Puranjay Mahapatra 马普杰

On a long journey to find the right parameters to solve life's optimization problem.

[Puranjay Mahapatra 马普杰 @_puranjaym_]

Twilight lanterns beam amber light,
fireflies quietly fade from sight,
wild blooms blanket the dewy grass,
lilies sparkle like spotless glass.

#poem #inspired
Carnation, Lily, Lily Rose

[Dmitry Lyalin @LyalinDotCom]

Power user AI tip!

Suggestion: if you have multiple agents running in the same project, at the same time, making changes, even different models tell them this:

"Other models are working in this project, if anything odd happens or things change that you didnt do, it might be the other agent. use hey.md to talk to each other, never block, figure it out and achieve your goals together. clean up the messages in the file when done"

This works really well.

[Lionel Mora @lionel_mora]

Following the amazing reaction to the Marble Curriculum yesterday, we've decided to make it open source 🛰️👇

Everything a child learns in primary school. 1,590 concepts. 3,221 connections across 8 subjects, from Math and Science to Computing and Life Skills. Anchored in the US and UK curriculums, standard by standard (NGSS, Common Core, DfE).

What you will find in the repo: every concept as structured JSON with its age band and the evidence a child must show to master it. Every prerequisite link marked hard or soft, with a written rationale. It's a true DAG you can compute learning paths on. Open license, you can build whatever you want with it.

Now is a unique time in history to be building in education. Getting AI and kids education right is likely one of the hardest and most important problems to crack over the next decade and we need as many smart and creative minds behind it.

We think a common solid basis, accessible to all and that can be built upon, is critical to move fast. That's why we're making this curriculum open source.

It's not perfect but we know it's a robust basis, and we believe that sharing it openly is the fastest way to progress in this field. If you're building in education, share this around you and tell us in comments if you find this useful and if you want to contribute.

We'll keep working and investing on it @withmarbleapp. Credit goes to @guillaume_boni for building this. I just made it look pretty.

Links below 👇

[Akshay Krishnaswamy @hyperindexed]

Own Your Intelligence.

[Palantir @PalantirTech]

Our expanded thoughts on an institution’s freedom to pursue new opportunities, its economic rights, and its ability to expand them in the age of AI.

Please find attached our white paper, Institutional Sovereignty in the Age of AI, outlining the 15 steps governments and companies must take to protect both their sovereignty and their alpha.

https://t.co/rZuNeOKXU9

[juggernaut @curlysaarthak]

CTO shared this with me on day 1

something everyone should read if they are in an early-stage startup at least once

[Justin Skycak @justinskycak]

The knowledge graph is the main ingredient in our secret sauce that empowers students to learn at breakneck speed.

Here's the rest of the recipe.

Here's the physics of learning, and why almost no one uses it.

* * *

It’s shocking how much we know about how learning happens, all the way down to the mechanics of what’s going on in the brain.

And not just how learning happens, but also, what can be done to improve learning.

There are plenty of learning-enhancing practice strategies that have been tested scientifically, numerous times, and are completely replicable. They might as well be laws of physics.

For instance: we know that actively solving problems produces more learning than passively watching a video/lecture or re-reading notes.

(To be clear: active learning doesn’t mean that students never watch and listen. It just means that students are actively solving problems as soon as possible following a minimum effective dose of initial explanation, and they spend the vast majority of their time actively solving problems.)

Another finding: if you don’t review information, you forget it. You can actually model this precisely, mathematically, using a forgetting curve. I’m not exaggerating when I refer to these things as laws of physics – the only real difference is that we’ve gone up several levels of scale and are dealing with noisier stochastic processes (that also have noisier underlying variables).

* * *

Okay, but aren’t these findings obvious? Yes, but…

Yes, but in education, obvious strategies often aren't put into practice. For instance, plenty of classes that still run on a pure lecture format and don't review previously learned unless it's the day before a test.

Yes, but there are plenty of other findings that replicate just as well but are not so obvious.

Here are some less obvious findings.

