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Mapping technique: use H3 hexagonal binning to make dense point data intelligible at low zooms. Shown here: the new H3 integration in the freestiler vector tiling tool in action. 146 million jobs are binned by hierarchical hexagons, then mapped by the most common job category. Hexagons give way to... show more

Kyle Walker

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10,663 просмотров • 24 дней назад •via X (Twitter)

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🚨🚨🚨 We have released a database with the most searched keywords on the App Store, free for everyone! Just yesterday, we sent a message to all Astro ASO Tool users informing them that we had completed the integration of a database with one million keywords into our backend. This database allows us to access increasingly reliable data on popularity Today, we want to take a further step forward by making this database with 1 million keywords with a popularity value > 5 publicly available to everyone on our website! In this video, Alice Ercolani explains how to use this tool to discover new keywords to track in Astro! Link to the tool in the next post!

🚨🚨🚨 We have released a database with the most searched keywords on the App Store, free for everyone! Just yesterday, we sent a message to all Astro ASO Tool users informing them that we had completed the integration of a database with one million keywords into our backend. This database allows us to access increasingly reliable data on popularity Today, we want to take a further step forward by making this database with 1 million keywords with a popularity value > 5 publicly available to everyone on our website! In this video, Alice Ercolani explains how to use this tool to discover new keywords to track in Astro! Link to the tool in the next post!

Matteo Spada

53,383 просмотров • 9 месяцев назад

It looks to me like that guy was just protecting the lady with a backpack, when he was pepper sprayed at first. That is when he got tackled by the agents, and dragged back onto the street. Here it is zoomed in, and @ 50% speed. Much easier to see it this way. He clearly did not undertake any offensive action towards the agents at all. The most he did was to raise his left hand, protecting himself from the pepper spray. The lady with a back pack also seems to be getting back onto the street. but only because the guy was holding her by the hood of her jacket. So, when got pulled back onto the street, she also got pulled out with him.

It looks to me like that guy was just protecting the lady with a backpack, when he was pepper sprayed at first. That is when he got tackled by the agents, and dragged back onto the street. Here it is zoomed in, and @ 50% speed. Much easier to see it this way. He clearly did not undertake any offensive action towards the agents at all. The most he did was to raise his left hand, protecting himself from the pepper spray. The lady with a back pack also seems to be getting back onto the street. but only because the guy was holding her by the hood of her jacket. So, when got pulled back onto the street, she also got pulled out with him.

US_did_ 911

31,443 просмотров • 5 месяцев назад

#WATCH | Delhi: On the #CasteCensus, author and Advaita teacher Acharya Prashant says, "Any action depends on the intention. Caste census means collecting more data, authentic data and data in itself are neither forward nor backwards, good nor bad. It depends on what you want to what you intend to do with the data. If you want to identify the marginalised groups in a better way, so that more accurate and more efficient policy making can happen, then it's a good step, a forward step... On the other hand, if you use this data to cement cost identification further, then obviously it's a step backwards. If you use this data for electoral gains by targeting particular caste groups concentrated in particular electorates or geographies, then obviously it's a backwards thing..."

#WATCH | Delhi: On the #CasteCensus, author and Advaita teacher Acharya Prashant says, "Any action depends on the intention. Caste census means collecting more data, authentic data and data in itself are neither forward nor backwards, good nor bad. It depends on what you want to what you intend to do with the data. If you want to identify the marginalised groups in a better way, so that more accurate and more efficient policy making can happen, then it's a good step, a forward step... On the other hand, if you use this data to cement cost identification further, then obviously it's a step backwards. If you use this data for electoral gains by targeting particular caste groups concentrated in particular electorates or geographies, then obviously it's a backwards thing..."

