Y Combinator

All 16 from this episode, along the running time →

Our reading — their words are on the recording

0:50
Our reading
Almost everything about starting a startup is the same whatever the technology of the era — microprocessors, AI or the internal combustion engine.
Their wordsMost of starting a startup is the same. Most of starting a startup is always the same, right? In you know, microprocessors or AI or like internal combustion engine, it's always the same stuff.
startups
1:17
Our reading
The complaint that Y Combinator has jumped the shark is permanent rather than observational — it has been made since 2008, and it concedes that YC was once great.
Their wordspeople are always saying that YC has jumped the shark. You know, and like I remember when people started saying this, which is in about like 2008, right? YC is always this thing that was great, but now it's not what it was, right? Cuz they can't say it was always terrible. They have to admit it was once great.
startups
4:23
Our reading
You do not displace Google by attacking it head-on; you wait until the world has changed enough to make its model obsolete, which is what OpenAI did.
Their wordsthe way you make a new Google is not by attacking them head on. You have to wait for things to change so much that their model is is obsolete and that's when you can do it. And that's what Open AI is.
OpenAI Google
5:23
Our reading
A founder needs ambition rather than a sense of duty, because a startup is grueling enough that dutifulness will not carry anyone through it.
Their wordswe knew that it was grueling. And you need you need something to drive you through all those obstacles and honestly being dutiful is not enough.
startups ambition
5:54
Our reading
What actually motivates founders day to day is fear of disaster and of looking like a fool, not the prospect of becoming rich.
Their wordsYou know what actually motivates founders day-to-day? The fear of failure. Even though they're doing this thing that if they succeed they'll be super rich. Um that does motivate them but the thing that motivates them at the at any given moment is the fear of like disaster or looking like a fool, the server crashing, right?
startups
7:12
Our reading
Ambition in founders is mostly inborn, which is why stories of a founder acquiring it during a batch are rare.
Their wordsThose stories are rare because most of this quality is inborn.
startups ambition

Michael KratsiosInside the White House's AI StrategyUS science and technology adviser to the president, director of the White House Office of Science and Technology Policy; previously US chief technology officer and chief operating officer of Scale AI.18 Aug 2026 · 39m · 20 korrentsOn America, government and AI and science

Michael Kratsios: Inside the White House's AI Strategy

Michael Kratsios

Our reading — their words are on the recording

2:08
Our reading
There is no place where a technologist can work on bigger problems than the US government — not even the biggest technology companies deal with problems of that scale.
Their wordsI think there is there is um no place where you can work on bigger problems than the US government. Even at the the biggest tech companies in the world, you're never going to be dealing with problems of this of this scale.
government
4:10
Our reading
Whatever the industry, the thing standing between a technology and its market is usually a government rule, which makes policy the lever that unlocks technologies.
Their wordsno matter what kind of industry you're looking at, there was this government angle where the government's policy actions could be ones that could actually unlock technologies.
government
4:52
Our reading
Technology policy is not decided by one or two people in the White House: the decisions are extraordinarily federated across agencies.
Their wordsthese decisions are extraordinarily federated. There isn't just like one or two people in the White House who decide something.
7:40
Our reading
The United States cannot lead in artificial intelligence with closed models alone: it needs a vibrant open-source ecosystem alongside them.
Their wordsif the US wants to lead in artificial intelligence, we have to have a vibrant closed and open source ecosystem, and that's the only way they can all work together.
America
7:58
Our reading
Washington cannot make good technology policy in a vacuum: without startups and industry saying what they believe, the decisions come out worse.
Their wordsif if DC is in a vacuum isn't hearing anything from the startup ecosystem or even from big tech ecosystem or even from financial services or all these different industries, we can't make the best decisions.
startups America
10:07
Our reading
Rules for a technology that reinvents itself every six months must not draw firm red lines, because firm lines ultimately do not work.
Their wordsthe answer to that really is you don't want to set very firm red lines in the sand because they ultimately don't work.

