u/Wise-Cardiologist-31

I have been developing and testing this for months and it is finally ready.

The problem: I audit websites for clients regularly and I always needed three or four separate tools to get a full picture. One for SEO basics, one for structured data, one for security headers, and none of them cross-referenced their findings.

So I built Canopy Guard. It runs a single scan against any domain and scores four layers:

• SEO: crawlability, H1 structure, meta descriptions, canonicals, internal linking depth
• AEO: schema markup validation, FAQ JSON-LD, Organization JSON-LD, Q&A content density
• GEO: chunking efficiency for generative AI, citation precision, llms.txt status
• Security: TLS, HSTS, 6 security headers, AI crawl policy, exposed endpoints, server disclosure

The part I am most proud of is the Cross-Reference Intelligence layer. It maps visibility data against security data and surfaces gaps that only exist in the overlap. Example: if your robots.txt lets every AI bot in but you have no llms.txt, your content is being ingested without citation guidance. No SEO tool or security scanner flags that individually.

Tech stack: React frontend on Vercel, Node.js/Express/TypeScript backend on Railway, 12 scan modules running in parallel, Notion for lead storage, PDF report generation in-browser. Looking for feedback on the scoring methodology, especially the AEO and GEO layers since those are newer concepts most tools ignore.

I was on a call with a potential business partner last week when he said something I keep hearing.

"AI is really only going to help big business. The small people are going to get left behind."

I let it sit for a second. Because here is what he did not know. My father is in his 80s. He uses Gemini every morning.

I set it up with his calendar so it reads him his day. His appointments, what time he needs to be where, a quote to start the morning. He talks to it. He looks forward to it. He told me last week he is going to start asking it for lottery numbers, and I am pretty sure he was only half joking.

This is a man who came up before personal computers were in homes. And here he is, in his ninth decade of life, in conversation with an AI before breakfast.

That is not big business. That is my dad.

I have used story-based AI with my own children. I have watched parents of nonverbal kids use the same tools and get reactions from their child that they do not get any other way. A story, a voice, a character that meets the child where they are and waits with them. I am not going to pretend that fixes everything. It does not. But for a parent who has spent years searching for a way in, a small door opens. That matters.

The research backs this up. A study out of Seongdong-gu in Korea followed 80 community-dwelling older adults using a conversational AI called CLOVA CareCall for biweekly check-ins. After 31 weeks, their depression scores went down and their memory scores went up. Over 90 percent said they wanted to keep going. Loneliness is not a soft problem. It raises the risk of dementia by 31 percent, Alzheimer's by 14 percent, and vascular dementia by 17 percent. That is comparable to the impact of smoking.

A phone call from an AI is not a replacement for a phone call from a grandchild. Nobody is arguing that. But for the senior who is not getting either, the AI is the difference between a quiet apartment and a connected morning.

The guy I was talking to saw the headlines about enterprise AI, the billion-dollar deals, the layoffs, the productivity stats, and reached the conclusion most people are reaching. AI is a tool the powerful are using to get more powerful.

I understand the read. I just think it is incomplete.

Because while the headlines are about enterprise, the real adoption is happening in homes. Parents using AI to plan meals, manage the family calendar, take some of the invisible labor off their plates. Seniors using it to feel less alone. Kids learning at their own pace with patience no overworked teacher can offer to thirty students at once. People with disabilities accessing a world that was not built for them.

These are not edge cases. These are the use cases.

The boom is not only happening in conference rooms. It is happening in living rooms.

Curious if anyone else has watched AI quietly help someone in their family the headlines do not talk about. Would like to hear it.

