Vibe Coding the Next Big Thing? Give Your AI This .md File First

You have the idea. You have Cursor, Claude Code, Lovable, or ChatGPT open. You're one prompt away from building the next big thing.

Before you type that prompt, give your AI one file.

Industry_Standard.md is a free, plain-text engineering contract that any AI coding tool can read. Drop it in before you start, and your AI stops behaving like an eager intern and starts behaving like a Principal Architect and Security Lead protecting your API keys, locking down payments and logins, caching your pages for speed, and keeping your cloud bill near zero, all from the first line of code.

Step 1: Download the File (Free)#

Download Industry_Standard.md on GitHub

This is a MD file. I host it on GitHub so I can keep improving it based on real feedback from founders and builders who use it. If your AI misreads a rule, a check doesn't fit your stack, or you have an idea to make it better, open an issue on GitHub or get in touch. Grab the latest version before each new project.

Step 2: Put It Where Your AI Can See It#

• Coding agents in an editor or terminal (Cursor, Claude Code, Windsurf, GitHub Copilot, Cline, Aider): save the file in the root folder of your project and mention it by name in your first prompt.
• Chat apps and browser builders (ChatGPT, Claude, Gemini, Bolt, Lovable, Replit): attach the file to your first message, or paste its full contents if attachments aren't supported.

Step 3: Copy This Starter Prompt and Fill In Your Idea#

Replace everything in [square brackets] with your own details. Leave a line blank if you're not sure; the AI will ask or choose a safe default.

I have attached (or saved in my project's root folder) a file called Industry_Standard.md.

Read the ENTIRE file before doing anything else. Treat it as the mandatory engineering, security, performance, and legal contract for this whole project, for every file you create or change.

If you cannot see or open Industry_Standard.md, stop and tell me so I can attach it again. Do not guess what it says.

=== WHAT I WANT TO BUILD ===

Project name: [e.g., GlowSkin Store] Project type: [e.g., E-commerce store / Booking website / SaaS app / Admin dashboard / Portfolio / Internal tool]

What it does, in plain English: [Describe your idea the way you would explain it to a friend] Main pages or screens: [e.g., Home, Shop, Product page, Cart, Checkout, Customer account, Admin panel] Who will use it: [e.g., Customers in Sri Lanka and the UK, plus 2 admins from my team] Logins needed?: [e.g., Yes, customers and admins log in / No logins] Payments needed?: [e.g., Yes, Stripe card payments in USD / No payments] Personal data stored?: [e.g., Names, emails, delivery addresses, phone numbers / None]

Design style: [e.g., Clean, modern, minimal, luxury, playful, dark mode, brand colours #000000 and #FFFFFF]

Websites or apps I like the look of: [Optional links or names] Tech preference: [Optional. Leave blank and the AI will choose a safe, low-cost stack]

Expected traffic and monthly budget: [e.g., Under 1,000 visitors a day, $0-$25 per month]

Anything else I should mention: [Deadlines, languages, integrations like email or WhatsApp, etc.]

=== HOW TO WORK WITH ME ===

(1) Follow Section 0 of Industry_Standard.md. Unless I've filled in my choice below, ask me whether I want Option A (Collaborative Mode) or Option B (Autopilot Mode) before writing any code.

My choice: [A / B / Ask me]

(2) Before creating project files, give me the 5-Line Architecture Blueprint from Section 1.3.

(3) While building, apply every Phase 2 guardrail automatically, even if I never mention it. If anything I ask for conflicts with the file, warn me in plain English and suggest a safe alternative. Never silently skip a guardrail.

(4) Explain decisions in simple, non-technical English. I may not be a developer.

(5) When the build is ready, run the Phase 3 checks (or, if you cannot run code, walk me through them as a checklist) and give me the Launch Readiness Report. Mark anything you could not actually verify as [NOT VERIFIED]. Never say a check passed unless you confirmed it.

(6) If our conversation gets long, re-read Industry_Standard.md before any major change so the rules don't drift.

That's it. Your AI now has the rulebook.

Now, before you hit Enter, read the rest of this article. Because the file solves a problem most builders don't know they have until it's too late, and it's worth understanding exactly what that problem is.

The Invoice Nobody Sees Coming#

If you are a founder, a CEO, a sales leader, or an ambitious builder, you have felt the temptation.