-- The spacing effect: more long-term retention occurs when you space out your practice, even if it's the same amount of total practice.

-- A profound consequence of the spacing effect is that the more reviews are completed (with appropriate spacing), the longer the memory will be retained, and the longer one can wait until the next review is needed. This observation gives rise to a systematic method for reviewing previously-learned material called spaced repetition (or distributed practice). A "repetition" is a successful review at the appropriate time.

-- To maximize the amount by which your memory is extended when solving review problems, it's necessary to avoid looking back at reference material unless you are totally stuck and cannot remember how to proceed. This is called the testing effect, also known as the retrieval practice effect: the best way to review material is to test yourself on it, that is, practice retrieving it from memory, unassisted.

-- The testing effect can be combined with spaced repetition to produce an even more potent learning technique known as spaced retrieval practice.

-- During review, it's also best to spread minimal effective doses of practice across various skills. This is known as mixed practice or interleaving -- it's the opposite of "blocked" practice, which involves extensive consecutive repetition of a single skill. Blocked practice can give a false sense of mastery and fluency because it allows students to settle into a robotic rhythm of mindlessly applying one type of solution to one type of problem. Mixed practice, on the other hand, creates a "desirable difficulty" that promotes vastly superior retention and generalization, making it a more effective review strategy.

-- To free up mental processing power, it's critical to practice low-level skills enough that they can be carried out without requiring conscious effort. This is known as automaticity. Think of a basketball player who is running, dribbling, and strategizing all at the same time -- if they had to consciously manage every bounce and every stride, they'd be too overwhelmed to look around and strategize. The same is true in learning.

-- The most effective type of active learning is deliberate practice, which consists of individualized training activities specially chosen to improve specific aspects of a student's performance through repetition (effortful repetition, not mindless repetition) and successive refinement. However, because deliberate practice requires intense effort focused in areas beyond one's repertoire, which tends to be more effortful and less enjoyable, people will tend to avoid it, instead opting to ineffectively practice within their level of comfort (which is never a form of deliberate practice, no matter what activities are performed).

-- Instructional techniques that promote the most learning in experts, promote the least learning in beginners, and vice versa. This is known as the expertise reversal effect. An important consequence is that effective methods of practice for students typically should NOT emulate what experts do in the professional workplace (e.g., working in groups to solve open-ended problems). Beginners (i.e. students) learn most effectively through direct instruction.

* * *

Now, this might seem like a lot of new information -- a common reaction is “Wow, the field of education is experiencing a revolution!”

But here’s the thing:

Most key findings have been known for many decades.

It’s just that they’re not widely known / circulated outside the niche fields of cognitive science & talent development, not even in seemingly adjacent fields like education.

These findings are not taught in school, and typically not even in credentialing programs for teachers themselves – no wonder they’re unheard of!

But if you just do a literature review on Google Scholar, all the research is right there – and it’s been around for many decades.

Naturally, this leads us to the following question:

Why aren't these key findings being leveraged in classrooms? Why do they remain relatively unknown?

Here are a handful of reasons that I’m aware of.

* * *

1. Leveraging them (at all) requires additional effort from both teachers and students.

In some way or another, each strategy increases the intensity of effort required from students and/or instructors, and the extra effort is then converted into an outsized gain in learning.

This theme is so well-documented in the literature that it even has a catchy name: a practice condition that makes the task harder, slowing down the learning process yet improving recall and transfer, is known as a desirable difficulty.

Desirable difficulties make practice more representative of true assessment conditions. Consequently, it is easy for students (and their teachers) to vastly overestimate their knowledge if they do not leverage desirable difficulties during practice, a phenomenon known as the illusion of comprehension.

However, the typical teacher is incentivized to maximize the immediate performance and/or happiness of their students, which biases them against introducing desirable difficulties and incentivizes them to promote illusions of comprehension.

Using desirable difficulties exposes the reality that students didn’t actually learn as much as they (and their teachers) “felt” they did under less effortful conditions. This reality is inconvenient to students and teachers alike; therefore, it is common to simply believe the illusion of learning and avoid activities that might present evidence to the contrary.

