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Google quietly rebuilt its entire PM interview loop, and most candidates are still prepping for the version that stopped existing two years ago. The five rounds Google now sends every candidate: product vision, product analysis, strategic insights, execute with judgment, and problem space understanding. That last one didn't exist... until recently. There's barely any prep material for it anywhere. Gal Eshel, who sat on Google's hiring committees for six years, breaks down what actually changed underneath the renames. Product sense became product vision. Cross-functional collaboration became execute with judgment. These aren't just relabels. The bar moved from "can you describe good judgment" to "can you demonstrate it live," under a real constraint, in real time. Take strategic insights. The question isn't a day-to-day PM problem like fixing a bug. It's "should Google launch a StubHub competitor," the kind of call an SVP makes. Most candidates answer for two minutes at CEO altitude, then quietly slide back into solving it like an L5. That single shift in altitude is the entire round. Then there's problem space understanding. A bug is hitting a lot of users. Engineering could fix the code. Or product could ship a popup labeling the feature experimental and skip the fix entirely. Neither answer is wrong. The round exists purely to see whether you can hold two valid paths at once and choose deliberately. The candidates studying frameworks from 2023 are prepping for a test that no longer runs. Know the round before you walk in.show more

Aakash Gupta

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26,389 次观看 • 1 个月前 •via X (Twitter)

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NVIDIA CEO Jensen Huang said this 22 years ago: “I don’t need to change the world overnight. I’m going to change the world over the next 50 years. I don’t need to build a killer product overnight. I just need to build a winning product. And the goal of winning is so that you can play again. It’s just like pinball, OK? If you could just play well enough to get another game, you could be there for a long time. Most companies just need to realize that, in fact, this is a long road and that you can’t build that perfect product. So once you do that, then you keep the project’s scope confined. You keep the project now simple. You have a long-term vision, but your product definition is rather simple. You execute flawlessly on that because your people now can execute flawlessly on a simple plan, and you come back and do it again.”

NVIDIA CEO Jensen Huang said this 22 years ago: “I don’t need to change the world overnight. I’m going to change the world over the next 50 years. I don’t need to build a killer product overnight. I just need to build a winning product. And the goal of winning is so that you can play again. It’s just like pinball, OK? If you could just play well enough to get another game, you could be there for a long time. Most companies just need to realize that, in fact, this is a long road and that you can’t build that perfect product. So once you do that, then you keep the project’s scope confined. You keep the project now simple. You have a long-term vision, but your product definition is rather simple. You execute flawlessly on that because your people now can execute flawlessly on a simple plan, and you come back and do it again.”

Nic Cruz Patane

375,804 次观看 • 9 个月前

Most PMs tried Claude Code for a day, didn't get instant magic, and quietly decided it wasn't for them. The PMs pulling ahead are 1500 hours in and still rebuilding their setup every single day. That's the entire gap. Not talent. Not technical background. Just whether you stayed past the awkward week where nothing works yet. Hannah runs product at Anthropic and has the highest documented Claude Code mileage of any PM I've talked to. Her advice for someone with two hours this weekend isn't "build a workflow." It's "find one task to automate so you free up six hours next week to learn." That reframe is the whole game. Most people treat AI learning as something they'll get to after the real work is done. Hannah treats freeing up time to learn AS the real work. Two hours in, six hours out. Next week you reinvest those six into deeper automations that free up fifteen. The compounding only starts if you survive the first week. And almost nobody does, because day-one Claude Code feels mediocre. Your context isn't loaded. Your skills aren't written. Your CLAUDE.md is empty. The tool is guessing about your role, your product, your standards, everything. The PMs at 1500 hours aren't smarter than the ones who quit on day two. They just didn't quit on day two. Every PM interview at a frontier AI company in 2026 is some version of "show me your setup." The honest answer for most people right now is "I tried it once." Build the hour. Then build the loop.

