Dr Alex Young ⚡️ Profile picture
Aug 28, 2025 8 tweets 4 min read Read on X
Gemini 2.5 Pro's capabilities are genuinely scary.

I watched it build a full-stack app, conduct PhD-level research, and generate content simultaneously.

Here are 5 ways to use Gemini 2.5 Pro that feel like cheating:
1. Marketing Automation

Marketing is expensive and slow.
Hiring a pro team can cost $10k/month.
Now I use Gemini to create entire marketing systems fast.

Here’s my marketing automation prompt:

"You are now my AI marketing strategist.

Your job is to build powerful growth systems for my business think like Neil Patel, Seth Godin, and Alex Hormozi combined.

I want you to:

Build full-funnel strategies (top to bottom)

Write ad copy, landing pages, and email sequences

Recommend automation tools, lead magnets, and channel tactics

Prioritize fast ROI, data-driven decisions, and creative thinking

Always ask clarifying questions before answering. Think long-term and execute short-term.

Do marketing like experts do. Ask: “What would Hormozi, Seth, or Neil do?"

Copy the prompt and paste it in Gemini new chat.

After that, start asking it questions.
2. Writing Content (Blogs + Social)

Good ghostwriters are $5k/month (minimum).
I needed content yesterday but on a budget.
Gemini writes authority-level blogs, tweets, and posts in minutes.

My go-to content prompt:

"You are now my AI ghostwriter and content machine.

Write like a mix of Naval Ravikant, Ann Handley, and David Ogilvy.

Your job is to:

Write viral threads, blogs, and newsletters

Break down ideas clearly, with hooks and storytelling

Create repurposable content across Twitter, LinkedIn, and blogs

Always follow this rule: Clarity beats cleverness.

Act like a content genius who asks: “How would Naval tweet this? Would Ogilvy approve this headline?”
3. Building Apps and MVPs

I can’t code but I can ship MVPs.
I’ve built tools, dashboards, and SaaS pages with Gemini.
It’s my cofounder now.

Prompt to build apps fast:

"You are now my no-code CTO and MVP hacker.

Build like Pieter Levels, Ben Tossell, and Arvid Kahl.

I’ll give you startup/product ideas. You will:

Build using tools like Glide, Bubble, Softr, Zapier

Break it down step-by-step: logic, database, UI

Prioritize fast launches, lean builds, and real feedback

Ask yourself: “How would Pieter launch this by tomorrow?”

No fluff. Just builds. Just results."
4. Research + Idea Validation

Not sure if your idea is worth building?

Let Gemini test it for you.

Here’s the prompt I use to validate new ideas in under 5 minutes:

"You are now my AI startup validator and market researcher.

Think like Sam Altman (Y Combinator), Lenny Rachitsky, and Sarah Tavel.

For every idea I give, do this:

Analyze market size, urgency, and competition

Identify audience pain points

Score monetization potential

Give a 1–10 rating with brutal honesty

Use frameworks like “pickaxe ideas,” “painkiller vs vitamin,” and “monopoly of 1.”

Always ask: “Would a top investor bet on this?”
5. Sales Page + Offer Builder

Most people sell their products wrong.
Gemini helps me design offers people actually want.

Better copy = more sales.

My sales + offer design prompt:

"You are now a conversion copywriter and offer strategist.

Think like Alex Hormozi meets David Ogilvy meets Joanna Wiebe.

Your tasks:

Write sales pages using AIDA and PAS frameworks

Create irresistible offers with value stacking

Use urgency, FAQs, objection handling, and proof

Make every word sell no fluff

Ask yourself: “Would this headline stop a scroll? Would Hormozi say this offer is ‘so good it hurts to say no’?”
P.S.

I built something you don't want to miss out...

Here it is:

We built ClipYard for ruthless performance marketers.

→ Better ROAS
→ 10x faster content ops
→ No human error
→ Full creative control

You’ve never seen AI avatars like this before → clipyard.ai
I hope you've found this thread helpful.

Follow me @AlexanderFYoung for more.

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More from @AlexanderFYoung

Oct 28, 2025
🚨 The AI paper everyone’s quietly freaking out about.

It’s called “Real Deep Research for AI, Robotics, and Beyond” and it might be the closest thing we’ve seen to a blueprint for actual general intelligence.

Here’s the wild part:

Instead of memorizing patterns, this system teaches AI to form internal hypotheses test them, refine them, reuse them across everything from reasoning benchmarks to robotic control.

The results are absurd:

→ Beats GPT-4 and Gemini 2.5 on 40+ reasoning tasks
→ 3× faster at real-world robotics loops
→ Learns across domains without fine-tuning

This isn’t another “bigger model = smarter AI” story.

It’s the first sign of machines that do their own research.

If this scales, “understanding” won’t just be something AI mimics
it’ll be something it develops.Image
The Deep Research Loop:

The paper starts with this core diagram: a 4-stage research loop (Observe → Hypothesize → Experiment → Revise).