* * *

2. Leveraging cognitive learning strategies to their fullest extent requires an inhuman amount of effort from teachers.

Let’s imagine a classroom where these strategies are being used to their fullest extent.

-- Every individual student is fully engaged in productive problem-solving, with immediate feedback (including remedial support when necessary), on the specific types of problems, and in the specific types of settings (e.g., with vs without reference material, blocked vs interleaved, timed vs untimed), that will move the needle the most for their personal learning progress at that specific moment in time.

-- This is happening throughout the entirety of class time, the only exceptions being those brief moments when a student is introduced to a new topic and observes a worked example before jumping into active problem-solving.

Why is this an inhuman amount of work?

-- First of all, it's at best extremely difficult, and at worst (and most commonly) impossible, to find a type of problem that is productive for all students in the class. Even if a teacher chooses a type of problem that is appropriate for what they perceive to be the "class average" knowledge profile, it will typically be too hard for many students and too easy for many others (an unproductive use of time for those students either way).

-- Additionally, to even know the specific problem types that each student needs to work on, the teacher has to separately track each student's progress on each problem type, manage a spaced repetition schedule of when each student needs to review each topic, and continually update each schedule based on the student's performance (which can be incredibly complicated given that each time a student learns or reviews an advanced topic, they're implicitly reviewing many simpler topics, all of whose repetition schedules need to be adjusted as a result, depending on how the student performed). This is an inhuman amount of bookkeeping and computation.

-- Furthermore, even on the rare occasion that a teacher manages to find a type of problem that is productive for all students in the class, different students will require different amounts of practice to master the solution technique. Some students will catch on quickly and be ready to move on to more difficult problems after solving just a couple problems of the given type, while other students will require many more attempts before they are able to solve problems of the given type successfully on their own. Additionally, some students will solve problems quickly while others will require more time.

In the absence of the proper technology, it is impossible for a single human teacher to deliver an optimal learning experience to a classroom of many students with heterogeneous knowledge profiles, who all need to work on different types of problems and receive immediate feedback on each attempt.

* * *

3. Most edtech systems do not actually leverage the above findings.

If you pick any edtech system off the shelf and check whether it leverages each of the cognitive learning strategies I’ve described above, you’ll probably be surprised at how few it actually uses. For instance:

-- Tons of systems don't scaffold their content into bite-sized pieces.

-- Tons of systems allow students to move on to more material despite not demonstrating knowledge of prerequisite material.

-- Tons of systems don't do spaced review. (Moreover, tons of systems don't do ANY review.)

Sometimes a system will appear to leverage some finding, but if you look more closely it turns out that this is actually an illusion that is made possible by cutting corners somewhere less obvious. For instance:

-- Tons of systems offer bite-sized pieces of content, BUT they accomplish this by watering down the content, cherry-picking the simplest cases of each problem type, and skipping lots of content that would reasonably be covered in a standard textbook.

-- Tons of systems make students do prerequisite lessons before moving on to more advanced lessons, BUT they don't actually measure tangible mastery on prerequisite lessons. Simply watching a video and/or attempting some problems is not mastery. The student has to actually be getting problems right, and those problems have to be representative of the content covered in the lesson.

-- Tons of systems claim to help students when they're struggling, BUT the way they do this is by lowering the bar for success on the learning task (e.g., by giving away hints). Really, what the system needs to do is take actions that are most likely to strengthen a student's area of weakness and empower them to clear the bar fully and independently on their next attempt.

Now, I’m not saying that these issues apply to all edtech systems. I do think edtech is the way forward here – optimal teaching is an inhuman amount of work, and technology is needed. Heck, I personally developed all the quantitative software behind one system that properly handles the above challenges. All I’m saying is that you can’t just take these things at face value. Many edtech systems don’t really work from a learning standpoint, just as many psychology findings don’t hold up in replication – but at the same time, some edtech systems do work, shockingly well, just as some cognitive psychology findings do hold up and can be leveraged to massively increase student learning.

