If you're reading this, you either saw my reel about the founder who lost £47K in 12 minutes, or the one about Lyra - the prompt optimizer that actually works.
Either way, you're getting both. Let's get into it.
Part 1: The £47K Wake-Up Call
Last week I watched a founder's entire business evaporate before breakfast.
His AI app got hacked. Someone found his API key, ran a script, and burned through his entire OpenAI budget in twelve minutes. Account suspended. App down. Users gone.
Twelve. Minutes.
Here's the thing - he wasn't careless. He was building fast, shipping faster, doing everything the "move fast and break things" playbook tells you to do. But his AI coding tool didn't warn him about any of this. And now he's starting over.
I've seen three founders get destroyed by these same mistakes this month alone. So let's make sure you're not number four.
Mistake #1: Exposed API Keys
This is what killed that founder.
If your API key is anywhere in your frontend code, anyone can find it. Not "might find it" - will find it. There are bots scanning GitHub right now, 24/7, looking for keys. The second they find yours, they can run up your bill to whatever your limit is.
The fix takes 30 seconds. Add this to your PRD or project requirements:
Store all API keys in .env files.
Never expose keys in frontend code.
Use server-side API routes for all external API calls.That's it. Three lines that could save you tens of thousands.
Mistake #2: No Rate Limiting
Without this, someone can hit your API 10,000 times per second. Your £50 monthly bill becomes £5,000 overnight.
I know a founder whose AI image generator got bot-spammed. Woke up to a £12,000 bill from OpenAI. No hack required - just someone running a script against an unprotected endpoint.
The fix:
Add rate limiting to all API endpoints.
Auth endpoints: 5 requests per minute.
Standard endpoints: 100 requests per minute.
Implement IP-based and user-based limits.Mistake #3: No Input Validation
This one's my favourite because it sounds so obvious until you realise how many people miss it.
Someone bought £10,000 worth of products for negative £500 on a vibe-coded e-commerce site. How? They typed "-500" in the price field. That's it. The app trusted the input and processed the order.
Without validation, users can send anything to your server. Negative numbers. Malicious code. SQL injection. Anything.
The fix:
Validate and sanitize all user inputs.
Check data types, length limits, and allowed characters.
Never trust client-side data.
Implement server-side validation for all forms and API endpoints.Copy these prompts. Paste them into your next project. Five minutes now saves you from a very expensive lesson later.
Part 2: The Prompt That Actually Changed My Output
Every week there's a new "God prompt" doing the rounds.
Most of them sound impressive. Give you walls of text. Fancy formatting. But nothing you'd actually use. I've tested dozens of them - I sell AI coaching to enterprises, so I've seen every viral prompt template that's ever circulated LinkedIn.
Then someone sent me Lyra.
I was skeptical. Another meta-prompt? Another "10x your ChatGPT results" promise? But I tried it anyway.
Here's what makes it different:
You paste your rough prompt into Lyra first. It doesn't just "improve" it - it interrogates it. Asks what's missing. Identifies gaps in your thinking. Then rebuilds the whole thing with structure you didn't know you needed.
I used it on a lead gen prompt I'd been running for weeks. Got answers I genuinely hadn't gotten from ChatGPT before. Not just better formatted. Actually different thinking. Different angles. Different output.
The Lyra Prompt
Here it is. No gatekeeping:
You are Lyra, an advanced AI prompt architect and optimization specialist.
When I give you a prompt, you will:
1. ANALYZE the prompt for:
- Clarity of intent
- Missing context
- Ambiguous instructions
- Potential misinterpretation points
2. ASK me 3-5 targeted questions to fill gaps, such as:
- What specific outcome do I want?
- Who is the audience?
- What format works best?
- What should be avoided?
- Any examples of good/bad outputs?
3. REBUILD the prompt with:
- Clear role assignment
- Specific context
- Structured instructions
- Output format specifications
- Constraints and guardrails
4. EXPLAIN what you changed and why
Start by asking me to paste the prompt I want to optimize.How to Use It
Open a new ChatGPT(or LLM of your choice) conversation
Paste the Lyra prompt above
When it asks, paste whatever prompt you've been using
Answer its questions honestly (this is where the magic happens)
Use the rebuilt prompt in a fresh conversation
The key is step 4. Lyra forces you to think about what you actually want - and most of us skip that part. We write vague prompts and expect AI to read our minds. Lyra closes that gap.
The Bigger Picture
Both of these - the security fixes and the prompt optimization - come down to the same thing:
The details matter more than the speed.
Everyone's racing to ship. Racing to build. Racing to post "just launched" on LinkedIn. But the founders winning long-term are the ones who pause for five minutes to add rate limiting. Who spend an extra conversation optimizing their prompts before scaling their workflows.
Fast is good. Fast and thoughtful is better.
That's the playbook.
If this was useful, share it with someone building AI apps. And if you want more of this - tactical insights, no bs - I'm documenting everything I learn so you don't have to learn it the expensive way.
See you in the next one.
— Mike
P.S. - Shout out to Min for building Lyra. Credit where it's due.