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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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Immediately after the Jacob/Anthropic tweet thread went viral, when it was at around 2m impressions, I screen recorded the first subtweeters as the diffusion/ propagation of the visit was obviously sync’d. I was certain, and said so at the time that the tweets were very highly suspect and bs. To do some light forensics, these days, as X cut off a lot of the tools/API I knew I had to catch the data in real time. I have , thru a shop I’m an investor in, the full x firehose, but whatever. Anyhoo, with this stuff it’s called ‘Graph Neural Networks’ = GNN It’s a graph of nodes that operate on graphs where nodes are connected by edges. A. Node (or a vertice) is a a network of users, the subtweeters B. edges are the relationships/interactions between them This is ‘neighborhood aggregation’ What you’re looking for, in the way I simplistically did it sans tools, is to look thru thr assorted influencers and subtweeters and see if their engagement is abnormal / too high And then also look for those accounts use of bots and inauthentic behavior Anyhoo
2:23

Immediately after the Jacob/Anthropic tweet thread went viral, when it was at around 2m impressions, I screen recorded the first subtweeters as the diffusion/ propagation of the visit was obviously sync’d. I was certain, and said so at the time that the tweets were very highly suspect and bs. To do some light forensics, these days, as X cut off a lot of the tools/API I knew I had to catch the data in real time. I have , thru a shop I’m an investor in, the full x firehose, but whatever. Anyhoo, with this stuff it’s called ‘Graph Neural Networks’ = GNN It’s a graph of nodes that operate on graphs where nodes are connected by edges. A. Node (or a vertice) is a a network of users, the subtweeters B. edges are the relationships/interactions between them This is ‘neighborhood aggregation’ What you’re looking for, in the way I simplistically did it sans tools, is to look thru thr assorted influencers and subtweeters and see if their engagement is abnormal / too high And then also look for those accounts use of bots and inauthentic behavior Anyhoo

Adam Townsend

203,816 Aufrufe • vor 8 Tagen

🛜 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!
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🛜 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

217,281 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 6 Monaten

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
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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 Aufrufe • vor 2 Monaten

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below
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context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below

Hanako

19,160 Aufrufe • vor 1 Monat

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. ↳ inside a unit: the loop. produce, check, correct, repeat until green ↳ between units: the graph. split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. ↳ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in ↳ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓
0:41

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. ↳ inside a unit: the loop. produce, check, correct, repeat until green ↳ between units: the graph. split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. ↳ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in ↳ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

73,867 Aufrufe • vor 29 Tagen

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 Aufrufe • vor 3 Monaten

$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

19,782 Aufrufe • vor 1 Jahr

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
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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

39,810 Aufrufe • vor 3 Monaten

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. ↳ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result ↳ between calls: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. ↳ it retries, it times out, it logs, it assembles context, it holds one call up beautifully ↳ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓
0:53

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. ↳ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result ↳ between calls: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. ↳ it retries, it times out, it logs, it assembles context, it holds one call up beautifully ↳ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

50,770 Aufrufe • vor 7 Tagen

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

1,439,310 Aufrufe • vor 1 Jahr

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

64,336 Aufrufe • vor 9 Monaten

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

168,153 Aufrufe • vor 1 Jahr

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
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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

1,335,409 Aufrufe • vor 1 Jahr

ComfyUI Tutorial for Beginners: Text-to-Image & Image-to-Image Workflows One graph, two workflows and Module 2 is where you build it node by node instead of loading someone else's. We start from a blank canvas and wire every connection, then change a single value and turn the same graph into image-to-image. What you'll learn: → The 5 core nodes, connected one at a time — checkpoint, CLIP encode, KSampler, empty latent, VAE decode → Why the data flows the way it does, not just where the wires go → Every KSampler setting: seed, steps, CFG, sampler, scheduler → Denoise from 0.9 to 0.0 — the one value that decides how much of your source survives → Locking seeds and generating takes instead of chasing one perfect image → Preview vs. save, and why every image carries the graph that made it → Reopen any result in your asset library as a live workflow → The 6 errors that break Comfy most often → How to read a red node like a sentence Click the links below to run the workflow ⬇️
37:05

