Your vibe coded app is a ticking time bomb.

UC San Diego studied how pros actually use AI coding tools.

They don't vibe. They control.

Meanwhile: mass produced code nobody can debug, maintain, or explain.

@verdent_ai built the fix. Here's what the research shows: The data is brutal:

→ Developers using AI are 19% SLOWER (while thinking they're faster)
→ Stack Overflow 2025: AI trust crashed from 43% to 33%
→ Pros NEVER let AI handle more than 5-6 steps before validating

The ones getting results aren't prompting and praying.

They're planning first.

R.I.P McKinsey.

You don’t need a $1,200/hr consultant anymore.

You can now run full competitive market analysis using Claude.

Here are the 10 prompts I use instead of hiring consultants: 1/ LITERATURE REVIEW SYNTHESIZER

Prompt:

"Analyze these 20 research papers on [topic]. Create a gap analysis table showing: what's been studied, what's missing, contradictions between studies, and 3 unexplored opportunities."

I fed Claude 47 papers on AI regulation.

It found gaps 3 human researchers missed.

Claude Sonnet 4.5 is the closest thing to an economic cheat code we’ve ever touched but only if you ask it the prompts that make it uncomfortable.

Here are 10 Powerful Claude prompts that will help you build a million dollar business (steal them now): 1. Business Idea Generator

"Suggest 5 business ideas based on my interests: [Your interests]. Make them modern, digital-first, and feasible for a solo founder."

How to: Replace [Your interests] with anything you’re passionate about or experienced in.

After 3 years of using Claude, I can say that it is the technology that has revolutionized my life the most, along with the Internet.

So here are 10 prompts that have transformed my day-to-day life and that could do the same for you: 1. Research

Mega prompt:

You are an expert research analyst. I need comprehensive research on [TOPIC].

Please provide:
1. Key findings from the last 12 months
2. Data and statistics with sources
3. Expert opinions and quotes
4. Emerging trends and predictions
5. Controversial viewpoints or debates
6. Practical implications for [INDUSTRY/AUDIENCE]

Format as an executive brief with clear sections. Include source links for all claims.

Additional context: [YOUR SPECIFIC NEEDS]

How to write prompts for ChatGPT, Claude, and Gemini to get extraordinary output (without losing your mind): Every good prompt has 3 parts:

1. CONTEXT (who you are, what you need)
2. TASK (what you want done)
3. FORMAT (how you want it delivered)

That's it. No 47-step frameworks. No PhD required.

Example:

CONTEXT: "I'm a startup founder pitching investors"
TASK: "Write a 1-minute elevator pitch for [product]"
FORMAT: "Hook + problem + solution + traction. Under 100 words."

You don't need a copywriter.
You don't need a data analyst.
You don't need an SEO specialist.

Claude Skills replaced all 5 freelancers I was paying $4,000-$10,000/month for.

Total cost now? $20/month.

Here's exactly how to set it up (takes 10 minutes): 👇 First, what are Claude Skills and why are they different from regular prompts?

A prompt is a one-time instruction. You explain your brand voice, your format, your preferences. Every. Single. Time.

A Skill is a reusable instruction set you build ONCE. Claude loads it automatically whenever you need that type of work done.

Think of it like hiring a specialist who never forgets your brand guidelines and never sends you an invoice.

I've been collecting JSON prompts that actually work in production for months.

Not the theoretical stuff you see in tutorials.

Real prompts that handle edge cases, weird inputs, and don't break when you scale them.

Here are the 12 that changed how I build with LLMs: 1. SCHEMA-FIRST ENFORCEMENT

Instead of: "Return JSON with name and email"

Use this:

"Return ONLY valid JSON matching this exact schema. No markdown, no explanation, no extra fields:
{
"name": "string (required)",
"email": "string (required, valid email format)"
}

Invalid response = failure. Strict mode."

Why it works: LLMs treat schema as hard constraint, not suggestion. 94% fewer malformed responses in my tests.

"Act as a marketing expert" is weak prompting.

"Act as a marketing expert + data analyst + psychologist" is 10x better.

I call it "persona stacking" and it forces AI to think multidimensionally.