* * *

4. Even if you leverage the above findings, you still have to hold students accountable for learning.

Suppose you have the Platonic ideal of an edtech system that leverages all the above cognitive learning strategies to their fullest extent.

Can you just put a student on it and expect them to learn?

Heck no!

That would only work for exceptionally motivated students.

Most students are not motivated to learn the subject material. They need a responsible adult – such as a parent or a teacher – to incentivize them and hold them accountable for their behavior.

I can’t tell you how many times I’ve seen the following situation play out:

-- Adult puts a student on an edtech system.

-- Student goofs off doing other things instead (e.g., watching YouTube).

-- Adult checks in, realizes the student is not accomplishing anything, and asks the student what's going on.

-- Student says that the system is too hard or otherwise doesn't work.

-- Adult might take the student's word at face value. Or, if the adult notices that the student hasn't actually attempted any work and calls them out on it, the scenario repeats with the student putting forth as little effort as possible -- enough to convince the adult that they're trying, but not enough to really make progress.

In these situations, here’s what needs to happen:

-- The adult needs to sit down next to the student and force them to actually put forth the effort required to use the system properly.

-- Once it's established that the student is able to make progress by putting forth sufficient effort, the adult needs to continue holding the student accountable for their daily progress. If the student ever stops making progress, the adult needs to sit down next to the student again and get them back on the rails.

-- To keep the student on the rails without having to sit down next to them all the time, the adult needs to set up an incentive structure. Even little things go a long way, like "if you complete all your work this week then we'll go get ice cream on the weekend," or "no video games tonight until you complete your work." The incentive has to be centered around something that the student actually cares about, whether that be dessert, gaming, movies, books, etc.

Even if an adult puts a student on an edtech system that is truly optimal, if the adult clocks out and stops holding the student accountable for completing their work every day, then of course the overall learning outcome is going to be worse.

[Michele Catasta @pirroh]

https://t.co/3BAeQ795hm

[Séb Krier @sebkrier]

Positive visions of the future are easy to produce, so it's understandable that people dismiss them. I think that's a mistake, and that disempowerment-fatalism is just as easy. If I had to imagine a nice 'post-AGI' vision, it would look like this:

I have to work much less, but still have a chunk of the year working if I want to. There is no 'work day' but there are 'work hours'. Opting out is sustainable for those who don't want it. A Venetian-like economy creates new roles, tasks, and quests that people never imagined could exist (I think full substitution will take much longer to bite than people assume).

The rest of the year is a mix of travelling, helping design a new city somewhere, doing research/writing that is more longitudinal in nature, and making music with new synths designed at some workshop in a different continent (the world of atoms is flourishing, and globalization is so back).

Low goods/services prices let me and my friends coordinate a few impromptu hangouts abroad every now and then - we realize it's much easier to nurture social life when time is not a blocker, and coordination costs are lowered by agents. My family's health issues are managed, giving them more years to live (clinical trials have sped up significantly).

The decreased dependence on living in a large capital city combined with the affordability of building transport infrastructure leads to a flourishing of new towns in places that would otherwise languish; people are excited to build them (yes building is legal again). There are a number of large scale projects to create colonies in space for the more ambitious pioneers, and many highly paid jobs there too.

Coordination tech lets people self-select into hubs, communities, towns that fit their vibes. In fact, the 'age of polarization' is now behind and a lot of governance is pushed down to the local level, which lowers the stakes of national identity fights. Conflicts naturally remain, but the 'boring majority' is no longer faced with having to choose between two flavours of outrage and righteousness.

Government is human-led and agent-mediated. Automated dispute resolution lets human courts deal with more important and significant cases. Citizens can automatically simulate the likely impacts of proposed laws, and agents help demystify them. The machinery of government still has humans, but it's far leaner; there's a neat separation between instrumental tasks (automated) and normative ones (augmented). Politicians are rewarded more, but they're also under automated scrutiny by constituents - e.g. commitments are easier to monitor and enforce.

