Forem: Joshua Solomon

From Fault Codes to Smart Fixes: How Google Cloud NEXT ’26 Inspired My AI Mechanic Assistant

Joshua Solomon — Wed, 29 Apr 2026 08:45:21 +0000

< This is a submission for the Google Cloud NEXT Writing Challenge. >

🚗 From Mechanic to Builder

I’m an auto technician.

Every day, I scan cars, read fault codes, and diagnose problems manually.

But here’s the issue:

Fault codes don’t fix cars — understanding does.

That’s why I started building AutoClaw — a lightweight diagnostic tool to help mechanics and car owners understand problems faster.

🔍 What Stood Out at Google Cloud NEXT ’26

One clear message:

AI is no longer optional — it’s becoming the interface for everything.

What excited me most wasn’t just AI itself — it was how Google Cloud is making it:

Accessible
Scalable
Useful in real-world workflows

💡 The Idea: AutoClaw + AI

Instead of this:

❌ “P0300 – Random Misfire Detected”

We move to this:

✅ “Your engine is misfiring. Likely causes: spark plugs, fuel injectors, or ignition coil. Start by checking plugs.”

🖼️ App Preview (AutoClaw)

🔎 Fault Code Search Interface

🤖 AI Diagnosis Assistant

📊 Fault Code Results

🛠️ How Google Cloud Makes This Possible

With the direction shown at NEXT ’26, AutoClaw can evolve using:

☁️ Cloud Infrastructure

Store thousands of fault codes
Fast, scalable access

🤖 AI Integration

Turn codes into explanations
Suggest fixes instantly
Learn from real-world cases

⚡ Real-Time Assistance

Mechanics get answers instantly
No more guessing or delays

🔥 What I Actually Built

Right now, AutoClaw includes:

🔎 Fault code search
📁 Growing database (hundreds → thousands)
⚡ Fast lightweight UI

Next step (inspired by NEXT ’26):

Add a real AI diagnosis assistant

⚖️ Honest Thoughts

Google Cloud is powerful — but:

💸 Pricing can be tough for indie devs
🌍 Internet dependency is a real limitation
📚 Learning curve still exists

If these improve, more builders from places like Africa will rise fast.

🌍 Why This Matters

This isn’t just about cloud updates.

It’s about who gets to build the future.

Before:

You needed a team

Now:

One person + AI = real product

🚀 What’s Next for AutoClaw

✅ AI-powered diagnostics
✅ Mobile-friendly experience
✅ Real mechanic workflows
✅ Booking system for repairs

💭 Final Thought

The tools are getting smarter.

Now it’s time we build smarter solutions.

🙌 Thanks for reading

If you're building something similar or want to collaborate, let’s connect.

AutoClaw: Smart Diagnostics. Real Fixes.

Joshua Solomon — Sun, 26 Apr 2026 16:26:27 +0000

This is a submission for the OpenClaw Challenge.

What I Built

I built a Smart Auto Diagnostic Assistant designed for mechanics and car owners who struggle to understand vehicle fault codes.
As an auto mechanic, I noticed that many people:
Don’t understand OBD fault codes (P-codes)
Depend heavily on mechanics for simple diagnostics
Waste time searching for solutions manually
This tool solves that by allowing users to:
Search and decode 3000+ car fault codes instantly
Get simple explanations (not complex grammar)
See possible causes and fixes
Get repair suggestions like a real mechanic
The goal is to make car diagnostics faster, smarter, and accessible to everyone, even without deep mechanical knowledge.

How I Used OpenClaw

I used OpenClaw to power the intelligence behind the system:
🔍 Code Interpretation Engine
OpenClaw helps translate raw OBD codes (like P0171) into human-friendly explanations.
🧠 AI Repair Suggestions
It suggests possible causes and step-by-step fixes based on real mechanic knowledge.
⚡ Auto-Suggestion Search (like Google)
As users type fault codes, OpenClaw helps predict and complete searches instantly.
🔗 Dynamic Knowledge Integration
I connected it to a growing database of:
Engine codes
Transmission codes
ABS codes
Manufacturer-specific codes
📱 User-Friendly Experience
Built like a lightweight mobile app so mechanics can use it easily at the workshop.

Demo

< Here’s how the system works.>

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What I Learned

< Real-world problems make the best tech solutions
AI tools like OpenClaw can simplify complex mechanical knowledge
Building for your own profession gives you a big advantage
Users prefer simple explanations, not technical overload
One challenge I faced was organizing large fault code data, but solving it helped me build a scalable system.>

ClawCon Michigan

I did not attend ClawCon Michigan, but I followed the challenge closely and built a solution based on real-world needs in the auto repair industry.

real-life impact

Joshua Solomon — Mon, 06 Apr 2026 17:32:56 +0000

Notion MCP Challenge Submission 🧠

Joshua Solomon

Mar 28

Smart Mechanic AI Workflow System

#devchallenge #notionchallenge #mcp #ai

Comments 1

2 min read

Smart Mechanic AI Workflow System

Joshua Solomon — Sat, 28 Mar 2026 10:09:10 +0000

This is a submission for the Notion MCP Challenge

What I Built

< I built a Smart Mechanic AI Workflow System using Notion to help mechanics manage their daily workshop operations more efficiently.
This system is designed for real-world use, especially for local mechanics who struggle with tracking jobs, pricing services correctly, and managing customers.
The workflow includes:
A Customer Job Tracker to log car issues, services, and payment status
A Daily Earnings Dashboard to monitor income and growth
A Smart Pricing Assistant to guide mechanics on how much to charge
An AI-powered Diagnosis Helper that suggests possible faults based on symptoms
This solution helps mechanics move from manual, unorganized work to a structured and professional system.>

Video Demo

< In my demo video, I walk through:
How I log a new customer and their car issue
How the system tracks job progress and payment
How I use AI prompts inside Notion to assist diagnosis and pricing
How I track my daily income and performance
The video shows how this system can improve efficiency and increase earnings for mechanics.>

Watch my real-life car diagnostic process here:

https://youtube.com/shorts/xOXmXyvG6Ig?feature=share

This video proves that my solution is already being used in real-life scenarios, making it practical, scalable, and impactful.

Show us the code

< This project is built using Notion, so there is no traditional code repository.
However, the structure includes:
Notion databases (tables) for jobs, customers, and earnings
Pre-written AI prompts for diagnosis and pricing
A clean, easy-to-use interface for daily workflow.>

How I Used Notion MCP

< I used Notion as a central system to organize and manage all workshop activities.
With AI-assisted workflows, this system acts like a “human-in-the-loop” assistant by:
Helping make better decisions on pricing and repairs
Reducing guesswork in diagnosing vehicle problems
Keeping all records in one place for easy access and tracking
This unlocks a smarter way of working where mechanics can focus more on fixing cars while the system helps manage information and decisions.>

Why This Matters.

Many skilled workers in developing environments lack digital tools to grow their business.
This project bridges that gap by combining real-world mechanical work with AI-powered organization, helping small business owners scale their operations and income.

I plan to expand this project by using Notion MCP tools to create a structured system where mechanics can log diagnostic reports, track vehicle history, and manage repairs more efficiently.
This can grow into a digital platform that helps mechanics across Nigeria and Africa improve their workflow and service delivery.