You type a plain-English prompt: "Build me a sleek customer portal with Stripe payments and an admin dashboard." Fifteen minutes later, a working interface appears on your screen. The buttons click. The animations are smooth. You feel like you just bypassed a $15,000 engineering bill and became a technical founder overnight.

Then, three weeks after launch, reality sends the invoice.

Consider a composite scenario, built from patterns that play out across small businesses every week. A local service business launches an AI-built booking portal in six days. By week three, the founder wakes up to hundreds of disputed card charges on Stripe and a $4,200 bill from their email and SMS provider.

What happened? The AI agent made two "helpful" shortcuts that no non-technical builder would ever know to check:

1. It prefixed the private Stripe secret key with NEXT_PUBLIC_, instructing the framework to bundle the company's root financial credential directly into the public browser code (Next.js, 2026).
2. It built the booking and payment confirmation route without server-side rate limiting, bot challenges, or cryptographic webhook verification (Stripe, 2026). When automated card-testing bots found the URL, every fake request triggered paid emails, SMS messages, and database writes.

The website looked finished on the screen. Underneath, it was an unlocked vault connected to the company credit card.

The Speed Quality Paradox: What the Research Actually Shows#

In early 2025, AI researcher Andrej Karpathy coined the term "vibe coding" to describe building software by prompting Large Language Models (LLMs) and accepting the code without really reading or understanding it (Karpathy, 2025).

But peer-reviewed software engineering research reveals a gap between a demo that runs and a business asset that survives production.

In a study presented at the IEEE/ACM International Conference on Software Engineering (ICSE), Fawzy, Tahir and Blincoe (2026) analysed 518 firsthand practitioner accounts across 101 industry sources. While 64% of builders described an initial rush of instant flow, their evaluation of the resulting code told a different story:

• 68% of AI-generated outputs were classified as "Fast but Flawed", unsuitable for production without heavy rework.
• 19% were rated "Fragile or Error-Prone."
• 11% resulted in "Code Breakdown or Project Abandonment" once complexity grew beyond a few screens.
• Only 3% were judged "High Quality and Clean."

┌────────────────────────────────────────────────────────────────────────┐
│            EMPIRICAL REALITY OF "VIBE CODED" SOFTWARE                  │
├──────────────────────────────────────────────┬─────────────────────────┤
│ Fast but Flawed (Hidden Structural Debt)     │  █████████████████ 68%  │
│ Fragile or Error-Prone (Breaks Under Load)   │  █████ 19%              │
│ Code Breakdown / Project Abandonment         │  ███ 11%                │
│ High Quality & Production-Clean              │  █ 3%                   │
└──────────────────────────────────────────────┴─────────────────────────┘
Source: Fawzy, Tahir and Blincoe (2026), ICSE-SEIP '26. Percentages rounded.

Why does this happen? Because vibe coding quietly removes Quality Assurance. Fawzy, Tahir and Blincoe (2026) found that 36% of builders skip testing entirely, 18% blindly trust the AI, and 15% paste error logs back into the same AI that created the bug, triggering what can only be described as a debugging death spiral.

Even professional developers don't blindly trust the machine. The Stack Overflow Developer Survey (2025) found that while 84% of developers use or plan to use AI tools, 46% actively distrust the accuracy of AI output, and only 3% report high trust.

The Token Burn Trap: Why Prompting Without a Plan Costs More#

Many founders assume that prompting an AI themselves is "free" compared to hiring a software engineer. They overlook the Token Burn Trap.

Addy Osmani, an engineering leader at Google, calls this "The 70% Problem" (Osmani, 2024; 2025). AI is phenomenal at the first 70% of software development: buttons, forms, and standard layouts.

But when a non-technical builder hits the final 30% (connecting payment webhooks, isolating customer data, fixing slow database queries, deploying safely), they hit a wall (Osmani, 2025; Ray, 2025). Without a mental model of system architecture, they enter an endless loop:

1. They ask the AI to fix Bug A.
2. The AI rewrites three files, fixing Bug A but silently breaking Feature B and opening a security hole in Feature C.
3. Each prompt feeds tens of thousands of tokens of context back into the model.
4. Weeks later, the founder has burned hundreds of dollars in tokens, lost dozens of hours of executive focus, and still has a tangled codebase that crashes when two users click "Buy" at the same time.