Susan KareDesigning Icons & Graphics For the Original MacGraphic designer and type designer, who created the original Macintosh icons and typefaces at Apple from 1982 and has since designed interface graphics for NeXT, Microsoft, Facebook and Pinterest.14 Aug 2026 · 51m · 19 korrentsOn design and simplicity

Susan Kare: Designing Icons & Graphics For the Original Mac

Susan Kare

All 19 from this episode, along the running time →

Our reading — their words are on the recording

1:33
Our reading
Design is about making things meaningful and memorable: get the idea right and the form follows from it.
Their wordsalways focus on making things meaningful and memorable. And I think the heart of that is have a good idea and then the form will follow.
design
7:00
Our reading
A computer meant for anyone has to be understandable without a manual, and it has to be friendly.
Their wordsAnd we wanted it to be as understandable as possible, so you wouldn't necessarily need a manual, and that it should be friendly
design
7:31
Our reading
You can be creative within any set of constraints, so long as you understand what they are and get excited about them rather than resent them.
Their wordsusually I think you can be creative in any constraints. You can make some pretty fun art with beach trash while you're walking around. And this you can be creative, but it's good to understand what the constraints are, and then be excited about that as opposed to being upset that you don't have a thousand colors.
design
12:33
Our reading
An interface should say what it means with animation and sound rather than text, because then one version works internationally.
Their wordsbut we tried not to have any any text so that it could be international without having to make a different version.
design
17:20
Our reading
A plain drawing of a pencil stands for writing better than a glossy realistic pen, because the more detail an image carries the more particular the thing it depicts.
Their wordswhich is why I think like a simple drawing of a pencil can stand for writing in some ways better than a chrome pen with a reflection, which is real very fancy, but it's a very particular kind of pen that not everybody might write with.
design
17:38
Our reading
An icon should carry one or two salient details and nothing else.
Their wordsSo, I my bias in icons is the a salient detail or two, but don't lard it with a lot of extra stuff.
design

Chelsea FinnThis is the State of the Art in RoboticsAssistant professor of computer science at Stanford, and co-founder of the robot foundation-model company Physical Intelligence.12 Aug 2026 · 58m · 22 korrentsOn robotics, LLMs and OpenAI

Chelsea Finn: This is the State of the Art in Robotics

Chelsea Finn

Our reading — their words are on the recording

4:06
Our reading
Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful.
Their wordsAnd in all of these applications, the customer is making a decision based off of the recommendation of the AI model more or less. Uh, and this means that if the customer is ultimately like kind of making the decision, this means that if the system makes a mistake, um, that's okay because usually the person can kind of recognize that or or decide what to do even despite that mistake.
AI agents
4:52
Our reading
Physical AI has to make far fewer mistakes than any AI deployed so far, because it is only useful when it acts autonomously on the world.
Their wordsUh and this means that they're going to be far more useful when they're operating fully autonomously. And as a result, this requires us to develop physical AI systems that make far fewer mistakes than the machine learning systems that have been deployed thus far.
robotics
7:18
Our reading
Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data.
Their wordsUm, and while this generally improves the reliability of the model, uh, people eventually get tired and it's hard to get really, really high reliability with a person that's manually tuning this. And so what would be even better is if the AI system itself can iterate on the scenario in which you want it to have higher reliability where it on its own automatically seeks out places where it needs more data, where it needs more supervision.
robotics
8:56
Our reading
Reinforcement learning cannot be scaled for robots the way it was for language models, because every attempt spends real robot hours instead of data-centre compute.
Their wordsNow maybe this isn't completely out of the question but this would be quite challenging uh to do and that's because the calculus is a little bit different. We're not just running compute to optimize for a use case. We're actually running the robot in the real world and using the hardware and attempting the task in the real world.
LLMs robotics reinforcement learning
10:00
Our reading
A robot going down a wrong path is not producing training data; the efficient move is for a human to intervene and show the recovery.
Their wordsand trying to fold two boxes together isn't useful data that will teach the model how to get better at the task. And so that would be kind of wasting a lot of time on the robot attempting to go down the wrong path for solving the problem. And so instead of spending a lot of time trying to do that task, what we'll do is we'll actually have a human intervene and show the robot what to do and how to recover from that situation.
robotics
14:26
Our reading
Whether a robot policy is any good is settled by running it for thirteen hours straight, not by whether it completes the task once.
Their wordskind of going back to this reliability question, we took this policy and we ran it not just once, but we ran it for 13 hours straight. Uh and we basically wanted to evaluate is this policy not only good at making a latte once, but can it do so reliably to the extent that it would be needed to be useful in the real world?
robotics

