Video yรผkleniyor...

Video Yรผklenemedi

Ana Sayfaya Dรถn

๐—›๐—ฒ๐—ฟ๐—ฒโ€™๐˜€ ๐—ฎ ๐—บ๐—ถ๐˜€๐˜๐—ฎ๐—ธ๐—ฒ ๐—œ ๐˜€๐—ฒ๐—ฒ ๐—ฎ๐—น๐—น ๐˜๐—ต๐—ฒ ๐˜๐—ถ๐—บ๐—ฒ. Teams collect robot data at 30Hz because โ€œthatโ€™s what our robot runs atโ€ and then wonder why their models underperform. The truth is that ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐˜๐—ฎ๐˜€๐—ธ๐˜€ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐˜๐—ฒ๐—บ๐—ฝ๐—ผ๐—ฟ๐—ฎ๐—น ๐—ฟ๐—ฒ๐˜€๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€. Pick-and-place often works best around 10Hz for smooth motions. Dynamic catching...

stepjamUK's profile picture

Stephen James

7,396 subscribers

22,574 gรถrรผntรผleme โ€ข 9 ay รถnce โ€ขvia X (Twitter)

EฤŸitimBilim & Teknoloji
LIVE

Anya Rossiโ€ข Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gรถnderinin yorumlarฤฑ burada gรถrรผnecek

Benzer Videolar

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: โ†ณ Your data stays in place. No ETL pipelines or data duplication โ†ณ Query Postgres, MongoDB, REST APIs, and more using consistent SQL โ†ณ JOIN across different sources in real-time with a unified interface โ†ณ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: โ†ณ Your data stays in place. No ETL pipelines or data duplication โ†ณ Query Postgres, MongoDB, REST APIs, and more using consistent SQL โ†ณ JOIN across different sources in real-time with a unified interface โ†ณ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay ๐Ÿš€

65,672 gรถrรผntรผleme โ€ข 9 ay รถnce

karpathy just broke the internet with something called auto research itโ€™s basically an ai research agent that runs experiments for you 24/7 you give it a goal like โ€œmake this model betterโ€ โ€œfind a higher converting landing pageโ€ โ€œlower customer acquisition costโ€ then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) ๐Ÿงƒ i break down: โ€ข what auto research actually is โ€ข how it works step by step โ€ข 10 business ideas you can build with it โ€ข how to install it and start using it this one is saucy because tools like this change how startups get built watch
24:21

karpathy just broke the internet with something called auto research itโ€™s basically an ai research agent that runs experiments for you 24/7 you give it a goal like โ€œmake this model betterโ€ โ€œfind a higher converting landing pageโ€ โ€œlower customer acquisition costโ€ then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) ๐Ÿงƒ i break down: โ€ข what auto research actually is โ€ข how it works step by step โ€ข 10 business ideas you can build with it โ€ข how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

436,482 gรถrรผntรผleme โ€ข 5 ay รถnce

I am happy to be finally able to post what I was able to build over the last few weeks. A full real-time high-frequency state estimation and mapping algorithm completely written line by line from scratch in Rust, which can be used by robots to navigate and reason within the 3D world also in complicated scenarios. TBH this took me longer than expected (which was still super fast :D) but you need to get a lot right: From the sensors over the drivers to their respective estimation pipeline and then fusing everything together - a covariance nightmare - and something that can be refined over years to come (currently using Fisher Information from the real measurements). What you see here is not the output of some structure from motion or Gaussian splatting, these are the points of a tight mesh (high res for the video) that a robot can use in real time to plan a path using any open-source planner. The flight you experience through the world is the actual state estimate of the scanner which is published at IMU rate. Yes, currently we have some artefacts of filtered-out humans (GDPR compliant of course :) ) and moving cars and there is still some calibration that could be improved. Offline refinement with SFM and Gaussian splats is possible as well but currently not on the road map. What is on the road map is an exciting step of now being able to collect data from customers at construction sites and in warehouses (currently handheld in the near future with a robot). This data can then be used by our physical agents to reason within this world and automate any customerโ€™s task related to 3D data. If you have anyone who wastes time manually looking ๐Ÿ‘€ through 3D data, or cannot collect enough 3D data and interpret: Tell me how to reach them!
1:15

I am happy to be finally able to post what I was able to build over the last few weeks. A full real-time high-frequency state estimation and mapping algorithm completely written line by line from scratch in Rust, which can be used by robots to navigate and reason within the 3D world also in complicated scenarios. TBH this took me longer than expected (which was still super fast :D) but you need to get a lot right: From the sensors over the drivers to their respective estimation pipeline and then fusing everything together - a covariance nightmare - and something that can be refined over years to come (currently using Fisher Information from the real measurements). What you see here is not the output of some structure from motion or Gaussian splatting, these are the points of a tight mesh (high res for the video) that a robot can use in real time to plan a path using any open-source planner. The flight you experience through the world is the actual state estimate of the scanner which is published at IMU rate. Yes, currently we have some artefacts of filtered-out humans (GDPR compliant of course :) ) and moving cars and there is still some calibration that could be improved. Offline refinement with SFM and Gaussian splats is possible as well but currently not on the road map. What is on the road map is an exciting step of now being able to collect data from customers at construction sites and in warehouses (currently handheld in the near future with a robot). This data can then be used by our physical agents to reason within this world and automate any customerโ€™s task related to 3D data. If you have anyone who wastes time manually looking ๐Ÿ‘€ through 3D data, or cannot collect enough 3D data and interpret: Tell me how to reach them!

Benedikt Seidel

16,656 gรถrรผntรผleme โ€ข 3 ay รถnce

Unpopular ๐ŸŒถ๏ธ take but it's time we call it out and tell the truth! Entitlement is a cancer. So many wonder why weโ€™re losing great coaches, leaders and struggling to find quality ones. No one wants to deal with this level of nonsense. Entitled parents are teaching entitled kids that they can whine, complain, and manipulate their way into what they want. But thatโ€™s not how life works. The best players earn their spot thatโ€™s how itโ€™s supposed to be. Donโ€™t like where you stand? Work harder or move on. Because in college and especially in the real world you canโ€™t just call the coach or your childโ€™s future employer and demand they get a position, scholarship, or job they didnโ€™t earn. Accountability builds character. Excuses donโ€™t. And itโ€™s sad, because these parents arenโ€™t helping theyโ€™re robbing their kids of the opportunity to build confidence and resilience through hard work. When you step in and do it for them, the message you send is: โ€œYouโ€™re not good enough on your own.โ€ But the truth is, theyโ€™ll never know what theyโ€™re capable of if you donโ€™t let them earn it. Thoughts? ๐Ÿ’ญ
0:48