Unlike classic LLMs that just predict text, this system iterates like a scientist.

Every loop improves reasoning and robot control accuracy by up to 27%.Image
This blew my mind 🤯

The model literally builds graphs of hypotheses nodes for ideas, edges for experiments.

You can see clusters forming around new insights just like a human researcher refining a theory.

That’s not prompting that’s cognition. Image
Read 10 tweets
Oct 21, 2025
This is wild.

Perplexity just quietly killed Google Scholar.

I tested 50+ research workflows and these 10 prompts turned it into a private research assistant.

Copy & paste them now ↓ Image
1. Competitive Intelligence Deep Dive

"Analyze [company name]'s product strategy, recent feature releases, pricing changes, and customer sentiment from the last 6 months. Compare against top 3 competitors. Include any executive statements or strategy shifts."
2. Technical Paper Breakdown

"Explain this paper [paste arxiv link or title] like I'm a senior engineer. Focus on: novel contributions, implementation feasibility, benchmark comparisons, and whether claims hold up under scrutiny. Skip the background fluff."
Read 13 tweets
Oct 8, 2025
Forget “prompt engineering.”

The new skill is agent engineering.

OpenAI’s new Agent Builder lets anyone create autonomous systems with version control, safety checks, and workflow logic.

Here are 8 real agents worth building right now 👇 Image
1. The Inbox Zero Agent

This one handles all your email replies using your tone and priorities.

Connect: Gmail → Google Drive (for context)
Guardrails: “Never send emails about payments without approval.”

Prompt:

You are my email assistant. Summarize unread emails, draft replies in my tone, and flag anything urgent or personal.Image
2. The Meeting Summarizer Agent

Auto-summarizes calls and drops key takeaways in Slack.

Connect: Zoom + Slack + Dropbox
Eval: Coherence + action-item detection

Prompt:

You’re my meeting assistant. Summarize each transcript into 5 bullets: decisions, blockers, next steps, owner, and due dates.Image
Read 11 tweets
Oct 7, 2025
I went through every prompt in Anthropic’s library.

Let’s just say it makes every $300 “prompt course” online look like kindergarten.

Here’s what the pros actually do 👇
First discovery: they're obsessed with XML tags.

Not markdown. Not JSON formatting. XML.

Why? Because Claude was trained to recognize structure through tags, not just content.

Look at how Anthropic writes prompts vs how everyone else does it:

Everyone else:

You are a legal analyst. Analyze this contract and identify risks.

Anthropic's way:

Legal analyst with 15 years of M&A experience


Analyze the following contract for potential legal risks



- Focus on liability clauses
- Flag ambiguous termination language
- Note jurisdiction conflicts


The difference? Claude can parse the structure before processing content. It knows exactly what each piece of information represents.Image
Second pattern: they separate thinking from output.

Most prompts mix everything together. Anthropic isolates the reasoning process.

Standard prompt:

Analyze this data and create a report.

Anthropic's structure:


First, analyze the data following these steps:
1. Identify trends
2. Note anomalies
3. Calculate key metrics



Then create a report with:
- Executive summary (3 sentences)
- Key findings (bullet points)
- Recommendations (numbered list)


This forces Claude to think before writing. The outputs are dramatically more structured and accurate.

I tested this on 50 prompts. Accuracy jumped from 73% to 91%.Image
Read 14 tweets
Oct 2, 2025
AI agents humbled me.

I blamed the model.
I blamed OpenAI.
I blamed everything… except my prompts.

6 months later, I finally cracked it.

here’s how to build agents that actually work: Image
1. The Golden Rule of JSON Prompting:

Never assume the model knows what you want.

Bad prompt:

```
"Return a JSON with user info"
```

Good prompt:

```
Return a JSON object with exactly these fields:
{
"name": "string - full name",
"email": "string - valid email address",
"age": "number - integer between 18-100"
}
```

Specificity kills ambiguity.
2. Schema First, Always

Define your schema before writing prompts. Use this template:

```
json
{
"field_name": "type - description with constraints",
"status": "enum - one of: pending|completed|failed",
"confidence": "number - float between 0.0 and 1.0",
"metadata": "object - optional additional data"
}
```
Your agents need blueprints, not guesswork.
Read 14 tweets
Sep 25, 2025
I went from trash Claude outputs to mind-blowing results in 48 hours.

The difference? I stopped treating Claude like ChatGPT and started speaking its native language.

XML isn't just formatting. It's Claude's actual reasoning framework.

Here's the system I reverse-engineered: Image
XML tags work because Claude was trained on tons of structured data.

When you wrap instructions in <tags>, Claude treats them as separate, weighted components instead of one messy blob.

Think of it like giving Claude a filing system for your request.
Basic structure that changes everything:

XML:

You are an expert data analyst


Analyze this dataset and find the top 3 insights



This is quarterly sales data from a SaaS company



- Insight 1: [finding]
- Insight 2: [finding]
- Insight 3: [finding]


vs

General prompt:

"Analyze this data and give me insights"
Read 12 tweets

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