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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 просмотров • 1 год назад •via X (Twitter)

Образование Финансы Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля Marc Lou

Marc Lou1 год назад

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

Фото профиля Lab4crypto

Lab4crypto1 год назад

🚀 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.

Фото профиля Thom van Lieshout

Thom van Lieshout1 год назад

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

Фото профиля Marc Lou

Marc Lou1 год назад

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>

Фото профиля Marc Lou

Marc Lou1 год назад

ho no no no no no

Фото профиля jack friks

jack friks1 год назад

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

Фото профиля Fraser

Fraser1 год назад

all visitors from india🥶

Фото профиля Marc Lou

Marc Lou1 год назад

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

Фото профиля Abdelmoghit

Abdelmoghit1 год назад

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

Фото профиля Timo

Timo1 год назад

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

Фото профиля Marc Lou

Marc Lou1 год назад

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 просмотров • 9 месяцев назад

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AI Notkilleveryoneism Memes ⏸️

74,490 просмотров • 2 лет назад

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StockX

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Veena Jain

10,476 просмотров • 7 дней назад

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Faheem Ullah

11,974 просмотров • 4 месяцев назад

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@levelsio

85,969 просмотров • 2 месяцев назад

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Dan Gambardello

29,125 просмотров • 3 месяцев назад

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

131,075 просмотров • 3 месяцев назад

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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 просмотров • 1 месяц назад

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

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

29,789 просмотров • 2 месяцев назад

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 просмотров • 10 месяцев назад

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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 просмотров • 1 год назад

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

60,831 просмотров • 26 дней назад

is live 🚨 > Psyop tactics X DR marketing X mathematics everything here and more to build our psywar Models This is just the beta, the scale in which our models will enhance will be legendary. And ONLY for marketing/VSL purposes. Operating without psywar intel will from now on will be negligent to your potential as a founder & marketer... the amount of data this is being trained on is unfathomable. literally every singe data point from my firm + a insane computational prediction engine + some more [redacted] stuff I can't disclose for privacy reasons. 3+ psywar manuals worth of data, hundreds of case studies for the top neuro data sources hundreds of case studies and mapping data for stimuli response patterns ungodly amount of DR data and testing there is also no shot anyone can make something close to the level of this engine will output data. the Information this has been built on has been lost data, BH data, sourced from the farthest corners of the internet and even FARTHER THAN THAT + Our own Psywar firm's data The edge this will give is immeasurable and I promise you I will make this shit work and connect with all the automations you need it to > this is just the beta, once I can turn this into a full model. everything will go exponential, so do not forget about this. Try out our tools now and see how you like it. We are Just getting started. You do NOT want to miss out.

is live 🚨 > Psyop tactics X DR marketing X mathematics everything here and more to build our psywar Models This is just the beta, the scale in which our models will enhance will be legendary. And ONLY for marketing/VSL purposes. Operating without psywar intel will from now on will be negligent to your potential as a founder & marketer... the amount of data this is being trained on is unfathomable. literally every singe data point from my firm + a insane computational prediction engine + some more [redacted] stuff I can't disclose for privacy reasons. 3+ psywar manuals worth of data, hundreds of case studies for the top neuro data sources hundreds of case studies and mapping data for stimuli response patterns ungodly amount of DR data and testing there is also no shot anyone can make something close to the level of this engine will output data. the Information this has been built on has been lost data, BH data, sourced from the farthest corners of the internet and even FARTHER THAN THAT + Our own Psywar firm's data The edge this will give is immeasurable and I promise you I will make this shit work and connect with all the automations you need it to > this is just the beta, once I can turn this into a full model. everything will go exponential, so do not forget about this. Try out our tools now and see how you like it. We are Just getting started. You do NOT want to miss out.

Krma

14,658 просмотров • 1 месяц назад

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

13,805 просмотров • 1 месяц назад