Unpopular ๐ŸŒถ๏ธ take but it's time we call it out and tell the truth! Entitlement is a cancer. So many wonder why weโ€™re losing great coaches, leaders and struggling to find quality ones. No one wants to deal with this level of nonsense. Entitled parents are teaching entitled kids that they can whine, complain, and manipulate their way into what they want. But thatโ€™s not how life works. The best players earn their spot thatโ€™s how itโ€™s supposed to be. Donโ€™t like where you stand? Work harder or move on. Because in college and especially in the real world you canโ€™t just call the coach or your childโ€™s future employer and demand they get a position, scholarship, or job they didnโ€™t earn. Accountability builds character. Excuses donโ€™t. And itโ€™s sad, because these parents arenโ€™t helping theyโ€™re robbing their kids of the opportunity to build confidence and resilience through hard work. When you step in and do it for them, the message you send is: โ€œYouโ€™re not good enough on your own.โ€ But the truth is, theyโ€™ll never know what theyโ€™re capable of if you donโ€™t let them earn it. Thoughts? ๐Ÿ’ญ

Wendi A. Irlbeck MS, RDN, LD, CISSN

35,999 gรถrรผntรผleme โ€ข 9 ay รถnce

Is Traditional Software Engineering Dead? โ€œDoes this mean that traditional software engineering is dead? Absolutely not. Software engineersโ€”even the ones who are not necessarily tuning or training AI modelsโ€”these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because theyโ€™re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know whatโ€™s going on underneath. And all abstractions are leaky. So when you have a computer programming for youโ€”when you have Claude Code or equivalent programming for youโ€”itโ€™s going to make mistakes. Itโ€™s going to have bugs. Itโ€™s going to have suboptimal architecture. So itโ€™s not going to be quite right. And someone who understands whatโ€™s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then youโ€™re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, theyโ€™ve seen countless examples of that, so theyโ€™re extremely good at it. But when you start getting out of their domainโ€”where you have to write very high-performance code, when youโ€™re running on architectures that are novel or brand new, when youโ€™re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their ownโ€ฆ And remember: there is no demand for average. The average appโ€”nobody wants it, at least as long as itโ€™s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe thereโ€™s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or itโ€™s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is thereโ€™s no point in being number two or number threeโ€”like in the famous Glengarry Glen Ross scene where Alec Baldwin says, โ€œFirst place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place youโ€™re fired.โ€ Thatโ€™s absolutely true in these winner-take-all markets. Thatโ€™s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, โ€œBecome the best in the world at what you do. Keep redefining what you do until this is true.โ€ And I think that still applies in this age of AI.โ€
2:56

Is Traditional Software Engineering Dead? โ€œDoes this mean that traditional software engineering is dead? Absolutely not. Software engineersโ€”even the ones who are not necessarily tuning or training AI modelsโ€”these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because theyโ€™re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know whatโ€™s going on underneath. And all abstractions are leaky. So when you have a computer programming for youโ€”when you have Claude Code or equivalent programming for youโ€”itโ€™s going to make mistakes. Itโ€™s going to have bugs. Itโ€™s going to have suboptimal architecture. So itโ€™s not going to be quite right. And someone who understands whatโ€™s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then youโ€™re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, theyโ€™ve seen countless examples of that, so theyโ€™re extremely good at it. But when you start getting out of their domainโ€”where you have to write very high-performance code, when youโ€™re running on architectures that are novel or brand new, when youโ€™re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their ownโ€ฆ And remember: there is no demand for average. The average appโ€”nobody wants it, at least as long as itโ€™s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe thereโ€™s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or itโ€™s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is thereโ€™s no point in being number two or number threeโ€”like in the famous Glengarry Glen Ross scene where Alec Baldwin says, โ€œFirst place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place youโ€™re fired.โ€ Thatโ€™s absolutely true in these winner-take-all markets. Thatโ€™s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, โ€œBecome the best in the world at what you do. Keep redefining what you do until this is true.โ€ And I think that still applies in this age of AI.โ€

Naval

853,816 gรถrรผntรผleme โ€ข 5 ay รถnce

We are thinking of robots all wrong. Why a $1,500 robot is far more important to buy than a $20,000 one. And why it will pay for itself within a year. First of all, for the last eight years I've been a Silicon Valley housewife. Picking the kids up from school. Doing a variety of tasks taking care of them from feeding them to laundry. And I've already bought a $20,000 Neo from 1X and have built the most complete list on X of the robotics industry. Just to set the tone for this conversation. We must ask ourselves "what is the goal of a robot?" before we go into why typical American homeowners might want one, and shell out quite a bit of money for one, like the Neo. I grew up in Silicon Valley back when it was all orchards and the farmers taught me "pick the low hanging fruit first." What is the low hanging fruit in the American home? Laundry? Cleaning the toilets or your home? Watering the plants? Bringing you a beer? Nope. It is the preparation of food. Yesterday I got a new Posha robot, and the attached video with founder Raghav Gupta gets into depth about what the $1,500 robot does. Cooks meals. Far more time in the home is spent cooking meals than the other tasks and is far more complex than, say, folding laundry. But there is something I think everyone is missing in the discussion of robots: "what is the goal?" I've been doing consumer research talking with many around the world about these things. People tend to have a few goals: 1. Improve their lives. 2. Save them time. 3. Save them money. 4. Enable a new business. What is the best way to improve your life? Upgrade your food. This is very hard to do when both parents in a family are working their butts off to try to improve their careers. It gets worse when a single parent is trying to keep everything going. How many times have you decided to go out to eat rather than spend an hour cooking food? Doing that for a family of four in Silicon Valley costs $100+. And guarantees your family will overeat. I've done that many times while raising my kids, and often I can't say no when desert comes around. It gets worse if you take the easy route out at home. Put a pre-processed meal into the microwave, or heat up a frozen pizza. Horrible for everyone's health. But what if you could have a robot at home that cooks your meals? Then costs go down to less than $20 and ingredients get way way better. It gets worse when you consider a $20,000 humanoid. They aren't safe enough to trust around stoves yet. And their hands aren't yet dexterous enough to do that. I doubt my Neo will be allowed to cook meals over an open flame, and if so I will have to watch it to make sure it doesn't do anything wrong. (The Neo that arrives next year will be teleoperated by a human remotely and the risks that person does something, or misses oil catching on fire is just way too high). While neither robot will be able to do all food preparation (cutting chicken up into cubes, or cutting carrots or other fruits, for instance) this robot dramatically reduces the time needed for a human to make a meal and dramatically reduces the costs to do so. And, as we discuss in the video, when the Neo does arrive the Neo will be able to use this machine too, reducing time even more (and will be able to set the table and wash the dishes, saving even more time so you can answer more emails or learn more AI programs or, even, pay attention to your kids and give them a few more minutes of quality time). The robot industry should focus on the low hanging fruit first. Cooking meals is the biggest one to improve your life, save you time, and make your family healthier. It's why I bought one. And they actually make two: Your money is way better spent getting one of these than buying a humanoid. And if you do get a humanoid, like I am, they go together like peanut butter and jelly. โšช๏ธ sierra catalina has been saying this for years that our focus on humanoids is overblown and that specialized robots (you see my Matic Robots in the background to prove this point) are way better for most families.
34:11

