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I've created a monster... DataFast Realtime Map now shows the likelihood of a visitor making a purchase 🤑 The algorithm analyzes 10 metadata points (browser, country, referrer, etc.) from your previous purchase patterns to make predictions. 🥵 Warmer color = higher chance to become a customer 🥶 Cooler color... show more

Marc Lou

357,105 subscribers

194,729 次观看 • 1 年前 •via X (Twitter)

教育财经科学技术

Anya Rossi• Live Now

Private livecam show

11 条评论

Marc Lou 的头像

Marc Lou1 年前

Just press "M" on your site analytics to pop the world map

Lab4crypto 的头像

Lab4crypto1 年前

🚀 Don't gamble with your portfolio! Use our advanced hybrid quant risk tool using on/off-chain data daily and make informed decisions. 📈 Acess to 1000+ charts for your crypto journey. 📚Receive free weekly quant analysis. 📊+21 projects supported. 🏗️ Beginners and experts.

Thom van Lieshout 的头像

Thom van Lieshout1 年前

I mean nice, but why do you want to know that?

Marc Lou 的头像

Marc Lou1 年前

Agree, it’s a first step. Now you get a glimpse on who converts best based on country, device etc Soon I’m adding full breakdown for all traffic (heat maps) Then you can adjust your marketing, for instance run a Facebook ads towards people in <country> using <device>

Marc Lou 的头像

Marc Lou1 年前

ho no no no no no

jack friks 的头像

jack friks1 年前

holy shit man you’re the monster time to make more money by giving you my money (worked last time)

Fraser 的头像

Fraser1 年前

all visitors from india🥶

Marc Lou 的头像

Marc Lou1 年前

Would be curious to see how PPP impacts this Because average salary is $400/month

Abdelmoghit 的头像

Abdelmoghit1 年前

visitors aren’t just numbers they’re $$ waiting to happen if u know how to read them this realtime map + prediction layer is insane combining intent + value = actual prioritization for growth

Timo 的头像

Timo1 年前

awesome what you cook there. it's gpdr compliant, right? haha (welcome to germany, lol)

Marc Lou 的头像

Marc Lou1 年前

thanks! it uses 1 cookie to track visitors (that’s the only way to get accurate long term session tracking). To be GDPR compliant, you can show a cookie banner.

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i got you - prediction markets need a dedicated trading terminal + analytics dashboard to attract more volume and provide greater clarity to traders on their performance. traders need data visualizations which are geared towards tracking prediction oriented metrics like calibration curve + brier score. after spending months talking to prediction market traders on twitter - i built out this prototype (mock data for now since Kalshi doesn't offer auth in their API). over the last few months i've become prediction market pilled - believing in a future where experts in topics can profit from their knowledge and a world where most people can make better decisions for their future based off of prediction market odds. a world where someone can see the odds of a jerome powell interest rate cut and decide whether or not to buy a house. a world where someone can directly see the odds of an election and have better data on whether they should move somewhere accordingly. (i want to work in prediction markets btw)

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steve

17,525 次观看 • 9 个月前

GoogleDeepmind Chief AGI Scientist Shane Legg: AGI by 2028 He’s had the same timelines for 12 years - insane! He gives a log-normal distribution with a mode of 2025. Importantly, while he puts a 50% chance of AGI by 2028, that means there is a 30% chance of AGI in the next three years. How have his timelines been so consistent since 2011? SHANE LEGG: I first formed those beliefs around 2001 after reading Ray Kurzweil's The Age of Spiritual Machines. There were two really important points in his book that I came to believe as true: 1) One is that computational power would grow exponentially for at least a few decades. And that the quantity of data in the world would grow exponentially for a few decades. And when you have exponentially increasing quantities of computation and data, then the value of highly scalable algorithms gets higher and higher. There's a lot of incentive to make a more scalable algorithm to harness all this computing data. So I thought it would be very likely that we'll start to discover scalable algorithms to do this. And then there's a positive feedback between all these things, because if your algorithm gets better at harnessing computing data, then the value of the data and the compute goes up because it can be more effectively used. And that drives more investment in these areas. If your compute performance goes up, then the value of the data goes up because you can utilize more data. So there are positive feedback loops between all these things. 2) And then the second thing was just looking at the trends. If the scalable algorithms were to be discovered, then during the 2020s, it should be possible to start training models on significantly more data than a human would experience in a lifetime. And I figured that that would be a time where big things would start to happen that would eventually unlock AGI. And I think we're now at that first part. I think we can start training models now with the scale of the data that is beyond what a human can experience in a lifetime. So I think this is the first unlocking step. DWARKESH: If we're in 2029 and it hasn't happened yet, if there was a problem that caused it, what would be the most likely reason for that? SHANE LEGG: I don't know. At the moment, it looks to me like all the problems are likely solvable with a number of years of research.

