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What if candlesticks weren't just data points on a timeline but nodes in a living network? Visibility graphs do exactly that. Take a rolling window of price, connect every pair of bars that have unobstructed geometric line of sight to each other, and you get a graph a real...

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gemchanger

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44,699 次观看 • 3 个月前 •via X (Twitter)

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Eon Systems' Dr. Alex Wissner-Gross, author of the viral "First Multi-Behavior Brain Upload" article, says there are many paths to AGI beyond LLMs: "My bet is intelligence is this very, very general effect, with many ways to implement it." "I published a paper a number of years ago arguing that not only is intelligent behavior a general process, I went further and argued that it is a general physical process, and that you can even formulate intelligent behavior in pure thermodynamic terms." "[The brain upload] looks a little bit more like a graph neural network. But really, that's such an AI way of framing it. It looks like a leaky integrate-and-fire, or LIF, model that we've had for decades from the neuroscience world. It's just a graph of nodes that have leaky integrate-and-fire dynamics, and they're firing at each other."
2:04

Eon Systems' Dr. Alex Wissner-Gross, author of the viral "First Multi-Behavior Brain Upload" article, says there are many paths to AGI beyond LLMs: "My bet is intelligence is this very, very general effect, with many ways to implement it." "I published a paper a number of years ago arguing that not only is intelligent behavior a general process, I went further and argued that it is a general physical process, and that you can even formulate intelligent behavior in pure thermodynamic terms." "[The brain upload] looks a little bit more like a graph neural network. But really, that's such an AI way of framing it. It looks like a leaky integrate-and-fire, or LIF, model that we've had for decades from the neuroscience world. It's just a graph of nodes that have leaky integrate-and-fire dynamics, and they're firing at each other."

TBPN

19,117 次观看 • 4 个月前

🛜 Remember LAN parties? I do LAN means "local area network" and it was essentially the internet but locally only in your home or company in the late 90s and early 2000s So you could connect to other computers to play games or share files, kinda like Airdrop but via a cable and 30 years ago, people would even meet up at some person's house and bring their entire computer (back then a big PC tower, CRT monitor, keyboard and mouse) and everyone would connect to each other Which is were you'd get all the WaReZ games, MP3 music, etc. cause nobody had internet yet, or if you did it was super slow, so LAN was much faster to transfer files I know Windows 3.11 did have support for LAN networking via NetBEUI and it "should" work, but of course on I don't have a network cable that goes to an Ethernet network hub to other computers But...we could just act like we do? I asked AI to build a virtual Ethernet hub (a hub routes traffic) that acts like a local LAN, but instead of connecting physical computers in a home, it connects other browser sessions on the internet that have running Windows 3.11 open at any time, and with DHCP it can assign an IP to every browser session dynamically, so they literally all become part of a local LAN on the internet! It runs on a virtual NE2000 network card that sends its network data not to a network cable but via Websockets to wss://pieter.com And it works, well kinda, I just started and its' not perfect, but I'm able to PING in MS-DOS from one tab to the other! Next is setting it up inside Windows 3.11!
1:00

🛜 Remember LAN parties? I do LAN means "local area network" and it was essentially the internet but locally only in your home or company in the late 90s and early 2000s So you could connect to other computers to play games or share files, kinda like Airdrop but via a cable and 30 years ago, people would even meet up at some person's house and bring their entire computer (back then a big PC tower, CRT monitor, keyboard and mouse) and everyone would connect to each other Which is were you'd get all the WaReZ games, MP3 music, etc. cause nobody had internet yet, or if you did it was super slow, so LAN was much faster to transfer files I know Windows 3.11 did have support for LAN networking via NetBEUI and it "should" work, but of course on I don't have a network cable that goes to an Ethernet network hub to other computers But...we could just act like we do? I asked AI to build a virtual Ethernet hub (a hub routes traffic) that acts like a local LAN, but instead of connecting physical computers in a home, it connects other browser sessions on the internet that have running Windows 3.11 open at any time, and with DHCP it can assign an IP to every browser session dynamically, so they literally all become part of a local LAN on the internet! It runs on a virtual NE2000 network card that sends its network data not to a network cable but via Websockets to wss://pieter.com And it works, well kinda, I just started and its' not perfect, but I'm able to PING in MS-DOS from one tab to the other! Next is setting it up inside Windows 3.11!

