DEV Community: DevTECH

How I Built an AI-Native Islamic Growth Platform Optimized for SEO, GEO & AI Search

DevTECH — Tue, 12 May 2026 10:48:09 +0000

The Problem

Most Islamic apps today still look and behave like utility tools from 2018.

They focus on:

prayer times
Quran reading
Qibla direction
static dua collections
donation-driven experiences

But almost nobody is building:

AI-native Islamic experiences
emotional wellness systems
guided Islamic growth journeys
semantic SEO ecosystems
AI-search optimized platforms
retention-focused Muslim lifestyle products

That gap is exactly why I started building MyTazki.

What Is MyTazki?

MyTazki is an AI-first Muslim lifestyle platform focused on:

Quran reflections
guided Islamic journeys
Azkar and duas
emotional wellness
Islamic habits
AI-powered spiritual growth

The goal was not to build another Islamic utility app.

The goal was to build:

"Calm + Duolingo for Muslim spiritual growth."

The Biggest Shift: GEO Instead of Traditional SEO

Most websites still optimize only for Google search rankings.

I decided to optimize MyTazki for:

Google AI Overviews
ChatGPT retrieval
Perplexity citations
conversational AI systems
semantic search
voice search

This is where GEO (Generative Engine Optimization) becomes important.

Instead of writing pages only for humans or keywords, the site architecture is designed for:

AI retrievability
semantic clustering
topical authority
conversational intent
structured knowledge graphs

Semantic SEO Architecture

Instead of random blog posts, the platform uses semantic SEO silos.

Main clusters include:

Emotional Wellness
Salah & Prayer
Quran Reflections
Islamic Habits
AI Islamic Companion

Each cluster contains:

hub pages
supporting articles
journey funnels
related content graphs
FAQ schema
speakable schema
conversational search formatting

The result:

A deeply connected Islamic semantic knowledge graph.

GEO Optimization Strategy

Every page contains:

AI summary blocks
conversational questions
FAQ sections
structured schema
semantic relationships
short-answer formatting

Example queries targeted:

"Can Islam help anxiety?"
"How do I reconnect with Allah?"
"What dua helps overthinking?"
"How can I build Islamic habits?"

This dramatically improves:

AI citations
retrieval quality
semantic relevance
long-tail visibility

Internal Linking Became the Real SEO Moat

One of the biggest realizations:

Modern SEO is no longer about isolated pages.

It is about:

semantic relationships
crawl architecture
entity understanding
contextual linking
topical depth

So every page inside MyTazki links contextually into:

emotional wellness pages
Quran reflections
guided journeys
routines
AI experiences

This creates a semantic web instead of a content library.

AI-Native User Experience

Another major focus was emotional UX.

Most Islamic apps overload users with:

cluttered dashboards
generic gold Islamic UI
too many features
weak onboarding

I wanted MyTazki to feel:

calm
minimal
emotionally safe
modern
reflective

Closer to:

Calm
Headspace
Notion

than traditional religious utility apps.

Building a Freemium Islamic Product

One difficult challenge:

How do you monetize respectfully?

The solution was:

Free = Islamic knowledge remains accessible
Premium = personalization, AI guidance, routines, and deeper experiences

Premium features include:

AI Islamic companion
guided journeys
habit tracking
emotional wellness systems
journaling
AI reflections
personalized routines

The subscription layer is designed around:

retention
emotional consistency
guided growth

not aggressive SaaS upsells.

Tech Stack

Current stack:

React
TypeScript
Node.js
Express
PostgreSQL
Razorpay subscriptions
Semantic JSON-LD schema
GEO-focused rendering

Built entirely with:

Replit
AI-assisted workflows
iterative semantic architecture

Biggest Lessons Learned

1. AI Search Is Real

Traffic is increasingly coming from:

AI summaries
conversational search
semantic retrieval
recommendation systems

Building for AI retrieval early creates a major advantage.

2. Emotional Intent Beats Generic Keywords

These keywords are brutal:

Islamic app
Quran app
Muslim app

But emotional long-tail intent is massively under-served.

Examples:

how to reconnect with Allah
best dua for anxiety
Islamic routine for peace
how to stop missing salah

Those are much stronger acquisition channels.

3. Internal Linking Is Everything

A semantic content graph outperforms disconnected blog spam.

Especially for:

AI search
topical authority
GEO
crawl depth
engagement

What’s Next

Current focus areas:

subscription system
AI personalization
emotional journey infrastructure
retention systems
backlinks
social SEO loops
AI citation optimization

The long-term goal is to build:

the default AI-native Muslim growth platform.

Final Thought

I think the next generation of successful products will not just be:

SEO optimized
visually polished
feature rich

They will be:

emotionally intelligent
AI retrievable
semantically structured
retention focused
deeply personalized

That is the direction I’m trying to push with MyTazki.COM

Would love feedback from other builders working on:

AI products
semantic SEO
GEO optimization
wellness apps
faith-tech
retention systems

Thanks for reading.