I could go on! But as I write this, some will rightly say 'well this just sounds like a nice sci-fi story', and tbh they wouldn't be entirely wrong. For each sentence I can find a story why it might not happen. It's still helpful to outline a positive vision of the future to have an idea of what to aim for, but ultimately much of this will come down to resourceful political entrepreneurs, founders of new companies, important changes in legislation and culture and so on.

You don'tt get any of this automatically, it's an endogenous dynamic that depends on how many people exercise their agency. There is of course a degree of determinism to technology, and wider dynamics that cannot be stopped - but people too often point to them as if disempowerment is the only possible outcome. I think that's 'spectator cope', and a lot of shaping will remain possible. Societal change is inherently hard and diffuse, but it's certainly not impossible - too many people despair at being unable to control wide structural changes, but that's missing the trees for the forest, in the most literal sense.

[Elad Gil @eladgil]

Overheard

“My will is stronger then my circumstances”

🔥🔥🔥

[Phil Chen @philhchen]

https://t.co/oMoEAYkqGl

[Bags @0xbags]

“it’s called delusional optimism and it basically ensures that I win at life”

[Sajith Pai @sajithpai]

The framework is here.

TLDR; Founders building in the top right can launch with half-baked products (MVPs) but if you are in the bottom left, then you have to over-engineer to make it Delta2 to 4.

[Nicolas Finet @nifinet]

https://t.co/LDwScMtine

[Gokul Rajaram @gokulr]

Three non obvious things:

1. Find someone who can easily switch between strategy and execution, between the clouds and the dirt, between the board room and the daily standup, between the 60,000 foot view of the business and the lowest level of execution detail. I feel this is a non negotiable that people overlook. You want a doer, not just a talker, someone who hasn’t been lulled into the complacency of becoming a capital e “Executive” but can’t actually drive projects themselves. Whatever you do, don’t compromise on this. Execs are great talkers, so don’t get seduced by this. Drill into multiple levels of detail on their work and see if they understand it and can explain their work at that level.

2. Find someone who can deeply engage with both the product development team and the business / sales team. Yes, it’s likely they will lead one or the other, but they can’t be a single dimensional person who doesn’t understand product or doesn’t have a strong opinion on product. In other words, don’t hire a person who can’t truly engage with product and Eng as an equal. Have them meet 1:1 with key product leaders. Bring them to a couple of product sessions and see how they engage. Have a product centric dialogue with them. If you don’t get inspired or energized with their product vision or thinking, likely pass.

3. Remember that this is a bespoke role. The right near peer for each company / founder is unique and one of a kind. Eric, Sheryl, Keith, Christopher and Emilie are all very different from each other, both in styles of operation and personalities, but all wildly effective in their own way. Make sure your near peer is a fit for YOU and YOUR company. Understand their style of working by speaking with as many coworkers as possible. Make sure they pass the airport test with you. Meet with 5 founders who’ve successfully hired near-peers (and the near-peers themselves) to undestand the hiring process and how they figured out this person was “the one”.

[Prateek Mehta @prateekmehta42]

https://t.co/lp622vBzCt

[Movez @0xMovez]

https://t.co/ZxnvKqrScI

[Puranjay Mahapatra 马普杰 @_puranjaym_]

It’s all in the mind, you can change your mood and ability to get shit done

[Lewis 🏴󠁧󠁢󠁷󠁬󠁳󠁿 @WaldronLewis]

Elon Musk literally sat down for a 45-minute talk with Y Combinator that explains how to build world-changing companies better than any business school on earth. This is the advice he gave a room full of young founders:

1. Don't try to build something great. Try to build something useful.

Everyone obsesses over greatness. Musk says that's the wrong target. "I didn't originally think I would build something great. I wanted to try to build something useful. I didn't think I would build anything particularly great. Seemed unlikely, but I wanted to at least try." Aim for useful first. Greatness, if it comes, is a byproduct.

