# visualization Published articles for visualization. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## TanStack Charts Introduced with a Framework Agnostic Grammar of Graphics for TypeScript DevFeed: [TanStack Charts Introduced with a Framework Agnostic Grammar of Graphics for TypeScript]() Original publisher: [Read original article]() Author: Daniel Curtis Published: 2026-09-17T05:53:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Library](), [TypeScript](), [Graphics](), [Frameworks](), [React]() Tags: [angular](), [data-visualization](), [development](), [framework](), [graphics](), [introduced](), [library](), [news](), [react](), [react-native](), [tanstack-charts-alpha-introduced](), [typescript](), [visualization](), [vue-js](), [web-development]() ### AI overview TanStack Charts is a new framework-agnostic TypeScript visualization library that uses a grammar-of-graphics approach instead of fixed chart types. It is in Alpha, has about 160,000 weekly downloads, and supports multiple frameworks and environments, though it is not yet stable for production use. ### Source excerpt TanStack Charts is a new, framework-agnostic visualization library for TypeScript. It allows developers to create visualizations by composing various elements instead of using fixed chart types. Currently in Alpha, it has about 160,000 weekly downloads. The library supports multiple frameworks and is designed for a variety of environments, though it is not yet stable for production use. By Daniel Curtis ## tota11y - an accessibility visualization toolkit DevFeed: [tota11y - an accessibility visualization toolkit]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-06-08T22:00:00Z Content type: release Language: en Sources: [Khan Academy]() Topics: [Accessibility](), [audit](), [Testing](), [Tooling](), [browser](), [Chrome]() Tags: [accessibility](), [accessibility-testing](), [audit](), [browser](), [chrome](), [developer-tools](), [engineering](), [frontend](), [linter](), [news](), [testing](), [tests](), [tooling](), [unit-tests](), [visualization](), [web](), [web-frontend]() ### AI overview Khan Academy announces tota11y, an accessibility visualization toolkit designed to make manual accessibility testing easier for developers. The article describes its development and use of browser-based annotations to help teams understand and fix accessibility issues. ### Source excerpt By Jordan Scales Today we're releasing tota11y (on GitHub), an accessibility visualization toolkit that aims to reduce the ... Read more ## Building Depth: Designing and Developing a 3D Renderer Inside Figma DevFeed: [Building Depth: Designing and Developing a 3D Renderer Inside Figma]() Original publisher: [Read original article]() Author: Aleksei Kipin Published: 2026-09-13T11:34:39Z Content type: article Language: en Sources: [Codrops]() Topics: [Figma 3D renderer](), [Figma]() Tags: [3d](), [3d-design](), [3d-modeling](), [3d-renderer-for-figma](), [3d-rendering](), [3d-visualization](), [3d-workflow](), [articles](), [case-study](), [creative-development](), [depth-3d-renderer](), [depth-figma](), [depth-figma-plugin](), [design-engineering](), [design-tools](), [figma](), [figma-3d](), [figma-3d-renderer](), [figma-plugin](), [figma-plugin-design](), [interactive-3d](), [plugin-development](), [product-design](), [product-design-case-study](), [real-time-rendering](), [three-js](), [ui-design](), [ux-design](), [visualization](), [webgl](), [workflow]() ### AI overview An article about building Depth, a Figma plugin for quickly rendering 3D models during design work. It describes the workflow, plugin architecture, rendering behavior, and export approach. ### Source excerpt A look at how Depth came together, from building a 3D renderer inside Figma to designing a website that explores what it can do. ## A visual guide to troubleshooting search performance using Query Insights dashboards DevFeed: [A visual guide to troubleshooting search performance using Query Insights dashboards]() Original publisher: [Read original article]() Author: Chenyang Ji Published: 2026-09-04T20:11:52Z Content type: tutorial Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [dashboards](), [Monitoring](), [Latency](), [cpu]() Tags: [blog](), [configuration](), [cpu](), [dashboards](), [distribution](), [how-to](), [index](), [latency](), [memory](), [monitoring](), [navigation](), [opensearch](), [performance](), [real-time](), [technical](), [troubleshooting](), [view](), [visualization]() ### AI overview A visual guide to using OpenSearch Query Insights dashboards to investigate slow searches and unexpected resource usage. It covers the Live Queries and Top N Queries views, interactive visualizations, configuration, and metrics such as latency, CPU, and memory. ### Source excerpt OpenSearch Query Insights dashboards provide interactive visualizations for monitoring live queries, analyzing top N query performance, and viewing individual query details. This post explores each visualization and shows how to use visualizations to troubleshoot search performance issues. The post A visual guide to troubleshooting search performance using Query Insights dashboards appeared first on OpenSearch. ## Build a real-time market data app with ClickHouse and Massive DevFeed: [Build a real-time market data app with ClickHouse and Massive]() Original publisher: [Read original article]() Author: Lionel Palacin Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [App](), [data](), [Node.js](), [Back end](), [API keys]() Tags: [analytics](), [api](), [app](), [backend](), [clickhouse](), [compression](), [data](), [demo](), [events](), [how-to](), [latency](), [low-latency](), [node](), [node-js](), [react](), [real-time](), [rest-api](), [storage](), [streams](), [visualization]() ### AI overview A tutorial for building a real-time market-data application that ingests, stores, and queries trade and quote ticks with Massive and ClickHouse, using Node.js for backend work and React for live