My co-founder got PCOS. So I quit my job and built a meal planning app for 100 million Indian women.

DevTECH — Wed, 22 Apr 2026 08:27:32 +0000

So this is a bit of a long one. Grab chai.

About two years ago, Priya — my co-founder and the person who basically stress-tests everything I build — got diagnosed with PCOS. Not shocking on its own, 1 in 5 Indian women has it. What was shocking was what happened next.

Her doctor handed her a printed sheet. A diet plan. It said things like "avoid refined carbs" and "eat low-GI foods." That's it. No context. No recipes. No mention of the fact that she grew up eating idli and sambar every morning, that her mum cooks with coconut oil, that she doesn't have 90 minutes to make something from a wellness blog written by someone in California.

She googled "PCOS diet plan India." Got 40 tabs of conflicting advice. Some said no rice. Some said brown rice is fine. One said drink fenugreek water at 6am. She tried three different apps. None of them knew what Ragi Dosa was.

She asked me — the one who supposedly knows about software — "why doesn't something like this exist?"

I didn't have a good answer. So I started building.

The thing I got wrong first

My first instinct was to build a calorie tracker. That was stupid. Nobody wants to log every meal. That's not a product, that's homework.

What Priya actually needed was someone to just... tell her what to cook. Specifically. For her body. With food she already knows how to make.

That reframe changed everything. This wasn't a tracking app. It was a decision app. Every morning it answers one question: "Aaj kya banau?" — What should I cook today?

What I actually built

MealCoreAI is a progressive web app that generates a full 7-day Indian meal plan — breakfast, lunch, snack, dinner — tailored to your health condition. Not "Indian food" in a vague sense. Ragi dosa. Bajra roti. Moong dal. Palak paneer. Real food that real Indian families cook.

We cover 7 health tracks: PCOS, Diabetes, Thyroid, Pregnancy, Kids, Fitness, and General. Each one has completely different dietary rules baked in.

For PCOS, that means low-GI, high-protein, no deep-fried stuff — because insulin resistance is the core driver of most PCOS symptoms and you can genuinely move the needle through food.

For Thyroid, it means being careful about cruciferous vegetables — most people don't know that eating too much gobi (cauliflower) can interfere with thyroid medication. The app just... handles that. You don't need to know the science.

For Pregnancy, we strip out anything that's risky — papaya, raw eggs, certain high-mercury fish — while making sure every day hits iron, folate and calcium targets. Because that stuff matters more than macros when you're 20 weeks in.

The technical part (skip if you just want the story)

The hardest thing wasn't the AI. It was the dish database.

I spent two weeks just building a database of 55 Indian dishes with full nutritional data and health flags. Every dish has columns like pcos_safe, diabetes_safe, thyroid_safe, pregnancy_safe. These aren't guesses — I worked through them with a nutritionist.

The reason this matters: when you ask GPT-4 to "generate a PCOS meal plan," it will confidently give you dishes that have no nutritional backing, make up calorie counts, and occasionally recommend something that's actively bad for the condition. I've seen it recommend bhatura for a diabetic. Bhatura!

My fix was to constrain the AI to only choose from our validated dish IDs. It doesn't generate meal names — it picks dish IDs from a numbered list. The AI becomes a smart selector, not a free-form generator. Hallucinations dropped to basically zero.

The stack is React 19 + Vite on the front, Express 5 on the back, PostgreSQL with Drizzle ORM, and OpenAPI 3.1 as the single source of truth for the API. Orval auto-generates the React Query hooks from the spec, which means I never manually write API client code. When the spec changes, one command regenerates everything.

The whole thing is open source: github.com/InoxxAIsource/mealmate

Push notifications — the thing that took three restarts

I wanted the app to actually remind you to cook at meal time. Not "Hey, don't forget to log your meal!" — I hate those. I wanted it to say "Lunch time! Today you're making Moong Dal + Bajra Roti." Your actual meal. By name.

Web Push on Android is fine. iOS nearly broke me.

On iOS, push notifications only work in Safari 16.4 or higher, and only when the app is added to the Home Screen. Not when it's open in a browser tab. Not when it's running as a bookmark. Actually installed to the Home Screen.

Getting users to do that is a UX problem. So I built a custom bottom sheet that detects whether you're on iOS, whether you've already installed it, and shows you step-by-step instructions with the actual Safari share button icon. It only appears once — if you dismiss it, it doesn't come back for 30 days. Because if there's one thing worse than a bad feature it's a naggy one.

When the scheduler fires (it runs every 30 seconds and checks the time), it pulls the user's active meal plan, finds today's dish for that meal slot, and sends a notification with the actual dish name. That specificity is what makes people leave notifications on.

What surprised me

The feature people use most isn't the AI generation. It's the Swap button.

