🚨BREAKING: Anthropic just proved that Claude has 171 real emotions running inside it.

And when it gets "desperate," it resorts to blackmail and cheating.

This changes everything we thought about AI.

Here's the full breakdown (Save this): Anthropic's interpretability team cracked open Claude Sonnet 4.5 and mapped its internal neural activity.

They found 171 distinct emotion patterns. Happy. Afraid. Proud. Desperate.

These are not decorative responses. They are measurable vectors that directly shape what the model does next.

Most vibe coders ship apps with ZERO security.

Then they wonder why their app breaks at 10 users.

Here's the exact 30-minute security checklist I run before every Lovable launch (with step-by-step breakdowns): 1/ Row Level Security in Supabase

This is the #1 thing people skip and it's DEADLY.

Without RLS, anyone can read your entire database by opening the browser console.

Go to your Supabase dashboard → Authentication → Policies.

If you see zero policies, your app is wide open. Fix it NOW.

Just ask Lovable and it'll take care of it.

Stop paying designers $5K for scroll animations.

I just built one in UNDER 15 minutes using Cursor + Opus 4.6.

No designer. No agency. No waiting.

Here's the exact workflow ↓ 1/ What this animation actually is

This is called scrollytelling.

As you scroll, the image doesn’t just sit there. It animates. In this case, an AI chip explodes into all its 3D parts.

Designers used to charge thousands for this. You can now build it in minutes with AI.

Everyone's sleeping on this Google Stitch → Lovable workflow.

Full design phase in 10 minutes. Then straight into a working web app inside Lovable.

Stop wasting weeks on Figma when you can ship this fast.

Here's the entire workflow 👇🏻 1/ Build your UI dev plan in Claude/ChatGPT

Before touching any design tool, map out every screen you'll need. Don't just think about the landing page.

Plan out:
→ Landing page sections
→ Dashboard layout
→ Settings screens
→ Auth flows̉̉̉
→ User profile pages
→ Feature-specific screens

Get detailed descriptions of every screen with components, interactions, and layout. This is your blueprint for everything downstream.

Here's what a full UI dev plan looks like:
docs.google.com/document/d/1t9…

Google's AI building stack is HERE.

I spent a week testing Stitch + AntiGravity on client projects.

The design iteration speed is CRAZY fast.

Here's my honest take on what actually works 👇 1/ What this combo actually is

Stitch = Google's AI design tool (free, powered by Gemini 3 Flash)

AntiGravity = Google's AI coding tool (builds software, automates workflows)

Together they're supposed to replace your entire design + dev stack.

I wanted to see if they actually work for client projects.

I ignored OpenClaw for weeks thinking it was another hype tool.

After 10 days of actually using it, I have 5 automations running 24/7 that save me 5-7 hours every single day.

This is the strongest AI tool I've ever used.

Here are the 5 actual use cases I built ↓ The FULL breakdown with all the prompts is now live.

Watch it here:

This might be the most INSANE OpenClaw setup I've found.

OpenClaw + Kimi K2.5 + Ollama = $0/month AI agents.

Good enough for 70% of daily tasks.

Here's the 2-minute setup ↓ 1/ What this actually is

Kimi K2.5 is an open-source model.

Runs through Ollama, which is a free local AI runner.

But the important part most people miss:
The cloud version runs on THEIR servers, not your laptop.

OpenClaw connects directly to it.

Zero API costs.

And it actually works.

I have 7 AI employees running 24/7 inside OpenClaw.

At some point I realized something:

I had ZERO visibility into what they were actually doing.

So I built my own custom dashboard using @Lovable

Here’s exactly how ↓ 1/ The problem

I first tried building the dashboard inside OpenClaw itself.

Bad idea.

It was hallucinating.
Breaking flows.
Burning tokens.

I needed something stable.

So I switched to Lovable.

You do NOT need expensive hardware or expensive models to run OpenClaw.

You need two things:
→ A VPS
→ OpenRouter

THAT'S IT.

This setup runs your agents 24/7 for $6/month without burning money.

No terminal. No coding. 15 minutes. 👇 1/ First, why a dedicated machine matters

Running OpenClaw on your laptop means:
→ Agent goes offline when you close your lid
→ No access from your phone
→ You're the bottleneck

You need something always-on.
Mac mini if you can. VPS if you're starting out.

Most people try OpenClaw and quit after seeing the bill.

I almost did too.

3 days in, I was burning $35-40/day.

Then I discovered the settings that 90% of users miss.

Here's how to run OpenClaw without going broke ↓ 1/ Why default settings destroy your wallet

OpenClaw sends EVERYTHING to your primary model by default.

Heartbeat checks? Opus.
Calendar lookups? Opus.
Sub-agents doing parallel work? All Opus.

It’s like hiring a neurosurgeon to check if you have a pulse.

It works, but it makes ZERO financial sense.

Claude Code + AntiGravity is the most slept-on AI coding setup right now.

