BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Servi
Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise code. This shift raises a central evaluation question: can an
https://arxiv.org/abs/2607.11042v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Moderate
How close the company’s idea is to your thesis statement
Sector
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 ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
Y
Yuzhe Guo
1.0
low confidence
M
Mengzhou Wu
1.0
low confidence
Y
Yuan Cao
1.0
low confidence
J
Jialei Wei
1.0
low confidence
D
Dezhi Ran
1.0
low confidence
W
Wei Yang
1.0
low confidence
T
Tao Xie
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
The Token Company
· Active
Compression middleware that improves LLM outputs
founders not scraped yet