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AI Notkilleveryoneism Memes ⏸️

74,490 次观看 • 2 年前

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StockX

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Kamesh 𝐎𝐩𝐞𝐧𝐋𝐞𝐝𝐠𝐞𝐫 🇺🇸

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Andrew Ng

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Faheem Ullah

11,959 次观看 • 4 个月前

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Dan Gambardello

29,090 次观看 • 2 个月前

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@levelsio

85,969 次观看 • 2 个月前

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Chris Barber

130,951 次观看 • 2 个月前

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GeoLibre v1.4.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings field data collection, image georeferencing, and a full Jupyter notebook experience right next to the map. What's new in v1.4.0 - Jupyter beside the map: Run notebooks alongside your map (JupyterLite in the browser, JupyterLab on the desktop). - Field data collection: Capture point observations in the field with custom, configurable forms. - Georeferencer: Pin a scanned map or image to the map using ground control points. - Spectral Index toolbox: Compute NDVI, NDWI, EVI, and more directly from your rasters. - Better internationalization: Correct rendering of right-to-left basemap labels (Arabic, Hebrew). - More reliable data handling: Render GeoPackages without GDAL's WASM thread crash. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python

Qiusheng Wu

29,659 次观看 • 1 个月前

Soybean farmer: We're not doing well at all as an industry. We have zero sales on the books of this new crop to the Chinese, who are our biggest export customer historically. And right now, with them being a big zero in the purchase column, that's a five-alarm fire for our industry.

Soybean farmer: We're not doing well at all as an industry. We have zero sales on the books of this new crop to the Chinese, who are our biggest export customer historically. And right now, with them being a big zero in the purchase column, that's a five-alarm fire for our industry.

FactPost

44,308 次观看 • 10 个月前

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I'm excited to release Latent Scope 1.0! Thanks to Fable clearing 2 years of backlog, you can now point your workhorse model at a directory of images, a text corpus or even existing RAG db and get back an interactive map for looking at your data through the eyes of a transformer. Latent Scope started as a set of scripts with a frontend to try and make it less tedious to run the process of projecting & clustering embeddings. Now we let agents do the tedium and the library is essentially a skill that reliably outputs data maps that you can explore. In this release we enabled a ton of stuff, starting with performance increases by switching the backend to LanceDB and enabling cuML GPU acceleration. We added support for images, and late interaction (ColBERT models) as well as 3D UMAP support. We also added Leland McInnes new EVoC clustering method. All that is topped off with a skill definition and an Impeccable powered UI revamp. See the thread for more videos and demos!

Ian Johnson 🔬🤖

17,648 次观看 • 27 天前

Donald Trump says states need to embrace Data Centers “You have to convince your community how great these things are — you're going to make a tremendous amount of money. You're going to reduce taxes — Your community and your state's going to become rich, and there's very little disruption” “And again, if you don't take all that money, somebody else is going to take it. You might as well do it yourself.” Okay surprisingly enough, I have found some evidence for data centers actually lowering taxes for some communities This happened in Loudoun County, Virginia in “Data Center Alley” Data centers now generate a very large share of the county’s property tax revenue (hundreds of millions to nearly $1 billion annually This has allowed the county to lower the residential real property tax rate every year for roughly a decade. Loudoun has one of the lowest rates in Northern Virginia

Donald Trump says states need to embrace Data Centers “You have to convince your community how great these things are — you're going to make a tremendous amount of money. You're going to reduce taxes — Your community and your state's going to become rich, and there's very little disruption” “And again, if you don't take all that money, somebody else is going to take it. You might as well do it yourself.” Okay surprisingly enough, I have found some evidence for data centers actually lowering taxes for some communities This happened in Loudoun County, Virginia in “Data Center Alley” Data centers now generate a very large share of the county’s property tax revenue (hundreds of millions to nearly $1 billion annually This has allowed the county to lower the residential real property tax rate every year for roughly a decade. Loudoun has one of the lowest rates in Northern Virginia

Wall Street Apes

60,753 次观看 • 15 天前

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 次观看 • 1 年前

NILE NECTAR IS NOW LIVE!! (Blend of Kiwi and Nectarine!!)🔆🌴 There is also a 10% chance of obtaining the LEGENDARY LABEL with black-haired Tricky if you purchase!! Be sure to use CODE: SCREE for a discount, and I hope you all enjoy!! IT'S PRETTY GOOD!

NILE NECTAR IS NOW LIVE!! (Blend of Kiwi and Nectarine!!)🔆🌴 There is also a 10% chance of obtaining the LEGENDARY LABEL with black-haired Tricky if you purchase!! Be sure to use CODE: SCREE for a discount, and I hope you all enjoy!! IT'S PRETTY GOOD!

🔆Trickywi🌴

72,889 次观看 • 1 年前

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Corinne Marie Riley

119,410 次观看 • 7 个月前

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

Adeniyi.sui

13,419 次观看 • 1 个月前

Do you have what it takes to forecast International trade? Today, we are launching AI4trade, a global challenge where teams compete for cash prizes by submitting their best trade forecasts. But it comes with a couple of twists: 1. This is a future out-of-sample challenge, so teams will need to forecast data that will not exist at the time of submission (impossible to train to the test 👹). 2. Forecasts must be better than simply using the latest trade data available at the time of submission (e.g. using July data as a prediction for October numbers ⏰). This should be a fun global competition. People from all parts of the world are welcome to participate and all methods are allowed. We hope the challenge stimulates more work at the intersection of AI and economics. Link in the first comment: 👇

Do you have what it takes to forecast International trade? Today, we are launching AI4trade, a global challenge where teams compete for cash prizes by submitting their best trade forecasts. But it comes with a couple of twists: 1. This is a future out-of-sample challenge, so teams will need to forecast data that will not exist at the time of submission (impossible to train to the test 👹). 2. Forecasts must be better than simply using the latest trade data available at the time of submission (e.g. using July data as a prediction for October numbers ⏰). This should be a fun global competition. People from all parts of the world are welcome to participate and all methods are allowed. We hope the challenge stimulates more work at the intersection of AI and economics. Link in the first comment: 👇

César A. Hidalgo

22,915 次观看 • 1 年前