Peter Steinberger"Fun Is Velocity"Founded PSPDFKit in 2011 and ran it for a decade. Came back from a break to work on AI agents — the OpenClaw project, and OpenAI, joined in February 2026. Writes at steipete.me.10 Aug 2026 · 41m · 22 korrentsOn startups, coding agents and AI agents

Peter Steinberger: "Fun Is Velocity"

Peter Steinberger

Our reading — their words are on the recording

4:55
Our reading
Product-market fit shows up as anger when you tell people they cannot have it yet, not as polite interest.
Their wordsBut each time there was a strong emotion and the best part there were quite a few people that really wanted it and like especially to my nontechnical friends I told them no this is not yet for you and they got mad. So if that's not an indicator to have product market fit I don't know what is.
startups product management
12:48
Our reading
Anything you build can be forked or cloned, but your name cannot — so your personal brand matters more than any single product you will ever start.
Their wordsEverything that everything you can build can be forked or cloned, but your name cannot. So your personal brand is way more important than any single product that you ever will start working on that before you need it.
startups
14:16
Our reading
A correction never travels as far as the scare it corrects: the press said a fifth of the project's skills were malicious, and the measured figure was 0.3 percent.
Their wordsWe did the the numbers and the numbers are more like 0.3%. We scanned all 67,000. We shipped the we shipped the paper. But you know a correction never travels as far as a scare.
journalism
15:19
Our reading
Users like security in the abstract and experience it as breakage: hardening the project made things slower, harder to update, and broke what people depended on.
Their wordsSure, they they like the abstract term of security, but in all practical terms, they updated. I broke something they depended on. I made things slower and I made things more difficult to update.
open source
16:33
Our reading
With agents a new feature is one prompt away, which means the whole cost of a feature has moved to what happens after you ship it.
Their wordsYou know features are the fun part. A new feature is is just a prompt away. The real cost comes after every feature we shipped of course with a configuration option because we didn't want to break everyone's setup.
coding agents
16:56
Our reading
Adding a config option so nobody breaks compounds into permutations no test suite can cover, because it is infinitely harder to evolve software that has users.
Their wordsIf you count all the permutations, you can write all the tests you want. It is impossible to cover all of these and not break things from time to time. It is infinitely harder to evolve software that has users.
software quality

Max HodakAverage Is Not Good EnoughCo-founder and chief executive of Science, a neurotechnology company whose retinal implant restores sight to blind patients; before founding it he spent five years at Neuralink.7 Aug 2026 · 57m · 22 korrentsOn management, startups and hiring