We are thinking of robots all wrong. Why a $1,500 robot is far more important to buy than a $20,000 one. And why it will pay for itself within a year. First of all, for the last eight years I've been a Silicon Valley housewife. Picking the kids up from school. Doing a variety of tasks taking care of them from feeding them to laundry. And I've already bought a $20,000 Neo from 1X and have built the most complete list on X of the robotics industry. Just to set the tone for this conversation. We must ask ourselves "what is the goal of a robot?" before we go into why typical American homeowners might want one, and shell out quite a bit of money for one, like the Neo. I grew up in Silicon Valley back when it was all orchards and the farmers taught me "pick the low hanging fruit first." What is the low hanging fruit in the American home? Laundry? Cleaning the toilets or your home? Watering the plants? Bringing you a beer? Nope. It is the preparation of food. Yesterday I got a new Posha robot, and the attached video with founder Raghav Gupta gets into depth about what the $1,500 robot does. Cooks meals. Far more time in the home is spent cooking meals than the other tasks and is far more complex than, say, folding laundry. But there is something I think everyone is missing in the discussion of robots: "what is the goal?" I've been doing consumer research talking with many around the world about these things. People tend to have a few goals: 1. Improve their lives. 2. Save them time. 3. Save them money. 4. Enable a new business. What is the best way to improve your life? Upgrade your food. This is very hard to do when both parents in a family are working their butts off to try to improve their careers. It gets worse when a single parent is trying to keep everything going. How many times have you decided to go out to eat rather than spend an hour cooking food? Doing that for a family of four in Silicon Valley costs $100+. And guarantees your family will overeat. I've done that many times while raising my kids, and often I can't say no when desert comes around. It gets worse if you take the easy route out at home. Put a pre-processed meal into the microwave, or heat up a frozen pizza. Horrible for everyone's health. But what if you could have a robot at home that cooks your meals? Then costs go down to less than $20 and ingredients get way way better. It gets worse when you consider a $20,000 humanoid. They aren't safe enough to trust around stoves yet. And their hands aren't yet dexterous enough to do that. I doubt my Neo will be allowed to cook meals over an open flame, and if so I will have to watch it to make sure it doesn't do anything wrong. (The Neo that arrives next year will be teleoperated by a human remotely and the risks that person does something, or misses oil catching on fire is just way too high). While neither robot will be able to do all food preparation (cutting chicken up into cubes, or cutting carrots or other fruits, for instance) this robot dramatically reduces the time needed for a human to make a meal and dramatically reduces the costs to do so. And, as we discuss in the video, when the Neo does arrive the Neo will be able to use this machine too, reducing time even more (and will be able to set the table and wash the dishes, saving even more time so you can answer more emails or learn more AI programs or, even, pay attention to your kids and give them a few more minutes of quality time). The robot industry should focus on the low hanging fruit first. Cooking meals is the biggest one to improve your life, save you time, and make your family healthier. It's why I bought one. And they actually make two: Your money is way better spent getting one of these than buying a humanoid. And if you do get a humanoid, like I am, they go together like peanut butter and jelly. โšช๏ธ sierra catalina has been saying this for years that our focus on humanoids is overblown and that specialized robots (you see my Matic Robots in the background to prove this point) are way better for most families.

Robert Scoble

178,294 gรถrรผntรผleme โ€ข 8 ay รถnce

I hear so often from the Dommes I work with that they struggle with people online fetichizing them and simply seeing them for how sexy and beautiful they are. They project their fantasies and their desires onto you. That stops immediately once you move the attention from you to them. From 'look at me' to 'I see you'. What does that look like? When you create content, think of them and what this scene or that narrative is evoking. What will they learn from you? What they want is not to passively watch how sexy you are, but for you to train them, to give them instructions, to teach them, to guide them, to be in charge, to command them. This is not being an object but the main subject. The Authority figure. How is your content already doing that. The sexy photos can still be there, they are important to already capture des attention. But what you do with that attention once you have it, is where the power dynamic is established. Positioning yourself as more than a stunning Goddess, but actually a woman who has a voice, opinions, perspective, a philosophy, a way to doing things, teaching them what you like, how you like it, why you like it, already makes them want to be that for you. You hold the attention, you hold the power, so you direct it. And for that, you want them to know you get them and you know what lives within them... that creates the desire for you to be the one exposing it. You instantly build trust. Not because you demanded it, but because you earned it: you showed them you know what you are doing. You have experience, you understand them. They are not told to come see you, they are seduced into it. They desire it. And they will work for it. This will attract better clients (real subs) and instead of you trying to get their attention, they will work to earn yours. If you want to learn more about power dynamics, building a brand as a Pro or the psychology behind BDSM, you can now access all my trainings and classes in one place for a fraction of the cost of The Dominatrix Academy. And you can reinvest the total amount towards the Program. Message me [SECRET] for the details. This offer is not available on my website.
0:51

I hear so often from the Dommes I work with that they struggle with people online fetichizing them and simply seeing them for how sexy and beautiful they are. They project their fantasies and their desires onto you. That stops immediately once you move the attention from you to them. From 'look at me' to 'I see you'. What does that look like? When you create content, think of them and what this scene or that narrative is evoking. What will they learn from you? What they want is not to passively watch how sexy you are, but for you to train them, to give them instructions, to teach them, to guide them, to be in charge, to command them. This is not being an object but the main subject. The Authority figure. How is your content already doing that. The sexy photos can still be there, they are important to already capture des attention. But what you do with that attention once you have it, is where the power dynamic is established. Positioning yourself as more than a stunning Goddess, but actually a woman who has a voice, opinions, perspective, a philosophy, a way to doing things, teaching them what you like, how you like it, why you like it, already makes them want to be that for you. You hold the attention, you hold the power, so you direct it. And for that, you want them to know you get them and you know what lives within them... that creates the desire for you to be the one exposing it. You instantly build trust. Not because you demanded it, but because you earned it: you showed them you know what you are doing. You have experience, you understand them. They are not told to come see you, they are seduced into it. They desire it. And they will work for it. This will attract better clients (real subs) and instead of you trying to get their attention, they will work to earn yours. If you want to learn more about power dynamics, building a brand as a Pro or the psychology behind BDSM, you can now access all my trainings and classes in one place for a fraction of the cost of The Dominatrix Academy. And you can reinvest the total amount towards the Program. Message me [SECRET] for the details. This offer is not available on my website.

Ms. Malissia

16,120 gรถrรผntรผleme โ€ข 3 ay รถnce

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."
1:37

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."

TBPN

384,058 gรถrรผntรผleme โ€ข 6 ay รถnce

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."
1:37

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."

