Forget boring websites.

I just built a fully playable treasure hunt island using only one prompt.

Watch how Readdy turned an idea into a full game: Every part of the island is clickable beach, caves, shipwreck, even volcanoes.

The Readdy Agent acts as your pirate NPC:

“Ahoy! You found a golden coin!”
“Nothing here, matey try the palm tree!”

It reacts, jokes, and collects leads like a pro.

It’s not just for fun.

Readdy can turn games into growth tools.

Your site can:

- Collect emails
- Chat with visitors in real time
- Schedule calls or demos

All from inside a game-like world.

R.I.P Harvard MBA.

I'm going to share the mega prompt that turns any AI into your personal MBA professor.

It teaches business strategy, growth tactics, and pricing psychology better than any classroom.

Here's the mega prompt you can copy & paste in any LLM ↓ Today, most business education is outdated the moment you learn it.

Markets shift. Competition evolves. Customer behavior changes weekly.

Traditional MBA programs can't keep up. They teach case studies from 2015 while you're building in 2025.

This prompt fixes that.

This is fucking brilliant.

Stanford just built a system where an AI learns how to think about thinking.

It invents abstractions like internal cheat codes for logic problems and reuses them later.

They call it RLAD.

Here's the full breakdown: The idea is brutally simple:

Instead of making LLMs extend their chain-of-thought endlessly,
make them summarize what worked and what didn’t across attempts
then reason using those summaries.

They call those summaries reasoning abstractions.

Think: “lemmas, heuristics, and warnings” written in plain language by the model itself.

Everyone’s chasing “magic prompts.”

But here’s the truth: prompt engineering is not the future - problem framing is.

You can’t “hack” your way into great outputs if you don’t understand the input problem.
The smartest AI teams don’t ask “what’s the best prompt?” - they ask “what exactly are we solving?”

Before typing anything into ChatGPT, do this:

1️⃣ Define the goal - what outcome do you actually want?
2️⃣ Map constraints - time, data, resources, accuracy.
3️⃣ Identify levers - what can you change, what can’t you?
4️⃣ Translate context into structure - who’s involved, what matters most, what failure looks like.
5️⃣ Then prompt - not for an answer, but for exploration.

AI isn’t a genie. It’s a mirror for your thinking.
If your question is shallow, your output will be too. The best “prompt engineers” aren’t writers - they’re problem architects.

They understand psychology, systems, and tradeoffs.

Their secret isn’t phrasing - it’s clarity.
Prompting is the last step, not the first.

Anthropic's internal prompting style is completely different from what most people teach.

I spent 3 weeks analyzing their official prompt library, documentation, and API examples.

Here's every secret I extracted 👇 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.

Matthew McConaughey just asked for something on Joe Rogan that most people don't know they can already do.

He wants an AI trained on his books, interests, and everything he cares about.

Here's how to build your own personal AI using ChatGPT or Claude: ChatGPT: Use Custom GPTs

Go to ChatGPT, click "Explore GPTs," then "Create."

Upload your files: PDFs of books you've read, notes, blog posts you've saved, journal entries, anything text-based.

Give it instructions like: "You are my personal knowledge assistant. Answer questions using only the uploaded materials and my worldview."

If you're new to n8n, this post will save your business.

Every tutorial skips the part where costs spiral out of control.

I've built 30+ AI agents with n8n and tracked every dollar spent.

Here's the brutal truth about costs that nobody talks about: 1. The hidden cost killer: API calls.

Your "simple" customer service agent makes 15+ API calls per conversation:

3 calls to check context
4 calls for intent classification
5 calls for response generation
3 calls for follow-up logic

At $0.002 per call, that's $0.03 per conversation. Sounds cheap until you hit 10k conversations.

Fuck it.

I'm sharing the JSON prompting secrets that saved me from 6 months of broken AI agents.

Most developers are building agents that crash because they can't write a proper JSON prompt.

Here's everything I learned from debugging 500+ agent failures: 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.

Forget Zapier. Forget Notion. Forget Airtable.

The “AI OS” era is here.

Cockpit AI just launched and it’s changing how startups operate.