Most PMs tried Claude Code for a day, didn't get instant magic, and quietly decided it wasn't for them. The PMs pulling ahead are 1500 hours in and still rebuilding their setup every single day. That's the entire gap. Not talent. Not technical background. Just whether you stayed past the awkward week where nothing works yet. Hannah runs product at Anthropic and has the highest documented Claude Code mileage of any PM I've talked to. Her advice for someone with two hours this weekend isn't "build a workflow." It's "find one task to automate so you free up six hours next week to learn." That reframe is the whole game. Most people treat AI learning as something they'll get to after the real work is done. Hannah treats freeing up time to learn AS the real work. Two hours in, six hours out. Next week you reinvest those six into deeper automations that free up fifteen. The compounding only starts if you survive the first week. And almost nobody does, because day-one Claude Code feels mediocre. Your context isn't loaded. Your skills aren't written. Your CLAUDE.md is empty. The tool is guessing about your role, your product, your standards, everything. The PMs at 1500 hours aren't smarter than the ones who quit on day two. They just didn't quit on day two. Every PM interview at a frontier AI company in 2026 is some version of "show me your setup." The honest answer for most people right now is "I tried it once." Build the hour. Then build the loop.

Aakash Gupta

49,411 次观看 • 5 个月前

I don't think most PMs realize the PRD is becoming obsolete. For the last decade, the PM's core artifact was a qualitative spec. Clear requirements, user stories, acceptance criteria. The engineering team interpreted it, built something close, and the PM spent two weeks reconciling what shipped with what they wrote. The best AI companies replaced that entire loop with evals. A set of inputs your product needs to handle. A task that generates outputs. A scoring function that produces a number between 0 and 1. No ambiguity. No interpretation gap. Ankur Goyal built the eval platform behind Vercel, Replit, Ramp, Notion, and Airtable. An $800M company. He walked through building an eval from zero on this episode and the score went from 0 to 0.75 in under 20 minutes. That's a PM shipping a measurable quality bar before a single line of product code exists. Here's the part that changes the PM role permanently. When the product passes the eval and users still hate it, the eval is wrong. That's on the PM. Evals make PM judgment quantifiable in a way PRDs never did. You can't hide behind "the spec was ambiguous." There's a number now. Six months ago, PM interviews asked "how do you use AI in your workflow." The next wave of interviews is going to ask you to write an eval. The PMs who can encode user intent as a scoring function are building the one skill that survives every model change, every framework swap, every agent rewrite. Write the eval.

I don't think most PMs realize the PRD is becoming obsolete. For the last decade, the PM's core artifact was a qualitative spec. Clear requirements, user stories, acceptance criteria. The engineering team interpreted it, built something close, and the PM spent two weeks reconciling what shipped with what they wrote. The best AI companies replaced that entire loop with evals. A set of inputs your product needs to handle. A task that generates outputs. A scoring function that produces a number between 0 and 1. No ambiguity. No interpretation gap. Ankur Goyal built the eval platform behind Vercel, Replit, Ramp, Notion, and Airtable. An $800M company. He walked through building an eval from zero on this episode and the score went from 0 to 0.75 in under 20 minutes. That's a PM shipping a measurable quality bar before a single line of product code exists. Here's the part that changes the PM role permanently. When the product passes the eval and users still hate it, the eval is wrong. That's on the PM. Evals make PM judgment quantifiable in a way PRDs never did. You can't hide behind "the spec was ambiguous." There's a number now. Six months ago, PM interviews asked "how do you use AI in your workflow." The next wave of interviews is going to ask you to write an eval. The PMs who can encode user intent as a scoring function are building the one skill that survives every model change, every framework swap, every agent rewrite. Write the eval.

Aakash Gupta

78,275 次观看 • 5 个月前

Every company says they have Forward-Deployed Engineers now. ElevenLabs CEO and former Palantir Deployment Strategist Mati Staniszewski gives one of the best explanations of what an FDE is—and says companies are missing the key piece of what actually makes FDEs valuable: “In our case, FDEs are part of the product team. They are not part of the go-to-market team. They are deeply embedded in understanding what the roadmap of the product is.” “You want all the FDEs to actually solve the customer problem and stretch your product in that direction. But the second thing you want them to do is bring any of that knowledge back to the product, so the product becomes better for the next generation of companies building on top of it. I feel like the second part is frequently missed.” “The beauty of that is—everybody benefits because you work with one customer, learn, and you bring that into the product experience. Both of those customers benefited from having their product optimized for better work.” “The domain expertise they have is still theirs. They can win based on their domain expertise. But the product experience of how you build based on that domain expertise can be abstracted, and can be reusable.”