ANI

82,442 просмотров • 1 год назад

"To be attacked for simply asking to see the evidence was beyond the pale. I personally thought it was atrocious." Jack Posobiec was in the courtroom when Defense Attorney Michael Burt lashed out at Erika Kirk. He tells me what he saw: “When Erika heard her name mentioned that way from the Defense team, let me just say that her head shot up and she focused directly on the individual, Attorney Michael Burt, and what he was saying, as did mine. What the family was asking for in that moment was to be shown the zoomed-in image—not to be broadcast to the world. They said, 'could we at least see the zoomed-in image,' which the judge had blocked earlier in the week, of the individual they believe is Tyler Robinson when he's crouched down in the prone position. He's already assembled the rifle, and now he's crawling across the building towards the sniper's perch right on the edge of the building, which has a direct line of sight to where Charlie's tent was. And the judge had ruled that 'we can show the video, but we can't zoom in on this spot.' They just want to be able to see the evidence, to see what happened to Charlie, to have some kind of, you know, I'm not going to say there's ever going to be closure or satisfaction, but at least some kind of understanding of what went on here.”

"To be attacked for simply asking to see the evidence was beyond the pale. I personally thought it was atrocious." Jack Posobiec was in the courtroom when Defense Attorney Michael Burt lashed out at Erika Kirk. He tells me what he saw: “When Erika heard her name mentioned that way from the Defense team, let me just say that her head shot up and she focused directly on the individual, Attorney Michael Burt, and what he was saying, as did mine. What the family was asking for in that moment was to be shown the zoomed-in image—not to be broadcast to the world. They said, 'could we at least see the zoomed-in image,' which the judge had blocked earlier in the week, of the individual they believe is Tyler Robinson when he's crouched down in the prone position. He's already assembled the rifle, and now he's crawling across the building towards the sniper's perch right on the edge of the building, which has a direct line of sight to where Charlie's tent was. And the judge had ruled that 'we can show the video, but we can't zoom in on this spot.' They just want to be able to see the evidence, to see what happened to Charlie, to have some kind of, you know, I'm not going to say there's ever going to be closure or satisfaction, but at least some kind of understanding of what went on here.”

Glenn Beck

206,778 просмотров • 9 дней назад

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

Andrew Ng

137,073 просмотров • 2 лет назад

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data. 2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the motion of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro -> RoboCasa produces N (varying visuals) -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are building tools to enable everyone in the ecosystem to scale up with us. Links in thread:

Jim Fan

364,380 просмотров • 1 год назад

NEW: Jay Bhattacharya says Warp Speed was the right call in 2020 — but the real trouble began when the data from the Vax came in 2021 and the mandates began. “I think Operation Warp Speed was absolutely the right thing to do in 2020. If you are in the middle of a new novel virus spreading around and you have the capacity to deploy amazing scientists to try to address that at a rapid pace, it was definitely the right call to make in 2020.” “When the data started coming in 2021, and the mandates started going out—well that’s when we started getting into a lot of trouble.” “Having an understanding of what was known, at what points in time and also making sure the people who were injured get an honest, an honest take rather than censor, the kind of the way they were in the Biden administration.” “For me, the most important thing is we need to have public health be honest with the American public. To me that’s upshot of the president’s take, which I really just admire.”

NEW: Jay Bhattacharya says Warp Speed was the right call in 2020 — but the real trouble began when the data from the Vax came in 2021 and the mandates began. “I think Operation Warp Speed was absolutely the right thing to do in 2020. If you are in the middle of a new novel virus spreading around and you have the capacity to deploy amazing scientists to try to address that at a rapid pace, it was definitely the right call to make in 2020.” “When the data started coming in 2021, and the mandates started going out—well that’s when we started getting into a lot of trouble.” “Having an understanding of what was known, at what points in time and also making sure the people who were injured get an honest, an honest take rather than censor, the kind of the way they were in the Biden administration.” “For me, the most important thing is we need to have public health be honest with the American public. To me that’s upshot of the president’s take, which I really just admire.”