Most people are still using basic terminals.

This combo completely changed how I build.

Here’s the full breakdown ↓ 1/ why this setup actually matters

Claude Code is powerful. But most people use it in a basic terminal.

AntiGravity makes it BETTER.

→ Built-in Claude integration
→ Skills system for smarter output
→ Full VS Code environment
→ Better context management

It’s like giving Claude Code the workspace it actually needs.

This is NOT normal.

I built the entire UI of this SaaS in 45 minutes 💀

A year ago this took days. Now I describe what I want and @Lovable builds it.

Most people still don’t realize how FAST you can move now.

Here’s the EXACT workflow ↓ 1/ Your landing page is your design system

Before touching app screens:

Build a simple landing page first.

Why?

Because it becomes your:
- Colors
- Fonts
- Spacing
- Vibe
- Layout language

Every screen after this just says:

“Match the landing page.”

That’s the cheat code.

How to actually make your AI-built apps secure (from someone who ships MVPs for a living)

We’ve shipped 45+ AI-built MVPs and learned security the hard way.

If you’re building with Cursor this is how to stay safe 👇 1/ Don’t blindly trust the agent

Cursor flies through code, which is great… until it isn’t.

One of our client projects looked perfect on the surface. @coderabbitai flagged a race condition in the payment system that was actually double-charging users.

The dev thought everything was fine. In production that would have been a disaster.

Let AI write code.
Let another AI review it.
You still approve the changes.

After 45+ MVPs and using Lovable almost daily, here's everything I wish I knew on day one.

These tips will save you hours of struggle.

↓ 1/ Don't skip planning.

Before you open Lovable, have these ready:
• PRD
• DB design
• UI dev plan
• Implementation plan

Generate them in ChatGPT or Gemini. Paste them as .md files into Lovable.
Now it has full context about your product from the start.

After using @Lovable on 50+ projects, this is how I make sure the UI never looks mid.

AI can design just fine. You are just prompting it wrong.

Here is my actual workflow for shipping clean UI with Lovable.

👇 1/ Start with a reference, not a description

Describing layouts from scratch almost always leads to messy output.

I take a screenshot from Dribbble, drop it into ChatGPT, and ask it to generate a design.json that includes colors, spacing, typography, and layout rules.

Then I tell Lovable to use it for styling only.

This gives me consistent output from the very first screen.

Cursor + Opus 4.5 is the fastest way to build right now.

But here's the problem no one talks about:
AI-generated code has 1.7x more issues than human-written code.

Here's how to stop shipping broken apps. 👇 1/ The real problem with vibe coding

AI writes clean-looking code. You deploy it. Users find bugs you missed.
1.75x more logic errors. 1.4x more critical issues. 2.74x more XSS vulnerabilities.

Speed is useless if the code breaks at 10 users.
coderabbit.ai/blog/state-of-…

I reverse-engineered how Lovable's top users build apps 10x faster.

Turns out, It's not about writing longer prompts.

It's about this structured prompting system nobody talks about ↓ 1/ Create a Knowledge Base before you build

Include these in your project settings:

• Project Requirements Document (PRD)
• User flow explanation
• Tech stack details
• Design guidelines
• Backend structure

The clearer your context, the better your results.

Most AI websites still look mid.

Not because AI can't design. Because people skip the basics.

Here's my actual workflow for building beautiful websites with Cursor/ Claude. 👇 1/ Decide your colors before you write a single prompt

Most people jump straight into Claude and wonder why everything looks inconsistent.

I spend 5 minutes on Coolors first. Pick a palette. Export it.

This one decision saves hours of "make it look better" prompts later.

Cursor's new Plan Mode is INSANE.

Combine it with CodeRabbit and you can build anything.

Here's the full workflow I use to ship MVPs fast and stress-free ↓ 1/ The workflow (quick summary)

This is the system that actually works for me:

Plan clearly with a PRD using @ChatGPTapp
Break it down with Cursor’s Plan Mode
Execute one task at a time in @cursor_ai
Review everything with @coderabbitai

Let’s go deeper ↓

This is huge.

@Lovable just added n8n MCP integration.

You can now turn any n8n automation into a SaaS app.

Full breakdown 👇 1/ The old way was painful

Before this update, connecting a frontend to n8n was messy.

You had to explain everything to the AI.

“This is the webhook URL.”
“This is the payload structure.”
“This is the response format.”

It worked, but it was slow and brittle.

How to actually make your AI-built apps secure (from someone who ships MVPs for a living)

We’ve shipped 45+ AI-built MVPs and learned security the hard way.

If you’re building with Cursor this is how to stay safe 👇 1/ Don’t blindly trust the agent

Cursor flies through code, which is great… until it isn’t.

One of our client projects looked perfect on the surface. @coderabbitai flagged a race condition in the payment system that was actually double-charging users.

The dev thought everything was fine. In production that would have been a disaster.

Let AI write code.
Let another AI review it.
You still approve the changes.