Max Hodak: Average Is Not Good Enough

Max Hodak

Our reading — their words are on the recording

4:25
Our reading
Making a highly paid team wait a week to buy a tool at half price destroys far more value than it saves.
Their wordsthen you also remember that you've hired some very highly paid and talented employees. And are you saying they can't get the tools that they need? That you're spending $100,000 a week. If you wait a week to get a power supply half off, you have certainly dwarfed any possible benefit from getting it.
4:44
Our reading
Approving purchases one at a time is the wrong place to control burn; spending review belongs earlier, in a budget people can make their own trade-offs inside.
Their wordsAnd so what you realize is not only is this very hard to keep burn under control, but also it's just this is the inappropriate place to exercise spending review. Spending review has to come earlier. You have to have some concept of budgeting.
management
6:54
Our reading
If nobody can say what an experiment costs, everyone behaves as though experiments are free — it does not cost dollars, it costs media, and media comes from the fridge.
Their wordsAnd so if nobody knows how much an experiment costs, like every time you like grow up a new cell line or every time we make a new a new probe in in the fab, how much does that loop cost? Nobody knows. Therefore, experiments are free. Um, it doesn't cost dollars. It costs media. And media comes from the fridge.
management
9:11
Our reading
The best companies come out of scenes: a moment and a community that nucleate the founding team, and once that moment passes it is hard to get back.
Their wordsI think the best companies in my experience come from what might be characterized as scenes. There's like a moment that enables a new company to be born and there's a bunch that comes together that really creates this like unique nucleation for the new company.
startups
10:09
Our reading
There is no right hiring process, but there is a wrong one: not having something you do religiously as a company.
Their wordsthere's no one right answer, but this is a thing where you need a really defined process. There is no right answer, but a wrong answer for sure is not having something that you do very religiously as a company.
hiring
11:47
Our reading
Any one person or small group placed at the top of the hiring funnel will bottleneck the whole organisation, so first-round review should be distributed across the company as a vote.
Their wordsAnd so we can distribute the voting across a lot of the company for this initial review, which is essential because if you're doing anything cool, by the time you get a couple years into it, that top of funnel is overwhelming. And if you place any in any small group of employees or any one person in the way as a bottle bottleneck on this, they will absolutely bottleneck the whole rest of the organization.
hiring

Dmitri DolgovCo-CEO of Waymo and a founding member of Google's self-driving car project, which he joined in 2009.3 Aug 2026 · 49m · 22 korrentsOn self-driving cars and reinforcement learning

Waymo Co-CEO Dmitri Dolgov: The Demo Is Only 1% Of The Work

Dmitri Dolgov

Our reading — their words are on the recording

2:11
Our reading
The best moments in physical AI will look like nothing happened at all.
Their wordsThat the best AI moments will look like nothing happened. It's just the task got done safely and smoothly.
3:07
Our reading
"Move fast and break things" is the wrong mantra when you are dealing with atoms; the job is to move fast and ship safely.
Their wordsin Silicon Valley, there's a common mantra to move fast and break things. However, when you're dealing with atoms instead of bits, breaking things is not really okay. So, the thing you have to do is to move fast and ship safely.
4:11
Our reading
In the physical world a model's mistake is measured in human lives rather than tokens, and there is no retry button.
Their wordsIn the physical world, the cost of a mistake can be measured in human lives, not tokens. There's simply not an undo and a retry button.
5:43
Our reading
A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases.
Their wordsGiven the high cost of errors, you need to have a very high level of safety and a very high level of confidence on day one before you deploy your first robot, before you drive your your first autonomous mile.
8:21
Our reading
A working demo is at best one per cent of the work; the nines that follow it are where the real work is.
Their wordsAnd a working demo is 1% at best of the work that you have to do. The many nines of performance, the many nines of reliability that follow, that's where the real work happens.
self-driving cars
10:30
Our reading
Waymo's demo took eighteen months and the product took fifteen years — the gap between the two is the whole job.
Their wordsIt took us about 10 more years to begin providing a service, and then 5 more years to scale to half a million trips per week. So, the demo took 18 months, the product took about 15 years.
self-driving cars

Patrick CollisonIs AI Breaking the Lean Startup Playbook?Co-founder and CEO of Stripe and co-founder of the Arc Institute. Keeps a personal site of reading lists, open questions and notes on scientific progress and how research is funded.31 Jul 2026 · 31m · 20 korrentsOn startups and AI and human skill

Patrick Collison: Is AI Breaking the Lean Startup Playbook?