TBPN

34,529 gรถrรผntรผleme โ€ข 5 ay รถnce

The uncomfortable truth about hospitals is this: Patients never really know. They donโ€™t know if the surgery is truly necessary. Or if itโ€™s being recommended because thatโ€™s how the system makes money. Sadly today, people donโ€™t go to hospitals expecting care. They go expecting a sales process. The data is clear, a medi-angels study showed that over 40% of all surgeries prescribed were not required. A recent FOGSI study showed 95% of hysterectomies were unnecessary. Which is exactly why we built Supersurgery, the most honest and hassle free surgery experience ever.๐Ÿ‘‡ Honest Opinion from Zero Commission doctors With Supersurgery, you get an honest opinion because our doctors are full-time and salaried. There are no commissions, no sales targets or referrals. They take the time to listen to you and are focused only and only on what is best for you. Fixed Prices We have a fixed price for hte procedure. It is all inclusive and doesn't change whether you pay yourself or via insurance. It doesn't change even if your case complexity changes or whether you need more tests or scans. in fact if your doctor says you need to stay a few more days to get better, we don't charge for that either. we absorb the cost. Magic Discharge Unlike other hospitals where at discharge you have to wait for hours and hours and hours, at Superhealth we pre-commit the discharge time and it is instant. as soon as hte doctor confirms you are fit to go, you can get up and leave. there is no drama, no paperwork, no running to 10 departments trying to get clearances. Today we prescribe about 50% fewer surgeries than the industry. And if you do need it, then it should be simple, honest and high quality. I really hope you never need surgery, but if you do, don't settle for just surgery, get Supersurgery. Learn more at
2:50

The uncomfortable truth about hospitals is this: Patients never really know. They donโ€™t know if the surgery is truly necessary. Or if itโ€™s being recommended because thatโ€™s how the system makes money. Sadly today, people donโ€™t go to hospitals expecting care. They go expecting a sales process. The data is clear, a medi-angels study showed that over 40% of all surgeries prescribed were not required. A recent FOGSI study showed 95% of hysterectomies were unnecessary. Which is exactly why we built Supersurgery, the most honest and hassle free surgery experience ever.๐Ÿ‘‡ Honest Opinion from Zero Commission doctors With Supersurgery, you get an honest opinion because our doctors are full-time and salaried. There are no commissions, no sales targets or referrals. They take the time to listen to you and are focused only and only on what is best for you. Fixed Prices We have a fixed price for hte procedure. It is all inclusive and doesn't change whether you pay yourself or via insurance. It doesn't change even if your case complexity changes or whether you need more tests or scans. in fact if your doctor says you need to stay a few more days to get better, we don't charge for that either. we absorb the cost. Magic Discharge Unlike other hospitals where at discharge you have to wait for hours and hours and hours, at Superhealth we pre-commit the discharge time and it is instant. as soon as hte doctor confirms you are fit to go, you can get up and leave. there is no drama, no paperwork, no running to 10 departments trying to get clearances. Today we prescribe about 50% fewer surgeries than the industry. And if you do need it, then it should be simple, honest and high quality. I really hope you never need surgery, but if you do, don't settle for just surgery, get Supersurgery. Learn more at

Varun Dubey

45,652 gรถrรผntรผleme โ€ข 5 ay รถnce

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (๐Ÿ”– Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. โฌ‡๏ธ Hereโ€™s a breakdown of what they found Pi0 (Original) โœ… Strongest overall performance in precise pick-and-place โœ… High success rate even in edge cases โœ… Longest training time (~11 hours, ~$30 per run) โœ… Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios wellโ€ฆ solid for high-precision tasks, but slow to train. Gr00t โœ… Trains fast (~2 hours, ~$5 per run) โœ… Performs almost as well as Pi0 on large-object tasks โœ… Struggles with fine precision; random movement in some trials โœ… More training didnโ€™t fix jitter or random offsets Best suited for tasks where exact precision isnโ€™t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast โœ… Promised faster training, but results were underwhelming โœ… Training at 6 hours still showed low success rates โœ… Inference was slower than expected โœ… Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesnโ€™t live up to the โ€œFastโ€ name yet. ACT (Baseline) โœ… 200MB modelโ€”lightweight, but limited โœ… Struggles with stacked objects or ambiguous scenes โœ… Success rates around 70% in best-case setups โœ… Canโ€™t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. ๐Ÿšจ Extra Notes All newer models share a common issue: โ€ขInference takes longer than a frame (80 ms vs 33 ms), so robots โ€œpauseโ€ between chunks. โ€ขThis results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldnโ€™t generalize to a third unseen combination using only text prompts. โœ… The good news? These models adapt well to new robot arms with quick fine-tuning. โŒ The bad news? Thereโ€™s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.
0:51

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (๐Ÿ”– Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. โฌ‡๏ธ Hereโ€™s a breakdown of what they found Pi0 (Original) โœ… Strongest overall performance in precise pick-and-place โœ… High success rate even in edge cases โœ… Longest training time (~11 hours, ~$30 per run) โœ… Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios wellโ€ฆ solid for high-precision tasks, but slow to train. Gr00t โœ… Trains fast (~2 hours, ~$5 per run) โœ… Performs almost as well as Pi0 on large-object tasks โœ… Struggles with fine precision; random movement in some trials โœ… More training didnโ€™t fix jitter or random offsets Best suited for tasks where exact precision isnโ€™t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast โœ… Promised faster training, but results were underwhelming โœ… Training at 6 hours still showed low success rates โœ… Inference was slower than expected โœ… Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesnโ€™t live up to the โ€œFastโ€ name yet. ACT (Baseline) โœ… 200MB modelโ€”lightweight, but limited โœ… Struggles with stacked objects or ambiguous scenes โœ… Success rates around 70% in best-case setups โœ… Canโ€™t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. ๐Ÿšจ Extra Notes All newer models share a common issue: โ€ขInference takes longer than a frame (80 ms vs 33 ms), so robots โ€œpauseโ€ between chunks. โ€ขThis results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldnโ€™t generalize to a third unseen combination using only text prompts. โœ… The good news? These models adapt well to new robot arms with quick fine-tuning. โŒ The bad news? Thereโ€™s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 gรถrรผntรผleme โ€ข 1 yฤฑl รถnce

If thereโ€™s one lesson people are beginning to realize about modern technology, itโ€™s this: The devices we rely on every day arenโ€™t just tools. Theyโ€™re surveillance systems. ๐Ÿ‘‰ Learn more: Your phone tracks your location. Your apps collect your behavior. And now, AI integrated directly into your computer can record what you do on your screen every few seconds. Your emails. Your messages. Your searches. Even things you type but never send. That isnโ€™t speculation. Itโ€™s the direction Big Tech has already taken. And the uncomfortable truth is that the people building these systems know exactly how powerful they are. Many of the same tech leaders who sell these devices to billions of users take extraordinary precautions with their own technology, covering cameras and limiting the very tools they encourage everyone else to use. Because data is power. And the more data that flows through these systems, the more influence the companies behind them gain over the digital world we all live in. But the playing field doesnโ€™t have to stay that way. On Thursday, privacy experts Glenn and Eric Meder are hosting a free Privacy Academy webinar explaining exactly how these systems work and what people can do about them. During the training, they walk through how modern operating systems collect and analyze your behavior, how AI tools can build detailed behavioral profiles, and why many of the privacy settings people rely on donโ€™t actually stop data collection. More importantly, they explain a practical alternative. Instead of relying on software designed around data collection, they show how privacy-focused systems like Linux can give users far more control over their own computers. And they break it down step by step so everyday peopleโ€”not just programmers or tech expertsโ€”can understand how to make the transition. Because once you understand how these systems work, protecting your privacy becomes far easier than most people think. The free webinar is happening Thursday, March 5 at 7 PM Central, and itโ€™s open to anyone who wants to understand how Big Tech is gathering dataโ€”and how to stop it. ๐Ÿ‘‰ Register here: You canโ€™t control the direction the tech industry chooses to take. But you can control how much access those systems have to your life. We want to thank Privacy Academy for helping everyday people understand how the digital world really works and for being a proud sponsor of this program. If youโ€™re interested in learning more, visit do your own research, and decide if the training is right for you. Because when it comes to privacy, the most important step is understanding the system youโ€™re living inside.
1:29

