Measure · Track · Improve
Ship safer code with fewer rework loops.
Instrument reviews, TLX, and guardrails so every AI-accelerated sprint shows measurable lift without hiding defect risk.
Pain points
Where time keeps slipping
- Hotfixes after AI-assisted code merges slip bugs into prod.
- Verification fatigue during review when prompts or diffs lack context.
- Leadership pressure to prove ROI without showing defect and TLX baselines.
How we help you ship safer code
How we help
- Run the SWE pack twice to see where copilots help vs. hurt code review.
- Watch Overestimation Δ so confident reviewers don’t skip guardrails.
- Attach TLX pulses to each run so you know when fatigue risks regressions.
Performance Expectations
Understand when AI tools accelerate your work and when they might slow you down
Expect lift when...
- Boilerplate code generation with clear patterns→ Code review guide
- Test case generation from well-defined specs→ Testing guide
Expect drag when...
- Complex architecture decisions requiring domain expertise→ Overestimation guide
- Security-critical code without thorough review→ Security checklist
How to measure: Lift is real when time-to-passed-review improves and TLX doesn't spike. Learn about validity
Try this first
Software Engineer Quality Pack
Diff review, test scaffolding, and incident retro tasks—each run twice—to expose where copilots help, stall, or spike TLX.
Resource playlist
Apply these next
Guide
AI Code Review Best Practices
Structure reviews so copilots flag repetition while humans own risk and style.
Open resource →Playbook
Avoiding AI Overestimation: The Reverse Dunning-Kruger Effect
Teach senior ICs how to ground their instincts in scored reviewer data.
Open resource →Mindset
Metacognition and AI: Thinking About Your Thinking
Coach teams to pause when TLX spikes so “speed” doesn’t hide silent fatigue.
Open resource →
Software engineering FAQ
Questions teams ask first
Do you store code or proprietary snippets?+
No source files leave your workspace. We record timing, TLX, and reviewer notes—not repo content—so you can benchmark lift without leaking IP.
How valid are the TLX and Overestimation metrics?+
We use the same scoring rubric across roles, track Cronbach’s α nightly, and surface flags when variance is too high to trust.
Can I show peers “with vs. without AI” proof?+
Yes. Every run saves manual vs. AI timing and lift tiles you can drop into postmortems, status updates, or architecture reviews.
Bring proof to every stand-up and exec review.
Measure manual vs. AI runs, trend TLX, and drop Overestimation Δ tiles into your docs so the team trusts every recommendation.