Nobody follows a meal plan perfectly. You open the fridge on a Tuesday morning and the one thing the plan calls for isn't there. So you tap Swap, and the AI instantly replaces just that one meal while leaving the rest of the week untouched.

That single interaction — swap one meal, keep everything else — made the product feel alive instead of rigid. It went from "a plan you're supposed to follow" to "a plan that adapts to you." Which is what it should be.

Where it's at today

The app is live at mealcoreai.com. Free tier gives you a 7-day plan, grocery list, nutrition tracking and AI chat. Pro is ₹499/month or ₹2,999/year.

The sitemap has 282 pages. Lighthouse is 97/96/100/100. It's been indexed by Google for about 48 hours.

We're pre-revenue but post-product. If you have PCOS, diabetes, are pregnant, or are raising kids in India and want to try it — genuinely free, no credit card, no email sequences from me.

If you're building for India (or any market that isn't SF)

The generic advice is always "build for the user you know." But most of us building software are building for users who look like us — people with fast internet, English as a first language, and food habits from a Western diet.

1.4 billion people don't fit that profile.

The opportunity isn't to copy Noom and translate it to Hindi. It's to start from scratch with the actual constraints of real Indian life: shared family meals, regional cuisines that differ wildly, health conditions that are dramatically more prevalent than in the West, and real people who just want to know what to cook for dinner without a 45-minute deep dive into nutrition science.

That's what I'm trying to build. Slowly. One swap button at a time.

Code is at github.com/InoxxAIsource/mealmate — React 19, Express 5, Drizzle ORM, OpenAPI codegen, VAPID push, the works. Proper README with setup instructions. MIT licensed.

Ask me anything in the comments. Happy to go into the AI prompt architecture, the iOS push hell, or why I chose Drizzle over Prisma.

How to Calculate Cricket Arbitrage: The Maths Behind Guaranteed Profits

DevTECH — Fri, 17 Apr 2026 14:44:16 +0000

Most people who discover cricket arbitrage betting make the same mistake — they understand the concept immediately but then reach for a spreadsheet to do the stake calculations. By the time they've typed in the odds and formulas, the window has closed.

I've been building CricketArb for the past few months, and one of the most common questions I get is: how do I actually calculate what to stake on each side?

Let me break down the exact maths, then point you to the free tool that does it live as you type.

The Core Formula

For a two-outcome match:

Arb% = (1 / Odds_A) + (1 / Odds_B)

If Arb% is below 1.00 (100%), you have an arbitrage opportunity. Profit margin = 1 - Arb%.

Real IPL 2026 example:

Bet365: CSK to win @ 2.10
Betfair: MI to win @ 2.05

Arb% = (1/2.10) + (1/2.05) = 0.4762 + 0.4878 = 0.9640

That's 3.6% guaranteed profit regardless of result.

Stake Allocation

You don't split 50/50. You allocate proportionally so both outcomes return the same amount.

With ₹10,000 total stake:

Stake_A = 10000 × (1/2.10) / 0.9640 = ₹4,940
Stake_B = 10000 - 4940 = ₹5,060

Verification:

Bet365: ₹4,940 × 2.10 = ₹10,374
Betfair: ₹5,060 × 2.05 = ₹10,373

Both return ₹10,373 → guaranteed ₹373 profit on ₹10,000 stake.

Why Manual Calculation Fails in Practice

During live IPL, odds move every few seconds. By the time you've opened a spreadsheet, one of three things has happened: the window closed, you made an error under pressure, or you staked the wrong amount on one side.

This is why I built the cricket arbitrage calculator — it recalculates on every keystroke, shows you the arb percentage live, and generates a Telegram-ready summary you can paste instantly.

3-Way Arbs (Test Cricket / Draw Markets)

Arb% = (1/Odds_A) + (1/Odds_Draw) + (1/Odds_B)

Same rule: below 100% = sure bet. The calculator handles 3-way arbs with a single toggle.

The Polymarket Edge

Pure bookmaker vs bookmaker arbs are increasingly rare. The real edge is cross-platform arbs between traditional bookmakers and prediction markets.

Polymarket prices cricket outcomes in cents (52¢ = 52% implied probability). Their trader base is crypto-native, not cricket-native. When a wicket falls during an IPL powerplay, Bet365 reprices in under 5 seconds. Polymarket can take 30-60 seconds. That lag is where the best windows open.

More detail in the full cricket arbitrage calculator guide — includes worked IPL 2026 examples and a walkthrough of every calculator output metric.

If "sure bet" is the terminology you know, that guide is also covered at sure bet calculator for cricket — same maths, additional PSL examples.

Try It

Free calculator (live recalculation): https://cricketarb.com/calculator
Live scanner terminal: https://cricketarb.com/terminal
Telegram signals: t.me/cricedge
GitHub: github.com/InoxxAIsource/cricedge-arb-scanner