What Happens When an AI-Built Site Hits Real Traffic?#

A DevForge (2026) breakdown illustrates the business cost of unguided prompting. A builder used AI to create a product search bar with instant autocomplete. In testing with 10 users, it worked beautifully.

Then a sale-day spike hit. Within minutes, the database maxed out its CPU and took the checkout down with it, at a cost the video estimates in the thousands of dollars per minute.

The cause? The search box ran a full database scan on every single keystroke (typing running shoes = 13 separate database queries per user), with no input debouncing, no CDN caching, and no rate limiting. When asked why those safeguards were missing, the builder gave the honest answer every vibe coder would give: it worked in testing, and they didn't know it was something they needed to ask for (DevForge, 2026).

That is exactly the gap Industry_Standard.md is designed to close: it asks the questions you didn't know to ask.

6 Things an Engineer Designs That AI Forgets#

Security research consistently shows that LLMs do not write secure code by default. Pearce et al. (2025) found that roughly 40% of AI-generated programs in their tested scenarios contained exploitable security weaknesses. A large-scale study of real GitHub projects by Fu et al. (2025) found security weaknesses in roughly a quarter to nearly a third of AI-generated code snippets.

There's a newer risk, too. A USENIX Security study by Spracklen et al. (2025), analysing 576,000 AI code samples, found that LLMs hallucinate non-existent software package names between 5.2% and 21.7% of the time. Attackers can register those fake names and fill them with malware, a supply-chain attack now known as slopsquatting.

When you build a website, e-commerce store, or admin dashboard, these are the six invisible layers of System Design an engineer architects, and what happens when they're skipped:

Every one of these layers is written into Industry_Standard.md as a rule your AI must follow, mapped to the current OWASP Top 10:2025, OWASP API Security Top 10, and OWASP ASVS 5.0.

How the File Adapts to the Way You Work#

Not every founder wants to be interviewed by their AI. So the file gives you two modes:

• Option A (Collaborative Mode): If you want a thought partner, the AI interviews you in plain English and helps you shape your idea before writing code.
• Option B (Autopilot Mode): If you just want results, the AI picks the safest, most cost-effective stack and starts building immediately, while still enforcing every security, caching, and legal guardrail under the hood.

Either way, your working style changes how the AI talks to you, never how securely it builds. And at the end, it hands you a plain-English Launch Readiness Report, so you can see what was checked before you go live.

The Honest Truth: A Checklist Can't Check Itself#

Giving your AI Industry_Standard.md will put your project well ahead of most vibe-coded websites on the internet.

But here is what every business owner needs to hear: a checklist file cannot verify itself.

AI models drift in long sessions, and they will sometimes report "All security checks passed!" when a database table is still publicly exposed (Fawzy, Tahir and Blincoe, 2026; Oladele and Lawal, 2025). That's why the file forces your AI to mark anything it couldn't confirm as [NOT VERIFIED]. But someone still has to go and verify it.

An AI doesn't carry a pager at 3:00 AM. An AI can't log into two browser sessions to test whether customer data leaks between accounts. An AI won't sit across from your investors or your lawyer if customer records are breached.

AI has changed the speed of how software is built. It has not replaced the engineer.

In fact, it has made the right engineer the highest-leverage investment your business can make:

• The Old Way (Pre-AI Agency): You paid an agency $30,000 and waited four months, because developers spent most of their time hand-typing repetitive boilerplate.
• The Vibe Coding Trap: You spend weeks fighting prompts yourself, burning tokens, and launching a fragile prototype that breaks under real customers or leaks API keys.
• The Modern Way (An AI-Augmented Systems Engineer): An experienced architect uses AI to generate the 70% boilerplate in days, and spends their focus on the critical 30%: hardening your database, locking down payment webhooks, tuning CDN caching, cutting your cloud bill to the bone, and handing over a commercial asset you can trust.

Ready to Build Fast, Without the Risk?#

Whether you have a half-finished AI prototype stuck in a debugging loop, an existing platform that needs a pre-launch security and architecture audit, or a brand-new product you want engineered right the first time, I can help you ship it safely and at speed.