The Vigilant Fox 🦊

161,943 просмотров • 10 месяцев назад

“Football is to recognise and anticipate on patterns. You see all kinds of patterns when you’re little…and you anticipate on the patterns…” Ruud Gullit 1. He’s seen it over and over (paying attention to the detail in his playing environment) 2. He’s made sense of what he’s seen and remembered it (thought about it to make meaning of it and processed it in both a visual and semantic way) 3. He can then make quicker and more effective decisions in games based upon what he sees in the moment (knowledge-in the game) with relation to what he’s seen and what he’s made sense of in the past (knowledge-of the game) Mastery of anticipation and decision-making… This requires… 1. Coach session design setting problems for players to detect and to solve 2. Coach behaviours that orient players’ attention to the problems to help them think about, work on, solve, and remember in-game problems 3. Player participation - attention, involvement, reflection-in-action, post session reflection-in-action to consolidate memories It’s a coach-player responsibility…

“Football is to recognise and anticipate on patterns. You see all kinds of patterns when you’re little…and you anticipate on the patterns…” Ruud Gullit 1. He’s seen it over and over (paying attention to the detail in his playing environment) 2. He’s made sense of what he’s seen and remembered it (thought about it to make meaning of it and processed it in both a visual and semantic way) 3. He can then make quicker and more effective decisions in games based upon what he sees in the moment (knowledge-in the game) with relation to what he’s seen and what he’s made sense of in the past (knowledge-of the game) Mastery of anticipation and decision-making… This requires… 1. Coach session design setting problems for players to detect and to solve 2. Coach behaviours that orient players’ attention to the problems to help them think about, work on, solve, and remember in-game problems 3. Player participation - attention, involvement, reflection-in-action, post session reflection-in-action to consolidate memories It’s a coach-player responsibility…

Daniel Abrahams

45,167 просмотров • 2 лет назад

MrBeast is launching a toy line... talk about bad timing. ‼️ The roll out may have been affected by the recent drama, not yet clear. Listing for the products are up on Target but the product doesn't appear to be in most stores yet. Details were suppose to come out in July, and the toys were shown off at SDCC. This line has been in the work since January, when MrBeast first announced a partnership with Moose Toys.

MrBeast is launching a toy line... talk about bad timing. ‼️ The roll out may have been affected by the recent drama, not yet clear. Listing for the products are up on Target but the product doesn't appear to be in most stores yet. Details were suppose to come out in July, and the toys were shown off at SDCC. This line has been in the work since January, when MrBeast first announced a partnership with Moose Toys.

DramaAlert

3,554,812 просмотров • 1 год назад

Elon Musk on autonomy: These things happen slowly, then all at once. “These things happen slowly, but then all at once. Peter Thiel has a book. Zero to One. Once you have a proof point, once you have it working, then scaling up is just a matter of time. So once it's working well in Austin, then, you know, we'll make sure it works well in other cities. I mean, there are obviously some unique cases, like downtown New York. But that's a highly unusual situation. Most cities in America are like Austin. A Tesla that you buy today and the self-driving just costs $99 a month, will give you autonomous driving anywhere in the country right now. The question is, when is it unsupervised? In order for that to be the case, we want the autonomous car to be much safer than a car driven by a person.” Interview with David Faber, CNBC, May 20, 2025

Elon Musk on autonomy: These things happen slowly, then all at once. “These things happen slowly, but then all at once. Peter Thiel has a book. Zero to One. Once you have a proof point, once you have it working, then scaling up is just a matter of time. So once it's working well in Austin, then, you know, we'll make sure it works well in other cities. I mean, there are obviously some unique cases, like downtown New York. But that's a highly unusual situation. Most cities in America are like Austin. A Tesla that you buy today and the self-driving just costs $99 a month, will give you autonomous driving anywhere in the country right now. The question is, when is it unsupervised? In order for that to be the case, we want the autonomous car to be much safer than a car driven by a person.” Interview with David Faber, CNBC, May 20, 2025

ELON CLIPS

45,256 просмотров • 1 год назад

COMMON DENOMINATOR OF GOOD OFFENSES There are many different ways to play offense… Fast,Slow, 5 Out, 4 out 1 in, continuity, run a million sets, etc. Based on personnel and philosophy, the system you put in might differ. But the common denominator between the best offensive teams is that they get High Quality shots. Layups, Free Throws, Catch n Shoot 3’s, etc. Wouldn’t it then make sense that when putting your system together, the most important part is the players having clarity on the type of shots you value most and the spacing/principles we need to get them?