Patrick Collison

Our reading — their words are on the recording

2:46
Our reading
Knowing something yourself will stay faster than asking a model for it, because a lookup in your own head beats a round trip to an agent.
Their wordsThat's a hell of a lot slower than knowing it in cognitive L1 cache. And you can have way more round trips in your brain than you can, you know, muttering through, you know, super whisper or typing it out or whatever.
AI and human skill
3:23
Our reading
Renouncing your own cognitive ability because the models are good is premature, because companies still pay an enormous premium for it.
Their wordsthere still seems to be an enormous premium on cognitive ability. And so, I wouldn't I I I I think, um, renouncing that before there's evidence that we've saturated, uh, those benefits would be premature.
AI and human skill
4:13
Our reading
Models can prove monumental theorems and still have never written an essay worth reading.
Their wordsI mean, it's very interesting, right? Because these can prove the Jacobian conjecture, you know, whatever. Uh and so clearly they're capable of these monumental feats. Um but somehow I still haven't read the LLM essay that I found super compelling.
7:23
Our reading
Dropping out of college is not a trapdoor and the reputational cost is nil: as far as anyone can tell, nobody has ever cared.
Their wordsYou know, I think a lot of parents think that dropping out is very risky and impune your reputation for the rest of your life and so forth. And as far as I can tell, nobody has ever cared.
education
8:20
Our reading
Startup opportunities are not fleeting; Silicon Valley has reliably had a surfeit of them for decades.
Their wordsYou know, in hindsight, I think that um that was a a poor intuition. Uh it's been pretty robustly and reliably the case over many decades in Silicon Valley has a surfeit of opportunities.
startups Silicon Valley
9:30
Our reading
This is not the last moment to start a company, and the belief that the window is about to close is a recurring millenarian instinct.
Their wordsI mean, obviously aviation was was a pretty big deal, but uh I I I don't think it was sort of quite the um the sociological rewriting that some of the you know excitable proponents at the time imagined. So I am you know it's it's hard to predict anything especially the future but I would I would take the under on this being the last couple of years to get a company going.
startups

Jeff DeanThe 1% Rule for Building in AIChief Scientist at Google DeepMind and Google Research, at Google since 1999, where he co-created MapReduce, Bigtable, TensorFlow and the TPU.30 Jul 2026 · 57m · 22 korrentsOn AI agents, coding agents and energy

Jeff Dean: The 1% Rule for Building in AI

Jeff Dean

Our reading — their words are on the recording

0:55
Our reading
AI models have reached the level of a junior engineer on longer-running, agent-based coding tasks.
Their wordsYeah, I mean I feel like uh the models have been getting a lot better at sort of agent-based longer running coding tasks and it seems pretty clear that they are now actually pretty capable and depending on exactly your definition of of junior engineer it seems pretty spot-on I would say.
coding agents AI and jobs
3:36
Our reading
Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency.
Their wordsYeah, I mean it's a little different, but I think uh you're going to see more and more uh uh high performance and um low energy uh inference hardware systems because I think everyone is now realizing that inference is the key to making you know these agent-based systems be available to more and more people and that latency is really important and that specialization of the hardware is a really key way you can make uh things that are more energy efficient and lower latency than more general purpose uh computational devices like say GPUs or TPUs
energy semiconductors
4:51
Our reading
Agents can already be run for days or weeks on a single hard problem, and almost nobody has internalised that.
Their wordsI mean I think um probably one thing is people don't quite realize how possible it is to have you know agent-based systems that can run not just for an hour or two hours on a problem you care about but for some problem domains and with highly capable models underlying them you can get them to run for days or weeks and do really really complicated tasks
AI agents
7:56
Our reading
A chip that can do nothing but low-precision dense linear algebra is enough for machine learning, even though it cannot run Chrome.
Their wordsAnd um if you build a specialized chip for low precision dense linear algebra and can't do anything else that turns out to be really useful for machine learning inference uh even though it can't run Chrome or Word or whatever.
semiconductors
10:05
Our reading
Good ideas come from asking how you would solve a problem from first principles, rather than staying anchored on how it is solved today.
Their wordsyou know, because sometimes if you just squint at a problem and you think about not necessarily being anchored on exactly how that problem is solved today, but how you would solve it from first principles, you can come up with really good ideas that are, you know, maybe not what other people are thinking about.
11:09
Our reading
The numbers an AI engineer should know by heart are memory bandwidths and the energy cost of a single operation.
Their wordsYeah, I mean I think if you looked at what is important in AI systems these days, you would want to know things like the bandwidth between you know your main memory system on your accelerator to the onchip memory to the um you know the multiplier unit or whatever. You want to know how much energy does it take to do a single multiplier operation.
energy