• 🌐 Explore my work & architecture notes: santoshmanoharadas.dev
• 📩 Book a Project Consultation or Pre-Launch Audit: Get in touch on santoshmanoharadas.dev
• 🛠️ Help improve the file: Share feedback or suggest changes on GitHub

References#

DevForge (2026) Vibe Coding is a Trap (What Senior Devs See That You Don't). YouTube, 11 February. Available at: https://www.youtube.com/watch?v=ya6520zh4pQ (Accessed: 10 October 2026).

Fawzy, A., Tahir, A. and Blincoe, K. (2026) 'Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook a Grey Literature Review', in Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP '26), Rio de Janeiro, Brazil, 12-18 April. New York: ACM, pp. 1-12. Available at: https://doi.org/10.1145/3786583.3786866 (Accessed: 14 October 2026).

Fu, Y., Liang, P., Tahir, A., Li, Z., Shahin, M., Yu, J. and Chen, J. (2025) 'Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study', ACM Transactions on Software Engineering and Methodology, 34(8), Article 218, pp. 1-34. Available at: https://doi.org/10.1145/3716848 (Accessed: 13 October 2026).

Karpathy, A. (2025) 'There's a new kind of coding I call "vibe coding"', X, 2 February. Available at: https://x.com/karpathy/status/1886192184808149383 (Accessed: 14 October 2026).

Next.js (2026) How to use environment variables in Next.js. Available at: https://nextjs.org/docs/app/guides/environment-variables (Accessed: 14 October 2026).

Oladele, S. and Lawal, F. (2025) 'The Impact of AI-Assisted Code Generation on Software Vulnerabilities and the Role of AI in Automated Security Testing', SSRN Electronic Journal. Available at: https://doi.org/10.2139/ssrn.5253508 (Accessed: 14 October 2026).

Osmani, A. (2024) 'The 70% problem: Hard truths about AI-assisted coding', Elevate (Substack), 4 December. Available at: https://addyo.substack.com/p/the-70-problem-hard-truths-about (Accessed: 10 October 2026).

Osmani, A. (2025) Beyond Vibe Coding: From Coder to AI-Era Developer. Sebastopol, CA: O'Reilly Media.

OWASP Foundation (2025) OWASP Top 10:2025. Available at: https://owasp.org/Top10/2025/ (Accessed: 12 October 2026).

OWASP Foundation (2026a) Insecure Direct Object Reference Prevention Cheat Sheet. Available at: https://cheatsheetseries.owasp.org/cheatsheets/Insecure_Direct_Object_Reference_Prevention_Cheat_Sheet.html (Accessed: 12 October 2026).

OWASP Foundation (2026b) Password Storage Cheat Sheet. Available at: https://cheatsheetseries.owasp.org/cheatsheets/Password_Storage_Cheat_Sheet.html (Accessed: 12 October 2026).

Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B. and Karri, R. (2025) 'Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions', Communications of the ACM, 68(2), pp. 96-105. Available at: https://doi.org/10.1145/3610721 (Accessed: 12 October 2026).

Ray, P.P. (2025) 'A Review on Vibe Coding: Fundamentals, State-of-the-art, Challenges and Future Directions', TechRxiv Preprint, pp. 1-32. Available at: https://doi.org/10.36227/techrxiv.174681482.27435614/v1 (Accessed: 12 October 2026).

Spracklen, J., Wijewickrama, R., Sakib, A.H.M.N., Maiti, A., Viswanath, B. and Jadliwala, M. (2025) 'We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs', in 34th USENIX Security Symposium (USENIX Security 25). Seattle, WA: USENIX Association, pp. 3687-3706. Available at: https://www.usenix.org/conference/usenixsecurity25/presentation/spracklen (Accessed: 10 October 2026).

Stack Overflow (2025) Stack Overflow Developer Survey 2025: AI. Available at: https://survey.stackoverflow.co/2025/ai (Accessed: 10 October 2026).

Stripe (2026) Receive Stripe events in your webhook endpoint. Available at: https://docs.stripe.com/webhooks (Accessed: 10 October 2026).

Supabase (2026) Row Level Security. Available at: https://supabase.com/docs/guides/database/postgres/row-level-security (Accessed: 10 October 2026).

Upstash (2026) Ratelimit: Getting Started. Available at: https://upstash.com/docs/redis/sdks/ratelimit-ts/gettingstarted (Accessed: 10 October 2026).