正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

How I map millions of features without breaking the bank: - Data prep: R - Data tiling: tippecanoe, PMTiles - Deployment: Cloudflare R2 / Workers - Mapping: MapLibre / R / mapgl Lightning-fast with edge caching, minimal hosting costs See it in action:

Kyle Walker

19,872 subscribers

31,134 次观看 • 7 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

14 条评论

ᑕᕼᗩᗪᗷᖇ 的头像

ᑕᕼᗩᗪᗷᖇ7 个月前

Curious if you have tried for the viz?

Kyle Walker 的头像

Kyle Walker7 个月前

yeah is great

Alex Allchin 的头像

Alex Allchin7 个月前

Your R usage seems to be a bit of an outlier in the GIS community. What drives your preference to it?

Kyle Walker 的头像

Kyle Walker7 个月前

It does absolutely everything I want! R has a very strong GIS community going back 20+ years. I was just on a podcast where I went into more detail on this topic, if you're interested:

Alex Allchin 的头像

Alex Allchin7 个月前

Yeah, maybe I'm just in the wrong bubble. I haven't used R in many years but might have to give it another look in.

First Miles 的头像

First Miles7 个月前

Amazing as always Kyle

Kyle Walker 的头像

Kyle Walker7 个月前

thanks!

Greg Field 的头像

Greg Field7 个月前

PMTiles with Cloudfrare / R2 with workers works great!

josek 的头像

josek7 个月前

wow : Deployment: Cloudflare R2 / Workers

HiddeKehrer 的头像

HiddeKehrer7 个月前

this is brilliant – cloudflare workers + r2 for geo-distributed features is exactly what teams need to avoid the aws trap. have you considered for the full stack?

. 的头像

.7 个月前

Can you divide it by ethnic groups, maybe categorize it from what country they are mostly? that would be interesting to see

martin 的头像

martin7 个月前

will be trying out your formula. it's so slick and fast in the browser! thank you for sharing Kyle.

Josh Santiago 的头像

Josh Santiago7 个月前

This looks great. Have you considered using H3 hexagonal grids for density aggregation? The point-level granularity is compelling, but at city scale I start to lose the broader spatial pattern, which the hex view might surface macro trends.

Kristian Freeman 的头像

Kristian Freeman7 个月前

This is super cool - is it open-source somewhere? We're working on something on my team at CF to share OSS projects, would love to feature this

相关视频

Introducing the GeoLibre R package! GeoLibre already runs as a desktop app, a web app, and a Jupyter widget. Now it runs in R too. The new geolibre R package is an htmlwidgets interface that embeds the full GeoLibre GIS in the RStudio Viewer, Quarto, R Markdown, and Shiny. What you get: - A real GIS, not a static map. Layer panel, styling, drawing tools, identify, and the processing toolbox are all there inside your R session. - Bring your own data. `sf` objects, GeoJSON, CSV and XY tables, Shapefile, GeoPackage, FlatGeobuf, GeoParquet, KML, Cloud Optimized GeoTIFF, XYZ, WMS, WMTS, WFS, PMTiles, vector tiles, 3D Tiles, and even video overlays. - Shiny, in both directions. `geolibre_proxy()` updates a live map without rebuilding it, and drawn features, selected features, identify results, and the current view come back as Shiny inputs. You can even run processing algorithms from the server. The package is on GitHub now while the first CRAN submission is prepared. Try it out - R package website: - Live interactive example: - Source: - GeoLibre Web: - Documentation: #RStats #GIS #Geospatial #OpenSource #Shiny #MapLibre #GeoLibre