If thereโ€™s one lesson people are beginning to realize about modern technology, itโ€™s this: The devices we rely on every day arenโ€™t just tools. Theyโ€™re surveillance systems. ๐Ÿ‘‰ Learn more: Your phone tracks your location. Your apps collect your behavior. And now, AI integrated directly into your computer can record what you do on your screen every few seconds. Your emails. Your messages. Your searches. Even things you type but never send. That isnโ€™t speculation. Itโ€™s the direction Big Tech has already taken. And the uncomfortable truth is that the people building these systems know exactly how powerful they are. Many of the same tech leaders who sell these devices to billions of users take extraordinary precautions with their own technology, covering cameras and limiting the very tools they encourage everyone else to use. Because data is power. And the more data that flows through these systems, the more influence the companies behind them gain over the digital world we all live in. But the playing field doesnโ€™t have to stay that way. On Thursday, privacy experts Glenn and Eric Meder are hosting a free Privacy Academy webinar explaining exactly how these systems work and what people can do about them. During the training, they walk through how modern operating systems collect and analyze your behavior, how AI tools can build detailed behavioral profiles, and why many of the privacy settings people rely on donโ€™t actually stop data collection. More importantly, they explain a practical alternative. Instead of relying on software designed around data collection, they show how privacy-focused systems like Linux can give users far more control over their own computers. And they break it down step by step so everyday peopleโ€”not just programmers or tech expertsโ€”can understand how to make the transition. Because once you understand how these systems work, protecting your privacy becomes far easier than most people think. The free webinar is happening Thursday, March 5 at 7 PM Central, and itโ€™s open to anyone who wants to understand how Big Tech is gathering dataโ€”and how to stop it. ๐Ÿ‘‰ Register here: You canโ€™t control the direction the tech industry chooses to take. But you can control how much access those systems have to your life. We want to thank Privacy Academy for helping everyday people understand how the digital world really works and for being a proud sponsor of this program. If youโ€™re interested in learning more, visit do your own research, and decide if the training is right for you. Because when it comes to privacy, the most important step is understanding the system youโ€™re living inside.

Vigilant Fox ๐ŸฆŠ

13,435 gรถrรผntรผleme โ€ข 5 ay รถnce

Jensen Huang on how to convey your vision to employees: Question: "How you convey your vision to your employees and how you keep that sense of urgency in them so that they continue improving themselves?" Jensen: "So the question is how do I convey my vision to the employees and how do we convey a sense of urgency? First of all, you convey your vision the good old-fashioned way. And it's about telling a story. I'm not the best storyteller in the world, and I'm not the best, I don't enjoy public speaking, actually. And if you were to give me a choice right now between doing this versus just answering one of your emails, and I'll give you all my email address, you could all send me an email, and I'd be glad to respond to it. I'd rather do that. You know, I'm still an engineer, and I'm introverted by design, I guess. And I don't find myself particularly articulate. And so I don't enjoy the process of public speaking. But you have to force yourself to do it. It's for a good reason. It's for a good cause. I have to admit that speaking to my employees or speaking to NVIDIA's employees is the single most intimidating thing that I do. It freaks me out. And the reason for that is because I respect their time so much, and I know how important the meeting is, that in your own mind, the bar and the responsibility is extraordinary. But you have to put yourself, and I'm speaking to engineers here, you have to force yourself to communicate at a bigger picture level. You have to force yourself to practice. And it's something that over time you get better at. In terms of how do we communicate a sense of urgency? Just through action. They have to see that when I make decisions or when I do something or when something is near my field of influence, my scope of influence, that I do it with a sense of urgency. And it's amazing what that does. People simply pick up those habits from you. If your CEO works hard, you'll work hard. If your CEO cares, you'll care. If your CEO loves this company, you'll love this company. If your CEO is passionate about the work that we do, you'll be passionate about the work that we do. If your CEO does everything with an extraordinary sense of purpose and intensity and sense of urgency, you will too. It's amazing what happens when you're a leader of anything, whether you're a leader of a project team or, right? As I say that, you could almost everybody just, yeah, I get it. Leader of a project team or a leader of a lab team. The behavior and the values and the habits of that leader has an amazing way of rubbing off on everybody else."
2:52

Jensen Huang on how to convey your vision to employees: Question: "How you convey your vision to your employees and how you keep that sense of urgency in them so that they continue improving themselves?" Jensen: "So the question is how do I convey my vision to the employees and how do we convey a sense of urgency? First of all, you convey your vision the good old-fashioned way. And it's about telling a story. I'm not the best storyteller in the world, and I'm not the best, I don't enjoy public speaking, actually. And if you were to give me a choice right now between doing this versus just answering one of your emails, and I'll give you all my email address, you could all send me an email, and I'd be glad to respond to it. I'd rather do that. You know, I'm still an engineer, and I'm introverted by design, I guess. And I don't find myself particularly articulate. And so I don't enjoy the process of public speaking. But you have to force yourself to do it. It's for a good reason. It's for a good cause. I have to admit that speaking to my employees or speaking to NVIDIA's employees is the single most intimidating thing that I do. It freaks me out. And the reason for that is because I respect their time so much, and I know how important the meeting is, that in your own mind, the bar and the responsibility is extraordinary. But you have to put yourself, and I'm speaking to engineers here, you have to force yourself to communicate at a bigger picture level. You have to force yourself to practice. And it's something that over time you get better at. In terms of how do we communicate a sense of urgency? Just through action. They have to see that when I make decisions or when I do something or when something is near my field of influence, my scope of influence, that I do it with a sense of urgency. And it's amazing what that does. People simply pick up those habits from you. If your CEO works hard, you'll work hard. If your CEO cares, you'll care. If your CEO loves this company, you'll love this company. If your CEO is passionate about the work that we do, you'll be passionate about the work that we do. If your CEO does everything with an extraordinary sense of purpose and intensity and sense of urgency, you will too. It's amazing what happens when you're a leader of anything, whether you're a leader of a project team or, right? As I say that, you could almost everybody just, yeah, I get it. Leader of a project team or a leader of a lab team. The behavior and the values and the habits of that leader has an amazing way of rubbing off on everybody else."