@levelsio

215,552 次观看 • 20 天前

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG
0:55

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG

QuantHood

16,376 次观看 • 13 天前

In graph theory, there are algorithms that find the shortest path between two nodes. I made one with pure CSS (including the graph drawing). Drag the nodes, and the shortest path will update in real-time! A demo powered by all the modern CSS features🤩
0:36

In graph theory, there are algorithms that find the shortest path between two nodes. I made one with pure CSS (including the graph drawing). Drag the nodes, and the shortest path will update in real-time! A demo powered by all the modern CSS features🤩

CSS by T. Afif

1,201,004 次观看 • 4 个月前

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!
0:47

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!

The Graph

425,326 次观看 • 2 年前

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure
0:24

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure

Seth Thompson

36,271 次观看 • 1 个月前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!
3:46

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,284 次观看 • 2 年前

"Its basically deadly attacks vs time on a graph - extrapolate that graph and it leads to the Slaughter of Europe" "The Numbers are clear" People still have no idea just hope precarious the situation for the whole of Europe is.
0:43

"Its basically deadly attacks vs time on a graph - extrapolate that graph and it leads to the Slaughter of Europe" "The Numbers are clear" People still have no idea just hope precarious the situation for the whole of Europe is.

Concerned Citizen

13,795 次观看 • 5 个月前

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?
1:01

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?

Herman Trading

20,079 次观看 • 1 个月前

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.
11:39

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

155,514 次观看 • 1 年前

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:
2:26

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,963 次观看 • 10 个月前

$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.
0:33

$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

Stoic

19,757 次观看 • 1 年前

Introducing: Mycelium Network A social network that connects people in Hyperliquid cyberspace. This network will facilitate decentralized communication and value exchange among individuals. Community members with various contributions to the ecosystem have been gathered to create the core of this network. The network will now start expanding permissionlessly, organically forming a decentralized social graph. Each member can nominate up to 3 others to join the network. Mycelium Network will power multiple creative decentralized applications. The first will be a unique investment platform that is only possible on Hyperliquid. If you’d like to become a contributor, you can join the network and create your own branch by inviting people. Early contributors will receive long term benefits in the applications built on this network. Members: nominate people at Nominees: join the network at
0:24

Introducing: Mycelium Network A social network that connects people in Hyperliquid cyberspace. This network will facilitate decentralized communication and value exchange among individuals. Community members with various contributions to the ecosystem have been gathered to create the core of this network. The network will now start expanding permissionlessly, organically forming a decentralized social graph. Each member can nominate up to 3 others to join the network. Mycelium Network will power multiple creative decentralized applications. The first will be a unique investment platform that is only possible on Hyperliquid. If you’d like to become a contributor, you can join the network and create your own branch by inviting people. Early contributors will receive long term benefits in the applications built on this network. Members: nominate people at Nominees: join the network at

FUNDAMENTAL

45,586 次观看 • 1 年前

ELON MUSK: You're clearly seeing a massive increase in the number of attacks in Italy and broadly in Europe And the media, the legacy media, downplays these attacks "But the attacks, the terrorism, the killing of innocent people is rising every week." "There's more and more... and eventually, I think we're going to see widespread slaughter in Europe." "That is the trend. If you simply extrapolate the trend of... what is the rate at which the terrorist attacks are occurring in Europe..." "And put those points - it's basically deadly attacks versus time - on a graph..." "And extrapolate that graph, it leads to the slaughter of Europe."
0:47

ELON MUSK: You're clearly seeing a massive increase in the number of attacks in Italy and broadly in Europe And the media, the legacy media, downplays these attacks "But the attacks, the terrorism, the killing of innocent people is rising every week." "There's more and more... and eventually, I think we're going to see widespread slaughter in Europe." "That is the trend. If you simply extrapolate the trend of... what is the rate at which the terrorist attacks are occurring in Europe..." "And put those points - it's basically deadly attacks versus time - on a graph..." "And extrapolate that graph, it leads to the slaughter of Europe."