Introducing the GeoLibre R package! GeoLibre already runs as a desktop app, a web app, and a Jupyter widget. Now it runs in R too. The new geolibre R package is an htmlwidgets interface that embeds the full GeoLibre GIS in the RStudio Viewer, Quarto, R Markdown, and Shiny. What you get: - A real GIS, not a static map. Layer panel, styling, drawing tools, identify, and the processing toolbox are all there inside your R session. - Bring your own data. `sf` objects, GeoJSON, CSV and XY tables, Shapefile, GeoPackage, FlatGeobuf, GeoParquet, KML, Cloud Optimized GeoTIFF, XYZ, WMS, WMTS, WFS, PMTiles, vector tiles, 3D Tiles, and even video overlays. - Shiny, in both directions. `geolibre_proxy()` updates a live map without rebuilding it, and drawn features, selected features, identify results, and the current view come back as Shiny inputs. You can even run processing algorithms from the server. The package is on GitHub now while the first CRAN submission is prepared. Try it out - R package website: - Live interactive example: - Source: - GeoLibre Web: - Documentation: #RStats #GIS #Geospatial #OpenSource #Shiny #MapLibre #GeoLibre

Qiusheng Wu

25,414 次观看 • 2 个月前

1.47 billion dots mapped across India - one for every person. Visualized in a browser on a laptop with MapLibre + PMTiles, built with freestiler. I developed freestiler earlier this year to make massive interactive mapping workflows more accessible. Until recently, I hadn't been able to push it above 1 billion points. The latest batch of optimizations - coming soon to the package - make these sorts of workflows possible. In this workflow, freestiler streams in data from a parquet file, keeps memory in check by partitioning the data, and tiles in parallel with Rust. This tiling session took about 21 minutes on my MacBook with a peak memory usage under 9 GB. I'm working on making this even faster. The map here shows 1 dot for every person in India when fully zoomed in, derived from WorldPop 2026 population projections at 100m grid cells (modeled data, which is why you see uniform densities in large cities). Dots are placed randomly within those cells. I'll write up an article showing you how to do this soon.

1.47 billion dots mapped across India - one for every person. Visualized in a browser on a laptop with MapLibre + PMTiles, built with freestiler. I developed freestiler earlier this year to make massive interactive mapping workflows more accessible. Until recently, I hadn't been able to push it above 1 billion points. The latest batch of optimizations - coming soon to the package - make these sorts of workflows possible. In this workflow, freestiler streams in data from a parquet file, keeps memory in check by partitioning the data, and tiles in parallel with Rust. This tiling session took about 21 minutes on my MacBook with a peak memory usage under 9 GB. I'm working on making this even faster. The map here shows 1 dot for every person in India when fully zoomed in, derived from WorldPop 2026 population projections at 100m grid cells (modeled data, which is why you see uniform densities in large cities). Dots are placed randomly within those cells. I'll write up an article showing you how to do this soon.

Kyle Walker

813,860 次观看 • 23 天前

Excited to share a new open-source project for the web-mapping and LiDAR community: maplibre-gl-lidar 🚀 With maplibre-gl-lidar, you can visualize large-scale LiDAR point clouds (LAS/LAZ, including COPC) directly in the browser using MapLibre GL JS. It supports point-cloud data stored locally or hosted on the web, and comes with an interactive GUI featuring customizable color schemes, classification legends, and point picking. It even works seamlessly inside Jupyter Notebooks. Visualizing tens of millions of LiDAR points has never been this simple or fast, making it easy to build modern, lightweight, and fully interactive web-based LiDAR viewers. Try it out: Live Demo Viewer: GitHub Repository: Full Video Tutorial: #MapLibre #LiDAR #WebGIS #OpenSource #PointCloud #3DMapping

Excited to share a new open-source project for the web-mapping and LiDAR community: maplibre-gl-lidar 🚀 With maplibre-gl-lidar, you can visualize large-scale LiDAR point clouds (LAS/LAZ, including COPC) directly in the browser using MapLibre GL JS. It supports point-cloud data stored locally or hosted on the web, and comes with an interactive GUI featuring customizable color schemes, classification legends, and point picking. It even works seamlessly inside Jupyter Notebooks. Visualizing tens of millions of LiDAR points has never been this simple or fast, making it easy to build modern, lightweight, and fully interactive web-based LiDAR viewers. Try it out: Live Demo Viewer: GitHub Repository: Full Video Tutorial: #MapLibre #LiDAR #WebGIS #OpenSource #PointCloud #3DMapping