Here are 7 persona combinations that crush single-persona prompts: STACK 1: Content Creation

Personas: Copywriter + Behavioral Psychologist + Data Analyst

Prompt:

"Act as a copywriter who understands behavioral psychology and data-driven content strategy. Write a LinkedIn post about [topic] that triggers curiosity, uses pattern interrupts, and optimizes for engagement metrics."

Result: Content that hooks AND converts.

You can clone anyone's writing voice using Claude Sonnet 4.5 easily.

I've cloned:

- Hemingway
- Paul Graham essays
- My CEO's email style

The accuracy is scary good (validated by blind tests: 94% can't tell).

Here's the 3-step process: Here's why I love this:

- Write emails in your boss's style (approvals go faster)
- Create content that matches your brand voice (consistency)
- Ghost-write for clients (they sound like themselves)
- Study great writers (by reverse-engineering their patterns)

I've saved 20+ hours/week using this.

I don't use one AI model anymore.

I route tasks to the best model for that specific job.

ChatGPT for coding
Claude for writing
Gemini for research
Perplexity for real-time info

This strategy increased my productivity by 4x.

Here's the routing framework: 👇 ChatGPT → The Code Machine

Use it for:

- Writing/debugging code (all languages)
- Complex problem-solving (o1 reasoning)
- Data analysis & visualization
- API integration
- Multi-step technical tasks

Why? Best at structured logic and step-by-step execution.

Claude explains complex topics better than any AI I've tested.

You can use it to learn machine learning, SQL, and statistics and go from zero coding to building ML models in weeks.

Here are 10 Claude prompts that teach you anything faster for free: 1. The Feynman Technique

"Explain [topic] like I'm teaching it to someone else tomorrow. Include:

3 core concepts I must understand
2 common misconceptions to avoid
1 simple analogy to remember it
3 questions to test my understanding"

Claude becomes your study partner.

Everyone's paying $20/month for ChatGPT Plus.

I switched to Gemini 3.0 Pro at $19.99 and got:

• Million-token context window
• Deep research with 100+ sources
• 2TB Google storage included

Here are 10 prompts that make Gemini worth every penny: 1. Deep researcher

When you need to analyze 50+ sources ChatGPT can't handle.

Prompt:

"
You have access to a million-token context window. I need you to research [TOPIC] by:

1. Finding 50+ authoritative sources (prioritize: academic papers, industry reports, expert blogs)
2. Extracting contradictory viewpoints and emerging consensus
3. Identifying gaps in current understanding

Output format:

- Executive Summary (3 key insights)
- Consensus View (what 80% of sources agree on)
- Contrarian Takes (what top 10% believe differently)
- Actionable Implications (what this means for [MY GOAL])

Think like a PhD researcher, not a summarizer. Show me what everyone else is missing.
"

Here's why I use Gemini:

- Million-token window = actually processes all 50+ sources
- Deep research mode = finds sources you didn't know existed
- ChatGPT maxes out at ~10 sources before hallucinating

clawdbot (now moltbot) broke the internet

people are automating insane things with it

10 wild examples 👇 1/ autonomously trade on polymarket

https://x.com/browomo/status/2015881478123479206?s=20

OpenAI and Anthropic engineers leaked these prompt techniques in internal docs.

I've been using insider knowledge from actual AI engineers for 5 months.

These 8 patterns increased my output quality by 200%.

Here's what they don't want you to know: 👇 1. Constitutional AI Prompting

Instead of telling the LLM what TO do, tell it what NOT to do.

Bad: "Write professionally"

Good: "Never use jargon. Never write sentences over 20 words. Never assume technical knowledge."

Anthropic's research shows negative constraints reduce hallucinations by 60%.

After 6 months of testing, Gemini 3.0 is the most underrated AI for financial analysis.

It's completely free and outperforms GPT-5.2 on market research.

Here are 8 prompts for investment research that actually work: 1. Earnings Call Decoder

Prompt:

"Analyze the last 3 earnings calls for [company ticker].

Don't summarize what they said - tell me what they're NOT saying.

