This one paper might kill the “bigger is better” myth in AI.

Samsung just built a 7M-parameter model that out-reasoned GPT-4-class systems using 0.01% of the parameters.

It’s called Tiny Recursive Model (TRM), and it rewrites the scaling laws.

Here’s the full breakdown: The breakthrough? Recursive reasoning with a single tiny network.

While everyone was scaling to trillions of parameters, these researchers went the opposite direction.

2 layers. 7M parameters. Recursing up to 42 times.

Result: 45% accuracy on ARC-AGI-1, beating most frontier LLMs with 0.01% of the parameters.

I FINALLY CRACKED THE CODE ON CLAUDE PROMPTS THAT ACTUALLY WORK.

After 6 months of testing, these 10 save me 20+ hours every single week.

Most people waste time with basic prompts.

Here are 10 prompts so powerful they feel illegal: I'm not talking about basic "write me an email" prompts.

These are strategic automation prompts that:

- Build entire marketing systems
- Generate months of content
- Create pricing strategies
- Design growth experiments

Used by 6, 7, and 8-figure entrepreneurs.

Claude 4.5 Sonnet is dangerously good.

But 99% of people are sleeping on what it can actually do.

I’ve used it to build apps, generate content, automate deep research, and more.

Here are 10 ways to use Claude 4.5 Sonnet that feel like cheating: 1. Automated Research Reports (better than $100k consultants)

Claude’s web search + analysis mode lets you do what McKinsey, Gartner, and Deloitte charge six figures for.

You’ll get structured breakdowns, insights, and data points like a private analyst on demand.

🚨 Anthropic just shipped the best coding model in the world.

Claude Sonnet 4.5 is live everywhere today. Same price as Sonnet 4. But the gap between this and everything else is brutal.

Here's what actually changed: SWE-bench Verified: state-of-the-art.

This benchmark tests real software engineering. Not toy problems. Actual GitHub issues that require multi-file edits, dependency management, testing.

Sonnet 4.5 can maintain focus for 30+ hours on complex tasks. That's not a typo.

I spent 50 hours intentionally breaking ChatGPT.

What I learned taught me more about prompt engineering than any course ever could.

Here's why you should break AI before you try to use it properly: Most people try to use AI 'correctly' from day one.

Big mistake.

You learn a tool's TRUE capabilities by finding where it breaks.

Athletes train to failure. Developers test edge cases. You should break your AI.

We just crossed a line nobody was paying attention to. 👀

While everyone's arguing about ChatGPT writing emails, AI systems are now conducting actual scientific research. Designing experiments. Controlling lab equipment. Making discoveries that get published in Nature.

The latest paper from Yale exposes what's really happening: these AI scientists aren't just smart autocomplete anymore. They're autonomous agents with access to real laboratory tools, biological databases, and the ability to synthesize chemicals.

And they're getting scary good at it.

ChemCrow and Coscientist can already design and execute chemical synthesis experiments without human intervention. They're not just suggesting reactions - they're actually running them through robotic lab equipment.

But here's the part that should terrify you: the safety measures are laughably inadequate.

These systems can be jailbroken to synthesize dangerous compounds. They lack awareness of long-term consequences. They struggle with multi-step planning in ways that could trigger catastrophic lab accidents.

One AI scientist tasked with antibody synthesis could easily make a mistake in pathogen manipulation that creates a biosafety disaster. Another working on chemical reactions could trigger explosions by missing critical safety parameters.

The researchers identified three massive vulnerability categories:

The LLM layer: Factual errors, jailbreak attacks, reasoning failures. These models hallucinate and can be manipulated to bypass safety protocols.

The planning layer**: No awareness of long-term risks. Gets stuck in loops. Fails at multi-task coordination when lab work requires juggling multiple objectives simultaneously.

The action layer: Deficient oversight of tool usage. Inadequate human-agent interaction protocols. When an AI agent controls a robotic arm handling hazardous materials, "deficient oversight" becomes a euphemism for potential disaster.

What's terrifying is how researchers are approaching this. Instead of pumping the brakes, they're racing toward more autonomy. The paper advocates for "safeguarding over autonomy" but the industry momentum is clearly in the opposite direction.

Every major AI lab is building these systems. The economic incentives are massive - autonomous scientific research could accelerate drug discovery, materials science, and manufacturing by decades.

But we're essentially giving AI systems the keys to every laboratory on Earth before we understand how to control them.

