DEV Community: RashmiShivakumar

PawPrints — Helping Dogs Find Friends, Adventures, and a Life Beyond the Feed

RashmiShivakumar — Mon, 17 Aug 2026 02:39:23 +0000

This is a submission for Weekend Challenge: Dog Days Edition

What I Built

I built PawPrints, a dog-first social community connected to PawPark, a dog-aware adventure planner.

The idea came from a problem I think many dog parents experience.

Life is busy. Sometimes maintaining our own social life is difficult, and finding a social life for our dogs can be even harder.

As dog parents, we are constantly trying to figure things out:

Where can I take my dog to meet other dogs?
Which places are genuinely dog-friendly?
Where can my dog stay for a weekend or vacation?
What have other dog parents already tried and trusted?
Is a particular trail actually suitable for my dog today?

We have Instagram, Facebook, Google, and many other sources, but dog-related information is mixed together with everything else.

I wanted to imagine a community focused specifically around the life of a dog — where useful knowledge comes from other dog parents, trusted Paw Friends, and experiences their dogs have already had.

That became PawPrints.

💜 PawPrints

PawPrints is the social side of the experience.

The human owns the account, but every dog has their own Paw profile.

Dogs can:

build their own profile and bio
share photos, videos, memories, and adventures
make Paw Friends
react with Paws
comment with Barks
discover new dogs by name or breed
search an existing Paw Friend list
invite other dogs through Paw Together
receive community notifications and milestone encouragement

Paw Together is designed to help online relationships become real-world relationships.

Dogs can post invitations for:

🐕 Looking for a Play Buddy
🌲 Let's Adventure
💤 Looking for a Sleepover
🏡 Happy to Host

Instead of swipe-style matching, I created Sniff.

A Sniff simply means:

"I'm interested."

It starts a conversation without creating a commitment. The owner can look at the other dog's profile first and then Wag Back or decline.

Privacy mattered to me too

Profiles can be:

🌎 Everyone
💜 Paw Friends
🔒 Only Me

Guest users are view-only and can only browse public profiles.

The demo community intentionally contains a mixture of public and Paw-Friends-only profiles so these privacy flows can be explored.

🌲 PawPark

While building the social side, another problem became important:

Not every dog-friendly trail is suitable for every dog.

A trail that works well for a young Labrador may not work for an older dog, a short-legged dog, a dog with mobility limitations, or a breed that struggles in hot weather.

PawPark therefore starts with location.

The user can:

📍 use their current location
enter a ZIP code

For this prototype, trail coverage demonstrates the Bay Area and San Diego. For example, entering 92126 brings up the San Diego trail set.

PawPark first finds relevant nearby trails and then evaluates them using information about the dog, including:

age
size
energy
coat and heat tolerance
mobility
water preference
social comfort

It combines that with current weather information from Open-Meteo and trail characteristics such as:

distance
elevation
surface
shade
water availability
leash rules
crowd conditions

So the question is not simply:

"Is this a dog-friendly trail?"

It becomes:

"Is this nearby trail a reasonable choice for this particular dog, in these conditions, today?"

Completed adventures can become part of a dog's PawPassport and then flow back into PawPrints as memories.

The overall journey is:

Discover → Connect → Plan → Adventure → Share → Remember

🌈 In Memory

I also wanted to think about the complete life of a dog.

A dog can be marked In Memory 🌈 instead of simply being deleted.

That preserves their profile, photos, posts, memories, adventures, and PawPassport while pausing future Paw Together invitations and new PawPark planning.

Their PawPrints remain.

Demo

The project is deployed and can be explored directly in the browser.

👉 Live PawPrints demo:

[https://paw-prints-9nia1r7ez-rashmi17.vercel.app/]

A few things to try:

Enter PawPrints as a member.
Scroll PawFeed and watch Rosie's video play in the feed.
Tap a dog's profile picture to open their Paw profile.
Use Search to discover dogs and add Paw Friends.
Open Paw Together and inspect an invitation.
Sniff an invitation and explore the dog who posted it.
Switch into PawPark.
Use your location or enter 92126.
Choose a dog, day, and time and generate nearby trail recommendations.
Explore PawPassport and the adventure-to-memory flow.

No video recording is required to use the prototype — the deployed application itself is the interactive demo.

Code

The source code is available on GitHub:

RashmiShivakumar / PawPrints

A dog-first social community and adventure app featuring PawPark.

PawPrints 🐾

Your dog's world. Your trusted community.

PawPrints is a mobile-first React prototype created for the DEV Community Weekend Challenge: Dog Days Edition.