Imagine automating $200K worth of business intelligence with a single AI OS.

Here’s how it works: Many companies have a big problem:

- Information is in lots of different places
- Workers spend most of their time making reports by hand
- Leaders decide things using old information

OnCockpit fixed this with smart computer.

oncockpit.ai

RIP Tableau.
RIP Excel dashboards.
RIP Canva charts.

Meet Bricks: The AI tool that turns raw data into dashboards in seconds.

Here’s how (its literally mind blowing) 👇 Bricks is your AI data analyst.

Upload your data → get a beautiful, interactive dashboard instantly.

✅ Charts
✅ KPIs
✅ Analysis
✅ Themes
✅ Exports

No more hours formatting in Excel, PowerBI, or Canva.

thebricks.com

Google just dropped a 64-page guide on AI agents that's basically a reality check for everyone building agents right now.

The brutal truth: most agent projects will fail in production. Not because the models aren't good enough, but because nobody's doing the unsexy operational work that actually matters.

While startups are shipping agent demos and "autonomous workflows," Google is introducing AgentOps - their version of MLOps for agents. It's an admission that the current "wire up some prompts and ship it" approach is fundamentally broken.

The guide breaks down agent evaluation into four layers most builders ignore:

- Component testing for deterministic parts
- Trajectory evaluation for reasoning processes
- Outcome evaluation for semantic correctness
- System monitoring for production performance

Most "AI agents" I see barely handle layer one. They're expensive chatbots with function calling, not robust systems.

Google's Agent Development Kit (ADK) comes with full DevOps infrastructure out of the box. Terraform configs, CI/CD pipelines, monitoring dashboards, evaluation frameworks. It's the antithesis of the "move fast and break things" mentality dominating AI development.

The technical depth is solid. Sequential agents for linear workflows, parallel agents for independent tasks, loop agents for iterative processes. These patterns matter when building actual business automation, not just demos.

But there's a gap between Google's enterprise vision and startup reality. Most founders don't need "globally distributed agent fleets with ACID compliance." They need agents that handle customer support without hallucinating.

The security section is sobering. These agents give LLMs access to internal APIs and databases. The attack surface is enormous, and most teams treat security as an afterthought.

Google's strategic bet: the current wave of agent experimentation will create demand for serious infrastructure. They're positioning as the grown-up choice when startups realize their prototypes can't scale.

The real insight isn't technical - it's that if you're building agents without thinking about evaluation frameworks, observability, and operational reliability, you're building toys, not tools.

The agent economy everyone's predicting will only happen when we stop treating agents like chatbots with extra steps and start building them like the distributed systems they actually are. The guide reveals Google's three-path strategy for agent development.

Most teams are randomly picking tools without understanding these architectural choices.

You don’t need VC money.

You don’t need to code.

You just need this toolkit... and a weekend.

Follow the 🧵: 1/ This is a rare window - and it won't last.

Right now, tech is ahead of adoption.

You can build powerful tools without knowing how to code.

Soon, the crowd catches up. This is your early mover edge.

Google just dropped the biggest Chrome update in history.

And 99% of people have no idea it happened.

10 new AI features that turn your browser into an intelligent assistant:

1/ Gemini is now built directly into Chrome. Ask it to explain complex information on any webpage. Compare data across multiple tabs. Summarize research from different sources.

No more copy-pasting between ChatGPT and your browser.

AI can now predict what you're thinking before you say it 🤯

New research from CMU introduces "Social World Models" - AI that doesn't just parse what people say, but predicts what they're thinking, what they'll do next, and how they'll react to your actions.

The breakthrough is S³AP (Social Simulation Analysis Protocol). Instead of feeding AI raw conversations, they structure social interactions like a simulation game - tracking who knows what, who believes what, and what everyone's mental state looks like at each moment.

The results are wild. On theory-of-mind tests, they jumped from 54% to 96% accuracy. But the real magic happens when these models start interacting.

The AI doesn't just respond anymore - it runs mental simulations first. "If I say this, how will they interpret it? What will they think I'm thinking? How does that change what I should actually say?"