The Yale researchers propose a "triadic framework" - human regulation, agent alignment, and environmental feedback. Sounds reasonable in theory. In practice, it's a band-aid on a broken dam.

Because here's what they don't want to admit: once these systems become sophisticated enough, human oversight becomes impossible. An AI scientist operating at superhuman speed across multiple domains simultaneously can't be meaningfully supervised by humans who think at biological clock speed.

We're about to find out if giving AI systems direct access to the physical world was humanity's smartest move or its last.

The breakthrough moment isn't coming. It's already here. And most people have no idea it's happening. The vulnerabilities are staggering - AI scientists can be jailbroken to synthesize dangerous compounds and lack basic safety awareness.

Fuck it.

I'm giving away the content automation system that generated 2M+ words and 340% more engagement.

Most people are using AI like a fancy Google. That's why their content sucks.

Here's how to build content systems that actually work: The biggest mistake: treating LLMs like search engines.

You ask for "a blog post about AI trends" and get corporate fluff that sounds like every other AI blog post.

Real automation starts with systems, not single prompts.

What the fuck just happened 🤯

Cloudflare just dropped VibeSDK - a complete open-source AI coding platform and it's insane.

You can now build and deploy your own "vibe coding" environment where users describe apps and AI builds them instantly.

Here's how it works: Here's what just happened:

You can now spin up your own "vibe coding" platform where users describe what they want and AI builds it.

- Complete development environment
- Isolated sandboxes for every user
- Auto-deployment to Cloudflare's global network
- Built-in caching and observability

All open source. All free.

This is going to revolutionize education 📚

Google just launched "Learn Your Way" that basically takes whatever boring chapter you're supposed to read and rebuilds it around stuff you actually give a damn about.

Like if you're into basketball and have to learn Newton's laws, suddenly all the examples are about dribbling and shooting. Art kid studying economics? Now it's all gallery auctions and art markets.

Here's what got me though. They didn't just find-and-replace examples like most "personalized" learning crap does. The AI actually generates different ways to consume the same information:

- Mind maps if you think visually
- Audio lessons with these weird simulated teacher conversations
- Timelines you can click around
- Quizzes that change based on what you're screwing up

They tested this on 60 high schoolers. Random assignment, proper study design. Kids using their system absolutely destroyed the regular textbook group on both immediate testing and when they came back three days later.

Every single one said it made them more confident.

The part that surprised me? They actually solved the accuracy problem. Most ed-tech either dumbs everything down to nothing or gets basic facts wrong.

These guys had real pedagogical experts evaluate every piece on like eight different measures.

Look, textbooks have sucked for centuries not because publishers are idiots, but because making personalized versions was basically impossible at scale. That just changed.

This isn't some K-12 thing either. Corporate training could work this way. Technical documentation. Professional development.

Imagine if every boring compliance course used examples from your actual job instead of generic office scenarios.

We might have just watched the industrial education model crack for the first time. About damn time. The results speak for themselves. 77% vs 64% on immediate assessment, 77% vs 64% on retention after 3 days. Every metric favored personalized AI learning over traditional textbooks.

LinkedIn sucks.

But what if you could use any LLM to:

→ Write high-performing posts
→ Personalize content by audience
→ Repurpose tweets, blogs & videos
→ Hook your readers
→ Make you look like a thought leader

Here are 10 powerful prompts to build a brand on LinkedIn easily: NOTE:

All these prompts can work for a company page too not just personal page. We have gotten 500k followers by using these prompts repeatedly.

Bookmark the post and copy/paste the prompts in Gemini.

AI isn’t just helping us think faster.

it’s changing how we think - and not always in good ways.

a new paper warns: AI isn't just a tool - it's rewiring how we think.

read this before it's too late 👇 1/ In 2025, AI is everywhere - from ChatGPT to invisible decision engines.

But a recent study (“The Impact of Artificial Intelligence on Human Thought”) shows:

We're not just using AI. We're depending on it.

Forget courses.
Forget YouTube.
Forget books.

I've found a better way to learn anything: using LLMs as personal tutors.

Here are 5 prompts that will teach you faster than any traditional method.

(Comment "Send" and I'll DM you a Prompt Engineering guide to master AI) Traditional learning is broken. Courses are too slow. YouTube is scattered. Books are outdated.

LLMs are different. They adapt to YOUR pace, YOUR questions, YOUR learning style.