Founder Mode

16,574 gรถrรผntรผleme โ€ข 6 ay รถnce

ELON MUSK: DONโ€™T TAKE ANYONEโ€™S OPINIONโ€ฆ.GO TO THE SOURCE MATERIAL AND COMMUNITY NOTES TO SEE WHAT REALLY HAPPENED As soon as any company steps out of line and is willing to actually have the truth debated on their platform, it forces the other platforms to allow things to be more truthful, to not censor. Because their censorship becomes glaringly obvious And, you know, the best thing I found as a rebuttal, like if somebody, if there's a hoax, is just go to the source material. You know, if you think if somebody thinks, you know, it's, you know, Trump said that we should put Liz Cheney in a firing squad, I'm like, let me send you a link to X so you can watch his video. That's the best way. Don't take my opinion for it. Don't take anyone's opinion for it. Go to the source material and Community Notes Yes, and Community Notes is awesome. It's incredible because everybody gets checked, including me. And with Community Notes, all the software is open source, and all the data is open source, so you can recreate any given note independently. That's amazing. Yeah. That's how it should be. Total absolute transparency in every way Sometimes I get asked like, 'Elon, can you remove a note?' You know, mostly by the left, but sometimes by the right. I'm like, I don't even remove notes on my own account. Nothing. And, and by the way, everything is totally open. So if I did that, it would stick out like a sore thumb immediately. Like it's not going to be subtle That is the best counter to misinformation. Yes, absolutely. Like let everybody look at it and say, 'Okay, here's what the actual facts say.' Yes, exactly. The counter to misinformation is better information Not just that, but having it checked in real-time by the community. So you have millions of people that can go over it and debate whether or not this is true or that's true. Yes, and, and like I said, the best way to understand the truth of things is don't take anyone's opinion for it. Look at the source material Look at what someone actually said, look at what someone actually did, look at the real videos of the situation, and then you'll actually know what's real
1:52

ELON MUSK: DONโ€™T TAKE ANYONEโ€™S OPINIONโ€ฆ.GO TO THE SOURCE MATERIAL AND COMMUNITY NOTES TO SEE WHAT REALLY HAPPENED As soon as any company steps out of line and is willing to actually have the truth debated on their platform, it forces the other platforms to allow things to be more truthful, to not censor. Because their censorship becomes glaringly obvious And, you know, the best thing I found as a rebuttal, like if somebody, if there's a hoax, is just go to the source material. You know, if you think if somebody thinks, you know, it's, you know, Trump said that we should put Liz Cheney in a firing squad, I'm like, let me send you a link to X so you can watch his video. That's the best way. Don't take my opinion for it. Don't take anyone's opinion for it. Go to the source material and Community Notes Yes, and Community Notes is awesome. It's incredible because everybody gets checked, including me. And with Community Notes, all the software is open source, and all the data is open source, so you can recreate any given note independently. That's amazing. Yeah. That's how it should be. Total absolute transparency in every way Sometimes I get asked like, 'Elon, can you remove a note?' You know, mostly by the left, but sometimes by the right. I'm like, I don't even remove notes on my own account. Nothing. And, and by the way, everything is totally open. So if I did that, it would stick out like a sore thumb immediately. Like it's not going to be subtle That is the best counter to misinformation. Yes, absolutely. Like let everybody look at it and say, 'Okay, here's what the actual facts say.' Yes, exactly. The counter to misinformation is better information Not just that, but having it checked in real-time by the community. So you have millions of people that can go over it and debate whether or not this is true or that's true. Yes, and, and like I said, the best way to understand the truth of things is don't take anyone's opinion for it. Look at the source material Look at what someone actually said, look at what someone actually did, look at the real videos of the situation, and then you'll actually know what's real

X Freeze

589,152 gรถrรผntรผleme โ€ข 8 ay รถnce

Why You Shouldnโ€™t Retire Your Family (Especially If Youโ€™re In Your 20s & 30s) Retiring your family sounds noble, right? Youโ€™re hustling to make enough money so Mom never has to lift a finger again. So Dad can stop working that job heโ€™s hated for decades. I respect that intention. I truly do. But let me give you the REAL gameโ€ฆ If youโ€™re still in your 20s or 30s, the best thing you can do for your family isnโ€™t paying their bills. Itโ€™s becoming so self-sufficient they NEVER have to pay yours again. Itโ€™s not needing a ride. Itโ€™s not needing to borrow money. Itโ€™s not needing them to pick up the slack every time life gets tough. Thatโ€™s real freedom. Thatโ€™s how you actually honor your family. Iโ€™ve talked to THOUSANDS of people and seen this pattern over and overโ€ฆ You finally start making money. You start paying for dinner. You cover the bills. You start playing โ€œprovider.โ€ And then something shifts. Youโ€™re no longer their son or daughter, youโ€™re the bank. The vibe changes. Especially with your dadโ€ฆ See, men are BUILT to provide. Thatโ€™s where we get our fulfillment from. And when you swoop in to โ€œsave the family,โ€ you can unknowingly steal that role from your father. You think youโ€™re helping. He thinks youโ€™re taking his purpose. No social media guru is gonna tell you that. Theyโ€™ll tell you to hustle 24/7 so you can โ€œretire your familyโ€. Iโ€™m telling you, donโ€™t take on burdens nobody even asked you to carry. This year, your focus needs to be simple: Become 100% self-sufficient. Become the shining example of what a successful human looks like. Thatโ€™s the best gift you can give your family. You win, because youโ€™re free. They win, because they successfully raised a child into a thriving adult. Thatโ€™s the real flex. So let go of the pressure to carry everyone on your back. Get yourself right first. Get so locked in, so dialed, that your family never has to worry about you again. THATโ€™S what they actually want. Drop your thoughts in the comments. Letโ€™s hear how YOU feel about this. Always the best, Dylan Madden
1:03

Why You Shouldnโ€™t Retire Your Family (Especially If Youโ€™re In Your 20s & 30s) Retiring your family sounds noble, right? Youโ€™re hustling to make enough money so Mom never has to lift a finger again. So Dad can stop working that job heโ€™s hated for decades. I respect that intention. I truly do. But let me give you the REAL gameโ€ฆ If youโ€™re still in your 20s or 30s, the best thing you can do for your family isnโ€™t paying their bills. Itโ€™s becoming so self-sufficient they NEVER have to pay yours again. Itโ€™s not needing a ride. Itโ€™s not needing to borrow money. Itโ€™s not needing them to pick up the slack every time life gets tough. Thatโ€™s real freedom. Thatโ€™s how you actually honor your family. Iโ€™ve talked to THOUSANDS of people and seen this pattern over and overโ€ฆ You finally start making money. You start paying for dinner. You cover the bills. You start playing โ€œprovider.โ€ And then something shifts. Youโ€™re no longer their son or daughter, youโ€™re the bank. The vibe changes. Especially with your dadโ€ฆ See, men are BUILT to provide. Thatโ€™s where we get our fulfillment from. And when you swoop in to โ€œsave the family,โ€ you can unknowingly steal that role from your father. You think youโ€™re helping. He thinks youโ€™re taking his purpose. No social media guru is gonna tell you that. Theyโ€™ll tell you to hustle 24/7 so you can โ€œretire your familyโ€. Iโ€™m telling you, donโ€™t take on burdens nobody even asked you to carry. This year, your focus needs to be simple: Become 100% self-sufficient. Become the shining example of what a successful human looks like. Thatโ€™s the best gift you can give your family. You win, because youโ€™re free. They win, because they successfully raised a child into a thriving adult. Thatโ€™s the real flex. So let go of the pressure to carry everyone on your back. Get yourself right first. Get so locked in, so dialed, that your family never has to worry about you again. THATโ€™S what they actually want. Drop your thoughts in the comments. Letโ€™s hear how YOU feel about this. Always the best, Dylan Madden