Qiusheng Wu

53,113 次观看 • 9 个月前

Data Models - Better AI Code in 5 Minutes Your AI isn't consistent because you're missing one file. I had the same problem until I started using a data model file. In this video, I'm showing you the exact two prompts I use: one generates the data model, the second uses it to generate features without breaking my app. Testing this with AnimeLeak (my actual production app) adding an admin approval feature. Using Droid with Sonnet 4.5 in spec mode, but this works in Cursor, Claude, or whatever you're using. KEY TIMESTAMPS 00:00 - The One File Your AI is Missing 00:44 - Prompt 1: Generate a Data Model 01:12 - The Result: Data Model Documentation 02:12 - Prompt 2: Implement a New Feature 03:00 - Reviewing the AI-Generated Spec 04:17 - Why This Workflow is So Powerful 04:32 - Your 3-Step Action Plan 05:12 - Next Video: Live Code Generation

Data Models - Better AI Code in 5 Minutes Your AI isn't consistent because you're missing one file. I had the same problem until I started using a data model file. In this video, I'm showing you the exact two prompts I use: one generates the data model, the second uses it to generate features without breaking my app. Testing this with AnimeLeak (my actual production app) adding an admin approval feature. Using Droid with Sonnet 4.5 in spec mode, but this works in Cursor, Claude, or whatever you're using. KEY TIMESTAMPS 00:00 - The One File Your AI is Missing 00:44 - Prompt 1: Generate a Data Model 01:12 - The Result: Data Model Documentation 02:12 - Prompt 2: Implement a New Feature 03:00 - Reviewing the AI-Generated Spec 04:17 - Why This Workflow is So Powerful 04:32 - Your 3-Step Action Plan 05:12 - Next Video: Live Code Generation

Ray Fernando

15,479 次观看 • 11 个月前

GeoLibre v1.6.0 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 multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography

GeoLibre v1.6.0 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 multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography

Qiusheng Wu

15,473 次观看 • 3 个月前

The chaos in France right now is just a dress rehearsal for something bigger. The Red-Green alliance is testing its limits, and it's not going to stop in Marseille. There's a global network operating behind this and we have the receipts to prove it. If you want to see who is actually pulling the strings and how this exact blueprint is already targeting our upcoming midterms, you need to see our special this Thursday: Mapping the DSA Machine. We partnered with DataRepublican (small r) to run the data, trace the money, and map out every single thread in this operation. So you can wake up to what’s coming, before they try to do it here.

The chaos in France right now is just a dress rehearsal for something bigger. The Red-Green alliance is testing its limits, and it's not going to stop in Marseille. There's a global network operating behind this and we have the receipts to prove it. If you want to see who is actually pulling the strings and how this exact blueprint is already targeting our upcoming midterms, you need to see our special this Thursday: Mapping the DSA Machine. We partnered with DataRepublican (small r) to run the data, trace the money, and map out every single thread in this operation. So you can wake up to what’s coming, before they try to do it here.

Glenn Beck

75,080 次观看 • 5 天前

🚨 BREAKING: Scott Presler is investigating potential major VOTER FRAUD in Minnesota "I have filed an email with the MN Sec. of State, submitting a data practices request to get the data of registrants, of people that vouched, and HOW MANY of those 500,000 voters actually came about because of vouching." He's now digging into their "same day voter registration data" after it was confirmed one person can vouch for 8 MIGRANTS to vote without proof of residency or voter ID "This vouching system opens the door for potential fraud and could swing statewide elections!" ThePersistence

🚨 BREAKING: Scott Presler is investigating potential major VOTER FRAUD in Minnesota "I have filed an email with the MN Sec. of State, submitting a data practices request to get the data of registrants, of people that vouched, and HOW MANY of those 500,000 voters actually came about because of vouching." He's now digging into their "same day voter registration data" after it was confirmed one person can vouch for 8 MIGRANTS to vote without proof of residency or voter ID "This vouching system opens the door for potential fraud and could swing statewide elections!" ThePersistence