X Freeze

64,318 次观看 • 7 个月前

Benedict Evans says infinite demand for tokens does not guarantee pricing power: "Right now, we're in this period of extreme disequilibrium of supply and demand and price and CapEx and capacity. But just because demand for tokens is infinite, that doesn't mean that you can't get to a different price equilibrium." "That's what happened with mobile data. Demand for bits is infinite. It's grown 1500, 2000x in the last fifteen years. But you still got to a supply and demand price equilibrium, and you still got a murderous price war between telcos in most parts of the world." "We're in this extreme scarcity, and then we have a pricing system, and we have a free market, and we have a surge of CapEx and like a trillion dollars of CapEx. So those multiples are going to move around. And then what?" Benedict Evans with Erik Torenberg
1:17

Benedict Evans says infinite demand for tokens does not guarantee pricing power: "Right now, we're in this period of extreme disequilibrium of supply and demand and price and CapEx and capacity. But just because demand for tokens is infinite, that doesn't mean that you can't get to a different price equilibrium." "That's what happened with mobile data. Demand for bits is infinite. It's grown 1500, 2000x in the last fifteen years. But you still got to a supply and demand price equilibrium, and you still got a murderous price war between telcos in most parts of the world." "We're in this extreme scarcity, and then we have a pricing system, and we have a free market, and we have a surge of CapEx and like a trillion dollars of CapEx. So those multiples are going to move around. And then what?" Benedict Evans with Erik Torenberg

a16z

39,637 次观看 • 1 个月前

American Grocery Stores are spying on you Take Kroger for example, “They have like alternative profit divisions that are solely focused on monetizing our personal information” 🚨 New Microsoft powered facial recognition technology is being implemented “Monetizing all the customer data they've actually taken in. And wildly enough, they've made more than a billion from this. A grocery store made a billion dollars from monetizing your information. Like this is a store that profits like three and a half billion a year. And one third of that comes from monetizing our information” “Another thing I learned was like, you know those little digital price things. Like instead of having the paper one, they got a digital one. Did you know that's there? So they can like change the prices in real time based upon the traffic pat patterns in the store. Like they will change it in real time if more people are in the store. — Even wilder, Kroger has this thing called an edge shelf which is developed with Microsoft, which will actually facially recognize you, determine your like age and gender and things like that, and then adjust prices based upon what the AI thinks you'd be willing to pay for. Something like that's f*cking nuts. Like I expected to see this type of technology in like CIA facilities or embassies, but in a grocery store.”
1:43

American Grocery Stores are spying on you Take Kroger for example, “They have like alternative profit divisions that are solely focused on monetizing our personal information” 🚨 New Microsoft powered facial recognition technology is being implemented “Monetizing all the customer data they've actually taken in. And wildly enough, they've made more than a billion from this. A grocery store made a billion dollars from monetizing your information. Like this is a store that profits like three and a half billion a year. And one third of that comes from monetizing our information” “Another thing I learned was like, you know those little digital price things. Like instead of having the paper one, they got a digital one. Did you know that's there? So they can like change the prices in real time based upon the traffic pat patterns in the store. Like they will change it in real time if more people are in the store. — Even wilder, Kroger has this thing called an edge shelf which is developed with Microsoft, which will actually facially recognize you, determine your like age and gender and things like that, and then adjust prices based upon what the AI thinks you'd be willing to pay for. Something like that's f*cking nuts. Like I expected to see this type of technology in like CIA facilities or embassies, but in a grocery store.”

Wall Street Apes

1,439,162 次观看 • 1 年前

Delta Airlines to begin using artificial intelligence algorithms to scrape your emails and more to determine the maximum amount you’re willing to spend on tickets, then adjust your price Example: If you have a death in the family, they’ll know it’s urgent and the price will go up ‘Delta says it's "fully reengineering how we price" airfare - leaning into Al algorithms that set the maximum price you're willing to spend, based on surveillance technology. About 3% of Delta's domestic ticket prices are now determined by artificial intelligence - and Delta plans to raise that to 20% by the end of the year.’ - More Perfect Union “Delta Airlines just announced on their earnings call that they're going to start using some of these algorithms to price some of their tickets. And imagine a world in which you have a death in the family, you have to go home for a funeral. And they know that through scraping your email, that is, you know, announcing when the service is going to be. They know you're in a tight spot, and so you're going to be charged much more because of that. There's so much data collected about individual people by data brokers, by this whole shadowy ecosystem that's just tracking you through your phone, through your browsing history. — There is a serious worry that companies will be able to do this.” Imagine how many other businesses are going to start doing the same thing
0:36