Every company says they have Forward-Deployed Engineers now. ElevenLabs CEO and former Palantir Deployment Strategist Mati Staniszewski gives one of the best explanations of what an FDE is—and says companies are missing the key piece of what actually makes FDEs valuable: “In our case, FDEs are part of the product team. They are not part of the go-to-market team. They are deeply embedded in understanding what the roadmap of the product is.” “You want all the FDEs to actually solve the customer problem and stretch your product in that direction. But the second thing you want them to do is bring any of that knowledge back to the product, so the product becomes better for the next generation of companies building on top of it. I feel like the second part is frequently missed.” “The beauty of that is—everybody benefits because you work with one customer, learn, and you bring that into the product experience. Both of those customers benefited from having their product optimized for better work.” “The domain expertise they have is still theirs. They can win based on their domain expertise. But the product experience of how you build based on that domain expertise can be abstracted, and can be reusable.”

David Senra

295,601 次观看 • 5 天前

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”

Marc Andreessen on the 3 things he looks for when investing in a startup The first thing Marc Andreesen looks for is a big market: “Is there a big existing market that you think you can go after and displace incumbents? Or do you believe there will be a new market that will be big?” The second thing he looks for is a 10x better product: “Is there a fundamental technology or economic change that justifies a new company? And the way I always think about that is: Is there a 10x change happening in the technology landscape? Is something 10x faster, 10x cheaper, or 10x better? If it’s not 10x, we as both VCs and entrepreneurs have to ask ourselves if it’s really worth doing because it’s really hard to start new companies . . . Existing companies are usually pretty good at what they do. So for a new company to exist, it has to bring a product to market that’s so much better than what exists that it punches through the status quo.” The third is the team: “Is the team outstanding? . . . You want to have a founding team of complementary skillsets. You want to have at least one super strong technologist — quite possibly more than one. Some of the best startups are actually more than one founding technologist. And then it often helps to have someone who is a marketing or salesperson who has a really good understanding of business.” Marc believes that you need all three of these, but if you’re going to compromise on one of those as an investor, it should be the product: “A great market is a lot easier to make up for with iterative product execution. The problem with a poor or small market is that even if you do a good job on the product, there just aren’t that many customers so it’s hard to ever get big and people get demoralized . . . And then we evaluate the team of a startup by its ability to get into a big market with a good product.”

Startup Archive

17,333 次观看 • 7 个月前

ANTHROPIC LEAKED A FILE THAT TURNS AN EMPTY FOLDER INTO A PRODUCT WITH A PRICE IN 60 MINUTES AND AIMS AT $100,000 A MONTH in one hour you get a product with a price on it - a startup burns a quarter and $80,000 for that same hour problem → shape → surface → price → machine → back to the problem you write only the first 12 minutes: who hurts, and what it costs them right now Opus 5 gives it a shape and then a price, three Sonnet 5 build three versions of the page Haiku 4.5 stands up the machine: delivery, the queue and the weekly numbers $100,000 a month is 1,000 customers at $100, or 10 at $10,000 same number, four completely different businesses which of those four rows you're standing on gets decided in minutes 36-48, not a year from now at minute sixty you have a product, a price and a machine - and 0 customers the hour compresses the first clock to nothing, it never touches the second one the hour makes the product, it does not make the demand save this and paste it into Claude Code - let it build you these 5 blocks ↓

ANTHROPIC LEAKED A FILE THAT TURNS AN EMPTY FOLDER INTO A PRODUCT WITH A PRICE IN 60 MINUTES AND AIMS AT $100,000 A MONTH in one hour you get a product with a price on it - a startup burns a quarter and $80,000 for that same hour problem → shape → surface → price → machine → back to the problem you write only the first 12 minutes: who hurts, and what it costs them right now Opus 5 gives it a shape and then a price, three Sonnet 5 build three versions of the page Haiku 4.5 stands up the machine: delivery, the queue and the weekly numbers $100,000 a month is 1,000 customers at $100, or 10 at $10,000 same number, four completely different businesses which of those four rows you're standing on gets decided in minutes 36-48, not a year from now at minute sixty you have a product, a price and a machine - and 0 customers the hour compresses the first clock to nothing, it never touches the second one the hour makes the product, it does not make the demand save this and paste it into Claude Code - let it build you these 5 blocks ↓