Built a bilingual AI nail design app called Nail Check. Started it as a WordPress membership product. Paid plans, Stripe checkout, the whole gated funnel.
It worked, sort of. Conversion was bad. The marketing site couldn’t breathe because the CTA was always “subscribe now.” People came to look at AI-generated nail designs, hit a paywall on the second click, and bounced.
A few weeks ago I tore it apart. Pulled Stripe out of WordPress entirely. Made the site free and marketing-focused, no gates. Moved all monetization into the mobile app via App Store and Google Play IAP. If you want the rough phase log: privacy and ToS pages, mobile hamburger nav, CTA rewrites, Stripe removal, AI generation limits in the free tier, premium feature gating.
Three things I’d do differently if I started over:
1. Separate the marketing site from the product on day one. Mixing WordPress (marketing) with a membership system (product) made every change a two-system change. The new stack is WordPress for marketing, Capacitor and React for the app. Clean boundary.
2. Price for the platform from the start. Mobile users expect IAP. Web users expect a Stripe checkout. One paywall across both meant neither felt right.
3. Launch the free version first. The free site is doing more for me as top-of-funnel right now than the membership ever did, and the app converts on intent instead of curiosity.
Next phase is wiring the gallery and supply hub to the Railway API, plus shipping the App Store and Play download buttons.
If anyone’s done a similar cutover, what surprised you most after the switch? Still trying to figure out the right churn signals to watch in the IAP world.

About me…
I’m Adam. I build AI-powered SaaS platforms, mobile apps, and WordPress websites end to end. From wireframe to working production app.
I don’t just design. I don’t just code. I do both.
What I build:
• AI-powered SaaS platforms (React, Node.js, PostgreSQL)
• Mobile apps (React Native, Capacitor, iOS/Android)
• WordPress websites (Elementor, custom plugins, WooCommerce)
• LLM integrations (OpenAI, Together.ai, Claude API)
• UI/UX Design (Figma, Framer — wireframes to prototypes)
• Security audits for SaaS and AI apps (CISSP certified)
Recent work:
• VeloxSync — AI-powered HR SaaS with fine-tuned LLaMA model
• VeloxSync Education — K-12 EdTech platform with 112 state standards
• Nail Check — AI nail design mobile app (iOS/Android)
• Omnisattva — Mindfulness mobile app
• Dr. Parker Faith & Finance — Bilingual WordPress platform
• The Talley Law Firm — Professional services WordPress site
Certifications:
• CISSP (ISC2 2024)
• Azure AI Engineer (Microsoft 2025)
• Anthropic AI Fluency & Claude API certified
• Figma certified designer
• Framer prototyping and animation
Rates:
• $75/hr for development
• $65/hr for UI/UX design
• Security audits from $500 flat
Portfolio: dribbble.com/adammcclarin
Website: merakislove.com
Email: hello@merakislove.com
Available immediately. US based. Friendswood TX.
— Adam, Meraki is Love LLC | Soulful Tech™`

CISSP certified engineer here. Not selling anything. Just sharing what I keep finding.

1. Prompt Injection is Almost Never Handled

Most founders add an AI chatbot, give it system context, and call it done.

A malicious user can override your system prompt and extract your entire knowledge base in seconds.

Treat prompts like SQL queries. Assume they're hostile. Sanitize everything before it hits your LLM.

2. Multi-Tenant Data Bleeds Through LLM Responses

If your AI has database access and you have multiple customers... test whether Customer A can extract Customer B's data through a clever prompt.

You'd be surprised how often this works.

Scope your AI's database access strictly to the authenticated user's org. Never give it global read access.

3. OAuth Tokens Stored Unsafely

Tokens in localStorage. Tokens in URLs. Tokens in console logs.

All exploitable. All common.

HTTPOnly cookies. Server-side sessions. Never expose tokens to client-side JavaScript.

Happy to answer questions. Been building in this space a while.

CISSP certified engineer here. Not selling anything. Just sharing what I keep finding.

1. Prompt Injection is Almost Never Handled

Most founders add an AI chatbot, give it system context, and call it done.

A malicious user can override your system prompt and extract your entire knowledge base in seconds.

Treat prompts like SQL queries. Assume they're hostile. Sanitize everything before it hits your LLM.

2. Multi-Tenant Data Bleeds Through LLM Responses

If your AI has database access and you have multiple customers... test whether Customer A can extract Customer B's data through a clever prompt.

You'd be surprised how often this works.

Scope your AI's database access strictly to the authenticated user's org. Never give it global read access.