Alexandr Wang“This is a Once-in-a-Civilization Opportunity”Founder of Scale AI and, since 2025, Chief AI Officer at Meta, where he leads the company’s superintelligence lab.29 Jul 2026 · 32m · 20 korrentsOn AI alignment, startups and ambition

Alexandr Wang: “This is a Once-in-a-Civilization Opportunity”

Alexandr Wang

Our reading — their words are on the recording

6:49
Our reading
The only way to build something that matters is to develop conviction in a set of beliefs nobody else agrees with yet, long before they become consensus.
Their wordsI think the the key thing is you need to develop conviction in a set of beliefs that nobody else um agrees with. Like, I think if you look at all the most successful companies in the world, um they were started at a time long before the sort of like core idea was popular.
startups
7:33
Our reading
A founder who takes their bearings from what everyone around them is saying will end up nowhere; you have to build your own compass for what the future looks like.
Their wordsyou just you can't base your business decisions based on what everyone else is saying around you. Like, if you go too much with the herd, you will get immensely confused and you will end up nowhere. And so you have to develop your own compass of what you think the future is going to look like because everyone else will just confuse you.
startups
9:37
Our reading
The bottleneck in AI is no longer the progress of the models but their diffusion — if the models froze today, the economy would still take decades to absorb what already exists.
Their wordsI think we're at this like in amazing moment in the world where the bottleneck is not the progress of the AI models, the bottleneck is diffusing that through the rest of the world and and helping the world adapt to this amazing technology that already exists. Like I think if the models didn't improve at all from today, there would still be like decades and decades of like total upheaval and change in the economy and how the world operates and and everything around us
10:09
Our reading
This is a once-in-a-civilization opportunity to have a vision and impose it on the world by building something.
Their wordsit's probably a like once in a civilization opportunity to be a dreamer and to have a vision and to have ambition and to impose a view of how the future world should look by building something amazing.
10:50
Our reading
AI has ended the David-versus-Goliath era of startups: a startup that properly embraces agents can now easily outcompete incumbents.
Their wordsAnd now I actually think with the power of agents um and AI broadly speaking, it's much closer to Goliath versus Goliath. Like I think but maybe the startup is like a Mecca Goliath that is like vastly enhanced by the power of agents and AI and you know, the the large companies are the sort of like more traditional Goliath, so to speak. But I think that startups now like if you properly embrace AI agents and um figure out the way to leverage their strengths in the most like ambitious ways, you can easily outcompete incumbents.
startups
11:49
Our reading
Every one of the billions of people on earth will have their own superintelligence, tailored to them, and its point is to expand their agency.
Their wordswe believe that everybody in the world, you know, all the billions of people in the world are going to have a super intelligence that is adapted and tailored to them, that is enables them to accomplish their goals, knows their context, and ultimately is an expander of their own agency.
AI alignment

Sam Altman"Never a Better Time to Do a Startup"Co-founder and CEO of OpenAI; previously president of Y Combinator.28 Jul 2026 · 39m · 20 korrentsOn startups, AI alignment and AI and jobs

Sam Altman: "Never a Better Time to Do a Startup"

