Loading video...

Video Failed to Load

Go Home

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:

31,034 views • 4 months ago •via X (Twitter)

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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

28,656 views • 5 months ago

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

22,418 views • 1 year ago

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

The Graph

425,326 views • 2 years ago

A Perfect Pairing! 5G & Edge Computing: Together they enhance responsiveness, capacity and reliability by processing #data closer to where it’s generated! 🌟See🔗 ◀️T-Mobile Business 🔸This is critical for applications such as augmented reality #AR and #customer interaction and especially in contexts where low latency and near-real-time data processing are critical ✅ 🌟 MEC allows data processing closer to the source, reducing latency, improving efficiency, and enhancing resilience, whilst moving computing closer to users. It reduces latency and centralizes data processing. 🔸This is useful for scenarios that demand rapid data action, data #confidentiality or the ability to function without relying on distant #Cloud servers ☁️ Although only about 10% of enterprise data is currently processed outside central servers, this figure is anticipated to grow to 75% by 2025 🌟MEC can help in industries like #manufacturing 🏭#Retail 🛍️#warehousing 🚚#agriculture🌾and #logistics 🚛through faster decision-making, lower costs and more reliable data processing. Fixed and mobile edge applications are emerging, with mobile edge becoming a key area for future growth. 🔸5G's speed, low latency, and #security make it a natural fit with MEC, offering solutions to businesses' needs for faster responses, local data processing and reliable operations ✅ 📈Business Adoption: Though still early, it is anticipated that some 75% of #enterprise data will be processed at the #edge by 2025, driven by advancements in #Smart devices and routers with built-in processing capabilities 📶 💡To find out more, please explore the 🎞️resource below 🔽 See🔗 ⬅️#5G #EdgeComputing #TechNews #TFBPartner BusinessIntelligence Franco Ronconi 🇮🇹 #IoT Jean CAYEUX #RaviVisvesvarayaSharadaPrasad #Telecom #InfoTech Yann Marchand Tony Moroney #DigitalTransformation #Telco Knut Jägersberg Jean-Baptiste Lefevre 💙 #TechForGood 💙 #Sustainability Fati Sule Aurelien Lallemant #IA 🔎 & #RSE 🌎 Lionel Costes #AI Mack ipfconline Greg Valancius Dr. Marcell Vollmer #StaySafe #CES2026 #IoT Dev Khanna Baskaran Ambalavanan Anand Narang #CX Ian Jones Hana Laurent Alaus Xavier Gomez Pinna Pierre Dr. Khulood Almani | د.خلود المانع Eric T. #VR Enrico Molinari #VivaTech2025 Chidambara .ML. Smaksked Skåne AB 🌐

Sen. Sally Eaves

10,484 views • 1 year ago

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

61,860 views • 7 months ago

95% of Healthcare data lives in petabytes of SQL databases The tools for AI to use that data haven't existed Today we fix that with TextQL Healthcare 100,000+ tables. Trillions of rows. Petabytes of data. 15 minutes to insights. Epic systems with 100,000+ tables. Cerner environments. Claims databases. Clinical notes. Prior authorizations. Healthcare organizations have more data complexity than any other industry - and exactly zero AI platforms built to handle it. Until now. Here's what makes it different: 1. Direct access to ALL your systems. No migration required. Epic + Cerner + Claims + Snowflake + Databricks. Everything. At once. Other platforms: 6-month ETL projects. TextQL: Connect Monday. Query Tuesday. First insights in 15 minutes. 2. Healthcare-compliant execution environment. Autonomous agents running production code in SOC 2 Type II, HIPAA-compliant infrastructure. Full audit trails. On-premise deployment available. 3. Structured AND unstructured data. Simultaneously. Everyone else: Claims records OR clinical notes. Us: Both. At the same time. Make sense of 100,000s of tables without months of data prep. We're not launching with pilots. We're launching with Lumeris - powering their Tom™ AI platform delivering care to millions of Americans. Live partnership. Production workloads. Enterprise healthcare data at scale. Advisory Board of operators who've run organizations serving 120M+ Americans: - Varsha Rao (former CEO Nurx, COO Clover Health) - David Griffith (Trinity Life Sciences, ex-Pfizer) - Sam Mohanty (former CDO, Prime Therapeutics) - Jean-Claude Saghbini (CTO, Lumeris) - Raghu Chandra (30 year EHR Veteran) These aren't advisors. They're the people who built the systems we're now optimizing. Meet us at HLTH Conference next week - Booth #4060 Or request a demo: Comment "HEALTHCARE" and we'll reach out for a customized demo!

