ali

ザクロ どろぼう - Philosophy, optimism, classical music, visual arts, wine, travel, making products with a dash of passion.

[@_rt* @literatura_rte]

Joan Miró (1893-1983)
Femmes et oiseaux
Painted on 27 February 1974

[Phil Hoyeck @PAHoyeck]

The critical thinking-maxxing continues!

[Kirill @kirillk_web3]

"how do you remember everything you read?"

I don't. Claude does.

every article, PDF, and transcript goes into one Obsidian folder. Claude reads it, breaks it into atomic notes, links it to the rest. I just ask.

that's Karpathy's second-brain method. three folders, one file.

full guide below.

[ali @alexmoto]

You are either Picasso or a slob. Let your life decisions prove it to you.

[Founder Tribune @Founder_Tribune]

Marc Andreessen on how a three-person startup with no revenue convinces top talent to turn down Microsoft or Google:

Marc is asked how early founders get their top choices to join a venture that's nothing but an exciting vision.

Stock options are part of it "which they'll tell you those aren't worth anything." But the real answer is the vision itself.

"A friend of mine likes to say the difference between a vision and a hallucination is that other people can see the vision. I think that's actually the core answer to the question."

The best entrepreneurs are exceptional at selling people on their company precisely because they can explain the way the world is going to look in a way that is deeply compelling. Marc points to the famous example:

"Steve Jobs has what he calls the reality distortion field. If you get within 10 feet of Steve Jobs, whatever he says the next 20 minutes, you're going to walk out of there believing. He can say the sky is purple and you'd be like, 'Yep, that makes total sense.' And 4 hours later over dinner, as you're explaining it to your wife or your husband, you're like, 'Well, I don't really know what he meant by that, but it was really compelling at the time.'"

"The best entrepreneurs all tend to have that in common. It's essentially sales, selling to employees. It's an incredibly valuable skill. That plus stock options."

But Marc adds a counterintuitive insight about hiring: the frustration of rejection is actually a good thing.

"You try to talk somebody into joining, they don't come, and you're like, 'Damn it, I wasted a lot of time.' But hiring is also a selection process and it's a self-selection process on the part of the candidate. Of all the people you interview, if you hired them all, it would turn out that a good two-thirds or three-quarters of them you probably shouldn't have hired anyway."

The best companies lean into this by presenting a very stark idea of what they are and what they aren't:

"We are a company where people are expected to work 18-hour days, and if you don't like that, don't come here. Or we are a company where people are expected to go home at 5:00 every day, and if you think that would be frustrating, don't come."

He gives a memorable example from his own portfolio:

"We have a company we've invested in where the whole company does yoga together. So if you like yoga, this is the company for you. If you don't like yoga, don't go there. You're going to be asked to put your feet in positions that you're just going to be completely uncomfortable with. Literally yoga every day. The company's called Asana, which means a yoga pose."

His conclusion:

"A very stark idea is very good because it's polarizing. The best companies tend to be polarizing. If in your hiring process you're turning people off as often as you're turning them on because they're deciding, 'This is clearly not the right fit for me' I think that's a good thing."

[Furiokao @furiokao]

A dead MIT professor watched generations of engineers fall in love with the wrong thing. Neural nets. Genetic algorithms. Bayesian probability. Every year, a new method. Every year, the same blind spot.

Patrick Winston ran the MIT AI Lab for twenty-five years. He died in 2019. He gave it a name: mechanism envy.

Falling in love with a tool before asking what the problem is.

He taught the fix for fifty years. Start with the competence you want to understand. Find a representation that exposes the constraints. Only then pick a method. Without the right representation, you are guessing with expensive machinery.

Wall Street has the same disease. A trader buys a Bloomberg terminal before learning expected value. A founder hires a data team before understanding base rates. A retail investor opens a brokerage app before knowing that the number on the fund brochure is not the number that hits their account.

Quant firms do not pay half a million for methods. They pay for five representations: expected value, base rates, ergodicity, conditional probability, signal versus noise. Five ways of seeing that change every bet before you touch the machinery.

The lecture is free. The five models fit in one article. Almost nobody installs the representations before reaching for the tools.

