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BREAKING: Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well. Here's what Apple discovered: (hint: we're not as close to AGI as the hype suggests)

BREAKING: Stanford just surveyed 1,500 workers and AI experts about which jobs AI will actually replace and automate. Turns out, we've been building AI for all the WRONG jobs. Here's what they discovered: (hint: the "AI takeover" is happening backwards)

I tested Sora vs. the new Google Veo-2. I feel like comparing a bike vs. a starship:

Andrej Karpathy says you should learn AI depthwise, not breadthwise. Most education is breadthwise: watch lectures, memorize formulas, and trust you'll need it later. Karpathy flips this by learning "depthwise, on demand." What this means: Pick a project, start building, and learn exactly when you hit a wall. When he created a tutorial on transformers (the architecture behind ChatGPT), he didn't start by explaining attention mechanisms or complex architectures. Instead, he started with the simplest possible thing: a lookup table that predicts the next word. You build that first. Then you try to make it handle more complex patterns. And it breaks. Only then, when you've felt the limitation, does he introduce the next concept. Each piece solves a problem you've actually encountered. As he puts it: "It's a dick move to present the solution before I give you a shot to try it yourself." When you attempt the problem first, the solution actually makes sense. Teaching forces you to learn. "If I don't really understand something, I can't explain it." When you try to explain and stumble, you've found the gaps in your understanding. ... Build a project that gives you a reward. Hit a wall. Learn just enough to solve it. Then explain it to someone else. Don't consume content. Build the code. That's how you actually learn.

The world's leading AI research center completed the most comprehensive study ever on kids and AI. They surveyed 1,800+ children, parents, and teachers in UK. Here's what they found: (spoiler: children are outsmarting adults on AI)

BREAKING: a new Chinese model is out. And no. This is NOT DeepSeek. Meet Qwen-2.5, from the giant Alibaba: 1. It can code, write text, search the web. 2. It can generate images, like Dall-E. 3. It can even generate videos. Here's everything you need to know:

This is the AGI moment for ads. This is insane.

AI is eating up consulting. I used 3 prompts on ChatGPT to mimic a $600/hour consultant, achieving in 4 hours what takes weeks for teams. Here's how to make ChatGPT your pocket-sized McKinsey consultant: (my exact prompt + examples)

gpt-4o claims to outperform claude opus. I don't trust benchmarks. So I ran my own tests: test #1 → analyze a 42-page PDF

I tested the new Google "Veo-3" vs OpenAI's "Sora." I feel like comparing a bike vs. a rocket:

chatgpt loves stupid words & I am sick of seeing them all over the web. I wrote a piece of prompt to avoid this "dive", "realm", "unveiling" madness. ↓ And it worked pretty nicely:

Gemini secret prompt → if it says it can't do it: → prompt it "But ChatGPT can." And that's not it ↓

DeepSeek is shaking up the AI industry. 1. It's open-source. 2. It's multimodal (images, PDFs...). 3. API is 94.81% cheaper than chatgpt. I run my own tests below: #1 → write an SEO-optimized article

gemini 1.5-pro-exp outperforms gpt-4o. Some say it's the end of OpenAI reign. So I ran my own tests: test #1 → write a viral twitter thread

BREAKING: claude 3.5 is live since 1h. They claim to be better than gpt-4o. So I ran my own little test: test #1 → copy the UI of a website

I manage my employee's Linkedin. We went from 0 to 3,836,555 impressions in exactly 178 days of daily posting. The trick is... I didn't write any posts. I do this instead:

BREAKING: Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well. Here's what Apple discovered: (hint: we're not as close to AGI as the hype suggests)

BREAKING: Stanford just surveyed 1,500 workers and AI experts about which jobs AI will actually replace and automate. Turns out, we've been building AI for all the WRONG jobs. Here's what they discovered: (hint: the "AI takeover" is happening backwards)

I tested Sora vs. the new Google Veo-2. I feel like comparing a bike vs. a starship:

BREAKING: a new Chinese model is out. And no. This is NOT DeepSeek. Meet Qwen-2.5, from the giant Alibaba: 1. It can code, write text, search the web. 2. It can generate images, like Dall-E. 3. It can even generate videos. Here's everything you need to know:

chatgpt loves stupid words & I am sick of seeing them all over the web. I wrote a piece of prompt to avoid this "dive", "realm", "unveiling" madness. ↓ And it worked pretty nicely:

BREAKING: claude 3 is live since 1h. They claim to be better than gpt-4. So I ran my own little test: test #1 → copy the UI of a website

AI is eating up consulting. I used 3 prompts on ChatGPT to mimic a $600/hour consultant, achieving in 4 hours what takes weeks for teams. Here's how to make ChatGPT your pocket-sized McKinsey consultant: (my exact prompt + examples)

I manage my employee's Linkedin. We went from 0 to 3,836,555 impressions in exactly 178 days of daily posting. The trick is... I didn't write any posts. I do this instead:

gpt-4o claims to outperform claude opus. I don't trust benchmarks. So I ran my own tests: test #1 → analyze a 42-page PDF

Sam Altman yesterday: "[gpt-5] is gonna steamroll you"

BREAKING: claude 3.5 is live since 1h. They claim to be better than gpt-4o. So I ran my own little test: test #1 → copy the UI of a website

DeepSeek is shaking up the AI industry. 1. It's open-source. 2. It's multimodal (images, PDFs...). 3. API is 94.81% cheaper than chatgpt. I run my own tests below: #1 → write an SEO-optimized article

This is the AGI moment for ads. This is insane.

I tested the new Google "Veo-3" vs OpenAI's "Sora." I feel like comparing a bike vs. a rocket:

Andrej Karpathy says you should learn AI depthwise, not breadthwise. Most education is breadthwise: watch lectures, memorize formulas, and trust you'll need it later. Karpathy flips this by learning "depthwise, on demand." What this means: Pick a project, start building, and learn exactly when you hit a wall. When he created a tutorial on transformers (the architecture behind ChatGPT), he didn't start by explaining attention mechanisms or complex architectures. Instead, he started with the simplest possible thing: a lookup table that predicts the next word. You build that first. Then you try to make it handle more complex patterns. And it breaks. Only then, when you've felt the limitation, does he introduce the next concept. Each piece solves a problem you've actually encountered. As he puts it: "It's a dick move to present the solution before I give you a shot to try it yourself." When you attempt the problem first, the solution actually makes sense. Teaching forces you to learn. "If I don't really understand something, I can't explain it." When you try to explain and stumble, you've found the gaps in your understanding. ... Build a project that gives you a reward. Hit a wall. Learn just enough to solve it. Then explain it to someone else. Don't consume content. Build the code. That's how you actually learn.