Dylan Madden

13,220 gรถrรผntรผleme โ€ข 1 yฤฑl รถnce

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. Thatโ€™s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You donโ€™t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
1:13

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. Thatโ€™s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You donโ€™t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,822 gรถrรผntรผleme โ€ข 1 yฤฑl รถnce

ANTHROPIC'S PRODUCT CHIEF HAS USED CLAUDE FABLE 5 FOR MONTHS BEFORE ANYONE ELSE. HERE'S WHAT HE LEARNED ABOUT THE MOST POWERFUL MODEL YET Mike Krieger co-founded Instagram and now runs product at Anthropic. He's had Claude Fable 5 for two months before the public, and his takeaway is that it changes how you have to work, not just how much you get done. Here's what stood out, and what to actually do with it 1. It holds the whole project, so stop chopping tasks small. The old habit was breaking work into model-sized pieces and stitching them. Fable keeps the whole thing in context. What to do: stop pre-slicing your prompts into tiny steps. Hand it the full goal and the intent behind it, the way you'd brief a senior engineer, and let it sequence the work itself 2. Delegate big, async, and overnight. He sets it on a hard task at night and wakes to it finished, including the model getting itself unstuck when a service died, scaffolding a workaround, and documenting it. What to do: stop babysitting one prompt at a time. Kick off long jobs and walk away. Run several sessions at once instead of one you watch 3. The skill is planning now, not typing. His day moved to long architecture conversations up front, then execution in chunks. What to do: spend your first prompts planning, not building. Then ask it to output an HTML page or markdown doc of the plan so your team aligns before any code is written. That early alignment is the new leverage 4. Match the effort level to the task. Fable's range is wide, so a heavy reasoning pass on a tiny UI tweak is overkill (and pricey). What to do: dial effort down for small jobs, save the deep thinking for hard ones. And don't use your most expensive model for quick questions, keep a fast model for those 5. Verification is the real bottleneck now. The hard part isn't getting output, it's trusting it. What to do: make every change ship with proof. Have Claude attach a screenshot or video of what it built, so you can see the result instead of reading the diff. Then stand behind the decisions yourself before you merge 6. Cost is per-result, not per-turn. Fable is expensive per call but often one-shots what other models need ten turns to get right. What to do: judge cost by what it takes to finish the task to your satisfaction, not the price of a single message. Give it a real task and see how far it gets before you jump in His bigger point: software engineering isn't over, it's different. The craft moved from writing code to owning intent, taste, and what actually ships. The floor rose so anyone can build, and the ceiling rose so experts go further than before Bookmark this
52:06

ANTHROPIC'S PRODUCT CHIEF HAS USED CLAUDE FABLE 5 FOR MONTHS BEFORE ANYONE ELSE. HERE'S WHAT HE LEARNED ABOUT THE MOST POWERFUL MODEL YET Mike Krieger co-founded Instagram and now runs product at Anthropic. He's had Claude Fable 5 for two months before the public, and his takeaway is that it changes how you have to work, not just how much you get done. Here's what stood out, and what to actually do with it 1. It holds the whole project, so stop chopping tasks small. The old habit was breaking work into model-sized pieces and stitching them. Fable keeps the whole thing in context. What to do: stop pre-slicing your prompts into tiny steps. Hand it the full goal and the intent behind it, the way you'd brief a senior engineer, and let it sequence the work itself 2. Delegate big, async, and overnight. He sets it on a hard task at night and wakes to it finished, including the model getting itself unstuck when a service died, scaffolding a workaround, and documenting it. What to do: stop babysitting one prompt at a time. Kick off long jobs and walk away. Run several sessions at once instead of one you watch 3. The skill is planning now, not typing. His day moved to long architecture conversations up front, then execution in chunks. What to do: spend your first prompts planning, not building. Then ask it to output an HTML page or markdown doc of the plan so your team aligns before any code is written. That early alignment is the new leverage 4. Match the effort level to the task. Fable's range is wide, so a heavy reasoning pass on a tiny UI tweak is overkill (and pricey). What to do: dial effort down for small jobs, save the deep thinking for hard ones. And don't use your most expensive model for quick questions, keep a fast model for those 5. Verification is the real bottleneck now. The hard part isn't getting output, it's trusting it. What to do: make every change ship with proof. Have Claude attach a screenshot or video of what it built, so you can see the result instead of reading the diff. Then stand behind the decisions yourself before you merge 6. Cost is per-result, not per-turn. Fable is expensive per call but often one-shots what other models need ten turns to get right. What to do: judge cost by what it takes to finish the task to your satisfaction, not the price of a single message. Give it a real task and see how far it gets before you jump in His bigger point: software engineering isn't over, it's different. The craft moved from writing code to owning intent, taste, and what actually ships. The floor rose so anyone can build, and the ceiling rose so experts go further than before Bookmark this

Yarchi

30,958 gรถrรผntรผleme โ€ข 2 ay รถnce

A lot of people think that if youโ€™re carnivore, you must be anti-plants. Iโ€™m not anti-plants. There are millions of people in the world who eat plants and do great on them. Some plants are absolutely fine for some bodies. Right now theyโ€™re not fine for mine. Thatโ€™s why I use carnivore as an elimination protocol. When I started, I had inflammation, digestive issues, likely SIBO, leaky gut, and a system that felt chaotic. I didnโ€™t know what foods were helping and what foods were hurting me. When youโ€™re eating a bunch of different plant foods every week, itโ€™s almost impossible to figure out whatโ€™s causing the problem. Carnivore removes the noise. Beef, butter, bacon, eggs, salt, water. Thatโ€™s it. Once the noise is gone, your body finally has a chance to tell the truth. Over the last 13 months Iโ€™ve tested a few plants one at a time like zucchini, cucumber, raspberries, strawberries, and pickles. I paid attention to how I felt after each one. Thatโ€™s how you collect useful data. Not guesses. Data. A lot of people try keto and donโ€™t feel much different, so they assume eating more meat and cutting carbs โ€œdidnโ€™t work.โ€ But many times theyโ€™re still eating the same plants that were triggering inflammation in the first place. Not always. But often enough that it matters. If your system is reacting to something youโ€™re eating, keto can lower carbs without actually removing the trigger. Carnivore does. This isnโ€™t for everyone. But if your joints hurt, your digestion is unpredictable, your sleep is off, or your inflammation feels constant, a 90-day carnivore elimination phase can give you answers fast. Remove the variables. Watch what changes. Then add foods back one at a time and see what your body actually tolerates. Thatโ€™s not restriction. Thatโ€™s investigation.
2:37

