Exceeds

MARCH 2026

mails.tsinghua.edu.cn Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the mails.tsinghua.edu.cn engineering team.

See how AI-active teams rank this week on the Exceeds Leaderboards.

The mails.tsinghua.edu.cn engineering team reports 92.7% AI adoption, 1.06× productivity lift, and 20.0% code quality across recent work.

These metrics track how AI integrates into delivery pipelines, how throughput changes when assistance is used, and the health of AI-supported code review outcomes.

What this report measures

We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.

How to interpret these metrics

Use these signals to understand how AI assistance fits into day-to-day development, where enablement efforts drive throughput, and how review practices keep quality steady.

AI Adoption Rate

HIGH

92.7%

AI assistance is present in 92.7% of recent commits for mails.tsinghua.edu.cn.

AI Productivity Lift

LOW

1.06×

AI-enabled workflows deliver an estimated 6% lift in throughput.

AI Code Quality

LOW

20.0%

Review insights show 20.0% overall code health on AI-supported changes.

How is the mails.tsinghua.edu.cn team performing with AI?

The mails.tsinghua.edu.cn engineering team reports 92.7% AI adoption, translating into 1.06× productivity lift while sustaining 20.0% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.

Manager Questions Answered

Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.

What's a good company AI adoption rate?

mails.tsinghua.edu.cn is at 92.7%. This is 48.9pp above the community median (43.8%)..

92.7%

↑48.9pp above43.8% Community Median

Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.

Does AI actually make developers faster?

mails.tsinghua.edu.cn operates at 1.06×. This is 0.07× below the community median (1.13×)..

1.06×

Roughly in line1.13× Community Median

Instrument reviewer assignment and AI summaries to trim the slowest merge steps and edge past the median.

How does AI affect code quality?

mails.tsinghua.edu.cn holds AI-assisted quality at 20.0%. This is 3.3pp below the community median (23.3%)..

20.0%

Roughly in line23.3% Community Median

Invest in AI-specific test checklists and shadow reviews to keep quality slightly ahead of peers.

How evenly is AI use distributed across our team?

AI impact is concentrated—61.7% of AI commits come from a few experts, raising enablement risk.

61.7%

Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.

How can I prove AI ROI to executives?

mails.tsinghua.edu.cn has a solid ROI signal with room to strengthen either adoption, lift, or quality before presenting to executives.

Document case studies where AI accelerates delivery while maintaining quality, and expand playbooks across teams.

See how your full organization compares

Unlock personalized insights across all your repositories, teams, and contributors.

Securely connect Exceeds with your codebase to get commit-level insights on AI adoption and performance.

How Your Company Ranks

See how top engineering organizations compare across AI adoption, productivity lift, and code quality.

AI Adoption

% of commits with AI assistance

Companies in this quartile:

ID

idesie.com

(2904.2%)

IN

inngest.com

(1429.6%)

PE

pevesoft.ro

(87.6%)

VL

vllmr.dev

(87.6%)

Top 25% of teams adopt AI in 65-75% of their commits.

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

AC

acad.pucrs.br

(1.12×)

MC

mcornholio.ru

(1.12×)

HA

hassan.host

(1.01×)

MU

musicaloft.com

(1.01×)

Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.

Code Quality

Post-merge defect rate

Companies in this quartile:

KO

konghq.com

(795.0%)

IN

inngest.com

(701.7%)

GZ

gzgz.dev

(20.0%)

GW

gwu.edu

(20.0%)

Top 25% maintain quality above 92% while expanding AI usage, pairing automation with rigorous guardrails.

Rankings based on aggregated Exceeds AI dataset of 1.2M commits across open-source and enterprise engineering teams (Q4 2025).

Top contributors

Top contributors combine high AI adoption and quality output. Encourage internal sharing of best practices.

WY

Wentao Ye

Commits27
AI Usage96.0%
Productivity Lift2.00x
Code Quality20.0%
SH

Shao823

Commits1
AI Usage30.0%
Productivity Lift2.00x
Code Quality20.0%
CH

chenziyi

Commits2
AI Usage20.0%
Productivity Lift1.34x
Code Quality20.0%
XZ

Xie Zhe

Commits1
AI Usage38.0%
Productivity Lift1.17x
Code Quality20.0%
XI

Xiang-cd

Commits6
AI Usage20.0%
Productivity Lift1.16x
Code Quality20.0%

Encourage knowledge transfer from top AI users to others through internal mentoring or recorded "AI coding walkthroughs." Balanced adoption across the team typically improves overall performance by 12-15%.

Cross-Organization Network

Shared Repositories

20

OldNew777

LuisaGroup/LuisaCompute

Atream

kvcache-ai/ktransformers

Shao823

eesast/web

Ador-N

eesast/THUAI8

timzsu

flashinfer-ai/flashinfer

jeejeelee/vllm

yiwenlu66

FellouAI/eko

punkpeye/awesome-mcp-servers

Activity

90 Commits

Your Network

21 People
zzdhybthu
Member
wx-csy
Member
ovowei
Member
Ador-N
Member
Hujh23
Member
yiwenlu66
Member
Ashitemaru
Member
Shao823
Member
JamesSand
Member

Why these metrics matter for engineering managers

Faster delivery

1.4x lift → predictable roadmaps

Safer velocity

93% quality → lower rollback risk

Equitable gains

AI less dependency on heroes

Governance

Depth monitoring audit-ready

ExceedsExceeds AI

Turns these insights into daily coaching and automatic alerts, helping managers balance speed with sustainability.

See the truth of AI impact

Adoption + lift + quality in one view

Learn more

Know where to act first

Repo and role level "lift potential"

Learn more

Prove ROI

Export executive snapshots and benchmarks

Learn more