ComfyUI Tutorial for Beginners: Text-to-Image & Image-to-Image Workflows One graph, two workflows and Module 2 is where you build it node by node instead of loading someone else's. We start from a blank canvas and wire every connection, then change a single value and turn the same graph into image-to-image. What you'll learn: → The 5 core nodes, connected one at a time — checkpoint, CLIP encode, KSampler, empty latent, VAE decode → Why the data flows the way it does, not just where the wires go → Every KSampler setting: seed, steps, CFG, sampler, scheduler → Denoise from 0.9 to 0.0 — the one value that decides how much of your source survives → Locking seeds and generating takes instead of chasing one perfect image → Preview vs. save, and why every image carries the graph that made it → Reopen any result in your asset library as a live workflow → The 6 errors that break Comfy most often → How to read a red node like a sentence Click the links below to run the workflow ⬇️

ComfyUI

57,175 Aufrufe • vor 18 Tagen

Tillman compares Zcash to CryptoPunks, a price held up by holders who don't need to sell "Here's the point I brought up earlier about like 40% of the public pool is owned by 100 wallets in Zcash." "They may all be diamond hands, like Bored Ape Yachts and CryptoPunks are. I'm just saying, that's the only reason why those things still command a price, because the people that own them are rich enough where they don't have to sell them." "And if the 40% of wallet holders on Zcash don't have to sell, because the price is up this high and they don't need the extra billion, then the price could go up higher." "But that's not real is the point. And people need to understand that." "That's not a real market, when you have concentration of that much of the currency in that few hands. What you want is a very, very shallow, very wide pool of holders."
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Tillman compares Zcash to CryptoPunks, a price held up by holders who don't need to sell "Here's the point I brought up earlier about like 40% of the public pool is owned by 100 wallets in Zcash." "They may all be diamond hands, like Bored Ape Yachts and CryptoPunks are. I'm just saying, that's the only reason why those things still command a price, because the people that own them are rich enough where they don't have to sell them." "And if the 40% of wallet holders on Zcash don't have to sell, because the price is up this high and they don't need the extra billion, then the price could go up higher." "But that's not real is the point. And people need to understand that." "That's not a real market, when you have concentration of that much of the currency in that few hands. What you want is a very, very shallow, very wide pool of holders."

The Wolf Of All Streets

43,302 Aufrufe • vor 10 Tagen

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.
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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

17,483 Aufrufe • vor 5 Monaten

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:
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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 Aufrufe • vor 1 Jahr

What if you could visualise how electricity flows from a wind farm in Scotland to your home in London? Ok it's not quite that simple, but it got me thinking that there must be a way of visualising the complexity of the electricity transmission grid in Great Britain. This is my first attempt… What you're seeing are the real power lines and substations on the transmission grid using OpenStreetMap data. The real connections between each substation are found using PyPSA-Eur as well as calculating the "electrical distance" or impedance between each substation. This is my incredibly basic and naive way of attempting to show the "speed" at which the energy is moving through the network, where the bottlenecks are, etc. I know it's not as simple as this, but it's a first attempt so… 😉 I've also added a way to pick any substation in the network and show how electricity pulses through the rest of the network from that point. This is the magic sauce that really brings to life how much infrastructure electricity has to pass through to get from point A to point B. Of course this isn't really what happens, and there's no demand to soak up the energy. However I think it still serves as an interesting visual of how complex the network actually is and the distances involved to get energy from one side of the country to the other. What do you think? Is this worth exploring further?
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What if you could visualise how electricity flows from a wind farm in Scotland to your home in London? Ok it's not quite that simple, but it got me thinking that there must be a way of visualising the complexity of the electricity transmission grid in Great Britain. This is my first attempt… What you're seeing are the real power lines and substations on the transmission grid using OpenStreetMap data. The real connections between each substation are found using PyPSA-Eur as well as calculating the "electrical distance" or impedance between each substation. This is my incredibly basic and naive way of attempting to show the "speed" at which the energy is moving through the network, where the bottlenecks are, etc. I know it's not as simple as this, but it's a first attempt so… 😉 I've also added a way to pick any substation in the network and show how electricity pulses through the rest of the network from that point. This is the magic sauce that really brings to life how much infrastructure electricity has to pass through to get from point A to point B. Of course this isn't really what happens, and there's no demand to soak up the energy. However I think it still serves as an interesting visual of how complex the network actually is and the distances involved to get energy from one side of the country to the other. What do you think? Is this worth exploring further?

Robin Hawkes

144,544 Aufrufe • vor 7 Monaten