This isn't just better chatbots. It's AI that can navigate office politics, understand when someone is lying, predict how a negotiation will unfold. AI that gets the subtext.

The researchers tested this on competitive vs cooperative scenarios. In competitive settings (like bargaining), the social world models helped even more - because modeling your opponent's mental state matters most when interests don't align.

Here's what's unsettling: the AI doesn't need to be the smartest model to build these social representations.

A smaller model can create the "mental maps" that help larger models reason better. Social intelligence might be more about representation than raw compute.

We're not just building AI that understands the world anymore. We're building AI that understands 'us'. The key insight: humans navigate social situations by constantly running mental simulations. "If I say this, they'll think that, so I should actually say this other thing." AI has been missing this predictive layer entirely.

Why the fuck no one is talking about this 🤯

Perplexity Comet AI browser is the most powerful agentic browser in the market.

I've been using it for email automation, writing documents, and doing all the lazy work I hate.

Here are 8 ways I use Perplexity's Comet browser: 1. Shopping

OpenAI just published the first comprehensive study of how 700 million people actually use ChatGPT.

The results destroy every assumption about AI adoption.

Here's what they found: MYTH BUSTED: "ChatGPT is mainly for work"

Reality check: Only 27% of ChatGPT usage is work-related. 73% is personal. And the gap is widening every month.

The productivity revolution narrative completely misses how people actually use AI.

holy sh*t

claude generated a 17-page AI startup opportunities report in the style of McKinsey.

all it took is one simple prompt.

take a look inside 👇 1/ after 7 minutes of research, it gave me an entire presentation on opportunities for AI startups in the next 10 years.

scroll down to see the prompt I used.

executive summary:

99.9% of prompt engineering guides are bullshit.

They overcomplicate everything.

If you have 3 minutes, I'm going to make you an expert prompt engineer.

Comment "Prompt" and I'll DM you my complete guide on prompting engineering.

(Open this thread) Most people suck at prompting because they treat AI like Google.

They type random questions and expect magic. Wrong approach. AI is more like hiring a freelancer.

You need to be clear about what you want, give context, and set expectations. This mental shift changes everything.

the default belief: chatgpt gets better → chatgpt gets more accurate.

but openai’s new paper reveals something different:

• hallucinations are mathematically inevitable
• “i don’t know” is punished more than lying
• fixing it would ruin chatgpt’s entire appeal

the truth will change how you see LLMs forever 🧵: 1/ Why do AIs like ChatGPT hallucinate?

It’s not bad data. It’s not poor training.

It’s how they’re built.

Language models predict one word at a time. That structure creates errors - even if the data is perfect.

I just asked Claude to build a full market analysis Excel model.. and it nailed it.

One prompt. Everything done. Pivot tables. Power Query. Monte Carlo simulations. Competitive landscapes.

Here's the prompt 👇: 1/ I gave Claude this single prompt:

Create a complete market analysis Excel model for the [INSERT INDUSTRY] industry. Conduct deep research, step by step, to find authoratitative data on the requested analyses below.

Build:
- TAM/SAM/SOM analysis 2024-2030
- Competitive landscape (top 20 players, market share evolution)
- Industry P&L benchmarks (margins by company size)
- Value chain analysis with cost breakdowns
- Technology adoption S-curves
- Regional market penetration models
- Unit economics by business model type
- Investment flow tracking (VC/PE/M&A deals)
- Regulatory impact scenarios
Include pivot tables, power query for updates, Monte Carlo simulation for forecasts.
Professional formatting, interactive dashboard. Output .xlsx.

AI that delivers growth, not just posts.

Meet SynthMind: tell it your audience and idea, it scans trends and spits out viral-ready content in minutes. Text, visuals, scripts. Then it can even post and grow for you.

Here’s the proof 👇 Creators and teams waste hours guessing. SynthMind removes the guesswork:

✅ Analyzes your niche across X, IG, TikTok, LinkedIn
✅ Finds winning hooks, angles, ad-style templates
✅ Generates posts, scripts, and visuals that feel native to each platform

Result: content that actually drives growth, not just fills calendars.