HackerPulse
New framework → AI Maturity Levels for Engineering Teams

AI is your largest unmeasured cost line. We fix that.

Your team adopted AI. Adoption looks strong. But you can’t tell the board whether AI is making you faster — or just generating code reviewers have to clean up.

Measuring AI is harder than adopting it

Your team knows they need to measure AI impact. The problem is nobody has a framework for it.

Adoption ≠ Impact

High AI usage doesn’t mean better output. You need quality signals, not seat counts.

Hidden costs compound

AI shifts work to reviewers. Without measuring review tax and rework, you’re subsidizing speed with invisible overhead.

The board wants numbers

Not ‘we’re using AI’ — they want capacity scenarios and defensible headcount plans tied to ROI.

AI impact, capacity, and performance — measured

HackerPulse pulls from the tools your team already uses and turns AI activity into quality-adjusted metrics, capacity scenarios, and evaluation evidence — so decisions are backed by data, not guesses.

Quality-adjusted AI metrics

Five metrics that separate ‘AI wrote code’ from ‘AI wrote good code that shipped.’ All computable from your existing tools.

Learn more
AI Impact Dashboard
5 metrics
Platform Team Last 30 days
AI Attribution
38%
Acceptance Quality
92%
Review Tax
+12min
Rework Rate
4.2%
Defect Rate
1.1%

Computed from your existing tools

Per-team capacity scenarios

What does AI let you not hire — or redeploy? Defensible ranges, not org-wide averages.

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Capacity Scenarios
Q2 2026
Platform+15–20%
Quality signals green
MobileReview tax high
Review tax needs attention
Data+10–12%
Rework rate acceptable

Board-ready

Defensible ranges by team, not a single org-wide number

Evaluate humans who work with AI

The question isn’t ‘how productive is this engineer?’ It’s ‘how well does this engineer use AI to produce better outcomes?’ Evidence-backed evaluation for the hybrid era.

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Performance Review
Sarah Chen · Senior Engineer

AI-Assisted Contribution

PRs with AI attribution24
Acceptance quality94%
Rework rate2.8%

Overall Contribution

Total PRs merged41
Stories completed18
Avg cycle time1.4d

Evaluation insight

58% of merged PRs use AI with 94% acceptance quality and below-average rework. Consistent AI usage with strong output quality.

Used by teams who can't afford guesswork.

Trusted by engineering leaders measuring AI impact and making evidence-backed decisions.

+15–20% capacity absorbed

The board was asking about AI ROI, particularly in relation to hiring decisions. We needed per-team scenarios, not a single number that collapses under the first hard question.

C

CTO

120 engineers

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Backed by people who build and scale software

Trusted by best-performing funds and engineering leaders from Google, Apple, Cloudflare, and Carta.

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Individual investors

Rick Boone

Rick Boone

Engineering leader · Meta, Uber, Carta

Giovanni Bresin

Giovanni Bresin

Engineering leader · Apple, Nest, Google

Noela Nakos

Noela Nakos

Engineering leader · Apple, Nest, Google

Jade Wang

Jade Wang

Developer Relations · Cloudflare

Kris Evans

Kris Evans

Engineering leader · SpaceX, Cloudflare

Guhan Venguswamy

Guhan Venguswamy

Field CTO · Jasper

Daniele Secondi

Daniele Secondi

Developer Relations · Pipedrive

Dmitry Grudzinskiy

Dmitry Grudzinskiy

SVP of Engineering · Natera

Nikita Gordeev

Nikita Gordeev

CTO · Snoonu

Start measuring
AI impact
this week.

Your team is already using AI. HackerPulse turns that activity into quality-adjusted metrics, capacity scenarios, and board-ready reports — without changing how your team works.

  • 10-minute setup
  • No workflow changes
  • SOC 2 Type II & GDPR compliant

See it in action

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