COMMON DENOMINATOR OF GOOD OFFENSES There are many different ways to play offense… Fast,Slow, 5 Out, 4 out 1 in, continuity, run a million sets, etc. Based on personnel and philosophy, the system you put in might differ. But the common denominator between the best offensive teams is that they get High Quality shots. Layups, Free Throws, Catch n Shoot 3’s, etc. Wouldn’t it then make sense that when putting your system together, the most important part is the players having clarity on the type of shots you value most and the spacing/principles we need to get them?

Steve Dagostino

19,137 просмотров • 1 год назад

Exploring a visual style is one of the most exciting parts of the creative process. Color grading is a powerful way to define the atmosphere and give your story its own signature. #FindYourFlow A simple way to maintain your color grading throughout your story is by… 1️⃣ Including “match the color grading” in your prompt when generating your first or last frames with Nano Banana Pro in Flow. 2️⃣ Then, explore new settings and scenes while maintaining your look across clips. 3️⃣ Finally, use Frames to Video to animate your scenes. Here’s a look at the workflow:

Exploring a visual style is one of the most exciting parts of the creative process. Color grading is a powerful way to define the atmosphere and give your story its own signature. #FindYourFlow A simple way to maintain your color grading throughout your story is by… 1️⃣ Including “match the color grading” in your prompt when generating your first or last frames with Nano Banana Pro in Flow. 2️⃣ Then, explore new settings and scenes while maintaining your look across clips. 3️⃣ Finally, use Frames to Video to animate your scenes. Here’s a look at the workflow:

Google Flow

13,411 просмотров • 5 месяцев назад

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

Clara Shih

49,620 просмотров • 2 лет назад

As machine learning models grow in size, so too does their carbon footprint. As AI scales, it's important that we quantify and mitigate these emissions. In our new short course Carbon Aware Computing for GenAI Developers you'll learn from Google Cloud Developer Advocate Nikita Namjoshi how to: - Query the ElectricityMaps API for real-time data on regional electricity grid carbon intensity - Route your model training jobs to data centers in regions primarily powered by low-carbon sources like wind, solar, hydro and nuclear - Measure the carbon footprint of your ML training, inference, storage and API usage with the Google Cloud's Carbon Footprint tool - Optimize training job scheduling to run when clean energy is most abundant in a given region. You can sign up here:

As machine learning models grow in size, so too does their carbon footprint. As AI scales, it's important that we quantify and mitigate these emissions. In our new short course Carbon Aware Computing for GenAI Developers you'll learn from Google Cloud Developer Advocate Nikita Namjoshi how to: - Query the ElectricityMaps API for real-time data on regional electricity grid carbon intensity - Route your model training jobs to data centers in regions primarily powered by low-carbon sources like wind, solar, hydro and nuclear - Measure the carbon footprint of your ML training, inference, storage and API usage with the Google Cloud's Carbon Footprint tool - Optimize training job scheduling to run when clean energy is most abundant in a given region. You can sign up here:

Andrew Ng

105,900 просмотров • 2 лет назад

Just in case you forgot: Trump wants to give every F1 Visa holding Foreign Student who graduates from an American college (even jr. college) a Green Card. Not a temp work visa - he wants to give them permanent residency which also means the ability to apply for citizenship. Facts: There are about 11 million 'Degreed Stem' Jobs in the U.S. Between foreign born citizens and foreign work visa holders, about 7.5 million of those jobs are already held by foreigners. There are about 4.2 million native born Americans working in these degreed STEM jobs today. We are already a minority in degreed STEM jobs. There are 12 million American citizens with STEM degrees not working in STEM. How many degreed STEM jobs does Trump think there are? Fyi, you'll often hear numbers for STEM jobs in the 30-35 million range. But that includes nurses, electricians, electronics technicans and many important but lower level jobs. When it comes engineers and scientists. there are only about 11 million jobs available. Why does Trump believe we can just flood in and endless supply of foreigners into the STEM workforce? With 1.2 million F1 visa holders here, the numbers just don't work. Note that he's speaking with the Tech Bros on this podcast. They fill his head with endless lies about shortages and skills gaps. But he never seems to ask: How did we build the entire tech world here in the U.S from the '50s through to the late '90s with mostly American engineers and scientists? There was never a shortage. Rather, what we did is crowd Americans out of universities and entry level jobs. Now the labor market for these jobs is filled with the foreign born. It makes me sick to my stomach listening to him prattle on like this.