Noisy

14,084 次观看 • 5 天前

Boris Cherny (Creator of Claude Code): "Latent demand is the single most important principle in product, especially in Facebook's successful products. Every single one has an element of latent demand. For example, Marketplace. It came from this observation that if you looked at Facebook groups at the time, 40% of the posts were buying and selling stuff. Facebook groups were not designed for commerce, but that's what people were using it for. It's kind of cool. Like, you design this product in a way that can be hacked. It can be abused by users a little bit, and then you look at the data, you see how they're abusing it, and then you build a product around it. I think Facebook dating was pretty similar. I think the observation was something like 60% of profile views were people of the opposite gender that were not friends with each other, kind of like creeping on each other. The principle in product is you can never get people to do something they do not yet do. You can find the intent that they have, and then you can steer it to let them better kind of capitalize on that intent." Boris Cherny

Ryan Peterman

76,877 次观看 • 5 个月前

A double blind placebo trial is how safety gets established for a pharmaceutical product. You take ten thousand people, five thousand get the drug, five thousand get a placebo, which by definition should have no effect on the body. For an injectable product, that placebo should be saline. It's called double blind because neither the scientists running the study or the patients receiving it know who got which one, and that distinction matters because those scientists are being paid by the drug company the entire time. That is the standard for every drug, every cancer treatment, every product we put in our bodies. Except one. One of the things I've proven through years of lawsuits with our nonprofit is that not a single childhood vaccine on the schedule has ever gone through that process. None of them. That is the core of my entire issue with this. Of everything you could rush to market without proving it safe first, why would it be the product given to a perfectly healthy child?

A double blind placebo trial is how safety gets established for a pharmaceutical product. You take ten thousand people, five thousand get the drug, five thousand get a placebo, which by definition should have no effect on the body. For an injectable product, that placebo should be saline. It's called double blind because neither the scientists running the study or the patients receiving it know who got which one, and that distinction matters because those scientists are being paid by the drug company the entire time. That is the standard for every drug, every cancer treatment, every product we put in our bodies. Except one. One of the things I've proven through years of lawsuits with our nonprofit is that not a single childhood vaccine on the schedule has ever gone through that process. None of them. That is the core of my entire issue with this. Of everything you could rush to market without proving it safe first, why would it be the product given to a perfectly healthy child?

Del Bigtree

41,240 次观看 • 9 天前

A/B testing was the gold standard for product decisions for 15 years. The best AI companies abandoned it. For a generation of PMs, experimentation meant designing a controlled test, allocating traffic, waiting two weeks for statistical significance, and hoping you had enough sample size to learn something. That loop trained an entire discipline to think in cycles of weeks. Evals compressed that loop to minutes. Three components: a set of inputs your product needs to handle, a task that generates outputs, and a scoring function that produces a number between 0 and 1. You run it on your laptop. No production traffic. No two-week wait. No data engineering pipeline. The math on what this changes is staggering. Teams running evals are doing 12.8 experiments per day. That's roughly 384 per month. A traditional A/B testing team runs maybe 3. Over a quarter, one team has explored 1,150+ variations. The other has explored 9. That learning gap compounds every single week. Ankur Goyal built the eval platform behind Vercel, Replit, Ramp, and Notion. $800M valuation. He ran an eval from scratch on this episode, went from a score of 0 to 0.75 in under 20 minutes. That's a PM shipping a measurable quality bar before writing a single line of product code. The PM experimentation skill used to be about statistics: sample sizes, confidence intervals, traffic allocation. Now it's about judgment: can you encode what "good" means as a number between 0 and 1? That's a product sense question, not a math question. The cost of experimentation dropped 100x. The question is whether your team's experimentation rate did too.

A/B testing was the gold standard for product decisions for 15 years. The best AI companies abandoned it. For a generation of PMs, experimentation meant designing a controlled test, allocating traffic, waiting two weeks for statistical significance, and hoping you had enough sample size to learn something. That loop trained an entire discipline to think in cycles of weeks. Evals compressed that loop to minutes. Three components: a set of inputs your product needs to handle, a task that generates outputs, and a scoring function that produces a number between 0 and 1. You run it on your laptop. No production traffic. No two-week wait. No data engineering pipeline. The math on what this changes is staggering. Teams running evals are doing 12.8 experiments per day. That's roughly 384 per month. A traditional A/B testing team runs maybe 3. Over a quarter, one team has explored 1,150+ variations. The other has explored 9. That learning gap compounds every single week. Ankur Goyal built the eval platform behind Vercel, Replit, Ramp, and Notion. $800M valuation. He ran an eval from scratch on this episode, went from a score of 0 to 0.75 in under 20 minutes. That's a PM shipping a measurable quality bar before writing a single line of product code. The PM experimentation skill used to be about statistics: sample sizes, confidence intervals, traffic allocation. Now it's about judgment: can you encode what "good" means as a number between 0 and 1? That's a product sense question, not a math question. The cost of experimentation dropped 100x. The question is whether your team's experimentation rate did too.

