AI-Era Philanthropic Practice: From Inspiration to the Exploration of PawHaven

During the widespread AI revolution, new ideas are easy to come by—but "technology with a heart" that actually works needs something to keep it grounded. For me, that grounding came from real moments with my wife. She’s both a vet and a dedicated animal lover: how focused she is when treating hurt stray cats late at night, how worried she gets when finding foster homes for abandoned puppies, and how helpless she sounds when she says, "One person can’t rescue that many animals"—these small daily moments made me ask: Can AI help spread kindness, so more people can join in rescuing stray animals?

As someone who works with technology, I’m used to breaking down problems step by step. The main problems in stray animal rescue are basically two things: "not enough shared information" and "hard to join in": rescuers can’t find animals that need help, people who want to help don’t know where to start, and no one can see how rescue efforts are going. So, the first version of PawHaven started to take shape. It’s not just a "place to write down things," but a tech tool that connects "people who want to help" with "animals that need help"—and it’s my first try at mixing tech work with my wish to do good. Right now, PawHaven is still being tested and improved, but this process has already shown me clear ways AI can help with charity work.

1. From Inspiration to Requirements: AI Turns "Vague Ideas" into "Clear Scenarios"

Inspiration comes from life, but turning the fuzzy thought of "wanting to help stray animals" into real product features means figuring out exactly what users need. At first, I tried getting info from public groups (like pet rescue forums and Xiaohongshu’s stray animal topics), but sorting through all those talks by hand often left me with a mess of repeated or messy info—this is where AI really helped.

When I was gathering what the product needed, AI worked like a "fast organizer":

• Finding main problems: After I put over 2,000 community talks into AI, it quickly found three big issues: "rescue info is all over the place (stray animals in the same area get reported many times)," "hard to match volunteers (people want to help but can’t find nearby rescue spots)," and "no way to see rescue progress (people donate supplies but don’t know where they go)"—this helped me avoid making features that no one actually needs.
• Sorting user needs: It automatically grouped needs by "what kind of user you are"—people who start rescues (need to report animals and track progress), volunteers (need to sign up and get tasks), and regular users (need to donate or follow updates)—so the product’s features fit each group better.
• Filling in missing parts: For the "adoption process," AI used examples from other charity products to add details like "showing animal health papers publicly" and "checking if adopters are a good fit first"—this stopped me from missing important steps because I didn’t have enough experience.

But I always stuck to one rule: AI gives "ideas to check," not "final answers." For example, AI once suggested adding "AI that automatically tells an animal’s breed," but I thought, "In rescue work, breed doesn’t matter—health does." So I put that feature on hold and first made an "injury note template" (to help non-experts write down an animal’s injuries quickly).

2. Technical Feasibility Verification: AI Helps with "Trying Things Out," Humans Control the "Important Parts"

After figuring out what the product needs, the next step is to check if "the tech can actually be built." Since I’m working on this project alone, I needed to quickly test which features "can be made and are useful" without spending too much time. AI’s job here was to "make it easier to fix mistakes," but the key tech choices were still mine.

In practice, I did two things:

• First, build a "simple working version": I used React + Node.js to make an MVP (Minimum Viable Product)—a basic version with three key features: reporting stray animal info (with location tags), a volunteer sign-up page, and updating rescue progress. I didn’t use AI here because basic features have simple, well-known tech solutions, and making choices myself was faster.
• Then, use AI to find "ways to make it better": I put the MVP’s steps (like filling out a report form or checking rescue progress) into AI and asked it to act like a "new user." It found two problems: first, choosing a location meant switching to a map app (which was a hassle), and second, rescue status words were too hard to understand (like "pending transfer"). Using this feedback, I made address selection easier (letting people type in addresses or use auto-complete) and changed status words to simpler ones (like "waiting to go to a rescue station").