Just in case you forgot: Trump wants to give every F1 Visa holding Foreign Student who graduates from an American college (even jr. college) a Green Card. Not a temp work visa - he wants to give them permanent residency which also means the ability to apply for citizenship. Facts: There are about 11 million 'Degreed Stem' Jobs in the U.S. Between foreign born citizens and foreign work visa holders, about 7.5 million of those jobs are already held by foreigners. There are about 4.2 million native born Americans working in these degreed STEM jobs today. We are already a minority in degreed STEM jobs. There are 12 million American citizens with STEM degrees not working in STEM. How many degreed STEM jobs does Trump think there are? Fyi, you'll often hear numbers for STEM jobs in the 30-35 million range. But that includes nurses, electricians, electronics technicans and many important but lower level jobs. When it comes engineers and scientists. there are only about 11 million jobs available. Why does Trump believe we can just flood in and endless supply of foreigners into the STEM workforce? With 1.2 million F1 visa holders here, the numbers just don't work. Note that he's speaking with the Tech Bros on this podcast. They fill his head with endless lies about shortages and skills gaps. But he never seems to ask: How did we build the entire tech world here in the U.S from the '50s through to the late '90s with mostly American engineers and scientists? There was never a shortage. Rather, what we did is crowd Americans out of universities and entry level jobs. Now the labor market for these jobs is filled with the foreign born. It makes me sick to my stomach listening to him prattle on like this.

War for the West

68,342 просмотров • 5 месяцев назад

Billy Bragg explains why he's supporting The Green Party "People at the by-election have an opportunity to send a message to Westminster" "They don't like the way politics is done in Westminster" "To get to a place where we have affordable housing, affordable energy, affordable transport - we need a new settlement between the British people and Westminster" "There's a lack of vision on how we give voters a sense of agency, and make their votes actually matter" Calling for proportional representation In the clip of Billy Bragg at the start the BBC showed a scene of him at a protest standing alongside Jeremy Corbyn, then zoomed into Corbyn - this type of coverage is intentional and really quite sad to see

Billy Bragg explains why he's supporting The Green Party "People at the by-election have an opportunity to send a message to Westminster" "They don't like the way politics is done in Westminster" "To get to a place where we have affordable housing, affordable energy, affordable transport - we need a new settlement between the British people and Westminster" "There's a lack of vision on how we give voters a sense of agency, and make their votes actually matter" Calling for proportional representation In the clip of Billy Bragg at the start the BBC showed a scene of him at a protest standing alongside Jeremy Corbyn, then zoomed into Corbyn - this type of coverage is intentional and really quite sad to see

Farrukh

37,483 просмотров • 5 месяцев назад

Over 1 million clinical questions hit OpenEvidence every day. More than half the practicing physicians in the US rely on us at the point of care, mid-decision, with a patient in front of them. Downtime in that moment has real consequences. We partner with Baseten for our inference infrastructure to make sure answers are always there when physicians need them. They stopped by our office to talk about what that looks like under the hood.

Over 1 million clinical questions hit OpenEvidence every day. More than half the practicing physicians in the US rely on us at the point of care, mid-decision, with a patient in front of them. Downtime in that moment has real consequences. We partner with Baseten for our inference infrastructure to make sure answers are always there when physicians need them. They stopped by our office to talk about what that looks like under the hood.

OpenEvidence

107,233 просмотров • 3 месяцев назад

US job growth was just revised down by 818K (!) from April 2023 to March 2024. New jobs added were 30% less than initially reported. This is the largest downward revision since 2009! All-In saw this coming. David Sacks on August 2nd (E190): "The economic data that comes out keeps getting reforecast down." "So they put out a provisional number or an estimate, and then when they finalize the number three months or six months later, it always seems to go in one direction." "We've seen this over and over again with new jobs being reforecast down. And we saw this with the Q1 GDP." Chamath Palihapitiya on August 9th (E191): "We're in a low-key recession." "So I think that we're going to probably go through a couple of very difficult revisions of old data." "The thing to remember about nonfarm payrolls isn't as much what the number is, but if you actually look to the number of times it then gets revised, the reality is that these things get revised constantly." "And right now we're in this trend where we are overestimating and revising down."