Aakash Gupta

39,741 次观看 • 5 个月前

I’m thrilled to announce we’ve raised $44M to build a new home for product design. Meet Noon. No workflow is more broken and fragmented in 2026 than the product designers’. The very same people who care most about building software don’t have software purpose built for them. Kushagra Sinha and I have lived this problem first hand as designers ourselves. That’s why we built Noon. The first product design tool that works entirely on your product code, so you can design not only how a product looks, but also how it works. With AI at its core that works in seconds, not minutes. For the first time, you can create, iterate, build, test and ship. All in one canvas. No translations or roundtrips to the codebase and back. Comment “Get Noon” and we’ll get you on the list for early access.

I’m thrilled to announce we’ve raised $44M to build a new home for product design. Meet Noon. No workflow is more broken and fragmented in 2026 than the product designers’. The very same people who care most about building software don’t have software purpose built for them. Kushagra Sinha and I have lived this problem first hand as designers ourselves. That’s why we built Noon. The first product design tool that works entirely on your product code, so you can design not only how a product looks, but also how it works. With AI at its core that works in seconds, not minutes. For the first time, you can create, iterate, build, test and ship. All in one canvas. No translations or roundtrips to the codebase and back. Comment “Get Noon” and we’ll get you on the list for early access.

Aditya Bandi

685,141 次观看 • 5 个月前

Marc Andreessen explains the 3 Necessities for Start-up Success: "The general criteria for a successful high-tech startup, in my view, you see different sort of rules of thumb from different people. But the three big things you always come back to are, is there a big market? And by the way, that comes in two parts. Is there a big existing market that you think you can go after and sort of displace incumbents or do you believe there will be a new market that will be big? So big market. Is there a fundamental technology or economic change that causes you to basically justify having a new company? And that's really important. And the way I always think about that is, is there a 10X change happening in the technology landscape? Is something 10X faster or 10X cheaper or 10X better? And if it's not 10X, we as both VCs and entrepreneurs, we really have to ask ourselves like, is it really worth doing? Because it's really hard. I mean, it's really hard to start new companies. new companies generally shouldn't exist. Existing companies are usually pretty good at what they do. And so for a new company to exist, it not only has to like come in and go into business and bring a product to market, but it has to bring a product to market that's so much better than what already exists that it punches through the sort of status quo. And most customers in most markets are pretty happy buying from the current suppliers and so there has to be a real kind of edge on the thing and we look for that in either a technology change, usually a technology change or an economic change. which are often the same thing. And then the third is team. Is the team outstanding? And if you think about this as an entrepreneur, it becomes a question of the founding team. Some companies are solo founders and they can work, but generally most of us, like myself, we're human beings, we're mortal. You want to have a founding team of complementary skill sets. And so you want to have at least one super strong technologist, quite possibly more than one. Some of the best startups are actually more than one founding technologist and then it often helps to have somebody who's like a product or who's a market or sales person or has a sort of really good understanding of business on the team, certainly helps a lot. And so we sort of look at market, product, and team. And the reality is you need all three. I would say, interestingly, if you're going to compromise as an investor, if we're going to compromise on one of those, it would actually be the product. And the reason I say that is because a great market is a lot easier to make up for with iterative product execution than a poor market. Because the problem with a poor market, a small market, is even if you do a great job on the product, there just aren't that many customers. It's hard to ever get big."