Eric Daugherty

34,585 次观看 • 9 个月前

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,464 次观看 • 11 个月前

🚀 Visualize Big Vector Data in Python—FAST! Tired of slow and clunky tools for visualizing big vector dataset in Python? Discover this one-line Python trick using Leafmap + DuckDB to instantly render huge vector datasets—even gigabytes in size—right in your Jupyter notebook. In this video, you'll learn how to: ✅ Visualize millions of vector features with ease ✅ Use Leafmap + DuckDB to serve vector tiles dynamically ✅ Handle GeoParquet, GeoPackage, cloud-hosted data & more ✅ Create fully interactive maps—without crashing your browser! 📌 Watch the full video tutorial on YouTube: 👉 Notebook example: #Visualization #Leafmap #BigData #DuckDB #GeoPython #GIS #OpenSource

🚀 Visualize Big Vector Data in Python—FAST! Tired of slow and clunky tools for visualizing big vector dataset in Python? Discover this one-line Python trick using Leafmap + DuckDB to instantly render huge vector datasets—even gigabytes in size—right in your Jupyter notebook. In this video, you'll learn how to: ✅ Visualize millions of vector features with ease ✅ Use Leafmap + DuckDB to serve vector tiles dynamically ✅ Handle GeoParquet, GeoPackage, cloud-hosted data & more ✅ Create fully interactive maps—without crashing your browser! 📌 Watch the full video tutorial on YouTube: 👉 Notebook example: #Visualization #Leafmap #BigData #DuckDB #GeoPython #GIS #OpenSource

Qiusheng Wu

13,147 次观看 • 11 个月前

Nothing worse than letting unchecked AI agents use your data without guardrails. AI apps are becoming more useful, but with that, they are getting access to more of our data. Now imagine that access in a family context, where the app can access everyone's emails, calendars, chats, photos, etc. I'm not sure how this works in practice, but I'm looking forward to using this app for a couple of weeks to see exactly how much I can trust it.

Nothing worse than letting unchecked AI agents use your data without guardrails. AI apps are becoming more useful, but with that, they are getting access to more of our data. Now imagine that access in a family context, where the app can access everyone's emails, calendars, chats, photos, etc. I'm not sure how this works in practice, but I'm looking forward to using this app for a couple of weeks to see exactly how much I can trust it.

Santiago

17,063 次观看 • 3 个月前

WHO’S ACTUALLY RUNNING THE SYSTEM? “Everyone and no one.” Jennica Pounds, the data analyst known on X as DataRepublican, explains why the real power structure isn’t necessarily a single mastermind. It’s a self-reinforcing system built on institutional cycles that continue regardless of who appears to be in charge. The deaf database engineer rose to prominence through her data analysis and later assisted DOGE efforts by building tools to search government awards and map money flowing through the NGO industrial complex. In Episode 95, DataRepublican joins Lara Logan to expose how the system sustains itself, who benefits and what it will take to dismantle it. DataRepublican (small r) | DataInterprete(r) | Going Rogue with Lara Logan WATCH EPISODE 95: SUPPORT GOING ROGUE: #LaraLogan #DataRepublican #DeepState #DOGE #Corruption

WHO’S ACTUALLY RUNNING THE SYSTEM? “Everyone and no one.” Jennica Pounds, the data analyst known on X as DataRepublican, explains why the real power structure isn’t necessarily a single mastermind. It’s a self-reinforcing system built on institutional cycles that continue regardless of who appears to be in charge. The deaf database engineer rose to prominence through her data analysis and later assisted DOGE efforts by building tools to search government awards and map money flowing through the NGO industrial complex. In Episode 95, DataRepublican joins Lara Logan to expose how the system sustains itself, who benefits and what it will take to dismantle it. DataRepublican (small r) | DataInterprete(r) | Going Rogue with Lara Logan WATCH EPISODE 95: SUPPORT GOING ROGUE: #LaraLogan #DataRepublican #DeepState #DOGE #Corruption