The representations are free. Installing them is the entire edge.

[Swati Gupta @hrswatigupta]

🚨 Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude.

Taught by the people who built it.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch the session

[Abyzon @Abyzonn]

THE PROFESSOR WHOSE TEXTBOOKS SIT IN OVER 3000 UNIVERSITIES SHOWS IN 30 MINUTES WHY NEWTON'S METHOD HAS SURVIVED 400 YEARS UNCHANGED - AND WHY FOLLOWING A STRAIGHT LINE IS ALMOST ALWAYS GOOD ENOUGH

This is Gilbert Strang, MIT, a lecture on linear approximation and Newton's method. He opens with one sentence - both ideas come from the same place. Delta f over delta x. In one case you know x and want f. In the other case you know f and want x. The formula is the same. The insight is the same. Follow the tangent line instead of the curve.

First example - the square root of 9.06. You cannot compute it exactly in your head. But you know the square root of 9 is exactly 3, and you know the slope of the square root function at 9 is 1/6. Go across 0.06 on the tangent line, go up 0.01, and the answer is 3.01. The error is in the fourth decimal place.

Then Newton's method for the same problem. Set up the equation x squared minus 9.06 equals zero. Start at x equals 3. The function value there is minus 0.06. The slope is 6. Newton's formula gives a correction of 0.01. New guess: 3.01. Square it: 9.0601. The error is 0.0001 - way out in the fourth decimal place.

Then the second iteration of Newton's method. Start at 3.01. The error is 0.0001. The slope is 6.02. The correction is tiny beyond what you can compute in your head. Square the new x and the error has moved to somewhere around the eighth decimal place. Each step of Newton's method roughly doubles the number of correct decimal digits.

Watch the moment he connects linear approximation to Taylor series. The linear approximation of e to the x around zero is 1 plus x. The full Taylor series is 1 plus x plus x squared over 2 plus x cubed over 6 and so on. Linear approximation is just Taylor series stopped after the first two terms. Everything after that is the error of following the straight line instead of the curve.

A numerical methods engineer I know shows this lecture to every new hire before they implement any root-finding algorithm. Said it was the first time Newton's method felt like something you would naturally invent rather than something you memorize.

Free on YouTube, MIT OpenCourseWare, Gilbert Strang at a chalkboard.

bookmark this and watch later - after this lecture every equation you cannot solve exactly will feel like an invitation to follow a tangent line

[Khairallah AL-Awady @eng_khairallah1]

Don't waste 2 years learning to build LLMs like Claude & ChatGPT.

Andrew Ng, the godfather of AI, gave the complete playbook to become an AI agentic engineer in 2026.

• 00:00 - AI agent basics
• 12:12 - AI Agentic workflows & design patterns
• 53:27 - Practical tips for building AI agents
• 1:20:30 - self-improving AI agent loops
• 1:30:19 - multi-agent AI systems

Anthropic pays $750,000/year to engineers who understand the this exact knowledge of LLMs.

Bookmark this & give 2 hours today, no matter what. Then read the article below.

[Jota. @jota_snchez]

Shi Heng Yi es maestro Shaolin de 35ª generación y lleva 30 años entrenando la mente y el cuerpo.

Reveló los 5 obstáculos que te impiden avanzar en la vida.

1) Deseo sensual: persigues lo que te da placer y pierdes el camino.

[Zyphor @Zyphorrl]

HARVARD FILMED A PROFESSOR EXPLAIN IN 48 MINUTES WHAT TOOK MATHEMATICIANS 200 YEARS TO FIGURE OUT - AND SHOWS WHY THE SMARTEST MAN WHO EVER LIVED GOT THE INTUITION COMPLETELY WRONG

This is Joe Blitzstein, Harvard Statistics 110, lecture 4. He has won Harvard's Excellence in Teaching award multiple times, his textbook is used in over 200 universities worldwide, and his course has been taken by over 2 million people across 190 countries. He opens with one claim - conditioning is the soul of statistics. Everything else in the course follows from that.