Marc Andreessen explains the 3 Necessities for Start-up Success: "The general criteria for a successful high-tech startup, in my view, you see different sort of rules of thumb from different people. But the three big things you always come back to are, is there a big market? And by the way, that comes in two parts. Is there a big existing market that you think you can go after and sort of displace incumbents or do you believe there will be a new market that will be big? So big market. Is there a fundamental technology or economic change that causes you to basically justify having a new company? And that's really important. And the way I always think about that is, is there a 10X change happening in the technology landscape? Is something 10X faster or 10X cheaper or 10X better? And if it's not 10X, we as both VCs and entrepreneurs, we really have to ask ourselves like, is it really worth doing? Because it's really hard. I mean, it's really hard to start new companies. new companies generally shouldn't exist. Existing companies are usually pretty good at what they do. And so for a new company to exist, it not only has to like come in and go into business and bring a product to market, but it has to bring a product to market that's so much better than what already exists that it punches through the sort of status quo. And most customers in most markets are pretty happy buying from the current suppliers and so there has to be a real kind of edge on the thing and we look for that in either a technology change, usually a technology change or an economic change. which are often the same thing. And then the third is team. Is the team outstanding? And if you think about this as an entrepreneur, it becomes a question of the founding team. Some companies are solo founders and they can work, but generally most of us, like myself, we're human beings, we're mortal. You want to have a founding team of complementary skill sets. And so you want to have at least one super strong technologist, quite possibly more than one. Some of the best startups are actually more than one founding technologist and then it often helps to have somebody who's like a product or who's a market or sales person or has a sort of really good understanding of business on the team, certainly helps a lot. And so we sort of look at market, product, and team. And the reality is you need all three. I would say, interestingly, if you're going to compromise as an investor, if we're going to compromise on one of those, it would actually be the product. And the reason I say that is because a great market is a lot easier to make up for with iterative product execution than a poor market. Because the problem with a poor market, a small market, is even if you do a great job on the product, there just aren't that many customers. It's hard to ever get big."

Founder Mode

39,005 次观看 • 7 个月前

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

cvxv666

40,765 次观看 • 3 个月前

this ai agent is INSANE for creating & selling digital products... i built an agent that builds you digital products, helps you pick a niche, then gives you a full personalized blueprint to sell it. feed it a niche, the problem you're solving, and your desired outcome, one minute later you've got a ready-to-sell product and an ai assistant that helps you sell it. > it helps you find untapped niches in the digital product space > scans your niche for real problems, desires, and buyer personas > calculates whether the demand is actually worth launching into > once validated, it generates the full product in any format, pdf, notion template, ebook > gives you the exact path to sell it, even if you've never sold anything before. the output is a ready-to-sell product. go to your ai assistant, ask it how to sell, follow the blueprint. rt + reply "agent" and i'll send it to you so you can use it for your business (must be following so i can dm)

this ai agent is INSANE for creating & selling digital products... i built an agent that builds you digital products, helps you pick a niche, then gives you a full personalized blueprint to sell it. feed it a niche, the problem you're solving, and your desired outcome, one minute later you've got a ready-to-sell product and an ai assistant that helps you sell it. > it helps you find untapped niches in the digital product space > scans your niche for real problems, desires, and buyer personas > calculates whether the demand is actually worth launching into > once validated, it generates the full product in any format, pdf, notion template, ebook > gives you the exact path to sell it, even if you've never sold anything before. the output is a ready-to-sell product. go to your ai assistant, ask it how to sell, follow the blueprint. rt + reply "agent" and i'll send it to you so you can use it for your business (must be following so i can dm)

MONTE

13,930 次观看 • 2 个月前

Most PM job descriptions still ask for airtight PRD writing. The best AI teams have already stopped writing them. The reason is structural, not stylistic. A traditional PRD describes how a product should behave in specific situations. Gen AI products are expected to handle almost every situation, which makes that kind of document nearly impossible to write with any precision. The replacement is a pre baked set of trivia questions for the model, paired with an answer key that scores whether the response is actually good. Build a recipes app and instead of writing pages of edge cases, you write 100 prompts covering the ice cream flavors users might ask for, score the outputs, and use that offline set as a proxy for what real users will experience once it ships. Daniel McKinnon spent years as a PM on Llama at Meta and on Gemini at Google. He's been saying the same thing for two years now. Performance on that offline set is the best available signal for whether users will be satisfied. Score well offline, ship it, and let the online numbers confirm or deny the bet. The skill that used to separate strong PMs was writing a precise spec. Now it's writing a precise test. Most PM training hasn't caught up. Define the eval.

