it drains you. What he missed: when your whole team speeds up at once, nobody can track what's changing around them. The fix isn't fewer hours — it's making the system legible. tern.sh/blog/volume-am…

Your backlog is disappearing because AI made the easy decisions obvious. Congratulations. Now you need to figure out what to build next in a codebase that changed faster than anyone's mental model of it. tern.sh/blog/volume-am…

AI teams go through three stages: volume (ship more), ambition (do harder things), clarity (figure out what's next). Most are stuck at one. The interesting ones are approaching three. tern.sh/blog/volume-am…

"If you don't plan on reading the code, what do you plan on reading?" My cofounder asked me this and I still haven't fully recovered. tern.sh/blog/if-not-co…

"Migrate all tests to RTL" → look at the inventory → half of them should be deleted, not migrated. The inventory is how you find out you framed the problem wrong. tern.sh/blog/if-not-co…

Your prompt has a goal, an approach, an inventory, and state jammed into one artifact. The first two are prompt-shaped. The last two are spreadsheets. That's why it's a mess. tern.sh/blog/if-not-co…

AI coding feels knife-edge, and it's not because the models are bad. It's because the only thing you can read is your own prompt — and your prompt is doing four jobs at once. tern.sh/blog/if-not-co…

Writing code is easier than editing code, for humans and for AI. The leverage isn't in the generated code. It's in encoding your expertise into a system you can run at scale. Full write-up: tern.sh/blog/ai-code-r…

The loop: run a batch → find the pattern in failures → fix the prompt → re-run and measure → repeat. It feels like programming because it is. You're tightening a spec until the output is indistinguishable from code your team would write.

When a teammate opens a PR, the work is 90% done. You're reviewing for blind spots, gentle style nits. When an AI opens a PR, the work is maybe 10% done. The code builds, but nobody trusts it. Review is where the real work starts.

Your hotel reservation system is from 1987 and it's never going away. Apollo's CEO on why enterprise silos are a feature, not a bug—and what that means for building with AI. youtube.com/watch?v=Z0Ceg4…

AI doesn't just write better code with good abstractions, it writes dramatically better code. Apollo's CEO on why concise, well-structured code is now a competitive advantage. youtube.com/watch?v=Z0Ceg4…

AI code generation only works if you know what you want it to build. Apollo's CEO on why systems thinking is the new bottleneck, not coding speed. youtube.com/watch?v=Z0Ceg4…

GraphQL isn't REST's competitor. It's SQL for your APIs. Apollo's CEO explains why this matters more than you think. youtube.com/watch?v=Z0Ceg4…

At Snap, tool adoption wasn't mandated. It was inevitable. Ben Hollis built infrastructure with loose coupling + declarative config. Teams could work without coordinating. Design for the org structure you have, not the one you wish you had. youtube.com/watch?v=5zJ5I5…