3. OAuth Tokens Stored Unsafely

Tokens in localStorage. Tokens in URLs. Tokens in console logs.

All exploitable. All common.

HTTPOnly cookies. Server-side sessions. Never expose tokens to client-side JavaScript.

Happy to answer questions. Been building in this space a while.`

CISSP certified engineer here. Not selling anything. Just sharing what I keep finding.

1. Prompt Injection is Almost Never Handled

Most founders add an AI chatbot, give it system context, and call it done.

A malicious user can override your system prompt and extract your entire knowledge base in seconds.

Treat prompts like SQL queries. Assume they're hostile. Sanitize everything before it hits your LLM.

2. Multi-Tenant Data Bleeds Through LLM Responses

If your AI has database access and you have multiple customers... test whether Customer A can extract Customer B's data through a clever prompt.

You'd be surprised how often this works.

Scope your AI's database access strictly to the authenticated user's org. Never give it global read access.

3. OAuth Tokens Stored Unsafely

Tokens in localStorage. Tokens in URLs. Tokens in console logs.

All exploitable. All common.

HTTP Only cookies. Server-side sessions. Never expose tokens to client-side JavaScript.

Happy to answer questions. Been building in this space a while.`

Multi-tenant SaaS org isolation bugs are one of the most common and most underappreciated vulnerability classes in early stage products.

Here is the pattern I see constantly:

A developer builds a route that queries the database by an ID from the URL or request body. They validate authentication. They do not validate that the requested resource belongs to the authenticated user's organization. The result is a cross-org IDOR that lets any authenticated user access any other organization's data by manipulating a single parameter.

The fix is simple: every database query that returns tenant-specific data needs to be scoped to the organization ID from the JWT token, not just the ID from the request. One extra AND clause per query. That is it.

The reason it keeps shipping is deadline pressure combined with the fact that it does not break anything in testing. Your test user can access your test data just fine. The bug only surfaces when you try to access someone else's.

If you are building multi-tenant SaaS, audit every route that takes an ID parameter. Verify the resource belongs to the caller's org before returning or modifying it. No exceptions.

Happy to discuss specific patterns if useful.

One thing that frustrates me about most AI workflows is the cold start problem. Every new session you re-explain your business, your voice, your clients.

I started solving this with skill files. A skill file is a markdown document you upload to a Claude Project or paste into a Gemini Gem. It holds your context permanently so you never re-explain anything.

The three I use most:

brand-voice.md: defines tone, writing rules, and platform-specific formatting

client-router.md: when you say a client name, Claude loads their full project context automatically

seo-aeo-audit-checklist.md: structured audit that scores any website out of 100 across 7 sections including AI search visibility

Anyone else using a similar system? Curious what context you keep persistent across sessions.

I run a solo digital studio. No employees. Just me and AI tools that know exactly how my business works.

Most people using AI for their business get generic output because they never taught the AI who they are. I spent months building skill files that fix that.

Brand Voice: Your tone, your rules, your formatting per platform. Define it once. AI follows it every session.

Client Router: Say "working on [client name]" and the AI loads everything about that project. No more re-explaining.

SEO Audit Checklist: Give it any URL. Get a 100-point scored report in minutes. Same framework I charge clients for.

3 files for Claude, 3 for Gemini. Free. No email required.

Download

I built an AI tool for homeschool families because I couldn’t find what I wanted for my own daughter.

It shows you where your child stands across subjects, suggests what to focus on next, and generates lesson plans aligned to your state standards (TEKS, Common Core, NGSSS, and others) in minutes. It works across K through 12 and adapts to how your family actually learns.

It’s not a curriculum. It’s an intelligence layer that makes whatever curriculum you’re already using work better. If your kid is struggling in reading comprehension, it identifies the gap and builds a focused plan instead of you guessing what to work on next.

I’ve been a developer for over 20 years so this not a flyby night built it overnight program. It’s something I’ve been developing with a line of services.

Free trial, no credit card. I’m looking for homeschool families who want to test it and help shape what it becomes. Your feedback directly influences what gets built next.

veloxsync.app/education-home

Would love to hear what tools you’re currently using and what’s missing.