Most PM job descriptions still ask for airtight PRD writing. The best AI teams have already stopped writing them. The reason is structural, not stylistic. A traditional PRD describes how a product should behave in specific situations. Gen AI products are expected to handle almost every situation, which makes that kind of document nearly impossible to write with any precision. The replacement is a pre baked set of trivia questions for the model, paired with an answer key that scores whether the response is actually good. Build a recipes app and instead of writing pages of edge cases, you write 100 prompts covering the ice cream flavors users might ask for, score the outputs, and use that offline set as a proxy for what real users will experience once it ships. Daniel McKinnon spent years as a PM on Llama at Meta and on Gemini at Google. He's been saying the same thing for two years now. Performance on that offline set is the best available signal for whether users will be satisfied. Score well offline, ship it, and let the online numbers confirm or deny the bet. The skill that used to separate strong PMs was writing a precise spec. Now it's writing a precise test. Most PM training hasn't caught up. Define the eval.

Aakash Gupta

10,374 次观看 • 1 个月前

Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done."

Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done."

TBPN

58,374 次观看 • 8 个月前

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓

slash1s

10,864 次观看 • 3 个月前

Terence Tao, UCLA professor and the most decorated mathematician alive: "Hedge funds hand you $750K a year for a single skill: telling a real pattern from noise. I've spent a lifetime on it, and almost everyone has it flipped." this free lecture is the entire "find the signal" problem those firms are paying for, delivered at UCLA by the most awarded mathematician alive and put online for nothing. at the chalkboard it's plain. Tao's lifelong idea is that almost nothing is purely one thing. there's flawless structure, like a clock, and flawless randomness, like a coin toss, and nearly everything real sits somewhere between the two. the work is pulling them apart. the primes are the cleanest test. they look scattered and lawless, yet Tao and Ben Green proved they hold evenly spaced runs of any length you ask for. order was buried inside the apparent chaos the whole time. that's "signal detection" with the marketing stripped off. he gave this at UCLA and it has stayed free ever since. exactly the point of the article above: the mathematics those firms pay half a million for is public, written down and free to anyone tonight. the lecture costs nothing and anyone can press play. what no one can sell you is the judgment to tell when a pattern is real and when your own eyes invented it. that judgment is the entire job, and it takes years to earn.

Terence Tao, UCLA professor and the most decorated mathematician alive: "Hedge funds hand you $750K a year for a single skill: telling a real pattern from noise. I've spent a lifetime on it, and almost everyone has it flipped." this free lecture is the entire "find the signal" problem those firms are paying for, delivered at UCLA by the most awarded mathematician alive and put online for nothing. at the chalkboard it's plain. Tao's lifelong idea is that almost nothing is purely one thing. there's flawless structure, like a clock, and flawless randomness, like a coin toss, and nearly everything real sits somewhere between the two. the work is pulling them apart. the primes are the cleanest test. they look scattered and lawless, yet Tao and Ben Green proved they hold evenly spaced runs of any length you ask for. order was buried inside the apparent chaos the whole time. that's "signal detection" with the marketing stripped off. he gave this at UCLA and it has stayed free ever since. exactly the point of the article above: the mathematics those firms pay half a million for is public, written down and free to anyone tonight. the lecture costs nothing and anyone can press play. what no one can sell you is the judgment to tell when a pattern is real and when your own eyes invented it. that judgment is the entire job, and it takes years to earn.

Loran

790,780 次观看 • 1 个月前

Your social posts used to die in 48 hours. But Google… just changed everything. Now you can connect your social media profiles with your Google Search Console, just like you do with your website, you can see which posts show up in Google, which keywords they rank for, and how many clicks they drive. And that flips the entire ROI of social content. Because a post in your social feeds peaked in a day or two. Then it's gone. But then that same post ranking in Google can keep working for months. And the thing is Instagram content has been indexed in search for about a year now. But you just couldn't measure any of it. Now you can. So I’m telling all clients to create content around questions people are actually typing into Google. I use AnswerThePublic for this. Type in your topic, and it shows every question your audience is asking. Then answer one question in every piece of content you create. Because now, every post has the potential to become more than a social post. It can become a measurable search asset that keeps bringing you attention, traffic, and customers long after you publish it.