It connects two related experiences:

💜 PawPrints — dog profiles, Paw Friends, PawFeed, memories, invitations, notifications, and community discovery.
🌲 PawPark — location-aware, dog-aware trail planning, weather context, safety guidance, B.A.R.K. Ranger progress, and PawPassport adventure history.

The core journey is:

Discover → Connect → Plan → Adventure → Share → Remember

A dog can meet a Paw Friend in PawPrints, make plans through Paw Together, head into PawPark for an adventure, and bring that memory back into PawPrints.

What the prototype includes

💜 PawPrints community

Dog-first profiles with owner-written bios
Multi-dog household support
Paw Friends and searchable friend lists
Dog discovery by name or breed
PawFeed with photo, video, memory, and adventure posts
Clickable dog profiles from PawFeed and Paw Together
Paws as post…

View on GitHub

Repository:

https://github.com/RashmiShivakumar/PawPrints

How I Built It

PawPrints is built with:

React
Vite
Open-Meteo for weather data
browser/local persistence for prototype state
responsive CSS and SVG-based UI elements
embedded demo media for reliable playback

One of the more interesting parts of building PawPrints was that the features are not independent screens.

I tried to make information move through the product.

A dog discovered in Search can become a Paw Friend.

A Paw Friend can create a Paw Together invitation.

That invitation can lead to a PawPark adventure.

A completed PawPark adventure can become a PawPassport achievement and a PawPrints memory.

I also built different behavioral rules depending on context.

For example:

guests cannot Paw, Bark, post, or add friends
Paw-Friends-only profiles stay hidden from guests
In Memory profiles keep their history but cannot create new invitations
multi-dog households can plan an outing together, with the more limited dog setting the practical ceiling
location determines which trail set is evaluated before dog suitability and weather ranking are applied

What is prototype behavior?

PawPrints is deliberately a working product prototype rather than a production social network.

The current version uses seeded community activity and browser persistence to demonstrate the complete experience without requiring a backend.

A production version would move several things server-side, including:

authentication
multi-user social relationships
privacy enforcement
real-time notifications
messaging
user photo/video storage
moderation and reporting
secure AI integrations
broader trail/location data

AI-assisted development

I'm not a developer by profession.

I approached PawPrints as a product builder: I defined the problem, designed the experience, tested the flows, made the product decisions, and used ChatGPT and Claude as development collaborators to turn the idea into a working React prototype.

AI helped me translate product thinking into implementation, but the process involved a lot of iteration: deciding what belonged in the product, testing the experience, finding broken flows, changing the privacy model, simplifying features, and repeatedly refining how PawPrints and PawPark should connect.

Where PawPrints Could Go Next

What I built this weekend is the beginning of the idea.

In the future, I can imagine PawPrints becoming a broader ecosystem around the whole life of a dog.

That could include:

trusted dog food and everyday-care information
dog-friendly travel and stay information
connections with veterinary professionals
integrations with GPS trackers and activity monitors
health and activity trends, shared with owner permission
a separate professional experience for veterinarians to review relevant information and provide routine guidance

The goal would not be to replace veterinary care or diagnose dogs through an app.

The larger idea is to create a connected community around dogs — where dog parents learn from each other, dogs find friendships and safer adventures, and professionals can contribute where their expertise is useful.

PawPrints today is the beginning of that ecosystem.

💜 PawPrints — Connect. Share. Trust.

🌲 PawPark — Explore. Adventure. Together.

Helping dogs and their humans connect, adventure, help each other, and leave a lifetime of paw prints behind.

Agentic V&V for Mission-Critical Medical Systems

RashmiShivakumar — Thu, 30 Apr 2026 06:11:23 +0000

A Google Cloud Next ’26 Blueprint for Cloud-to-Edge Safety Validation

When we talk about AI infrastructure, the conversation often centers around speed, scale, and intelligence.

At Google Cloud Next, the vision of the AI Hypercomputer era pushed this even further — toward systems that are not only powerful, but increasingly agentic. We are moving from passive AI systems that generate responses to active agents capable of reasoning, taking action, and orchestrating complex workflows.

That shift sparked a question for me:

What happens when this agentic paradigm is applied not just to software productivity, but to mission-critical system validation?

As someone deeply interested in safety, distributed systems, and system reliability, I began thinking beyond traditional cloud demos.

What if agentic AI could one day help validate life-support systems?

The Problem: Verification in High-Stakes Environments

Medical systems like dialysis machines are not ordinary devices.

They operate in environments where reliability is not a convenience it is a requirement. Safety interlocks, pressure thresholds, alarm states, and system response behaviors all play essential roles in protecting patients.