US job growth was just revised down by 818K (!) from April 2023 to March 2024. New jobs added were 30% less than initially reported. This is the largest downward revision since 2009! All-In saw this coming. David Sacks on August 2nd (E190): "The economic data that comes out keeps getting reforecast down." "So they put out a provisional number or an estimate, and then when they finalize the number three months or six months later, it always seems to go in one direction." "We've seen this over and over again with new jobs being reforecast down. And we saw this with the Q1 GDP." Chamath Palihapitiya on August 9th (E191): "We're in a low-key recession." "So I think that we're going to probably go through a couple of very difficult revisions of old data." "The thing to remember about nonfarm payrolls isn't as much what the number is, but if you actually look to the number of times it then gets revised, the reality is that these things get revised constantly." "And right now we're in this trend where we are overestimating and revising down."

The All-In Podcast

666,449 просмотров • 1 год назад

COACHES: This is a great clip from the Flyers on modern ozone play. A couple highlights of positive things they do here: -They change sides multiple times and go low to high/high to low in the zone. This spreads out the opponents dzone coverage opening up ice particularly in the middle. -Everyone is moving! Movement is way harder to defend than standing still. -Weakside D activation. The weakside D activating allowed them to keep possession of the puck after the initial shot and then get the rebound for the goal. -Positionless hockey. The goal was scored off a shot from a defenseman in the slot and then another defenseman getting the rebound right at the net front. Great job by Philly!!!!

COACHES: This is a great clip from the Flyers on modern ozone play. A couple highlights of positive things they do here: -They change sides multiple times and go low to high/high to low in the zone. This spreads out the opponents dzone coverage opening up ice particularly in the middle. -Everyone is moving! Movement is way harder to defend than standing still. -Weakside D activation. The weakside D activating allowed them to keep possession of the puck after the initial shot and then get the rebound for the goal. -Positionless hockey. The goal was scored off a shot from a defenseman in the slot and then another defenseman getting the rebound right at the net front. Great job by Philly!!!!

Topher Scott

17,765 просмотров • 5 месяцев назад

AI is not replacing jobs. There is a fundamental difference between a collection of tasks & a job. Job = U { Tasks, Judgment, Relationships, Context, Ownership} Few jobs are truly at risk of replacement by AI - these involve more deterministic IO (Input Output) execution e.g. data entry, QA, CS etc. Most jobs are resilient because: (1) Human principals (business owners) always seek to assign responsibility & blame to a human agent. (2) Deals get done not because of outbound spam but because of years of nurturing a relationship (showing up at conferences, events, meet-ups) (3) The physical world is messy AF. Humans read non-verbal & subtle cues which are only possible via in-person interactions. The midwits are right - AI is taking over tasks. In fact, if AI hasn’t taken over tasks for you then you’re doing something very wrong. If tasks are automated & workflows are changing, jobs won’t be lost. In fact, to manage the messy transition, humans will be more in demand than ever before. The data from Foundation Labs itself points to this (watch to the end of this rant)

AI is not replacing jobs. There is a fundamental difference between a collection of tasks & a job. Job = U { Tasks, Judgment, Relationships, Context, Ownership} Few jobs are truly at risk of replacement by AI - these involve more deterministic IO (Input Output) execution e.g. data entry, QA, CS etc. Most jobs are resilient because: (1) Human principals (business owners) always seek to assign responsibility & blame to a human agent. (2) Deals get done not because of outbound spam but because of years of nurturing a relationship (showing up at conferences, events, meet-ups) (3) The physical world is messy AF. Humans read non-verbal & subtle cues which are only possible via in-person interactions. The midwits are right - AI is taking over tasks. In fact, if AI hasn’t taken over tasks for you then you’re doing something very wrong. If tasks are automated & workflows are changing, jobs won’t be lost. In fact, to manage the messy transition, humans will be more in demand than ever before. The data from Foundation Labs itself points to this (watch to the end of this rant)

Rahul Mathur

10,664 просмотров • 1 месяц назад