Lara Logan

170,148 次观看 • 1 个月前

I built a self-hosted Sentry clone that runs entirely on Cloudflare Workers, and I think it showcases one of the most underrated features in the Cloudflare ecosystem: Service Bindings. Let me explain why this matters. When you have multiple Cloudflare Workers (an API, a webhook handler, a cron job), they all need common things: error tracking, authentication, rate limiting, metrics. The typical solution? External HTTP calls to third-party services. That means: - 50-200ms latency per call - Egress fees - Your data leaving your infrastructure - Another vendor to manage Service bindings let Workers call each other directly inside Cloudflare's network. No HTTP. No internet. Just internal RPC with <5ms latency. With Workers Sentinel, any Worker in my account can just point Sentry-SDK into the Service binding, and have all errors flow into one centralized dashboard, stored in Durable Objects with SQLite. No external calls. No added latency. Service bindings aren't just for error tracking. You can centralize: 🔐 Authentication — One Worker that validates tokens for all your services 📊 Metrics — Centralized collection without external observability costs 🚦 Rate Limiting — Shared counters that actually work across Workers 🚩 Feature Flags — Instant propagation, no deployment needed Think of it as building your own internal microservices mesh, but at the edge, with zero network overhead. Workers Sentinel uses two Durable Objects: - AuthState (singleton) — users, sessions, projects - ProjectState (per-project) — issues, events, stats Events are fingerprinted and grouped intelligently. The dashboard is a Vue.js app served from the same Worker. I could say i built this to learn Durable Objects or that I needed error tracking for side projects, but honestly I just need a way to show my wife why I'm sending $200/month to some guy named Claudio who apparently helps me write code. The whole thing is open source. Deploy it to your Cloudflare account, point your Sentry SDKs at it, and you're done. But more importantly: take a closer look at service bindings. They're the glue that turns a collection of Workers into an actual platform. Most Cloudflare customers I talk to aren't using them, and they're missing out. To the Sentry team: I love your work. Genuinely. Sentry is battle-tested, has incredible features, and is what you should use for anything that matters. This project is a toy. A learning exercise. A weekend hack that got slightly out of hand. Please do not trust your production errors to this dummy clone. If your startup goes down at 3 AM because Workers Sentinel missed an edge case, that's on you. I warned you. Use the real thing. But if you want to learn about Durable Objects, service bindings, and how error tracking works under the hood? Clone away. Your Workers shouldn't be islands. Connect them.

I built a self-hosted Sentry clone that runs entirely on Cloudflare Workers, and I think it showcases one of the most underrated features in the Cloudflare ecosystem: Service Bindings. Let me explain why this matters. When you have multiple Cloudflare Workers (an API, a webhook handler, a cron job), they all need common things: error tracking, authentication, rate limiting, metrics. The typical solution? External HTTP calls to third-party services. That means: - 50-200ms latency per call - Egress fees - Your data leaving your infrastructure - Another vendor to manage Service bindings let Workers call each other directly inside Cloudflare's network. No HTTP. No internet. Just internal RPC with <5ms latency. With Workers Sentinel, any Worker in my account can just point Sentry-SDK into the Service binding, and have all errors flow into one centralized dashboard, stored in Durable Objects with SQLite. No external calls. No added latency. Service bindings aren't just for error tracking. You can centralize: 🔐 Authentication — One Worker that validates tokens for all your services 📊 Metrics — Centralized collection without external observability costs 🚦 Rate Limiting — Shared counters that actually work across Workers 🚩 Feature Flags — Instant propagation, no deployment needed Think of it as building your own internal microservices mesh, but at the edge, with zero network overhead. Workers Sentinel uses two Durable Objects: - AuthState (singleton) — users, sessions, projects - ProjectState (per-project) — issues, events, stats Events are fingerprinted and grouped intelligently. The dashboard is a Vue.js app served from the same Worker. I could say i built this to learn Durable Objects or that I needed error tracking for side projects, but honestly I just need a way to show my wife why I'm sending $200/month to some guy named Claudio who apparently helps me write code. The whole thing is open source. Deploy it to your Cloudflare account, point your Sentry SDKs at it, and you're done. But more importantly: take a closer look at service bindings. They're the glue that turns a collection of Workers into an actual platform. Most Cloudflare customers I talk to aren't using them, and they're missing out. To the Sentry team: I love your work. Genuinely. Sentry is battle-tested, has incredible features, and is what you should use for anything that matters. This project is a toy. A learning exercise. A weekend hack that got slightly out of hand. Please do not trust your production errors to this dummy clone. If your startup goes down at 3 AM because Workers Sentinel missed an edge case, that's on you. I warned you. Use the real thing. But if you want to learn about Durable Objects, service bindings, and how error tracking works under the hood? Clone away. Your Workers shouldn't be islands. Connect them.