Also, for the key feature of "matching volunteers by location," AI helped me compare two tech options: using a third-party map tool (good because it’s ready to use, bad because it costs money) vs. saving location data locally (good because it’s free, bad because it’s not super accurate). Since the project is "non-commercial and cheap," I chose the free option—prioritizing "rough location matching" and planning to make it more accurate later if I get more resources.

3. UI Design and Experience Optimization: AI Makes "First Drafts," Humans Fix the "Small Things"

A charity product’s design doesn’t need to be "fancy," but it must be "warm and easy to use." For example, photos of stray animals should look real (not over-edited), and buttons should be clear enough for both old and young users. But as a developer without design training, I often struggled with making things look good and easy to use—this is when AI became a "fast draft helper."

Here’s how I worked with AI:

• Set the style, AI makes drafts: First, I told AI I wanted a "warm, simple" look: "A stray animal rescue platform, main colors soft orange + white (no cold colors); rounded buttons to feel friendly." It quickly made 3 homepage layouts (different ways to show info lists and report buttons), so I didn’t have to start drawing from nothing (which saves time).
• Fix small details myself: AI’s first draft had a "report button" that was too bright (a loud red, which felt pushy), so I changed it to a softer orange-red. Also, AI put "animal injury info" at the bottom of the page—but since injury info is super important for rescues, I moved it under the animal’s photos to show it first.
• Use AI to help with different devices: For "responsive design" (making it work on phones and computers), AI automatically suggested layout changes for different screen sizes—like hiding side menus on phones (using bottom tabs instead) and adding "rescue count cards" on computers (to show how many animals have been rescued).

The current design is still a "test version," but compared to designing everything by hand, AI saved me at least 40% of the time—letting me focus on "testing how easy it is to use." For example, when I asked pet lovers to try it, they said that after adding "batch photo upload," we needed a "preview and delete" button. AI didn’t think of this, which made me realize: "How easy a product is to use" finally depends on "real users’ feedback"—AI can only give suggestions.

4. Summary and Future Outlook: AI is a "Tool," Philanthropy is the "Heart"

Looking back at how PawHaven was built, AI has really been a helpful "assistant": it helped me sort needs quickly, made it easier to fix tech mistakes, and made design simpler—so I could turn a personal project into a test version fast. But I know clearly that AI can’t replace the "heart of charity": caring about stray animals, respecting what users need, and knowing which features matter.

Right now, there are still many things to test for PawHaven:

• Short-term: Make "rescue record keeping" better (letting users upload medical reports and supply lists) so everyone can see how things are going.
• Mid-term: Try "AI that helps check injuries at first" (users upload animal photos, and AI says, "Possible injuries—check the legs first") to help non-experts write down key info fast.
• Long-term: Make a "volunteer trust system" (matching reliable volunteers based on how often they help and feedback) to fix the problem of "people signing up but not showing up" in charity work.

But no matter how tech changes, one rule will stay the same: PawHaven’s main job is "connecting people"—linking those who want to help with animals that need it. AI just makes this connection faster and warmer.

5. Participation and Support: Inviting "Fellow Helpers" to Improve Together

Right now, PawHaven is still a "test version"—it hasn’t been launched widely, and there are no plans to make money from it. If you, like me, believe "tech can help charity work," you’re welcome to join in these ways:

• For developers: The project code is open-source FrontEnd and BackEnd. If you have ideas to make the code better, please submit a PR. Developers who know about "location matching" and "multilingual support" are especially welcome.
• For volunteers/rescue groups: You can test the test version and share feedback on "which features work and which don’t" in real rescue work—like whether you need a "temporary foster info page" or a "rescue supply connection" tool.
• For regular users: You can star the project to follow updates. When the platform launches later, every time you "report an animal" or "share a rescue update," you’ll be helping stray animals.

Charity has never been "one person’s job," and tech isn’t just "cold code." I hope PawHaven can be a "starting point"—bringing more people to use their skills (tech, time, resources) to do more for stray animals. Even if it just helps one stray cat avoid one night of hunger or one injury, it’s worth it.