Know Before Fix: QA-Driven Repository Knowledge Acquisition for Software Issue R
LLM-based coding agents have significantly advanced automated software issue resolution, yet they remain highly prone to factual errors caused by insufficient repository understanding. Recent methods attempt to mitigate this limitation thro
https://arxiv.org/abs/2607.11111v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Light
How close the company’s idea is to your thesis statement
Sector
agents, llm, ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder → stable
Traction → stable
Idea vs market → stable
▸ Add / edit details
Correct facts used on the next screening or memo.
People
H
Haotian Lin
1.0
low confidence
S
Silin Chen
1.0
low confidence
X
Xiaodong Gu
1.0
low confidence
Y
Yuling Shi
1.0
low confidence
C
Chengxi Pan
1.0
low confidence
J
Jiaqi Ge
1.0
low confidence
M
Mengfan Li
1.0
low confidence
J
Jianghong Huang
1.0
low confidence
M
Mengchieh Chuang
1.0
low confidence
B
Beijun Shen
1.0
low confidence
H
Haibing Guan
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Real Artists
· Inactive
We make a fast and native issue tracker for software projects.
founders not scraped yet
PullRequest
· Acquired
Code review as a service - combining automation with a network of…
Lyal Avery
HyperProbe
· Active
Your coding agent writes code. Now let it fix prod too.
founders not scraped yet