TextQL

48,310 views • 9 months ago

Here's a copy/paste prompt recipe and vid showing exactly how to ask an LLM for an interactive map with satellite/map layers + a georeferencer that lets you see how old maps correspond with modern geography. Today the computer can’t make good print maps (that's your hill to climb ) but it can, with five bucks and twenty minutes, make good interactive maps. No software/GIS knowledge necessary, you just need a few nouns and an LLM. Scroll to the bottom for the repo/live map if you want those. I'm using Claude Code as an extension in VS Code but you can use the Claude CLI, Cursor, whatever. 1) Let's grab an old cadastral map and see who owned big tracts of a city; I found this an 1854 map of Niagara Falls, NY I found in the Library of Congress: , grabbed the .jp2, saved as a jpg from photoshop. 2) Let's ask Claude Code for a map. You can see exactly what I did in the video but my prompt, sans simple "hey it's busted" debugging, is written out in the following paragraphs. I explain the map-specific nouns in brackets. You can likely dump this whole thing in your LLM window and it'll work; I'd try plan mode + skip permissions. THE PROMPT Make an interactive map with MapLibre GL JS [maplibre is a javascript mapping library, a FOSS version of Mapbox GL JS. This lets us display tiled map data and arbitrary images on the map] Add basemap toggles with Esri satellite, Carto Positron, and OSM [these map layers require no API keys for light usage; Carto Positron is a nice road map layer and OSM is ugly but comprehensive] Add a globe/mercator projection toggle [I think the globe looks better at low zooms] Add a layer panel on the left with visibility checkboxes and delete buttons. Add a search box on the map that flies to results, with deletable pin markers [Makes this easy to get to your area of interest] Include an interactive local georeferencer: drop a JPG, pick ground control points on a zoomable/pannable image viewer, place them on the map, watch it warp with a progress bar centered on the map. [The georeferencer uses math ("affine transform"??) to match points on the old map to points on the new map; generally you click road intersections on the old map, match them on the new map, repeat a dozen times and everything aligns] The georeferenced map overlay defaults to 25% opacity with a slider above the control point list. [I want it easy to see the underlying modern geography] Add Export/import control point buttons [this saves the control points as a JSON so you can save and reimport your work] Add a button to export the warped image as a GeoTIFF with a .prj [In case you want to add the georeferenced image to a real GIS program like QGIS] Look up all relevant docs before starting [Claude sometimes uses outdated stuff] Split everything into separate HTML/CSS/JS files [Claude tends to pile everything in index.html, which is hard to read] Use Optima font, base color #FEFAF6 [I just like this style] Let me test with a local server [it serves it on a simple server so you can nav your host to localhost:8000 and try it out] Log all errors [so you don't have to play telephone with the LLM describing what's busted] 3) Once your LLM finishes, test it out in your browser; if it doesn't work, ask the LLM to check logs. Repeat 'til functional. 4) After this works on your computer, you can show it to everyone by hosting it on GitHub: prompt with "write a README explaining what everything does, add it to a new GitHub repo, deploy using GitHub pages, gimme the live URL" Here's what Claude made for me, try it yourself: • Upload the JPG in the repo, which is linked below • "Add GCP" • Click somewhere recognizable on the old map, like the tip of an island or a road intersection • Click the matching point on the new map • Repeat til you have least 3x points • Hit "georeference" • You'll see the old map atop the new map; if you want a better fit, delete bad points or add a dozen new ones, hit georeference again, repeat Repo: Is this map robust? Human-maintainable? Elegant? Performant? Secure? No, but *your* personal web map need not be. It just needs to work for *your* narrow use case, because it’s *your* map.

Evan Applegate

15,772 views • 4 months ago