Focus on:

1) Questions the CEO dodged or gave vague answers to,
2) Metrics they stopped reporting compared to previous quarters,
3) Language changes - where they went from confident to cautious or vice versa,
4) New talking points that appeared suddenly,
5) Guidance changes and the exact wording they used to frame it. Then connect this to their stock performance in the 2 weeks following each call.

What pattern emerges?"

Gemini can process multiple transcripts simultaneously and catch subtle language shifts. I caught a revenue recognition issue 3 weeks before the stock tanked because the CFO changed how he talked about "bookings." Made 34% shorting it.

After spending $2,000 on prompt engineering courses, I realized they're all teaching outdated techniques.

Here are 6 powerful prompts that actually matter in 2026 (copy & paste into Grok, Claude, or ChatGPT): 1. Deep researcher

Prompt:

"I'm researching [topic]. First, break down this topic into 5 key questions that experts would ask. Then for each question: 1) Provide the mainstream view with specific examples, 2) Identify 2-3 contrarian perspectives that challenge this view, 3) Explain what data or evidence would prove each side right. Finally, synthesize this into a framework I can use to evaluate new information on this topic."

Researchers waste weeks reading scattered sources.

This structures your entire research process upfront. I used this to write a market analysis that landed a $50k client.

How to get consistent AI outputs every single time (you should bookmark this thread): Step 1: Control the Temperature

Most AI interfaces hide this, but you need to set temperature to 0 or 0.1 for consistency.

Via API:

ChatGPT: temperature: 0
Claude: temperature: 0
Gemini: temperature: 0

Via chat interfaces:

ChatGPT Plus: Can't adjust (stuck at ~0.7)
Claude Projects: Uses default (~0.7)
Gemini Advanced: Can't adjust

This is why API users get better consistency. They control what you can't see.

If you're stuck with web interfaces, use the techniques below to force consistency anyway.

I finally understand how LLMs actually work and why most prompts suck.

After reading Anthropic's internal docs + top research papers...

Here are 10 prompting techniques that completely changed my results 👇

(Comment "Guide" and I'll DM Claude Mastery Guide for free) 1/ Assign a Fake Constraint

This sounds illegal but it forces the AI to think creatively instead of giving generic answers.
The constraint creates unexpected connections.

Copy-paste this:

"Explain quantum computing using only kitchen analogies. Every concept must relate to cooking, utensils, or food preparation."

I've hired 3 different AI consulting firms in the last year.

All of them delivered beautiful PowerPoints. None of them shipped working code.

Then I met this team of engineers who just finished building a multi-agent system for an EU government contract. These guys built production AI apps for the Albanian government and large investment firms.

Not demos. Not "proof of concepts."

Actual systems processing real transactions. Handling messy legacy data.

Deployed and running.

I finally cracked how to use LLMs for marketing that actually converts.

After testing 1,000+ campaigns and analyzing what worked...

Here are 10 prompts that completely changed my marketing results: 👇 1. Hyper-Targeted Audience Persona

Copy/paste this prompt with your details and input:

"Build 3 detailed buyer personas for [product/service, e.g., productivity app for freelancers].

Include:

> Demographics (age, job, income)
> Pain points & desires
> Where they hang out online
> Objections to buying
> Exact language they use

Make them realistic and actionable for ad targeting."

Anthropic just mapped the neural architecture that controls whether AI stays helpful or goes completely off the rails.

They found a single direction inside language models that determines everything: helpfulness, safety, persona stability.

It's called "The Assistant Axis."

When models drift away from this axis, they stop being assistants. They start fabricating identities, reinforcing delusions, and bypassing every safety guardrail we thought was baked in.

The fix? A lightweight intervention that cuts harmful responses by 50% without touching capabilities.

Here's the research breakdown (and why this matters for everyone building with AI) 👇 When you talk to ChatGPT or Claude, you're talking to a character.

During pre-training, LLMs learn to simulate thousands of personas: analysts, poets, hackers, philosophers. Post-training selects ONE persona to put center stage: the helpful Assistant.

But here's what nobody understood until now:

What actually anchors the model to that Assistant persona?

And what happens when that anchor slips?