Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM P
Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce computational resources that users must share. When should a user invest in e
https://arxiv.org/abs/2607.14371v1 ↗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
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
F
Fengzhuo Zhang
1.0
low confidence
Z
Zhuoran Yang
1.0
low confidence
D
Dirk Bergemann
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Automorphic
· Active
Infuse knowledge into language models with just 10 samples
founders not scraped yet