Gabriel Massadas

28,840 次观看 • 8 个月前

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

15,670 次观看 • 2 个月前

How does an FDE map the processes to deploy AI in a real business? We use 3 sources. Source 1 is people. - Companies tell us: "this person's been at the firm for 20 years and they just handle it." - We hear this from Fortune 50 companies and from SMBs. - So in finance, you talk to the head of AP, AR, reconciliations, billing, banking, and FP&A. You make a "human API." Source 2 is the systems of record. - With real-time access to Salesforce for 3 to 4 weeks, you see what data comes in and how often people correct it. Source 3 is the documents in SharePoint, Drive, Notion, Slack, Teams, and Gmail. If you use the system data without the interviews, you miss half of the picture. The split is different for each company. A large company can have 10 years of Salesforce data. An SMB has most of it in people's heads. Choose your angle for each company before you build.

How does an FDE map the processes to deploy AI in a real business? We use 3 sources. Source 1 is people. - Companies tell us: "this person's been at the firm for 20 years and they just handle it." - We hear this from Fortune 50 companies and from SMBs. - So in finance, you talk to the head of AP, AR, reconciliations, billing, banking, and FP&A. You make a "human API." Source 2 is the systems of record. - With real-time access to Salesforce for 3 to 4 weeks, you see what data comes in and how often people correct it. Source 3 is the documents in SharePoint, Drive, Notion, Slack, Teams, and Gmail. If you use the system data without the interviews, you miss half of the picture. The split is different for each company. A large company can have 10 years of Salesforce data. An SMB has most of it in people's heads. Choose your angle for each company before you build.

The Startup Ideas Podcast (SIP) 🧃

35,097 次观看 • 10 天前

‼️🇺🇲🇻🇪 BREAKING — Donald Trump now appears closer than ever to acting on his earlier threats, with signs pointing to a potential military response against Nicolás Maduro. After a five-day port visit to St. Thomas in the U.S. Virgin Islands, the USS Gerald R. Ford (CVN-78) was spotted this morning departing Frenchman’s Cove. The carrier has now resumed its deployment in the Caribbean as part of Operation Southern Spear, placing it in a prime position should Washington decide to initiate military action. See the latest updates with us: NSTRIKE

‼️🇺🇲🇻🇪 BREAKING — Donald Trump now appears closer than ever to acting on his earlier threats, with signs pointing to a potential military response against Nicolás Maduro. After a five-day port visit to St. Thomas in the U.S. Virgin Islands, the USS Gerald R. Ford (CVN-78) was spotted this morning departing Frenchman’s Cove. The carrier has now resumed its deployment in the Caribbean as part of Operation Southern Spear, placing it in a prime position should Washington decide to initiate military action. See the latest updates with us: NSTRIKE

NSTRIKE

50,581 次观看 • 10 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!

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

426,146 次观看 • 2 年前

In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇

In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇

Ben Jacobs

22,468 次观看 • 1 年前