He starts with De Montmort's matching problem. A deck of n cards labeled 1 through n, flipped one by one. What is the probability at least one card lands in its own position? The exact answer collapses into 1 minus 1 plus 1 over 2 factorial minus 1 over 3 factorial, continuing to n terms - which is exactly the Taylor series for e to the x at minus 1. The probability of no match converges to 1 over e, which is 0.37, no matter how large the deck gets.

Then the Newton-Pepys problem, 1693. At least one six from six dice, at least two sixes from twelve dice, or at least three sixes from eighteen dice - which is most likely? Pepys bet on the third. Wrote to Newton. Newton calculated correctly and showed it was the first, probability 0.665. Then the real punch - Newton's intuitive argument was wrong, and a statistician named Stigler proved it without even understanding what Newton wrote. Newton's argument never used the fact that the dice were fair. So it could not possibly be correct.

Then conditional probability. The definition is a single fraction - probability of A given B equals probability of A and B divided by probability of B. Blitzstein explains it two ways. First, pebble world - learning B occurred simply erases every outcome outside B and renormalizes what remains to sum to 1. Second, frequentist world - repeat the experiment many times, circle every run where B occurred, ask what fraction of those also had A.

Watch the moment he derives Bayes' rule in 10 seconds - divide both sides by P of B, end of proof. Then says controversies about this one line have raged for centuries and an entire field of statistics was built on top of it.

A data scientist I know rewatched this before switching careers into statistics. Said it was the first time probability felt like a system with rules rather than a collection of tricks.

Free on YouTube, Harvard, over 2 million views.

bookmark this and watch later - after this lecture every time you learn something new you will feel yourself updating a probability rather than changing your mind

[Swati Gupta @hrswatigupta]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

In 45 minutes, she breaks down how Anthropic builds agents that remember, learn from their mistakes, and get smarter with every run.

Worth more than any paid course you'll find on building agents.

Watch this and bookmark

[Anatoli Kopadze @AnatoliKopadze]

instead of watching 2 hours of Netflix tonight, watch this Stanford lecture given by Anthropic engineers

it's the clearest explanation I've seen of how AI agents actually work

useful whether you've never touched AI in your life or have been building with it every day

i took the key ideas and turned them into a practical guide, with ready-to-copy prompts

watch it, then read the guide below on how to build a system that improves itself

[Dalai Lama @DalaiLama]

If you concern yourself with taking care of others, there’ll be no room for lies, bullying and cheating. If you’re truthful you can live transparently, which will enable you to establish trust, the basis for making friends. We all tend to be driven by self-interest; the trick is to pursue wise self-interest, which takes other beings into account.

[Alvin Foo @alvinfoo]

In the age of AI, the real competitive advantage is not AI. It’s your brain.

Everyone is talking about prompts, agents, automation, and productivity hacks.

But there’s a danger nobody talks about:

If AI does all the thinking, we slowly lose the ability to think deeply ourselves.

This image is a timely reminder that the future belongs to people who use AI to amplify intelligence, not replace it.

Here’s my modern version for the AI era:

1. Seek novelty
Don’t let algorithms decide everything you consume. Learn a new skill. Read outside your industry. Travel. Have conversations with people who think differently. Novelty creates the raw material for innovation.

2. Challenge yourself
AI can make everything easier. That’s exactly why you should regularly do things that are hard. Write without AI first. Solve problems before asking for an answer. Struggle is where cognitive growth happens.

3. Think creatively
The highest-value ideas often come from connecting unrelated fields. AI is excellent at finding patterns in existing data. Humans are exceptional at imagining possibilities that don’t yet exist.

4. Do some things the hard way
Remember phone numbers. Navigate without GPS occasionally. Draft ideas by hand. Mental resistance training is becoming as important as physical exercise.

5. Network intentionally
Your next breakthrough is more likely to come from a conversation than from another AI tool. Diverse perspectives create insights that no model can generate from your personal data alone.

My biggest takeaway:

AI rewards people who are curious, adaptable, and intellectually disciplined.

The winners of the next decade will not be those who simply know how to use AI.

They will be those who continue to learn faster, think deeper, and connect ideas better than everyone else with AI as their copilot, not their replacement.

Don’t outsource your curiosity. That is the one thing AI cannot automate.