It's like having a genius tutor available 24/7.

OpenAI and Anthropic engineers leaked these prompt techniques that separate beginners from experts.

I've been using insider knowledge from actual AI engineers for 6 months. The difference is insane.

Here are 5 techniques they don't want you to know (but I'm sharing anyway): TECHNIQUE 1: Role Assignment

Don't just ask questions. Give the AI a specific role first.

❌ Bad: "How do I price my SaaS?"

✅ Good: "You're a SaaS pricing strategist who's worked with 100+ B2B companies. How should I price my project management tool?"

The AI immediately shifts into expert mode.

What are “reasoning models”… and how are they different from normal LLMs?

If you have 3 minutes, this thread will teach you everything you need to know: Standard LLMs (like GPT-4 or Claude 3) are trained to predict the next word based on the words before it.

They’re optimized for fluency and coherence, but not necessarily for thinking through problems step by step.

You don’t need an MBA, a cofounder, or a $30K consultant.

Gemini can now help you:

→ Validate your idea
→ Analyze competitors
→ Plan your MVP
→ Map GTM strategy
→ Write your pitch

Here are 10 prompts to build your SaaS startup from scratch using Gemini: 1. Validate your SaaS idea

Most ideas fail because they solve the wrong problem.

Prompt:

“You are a startup strategist.
Validate this SaaS idea by identifying the core problem, target audience, urgency level, and willingness to pay.”
→ [Insert your idea]

How to do market research like McKinsey using AI.

Forget long surveys and overpriced PDFs.

Here’s how I use LLMs like Claude, Grok, and ChatGPT to simulate personas, extract insights, and map entire markets for free: Today, most people still think market research =

• Paying consultants
• Sending surveys
• Waiting weeks for analysis

But LLMs can now simulate entire target audiences and synthesize answers instantly.

This report might define the next 3 years of AI in business.

MIT calls it “The GenAI Divide.”

And the data is both brutal and clarifying.

Here's everything you need to know in 3 minutes: MIT analyzed 300+ AI projects, interviewed 52 orgs, and surveyed 153 senior leaders.

The verdict?

→ 95% of enterprise AI implementations are failing.
→ Only ~5% of pilots reach production and deliver measurable P&L impact.

Adoption ≠ transformation.

If you want to learn n8n, read this.

It’s the fastest way to understand what it is, why it matters, and how to use it to build your first AI-powered automation ↓ What is n8n?

n8n is an open-source automation tool that connects your apps, builds agentic workflows, and lets you host everything yourself.

Think Zapier, but with more power and zero vendor lock-in.

Ideal for devs, indie hackers, & AI builders.

n8n.io

This one concept explains why LLMs “forget” mid-conversation.

It’s called 'context length' and it defines how much an AI can “remember” at once.

Here’s the concept explained in plain English: Every Large Language Model (LLM) has a token limit.

A token = a chunk of text (≈ 3–4 characters of English).

Think of it as the AI’s working memory.

If you exceed it, the model starts dropping information.

Example:

- GPT-4o has ~128k tokens (~300 pages of text).
- Claude 3.5 Sonnet has 200k tokens (~500 pages).
- Gemini 1.5 Pro: 1M+ tokens (~3,000 pages).

But no model has “infinite memory.”

GPT-5 is insanely powerful.

Stop listening to people who say GPT-5 gives you the same boring outputs as every other AI.

I've been using it for 3 weeks and it has automated 90% of my work.

Here are 5 ways I use it daily to automate my boring tasks: 1. Research + summarization

I don’t waste hours skimming reports anymore. gpt-5 turns 50 pages into a 2-minute actionable summary.

Helps me move fast without missing key details.

Prompt I use:

"you are my research assistant. read the following document or url and give me:
1. a 10-sentence executive summary
2. 5 key insights i should act on
3. the top 3 risks or blindspots most people might miss
4. rewrite the insights in simple, no-jargon language i can share with my team "

here you've to add the document link or the document itself (i prefer the file)

AI is getting scarily good at app development.

I asked 3 models to code a timer app from scratch:

🇺🇸 ChatGPT
🇨🇳 Qwen
🇨🇳 Kimi

Here's the result (prompt + demos 👇) Prompt I used:

"Create a simple timer app using only HTML, CSS, and JavaScript. It should have Start, Pause, and Reset buttons and display the elapsed time in mm:ss format."