Sam Altman

Our reading — their words are on the recording

4:52
Our reading
Startups are the main thing that keeps an economy from going stagnant, because established companies reliably drift towards being bad.
Their wordsI think startups are really like the main thing that keeps the economy from becoming stagnant. Um I think companies do just like drift towards suckiness and startups will continue to be important forever.
startups
5:06
Our reading
Startups matter more the bigger AI turns out to be, because they are what stops the technology's power from pooling in a few companies and models.
Their wordsIn fact, if if we are right that AI is going to be such a big change, startups will be much more important to making sure that the power of this technology gets widely distributed throughout the economy and society and is not just concentrated in a few companies or models.
startups
9:50
Our reading
Startups beat incumbents when the technology is moving fast, costs are falling and cycle times are short — and all three are true right now.
Their wordsI think startups tend to win when um the sort of like technology landscape is moving very quickly. uh when costs are coming down when cycle times are short and all of those things are happening right now.
startups
10:30
Our reading
Great startups do not arrive evenly; they cluster at the moments when the ecosystem shifts and incumbents lose most of their advantage.
Their wordsthe great startups tend to cluster when the ecosystem shifts and incumbents lose a lot of their advantage.
startups
11:31
Our reading
In this era fluency with AI tools beats years of accumulated experience, so seniority is now a liability rather than an edge.
Their wordsbut I would I would bet that this generally will cut against many years of experience in favor of people who have a lot of fluency with the tools.
AI and jobs
12:44
Our reading
The single most valuable thing a founder can find is a belief they can hold with reason that the conventional wisdom says is wrong.
Their wordsis find the things that you can develop like reasonable conviction in that people decide like the conventional wisdom is they're just wrong. And be okay with it taking a long time and having people be like very you know, very frustratingly wrong and dismissive.
startups

Blake SchollHow 50 People Built a Supersonic JetFounder and CEO of Boom Supersonic, the Denver company building the Overture supersonic airliner and its Symphony engine.28 Jul 2026 · 50m · 22 korrentsOn aviation and startups

Blake Scholl: How 50 People Built a Supersonic Jet

Blake Scholl

Our reading — their words are on the recording

0:14
Our reading
Half a century after Apollo and Concorde we can neither reach the moon nor fly faster than sound: the future stopped getting faster.
Their wordsIn 1969, we landed on the moon for the first time and the same year we flew Concord, the faster than the speed of sound airliner. The future was supposed to be faster and better. where we're supposed to look forward to innovation in air and in space. And yet, half a century later, we can't go to the moon and we can't fly faster than the speed of sound.
0:49
Our reading
A defence industry that needs more than two years to build a single missile interceptor can neither build the future nor win a war.
Their wordsIt now takes Lockheed more than two years to build a new Patriot missile interceptor. This is crazy. This is no way to build the future and this is no way to win a war.
war
1:09
Our reading
Airliner design has not progressed in fifty years — the newest jet is no faster and makes the world no more accessible than the one that opened the jet age.
Their wordsBut if we fast forward half a century, this is the 787, their latest airliner, which by the way was launched more than 20 years ago. You can't spot the difference. It doesn't fly any faster. It doesn't make the world any more accessible.
design aviation
2:26
Our reading
When air travel doubles in speed the important changes happen on the ground: Hawaii was not a tourist destination until the jet made it reachable.
Their wordsThose first jetliners truly did double the speed of what had come before them. And was they doubled the speed of air travel, the most important changes were on the ground. Hawaii was simply not a tourist destination before the speed of the jet.
aviation
4:52
Our reading
The incumbent manufacturers will never carry aviation into a supersonic age, because they have stopped building new aeroplanes at all.
Their wordsWell, no. They're not even building new airplanes anymore, let alone innovative new airplanes.
aviation
10:12
Our reading
The way to settle an argument about how quiet supersonic flight must be is to remove the boom entirely, so there is nothing left to argue about.
Their wordsby making the sonic boom disappear entirely, we are able to put aside a question that had long plagued people talking about supersonic flight, which is how quiet is quiet enough? If there's no boom at all, there's nothing to argue about.
aviation

