Run Locally
Prerequisites: Node.js
- Install dependencies:
npm install - Set the
GEMINI_API_KEYin .env.local to your Gemini API key - Run the app:
npm run dev
Open source/paper-pal
Project dossier / README
paper-pal
yizhiyanhua-ai/paper-pal
Research reading as an active workflow of return, notes, and synthesis.
Serious AI work depends on reading systems, not just browsing papers. This project treats comprehension as production work.
Repository reading order
Understand the workflow it replaces before deciding whether to stay in the README, open a topic path, or move toward collaboration.
01 / Workflow
PDF hoarding and one-pass reading that never turns into reusable understanding.
Start by naming the old workflow this repo is replacing.
02 / README
<div align="center"> <img width="1200" height="475" alt="GHBanner" src="unnamed.jpg" /> </div> Run Locally **Prerequisites:** Node.js Install dependencies: npm install Set the GEMINI_API_KEY in .env.local to your Gemini API key Run the app: npm run dev
Then use the README and site interpretation to confirm the project boundary and delivery surface.
03 / Proof graph
Return to thesis and the wider proof system
Finally, place the repository back into a bigger market question and working conversation.
Dossier links
Turn raw repository facts into a founder-readable evidence chain.
github.com/yizhiyanhua-ai/paper-pal
Proof-system routes
This repository is not the endpoint. It should keep flowing into archive, forum, thesis, and collaboration.
Longform route
Return to the archive for the argument behind this build
Read the repository and the writing as one public proof chain.
Forum route
Reconnect the project to launches, field notes, and shorter experiments
The forum layer carries pace, live context, and iteration.
Collaboration route
Turn this project page into a concrete collaboration path
Founding engineer, design partner, and contract build paths converge here.
Related clusters
A project page should also return to the bigger market question.
01 / Cluster 01
3 routesHarness engineering for agentic AI products
What has to surround the model before an AI workflow becomes a product?
02 / Cluster 02
3 routesLong-running agents and resumable work
Where does the user lose trust when an agent runs for hours?
03 / Cluster 03
3 routesDeveloper communication for AI infrastructure
Can someone understand the product before they understand the whole codebase?
Commercial outcomes
Founding engineer fit
Design partner sprint
Developer-facing proof work