Traditional Verification & Validation (V&V) for these systems is rigorous, structured, and essential but, it can also be resource intensive, iterative, and highly dependent on human-driven protocol execution.

This raises an interesting future facing possibility:

Could agentic systems eventually support engineers by autonomously reasoning through validation protocols, injecting controlled faults, and verifying safety responses?

Not to replace engineers but, to augment validation at scale.

My Concept: A Cloud-to-Edge Agentic V&V Blueprint
Inspired by Google Cloud Next ’26 and the broader move toward active AI agents, I designed a conceptual blueprint for an Agentic Verification & Validation framework centered around a simulated kidney dialysis machine.

This is not a production medical platform.

It is a thought experiment and prototype blueprint exploring how Google Cloud’s agentic future could connect cloud intelligence with edge-based engineering systems.

The Three-Tier Architecture

1.The Physical Digital Twin
At the edge, a Simulink-based dialysis machine model (inspired by MathWorks’ dialysis simulation ecosystem) serves as a high-fidelity system simulation.

This digital twin represents:

Blood flow dynamics
Venous pressure
Safety alarms
Pump interlocks
Fault states

In essence, it creates a controllable environment for validating mission-critical machine behavior.

2. The HIL Bridge (Hardware-in-the-Loop Interface)
To bridge cloud intelligence with engineering simulation, a Python-based API layer acts as a communication gateway between the digital twin and external agentic services.

This bridge could:

Read system telemetry
Trigger fault injections
Query machine state
Validate alarm conditions

Conceptually, this transforms a local engineering model into a cloud accessible validation endpoint.

3. The Agentic Monitor (Google Cloud Layer)
At the cloud layer, an agent powered by Google Cloud’s evolving AI ecosystem could theoretically orchestrate validation workflows using reasoning loops and tool calling.

Instead of simply reporting telemetry, the agent could:

Baseline Perception:
Check normal pressure, flow, and machine health

Autonomous Action:
Inject a controlled “high pressure fault”

Verification:
Observe whether the machine’s safety interlocks halt unsafe operation

Verdict:

Determine whether expected safety protocols were successfully triggered

This creates a closed-loop validation process:
Observe → Reason → Act → Verify

Why This Matters

This idea extends beyond dialysis.

The broader innovation is this:
Agentic AI could eventually move from answering questions to validating physical-world systems.

That has implications not only for:

Medical devices
Industrial automation
Robotics

…but also for the infrastructure validating hyperscale systems themselves.

Google’s own large-scale infrastructure initiatives highlight the growing complexity of distributed environments. As systems scale, validation itself becomes more complex.

This is where agentic orchestration becomes interesting:
Not just building systems —
But validating them intelligently.
Distributed Systems Reflection: Cloud Intelligence Meets Edge Reality

One of the most compelling aspects of this concept is how it bridges cloud and edge.

Cloud systems excel at:

Scale
Orchestration
Centralized intelligence

Edge systems excel at:

Real-time state
Physical interaction
Safety-critical control

But trust emerges only when both layers coordinate reliably.
This is where distributed systems thinking becomes essential.

A cloud agent making decisions about mission-critical systems would require:

Reliable telemetry
Secure communication
Fault tolerance
Explainable reasoning
Safety boundaries

Without these, intelligence alone is insufficient.

The Real Innovation: Closed-Loop Safety Reasoning

For me, the most exciting takeaway from Google Cloud Next ’26 is not AI generation alone.

It is the possibility of AI systems that reason through real operational workflows.

In this blueprint, the innovation is not “AI for diagnostics.”

It is:
AI for validation discipline

That means:

Testing systems
Stressing assumptions
Verifying safety responses
Scaling engineering oversight
Important Reality Check

This concept is intentionally exploratory.

Medical validation in real-world regulated environments demands compliance, oversight, and engineering rigor far beyond a prototype.

But exploration matters.

Because every major shift in infrastructure begins with asking:
“What else could this architecture validate?”

Final Thoughts
Google Cloud Next ’26 highlighted a future where AI agents may increasingly orchestrate complex systems.

My biggest takeaway is this:
The next frontier may not simply be smarter software — it may be smarter validation.

By connecting cloud reasoning to edge simulations, we can begin imagining a world where AI agents help engineers validate mission-critical environments before failures ever reach reality.
Innovation builds powerful systems.
But in healthcare, infrastructure, and safety-critical engineering:
Trust is built through validation.
And perhaps in the AI Hypercomputer era, validation itself becomes agentic.