Boris ChernyWe Cut 80% of Claude Code's PromptCreator of Claude Code at Anthropic.27 Jul 2026 · 35m · 20 korrentsOn LLMs, AI agents and coding agents

Boris Cherny: We Cut 80% of Claude Code's Prompt

Boris Cherny

Our reading — their words are on the recording

2:01
Our reading
Prompt injection is no longer a live attack on the frontier model: it simply does not follow instructions it reads on the internet any more.
Their wordsthe model does not seem to be prompt injectable anymore.
prompt injection
3:01
Our reading
Prompt injection can be caught by watching the neurons that fire when it happens, so a defence no longer depends on the model reporting the attack.
Their wordsit's it's literally we're looking at neurons in the model's brain that light up when prompt injection happens. So the model won't even tell you but we can actually see those neurons and we can figure out and diagnose that it's happening. And then you combine that with the auto mode classifier and with these three layers we just cannot demonstrate prompt injection anymore.
prompt injection neural networks
4:01
Our reading
Prompt work does not transfer across model generations: what you wrote three months ago for one model may be worth nothing to the next.
Their wordsAnd the reason is every model is very different. So, something that you did for one model maybe 3 months ago, it just might not translate at all to the next model.
LLMs
5:02
Our reading
A frontier model is measurably more intelligent with no system prompt at all; the prompts that remain are there for the product, not the model.
Their wordsAnd what's interesting is that the model is actually a little bit more intelligent without these prompts.
LLMs
6:55
Our reading
Every six months you should delete your whole AI configuration — instructions, skills and hooks — and find out how much of it the model never needed.
Their wordsfor people that aren't building agentic products, but you're using Claude code, every 6 months delete your Claude MD. Delete your skills. Delete your hooks.
coding agents Anthropic simplicity
7:49
Our reading
You cannot predict which instructions a model needs, so a system prompt has to be discovered by running the thing rather than designed in advance.
Their wordsAnd you don't want to guess what's the instruction that the model needs because you might not predict it correctly. The thing that you want to do is you want to run it.
LLMs

Jensen HuangThe Mindset That Built NVIDIACo-founder and CEO of NVIDIA since 1993.26 Jul 2026 · 49m · 20 korrentsOn semiconductors, startups and management

Jensen Huang: The Mindset That Built NVIDIA

Jensen Huang

Our reading — their words are on the recording

4:23
Our reading
The technology a company is founded on can be exactly wrong and still not matter, so long as the founders confront reality and can learn.
Their wordsthe the big lesson is that for me is technology is changing all the time, and so long as you're able to confront the reality, so long as you are able to learn, the technology itself actually doesn't matter.
startups
6:07
Our reading
The business is not building a great chip; it is accelerating an algorithm domain.
Their wordswe realized early on uh that it's not about building a great chip, it's about accelerating an algorithm domain.
semiconductors
6:17
Our reading
What makes a great company is a unique perspective about the world that its founders deeply believe in — not the technology and not even the market.
Their wordsone of the things I've always believed believed in is what makes great companies is a unique perspective about the world that you deeply believe in. It's not so much the technology, it's not so much uh the market even.
startups
9:12
Our reading
You do not invest in companies, you invest in people — which is why an honest founder who cannot deliver still gets kept alive.
Their wordsYou don't invest in companies, you invest in people.
venture capital
11:26
Our reading
AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function.
Their wordsthe the breakthrough for us was realizing that AlexNet was not AlexNet. That AlexNet was an approach with deep deep learning that allows you to learn any function.
computer vision
15:21
Our reading
A CEO is in service of the company, so going deep into the technical weeds is a personality technique rather than a management technique.
Their wordsin a lot of ways I think a a CEO is in service of the company, in service of all the people that are working there. And you want to empower them with some insight. And so that's really where it's coming from. It's not so much a management technique, but a personality technique.
management