Delta Airlines to begin using artificial intelligence algorithms to scrape your emails and more to determine the maximum amount you’re willing to spend on tickets, then adjust your price Example: If you have a death in the family, they’ll know it’s urgent and the price will go up ‘Delta says it's "fully reengineering how we price" airfare - leaning into Al algorithms that set the maximum price you're willing to spend, based on surveillance technology. About 3% of Delta's domestic ticket prices are now determined by artificial intelligence - and Delta plans to raise that to 20% by the end of the year.’ - More Perfect Union “Delta Airlines just announced on their earnings call that they're going to start using some of these algorithms to price some of their tickets. And imagine a world in which you have a death in the family, you have to go home for a funeral. And they know that through scraping your email, that is, you know, announcing when the service is going to be. They know you're in a tight spot, and so you're going to be charged much more because of that. There's so much data collected about individual people by data brokers, by this whole shadowy ecosystem that's just tracking you through your phone, through your browsing history. — There is a serious worry that companies will be able to do this.” Imagine how many other businesses are going to start doing the same thing

Wall Street Apes

1,335,153 次观看 • 11 个月前

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.
2:02

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.

OptimAI Network

17,434 次观看 • 3 个月前

Lina Khan: "The holy grail for companies for a long time has been the ability to charge each person what their willingness to pay is. If you're really hungry for lunch, maybe they'll charge you 10 bucks for a burger, but if you're not that hungry, maybe they'll charge you three bucks... Now there's so much data collected about individual people that there is a serious worry that companies will be able to do this. And actually Delta Airlines just announced on their earnings call that they're gonna start using some of these algorithms to price some of their tickets. And so imagine a world in which you have the death in the family. You have to go home for a funeral. And they know that through scraping your email that is announcing when the service is gonna be, they know you're in a tight spot. So you're gonna be charged much more because of that." Watch the full Lina Khan Episode of Lemonade Stand here:
1:09

Lina Khan: "The holy grail for companies for a long time has been the ability to charge each person what their willingness to pay is. If you're really hungry for lunch, maybe they'll charge you 10 bucks for a burger, but if you're not that hungry, maybe they'll charge you three bucks... Now there's so much data collected about individual people that there is a serious worry that companies will be able to do this. And actually Delta Airlines just announced on their earnings call that they're gonna start using some of these algorithms to price some of their tickets. And so imagine a world in which you have the death in the family. You have to go home for a funeral. And they know that through scraping your email that is announcing when the service is gonna be, they know you're in a tight spot. So you're gonna be charged much more because of that." Watch the full Lina Khan Episode of Lemonade Stand here:

More Perfect Union

171,665 次观看 • 11 个月前

Ben Felix reveals the 5% rule for deciding whether to rent or buy a home "You account for all of those unrecoverable costs the owner has, and the renter investing in the stock market and investing the cost difference" "What you'll find looking at expected stock returns and expected real estate appreciation, you can very easily show that there is an equivalence. There is a level of rent where you are indifferent between renting and owning" "If you divide the price of a home by 5% and then divide that number by 12, you will get the monthly rent that is equivalent to the unrecoverable cost of owning that home"
1:54

Ben Felix reveals the 5% rule for deciding whether to rent or buy a home "You account for all of those unrecoverable costs the owner has, and the renter investing in the stock market and investing the cost difference" "What you'll find looking at expected stock returns and expected real estate appreciation, you can very easily show that there is an equivalence. There is a level of rent where you are indifferent between renting and owning" "If you divide the price of a home by 5% and then divide that number by 12, you will get the monthly rent that is equivalent to the unrecoverable cost of owning that home"

dank

83,117 次观看 • 2 个月前