A lot of people think that if youโ€™re carnivore, you must be anti-plants. Iโ€™m not anti-plants. There are millions of people in the world who eat plants and do great on them. Some plants are absolutely fine for some bodies. Right now theyโ€™re not fine for mine. Thatโ€™s why I use carnivore as an elimination protocol. When I started, I had inflammation, digestive issues, likely SIBO, leaky gut, and a system that felt chaotic. I didnโ€™t know what foods were helping and what foods were hurting me. When youโ€™re eating a bunch of different plant foods every week, itโ€™s almost impossible to figure out whatโ€™s causing the problem. Carnivore removes the noise. Beef, butter, bacon, eggs, salt, water. Thatโ€™s it. Once the noise is gone, your body finally has a chance to tell the truth. Over the last 13 months Iโ€™ve tested a few plants one at a time like zucchini, cucumber, raspberries, strawberries, and pickles. I paid attention to how I felt after each one. Thatโ€™s how you collect useful data. Not guesses. Data. A lot of people try keto and donโ€™t feel much different, so they assume eating more meat and cutting carbs โ€œdidnโ€™t work.โ€ But many times theyโ€™re still eating the same plants that were triggering inflammation in the first place. Not always. But often enough that it matters. If your system is reacting to something youโ€™re eating, keto can lower carbs without actually removing the trigger. Carnivore does. This isnโ€™t for everyone. But if your joints hurt, your digestion is unpredictable, your sleep is off, or your inflammation feels constant, a 90-day carnivore elimination phase can give you answers fast. Remove the variables. Watch what changes. Then add foods back one at a time and see what your body actually tolerates. Thatโ€™s not restriction. Thatโ€™s investigation.

Queen of Carni

11,751 gรถrรผntรผleme โ€ข 3 ay รถnce

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen
49:49

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,010 gรถrรผntรผleme โ€ข 5 ay รถnce

$ABCL CEO Carl Hansen explains the net negative overall value creation in biotech and how AbCellera plans to beat it: โ€œI really think it can be beat because thereโ€™s a lot of mistakes that get made in where you allocate capital.โ€ โ€œIf youโ€™re a biotech and you start a company on a drug, and you go public and have a drug, and now the data is looking not that great, you just keep running that drug. โ€ฆ Because if you quit that drug, the company is done.โ€ โ€œSo, because youโ€™re so concentrated on one thing, you end up having blinders. Itโ€™s like a cult of a molecule. And even a real, practical, capital access problem that makes you continue to move things forwardโ€ ______________________________________ โ€œAnother problem is that many of the drugs that get started, get started by people who donโ€™t understand the competitive or commercial landscape and have no intent of ever actually manufacturing it. โ€ฆ Because they know someone else is going to buy it. So then their framework is not โ€˜Am I making a drug thatโ€™s going to make a difference for people?โ€™ The framework is โ€˜Can I make something that I think someone will take off my hands?โ€™ And that happens a lot.โ€ โ€œThere are many acquisitions where a big pharma company spends $10B, $20B, and itโ€™s a complete bust. So really no value is generated for patients but someone was able to get a good exit on that because they anticipated what someone would want. And a lot of decision making gets done like thatโ€. _______________________________________ โ€œIโ€™m not saying that we have this all solved. But what weโ€™re trying to do is create a framework where we are able to take many bets so that we donโ€™t fall in love with anything, or we donโ€™t have to fall in love with anything. โ€ฆ And then always hold up very explicitly what we know and what we donโ€™t know along the dimensions that matter. The dimensions that matter are - Will it work? Thatโ€™s a science. - When it gets where will people care? - Is it differentiated? - Does it solve a big problem? In order to get it there, is there a path with our resources and expertise that would allow us to see it through?โ€ โ€œWhen you start to hold things up like that, you start to see where the mistakes are, where the uncertainties are, and you can be more rational in deciding โ€˜Weโ€™re going to run it to here because we get to flip the card and if the card goes the wrong way weโ€™re going to kill it because weโ€™ve got something else behind it.โ€™โ€ โ€œI do think that if done right, and if you can pick the right opportunities, the success rate can be well higher than an order of magnitude better. โ€ฆ And there are examples of thisโ€ฆ companies Iโ€™ve mentioned, you know, Regeneron, their success rate is at least 10x the industry in bringing programs forward and getting them approved. โ€ฆ So itโ€™sโ€ฆ you can do it better that wayโ€
3:50

$ABCL CEO Carl Hansen explains the net negative overall value creation in biotech and how AbCellera plans to beat it: โ€œI really think it can be beat because thereโ€™s a lot of mistakes that get made in where you allocate capital.โ€ โ€œIf youโ€™re a biotech and you start a company on a drug, and you go public and have a drug, and now the data is looking not that great, you just keep running that drug. โ€ฆ Because if you quit that drug, the company is done.โ€ โ€œSo, because youโ€™re so concentrated on one thing, you end up having blinders. Itโ€™s like a cult of a molecule. And even a real, practical, capital access problem that makes you continue to move things forwardโ€ ______________________________________ โ€œAnother problem is that many of the drugs that get started, get started by people who donโ€™t understand the competitive or commercial landscape and have no intent of ever actually manufacturing it. โ€ฆ Because they know someone else is going to buy it. So then their framework is not โ€˜Am I making a drug thatโ€™s going to make a difference for people?โ€™ The framework is โ€˜Can I make something that I think someone will take off my hands?โ€™ And that happens a lot.โ€ โ€œThere are many acquisitions where a big pharma company spends $10B, $20B, and itโ€™s a complete bust. So really no value is generated for patients but someone was able to get a good exit on that because they anticipated what someone would want. And a lot of decision making gets done like thatโ€. _______________________________________ โ€œIโ€™m not saying that we have this all solved. But what weโ€™re trying to do is create a framework where we are able to take many bets so that we donโ€™t fall in love with anything, or we donโ€™t have to fall in love with anything. โ€ฆ And then always hold up very explicitly what we know and what we donโ€™t know along the dimensions that matter. The dimensions that matter are - Will it work? Thatโ€™s a science. - When it gets where will people care? - Is it differentiated? - Does it solve a big problem? In order to get it there, is there a path with our resources and expertise that would allow us to see it through?โ€ โ€œWhen you start to hold things up like that, you start to see where the mistakes are, where the uncertainties are, and you can be more rational in deciding โ€˜Weโ€™re going to run it to here because we get to flip the card and if the card goes the wrong way weโ€™re going to kill it because weโ€™ve got something else behind it.โ€™โ€ โ€œI do think that if done right, and if you can pick the right opportunities, the success rate can be well higher than an order of magnitude better. โ€ฆ And there are examples of thisโ€ฆ companies Iโ€™ve mentioned, you know, Regeneron, their success rate is at least 10x the industry in bringing programs forward and getting them approved. โ€ฆ So itโ€™sโ€ฆ you can do it better that wayโ€

Jack Prescott

27,317 gรถrรผntรผleme โ€ข 13 gรผn รถnce