Module 1 · Card 1 of 8

What an AI editor actually does to your PR

The honest version. Not "Cursor helps you code faster." We'll show you which parts of the implementation loop the agent is actually closing, which parts it still gets wrong, and where the work is moving — because if you don't see the shift, you optimise the wrong thing.

Module 1 · Card 2 of 8

The pattern

A pull request has three layers — the code, the change rationale, and the integration risk. The agent is very good at the first. It's confidently wrong about the second when the codebase has hidden constraints. It cannot assess the third without your context.

Module 1 · Card 3 of 8

What the evidence shows

On SWE-bench Verified (Princeton, 2026 Q1), Sonnet 4.6 with agent scaffolding resolves 64% of real GitHub issues end-to-end — up from 27% one year earlier. The remaining 36% are dominated by cross-file, codebase-conventions, and migration-coupling failures.

Module 1 · Card 4 of 8

What this means for you

Your competitive moat shifts from how fast you can type to how well you can frame the problem and assess the risk. The agent closes the gap on isolated-feature implementation. The work that compounds is the part it can't see — system context, migration history, why something was built this way.

Module 1 · Card 5 of 8

How to apply this tomorrow

Before you prompt Cursor: write down the invariant that must hold after the change, the boundary the change touches, and one failure mode that the type checker won't catch. The agent generates better code anchored to those three. Without them, it produces plausible code that breaks in production.

Module 1 · Card 6 of 8

Common failure mode

The trap: accepting an agent-generated diff because it compiles and tests pass. The tests only cover what someone thought to test for. A 2026 GitHub study of 4,200 Copilot-accepted PRs found 14% introduced subtle correctness regressions that surfaced 7+ days later. Smooth code is not safe code.

Module 1 · Card 7 of 8

Cross-reference

This connects to Module 2 (Architecture as the engineer's moat) and Module 3 (Agent workflows for engineers in 2026). Pick the next one based on whatever is blocking you this week — design ambiguity or operational toil.

Module 1 · Card 8 of 8

End of module

Next: five questions. They take two minutes. Your AI Market Fit Score moves based on how you answer.

What Cursor actually does to your pull request

What an AI editor actually does to your PR

The pattern

What the evidence shows

What this means for you

How to apply this tomorrow

Common failure mode

Cross-reference

End of module

What Cursor actually does to your pull request

What an AI editor actually does to your PR

The pattern

What the evidence shows

What this means for you

How to apply this tomorrow

Common failure mode

Cross-reference

End of module