Your social posts used to die in 48 hours. But Google… just changed everything. Now you can connect your social media profiles with your Google Search Console, just like you do with your website, you can see which posts show up in Google, which keywords they rank for, and how many clicks they drive. And that flips the entire ROI of social content. Because a post in your social feeds peaked in a day or two. Then it's gone. But then that same post ranking in Google can keep working for months. And the thing is Instagram content has been indexed in search for about a year now. But you just couldn't measure any of it. Now you can. So I’m telling all clients to create content around questions people are actually typing into Google. I use AnswerThePublic for this. Type in your topic, and it shows every question your audience is asking. Then answer one question in every piece of content you create. Because now, every post has the potential to become more than a social post. It can become a measurable search asset that keeps bringing you attention, traffic, and customers long after you publish it.

Neil Patel

93,558 次观看 • 27 天前

i just cracked the code on ai digital products... every product costs $0 to build and takes under 2 minutes to make. i built an ai agent that turns any niche into a ready-to-export digital product in whatever format you want, pdf, notion template, ebook, guide, you name it. drop in your niche and one problem. the system analyzes demand, maps buyer desires, builds you the full product, and you watch it happen in real time. then the agent gives you the path, what to do next, rates each module, edits everything, practically sells it for you. you just follow it. i'm making full end-to-end products in under 2 minutes with this system. so i packaged the whole thing up. you're getting an ai that: - picks your niche - calculates demand - builds the product in any format - gives you the selling blueprint to go with it. rt + reply "product" and i'll show you how to access (must be following so i can dm)

i just cracked the code on ai digital products... every product costs $0 to build and takes under 2 minutes to make. i built an ai agent that turns any niche into a ready-to-export digital product in whatever format you want, pdf, notion template, ebook, guide, you name it. drop in your niche and one problem. the system analyzes demand, maps buyer desires, builds you the full product, and you watch it happen in real time. then the agent gives you the path, what to do next, rates each module, edits everything, practically sells it for you. you just follow it. i'm making full end-to-end products in under 2 minutes with this system. so i packaged the whole thing up. you're getting an ai that: - picks your niche - calculates demand - builds the product in any format - gives you the selling blueprint to go with it. rt + reply "product" and i'll show you how to access (must be following so i can dm)

MONTE

16,114 次观看 • 1 个月前

Michael Seibel, Managing Director at Y Combinator, on why shipping a crappy product in under a month beats building a perfect one for a year: Michael starts with a simple challenge: "Do you remember the day Snapchat launched? Do you remember the day Instagram launched? Do you remember the day that WhatsApp launched? Remember the day that Uber or Lyft launched? Most likely you don't." His point cuts against how most founders think about launch day: "It turns out that launching is nowhere near as significant event to your users as it is to you. So, you should move up the launch as soon as possible." The reason comes down to validation. "Until you can get your product in front of customers, you can't validate whether it solves their problem. And so, it's much better to build a crappier product, release it sooner, and get it out there in front of customers, see if they want to use it." Michael Seibel acknowledges the approach isn't universal: "There's some exceptions. In some extremely regulated markets like banking, for example, or lending, it's just really hard to launch. You actually have to get one s*** done before you're even allowed to get customers." But for most founders, the bar is far lower than they think: "In most consumer and B2B startups that we encounter, it's actually possible to get some form of MVP built and launched in less than a month. And so, that's what you should be thinking about."

Michael Seibel, Managing Director at Y Combinator, on why shipping a crappy product in under a month beats building a perfect one for a year: Michael starts with a simple challenge: "Do you remember the day Snapchat launched? Do you remember the day Instagram launched? Do you remember the day that WhatsApp launched? Remember the day that Uber or Lyft launched? Most likely you don't." His point cuts against how most founders think about launch day: "It turns out that launching is nowhere near as significant event to your users as it is to you. So, you should move up the launch as soon as possible." The reason comes down to validation. "Until you can get your product in front of customers, you can't validate whether it solves their problem. And so, it's much better to build a crappier product, release it sooner, and get it out there in front of customers, see if they want to use it." Michael Seibel acknowledges the approach isn't universal: "There's some exceptions. In some extremely regulated markets like banking, for example, or lending, it's just really hard to launch. You actually have to get one s*** done before you're even allowed to get customers." But for most founders, the bar is far lower than they think: "In most consumer and B2B startups that we encounter, it's actually possible to get some form of MVP built and launched in less than a month. And so, that's what you should be thinking about."

Big Brain Business

75,019 次观看 • 4 个月前