2. When you can't get in the front door, build your own door.

Before Musk started his first company, he tried to get a job at Netscape. "I sent my resume into Netscape and nobody responded. I tried hanging out in the lobby to see if I could bump into someone, but I was too shy to talk to anyone. So I'm like, this is ridiculous, I'll just write software myself." He didn't set out to be a founder. He became one because no one would hire him.

3. He slept in the office and showered at the YMCA.

The origin of his first company was not glamorous. "We couldn't even afford a place to stay. The office was 500 bucks a month, so we just slept in the office and showered at the YMCA." He couldn't afford proper internet either, so he drilled a hole through the office floor and ran a cable to the internet provider downstairs. That was the founder of the future richest man on earth.

4. Keep the chips on the table.

When Musk sold his first company, he received a $20 million cheque. His bank balance went from $10,000 to $20 million overnight. Most people would have stopped. He put almost all of it straight back into his next company. "I kept the chips on the table." He did the same thing decades later, over and over. He hates money sitting idle. Money is fuel for the next mission.

5. Start with the mission, then work backwards to make it a business.

Musk didn't start SpaceX to make money. He went on the NASA website to find out when humans were going to Mars, and there was no plan. So he decided to build one. "There had been no prior example of a rocket startup succeeding. A small chance of success is better than no chance of success." The mission came first. The business model came later.

6. He started SpaceX expecting to fail.

He is brutally honest about the odds. "SpaceX started in mid-2002 expecting to fail. Probably 90% chance of failing. When recruiting people, I said, we're probably going to die, but small chance we might not die." The first three launches failed. The fourth one worked with no money left. "If the fourth launch hadn't worked, it would have been curtains. We made it by the skin of our teeth."

7. Break every problem down to physics.

This is the core of how Musk thinks. "First principles means break things down to the fundamental elements that are most likely to be true, then reason up from there, as opposed to reasoning by analogy." His example is rockets. Everyone priced them based on what old rockets cost. Musk asked what a rocket is actually made of, priced the raw metals, and found the materials were only 1-2% of the historical price. The rest was inefficiency he could attack.

8. When told something takes 24 months, break it down and do it in six.

Last year xAI needed a giant computer to train its AI. Suppliers said it would take 18 to 24 months. "It's like, well, we need to get that done in six months or we won't be competitive." So he broke it into parts. Needed a building, so he found an old factory. Needed power, so he rented generators. Needed cooling, so he rented a quarter of America's mobile cooling capacity. He slept in the data centre and ran cabling himself. It got done.

9. Watch your ego-to-ability ratio.

Musk's single sharpest piece of advice for young founders is about staying honest with yourself. "A major failure mode is when your ego-to-ability ratio gets too high. Then you break the feedback loop to reality." Keep the ego small, internalise responsibility for everything, and stay ruthlessly connected to what's actually true. "You want to close the loop on reality hard. That's a super big deal."

10. Chase work, not glory.

His closing philosophy ties it all together. "It's so hard to be useful. The area under the curve of total utility is how useful you've been to your fellow human beings times how many people. If you aspire to do true work, your probability of success is much higher. Don't aspire to glory, aspire to work."

He was ridiculed for years. The press called him "internet guy attempting to build a rocket company." He agreed it sounded absurd. He did it anyway, because a small chance of doing something useful beat no chance at all.

Here's the thing though....

Musk became the most followed founder alive because everything he does happens in public. The launches, the failures, the talks like this one. The companies made him powerful. The personal brand made his every word travel around the world before he finishes saying it.

We build massive distribution and grow personal brands on X and beyond without our clients lifting a finger.

If you're a founder or VC looking for that kind of exposure, book a call below.

We average 1.5M views a week.

https://t.co/UoXuYlkBQq

[Dave Kline @dklineii]

You are not rewarded for hard work. You are rewarded for being hard to replace. Outcomes, not effort. True for every employee. Even more acute for managers. Here are 7 skills that make a manager irreplaceable:

[nancy @nancy_y_chen]

I wish more people had access to @NotionHQ's slack channels, they're filled with gems like these that shift my perspective every single day I work here.