visualization. ### Source excerpt Learn how to build a real-time financial analytics application with Massive and ClickHouse that scales to thousands of events per second. ## Visualize how CUPED adjusts experiment results with Datadog DevFeed: [Visualize how CUPED adjusts experiment results with Datadog]() Original publisher: [Read original article]() Author: Tyler Buffington; Lukas Goetz-Weiss; Ryan Lucht Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments]() Tags: [analysis](), [digital-experience-monitoring](), [experiment](), [experiments](), [how-to](), [metrics](), [product-analytics](), [visualization]() ### AI overview This tutorial explains Datadog Experiments' CUPED adjustments visualization, which breaks the difference between raw and CUPED-adjusted experiment lift into individual covariate contributions. ### Source excerpt Learn how Datadog visualizes CUPED adjustments so you can trace which covariates change experiment lift estimates and improve precision. ## How to build an exposure management program the business trusts: Lessons from Tenable's CSO DevFeed: [How to build an exposure management program the business trusts: Lessons from Tenable's CSO]() Original publisher: [Read original article]() Author: Robert Huber Published: 2026-08-27T14:30:00Z Content type: tutorial Language: en Sources: [Tenable Blog]() Topics: [Cybersecurity](), [Security & Privacy](), [Artificial Intelligence](), [data](), [Business Security]() Tags: [ai](), [blog](), [cybersecurity](), [data](), [enterprise](), [exposure-management](), [how-to](), [metrics](), [security](), [tools](), [visualization](), [workflows]() ### AI overview Tenable's article explains how an exposure management program can reduce security tool sprawl, unify fragmented security data, improve visibility across the attack surface, and connect cyber-risk metrics with business priorities. It also discusses the challenges of maintaining security and speed while organizations rapidly adopt AI. ### Source excerpt Discover how Tenable's shift to an AI-driven exposure management program helped Tenable's CSO, Robert Huber, overcome tool sprawl, unify data silos, mitigate the risk of rapid AI adoption, and shift from presenting granular, technical metrics to communicating business risk that the C-suite and the board can understand. Key takeaways Security tool sprawl and data silos make it difficult for CISOs to holistically and accurately assess their organizations' cyber risk. An exposure management program consolidates fragmented security data into a single unified view of cyber risk across the entire attack surface. Aided by exposure management, CISOs can align security metrics with business priorities and quantify risk for key revenue-generating business units, answering the board's main question: "Are we secure?" What is trust in cybersecurity? And more importantly, how do you earn it? Here's a hint: It's not easy, especially in this AI era. As the Chief Security Officer at Tenable, my mandate is to ensure our organization operates securely, but with the speed required to succeed in a very competitive business environment. In recent years, achieving this delicate balance -- an agile yet cyber secure business -- had become progressively more difficult, as we grappled with increasingly fragmented data, siloed teams, and security tool sprawl. In this blog, I'll explain how exposure management helped my team: Tackle security tool sprawl Bridge operational and data silos Take a more proactive approach to security Attain visibility and control over Tenable's attack surface Continuously and precisely assess our cyber risk posture The operational impact of security data silos and tool sprawl For years, the cybersecurity industry's answer to every new threat or policy mandate was simple: Buy another tool, which in many -- maybe most -- organizations resulted in a bad case of tool sprawl. A typical large enterprise might juggle 70 or more security technology vendors, each promising to so ## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### AI overview This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy. ### Source excerpt Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications... ## Add API visualizations in Port with Swagger UI | Port DevFeed: [Add API visualizations in Port with Swagger UI | Port]() Original publisher: [Read original article]() Author: Dudi Elhadad Published: 2026-07-30T10:09:05Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [OpenAPI Specification](), [Markdown](), [Microservice](), [dev-tools]() Tags: [api](), [developer-portal](), [documentation](), [integration](), [markdown](), [openapi-specification](), [software-catalog](), [visualization]() ### AI overview Port adds visualization features to its software catalog and developer portal, including Markdown documentation, OpenAPI specifications rendered with Swagger UI, and embedded URLs. These features centralize documentation, API discovery, monitoring dashboards, and other developer tools in context, reducing search and context switching. ### Source excerpt Here are the visualization features we just added: Markdown documentation visualization, Open API - swagger and embedded URLs. ## Open Sauce and GPS time were my summer AI Antiseptics DevFeed: [Open Sauce and GPS time were my summer AI Antiseptics]() Original publisher: [Read original article]() Author: jeff@jeffgeerling.com (Jeff Geerling) Published: 2026-07-22T14:00:00Z Content type: opinion Language: en Sources: [Jeff Geerling]() Topics: [Artificial Intelligence](), [Data visualization](), [MicroPython](), [Claude](), [datasets](), [Code]() Tags: [ai](), [badge](), [claude](), [coding](), [data-visualization](), [electronics](), [experimentation](), [gps](), [micropython](), [open-sauce](), [projects](), [time](), [videos](), [visualization]() ### AI overview The author describes using Claude to build a 2,141-line MicroPython Badgeware app for a Pimoroni Tufty 2350 and Adafruit PA1010D GPS module. The GPS Time Badge uses GPS data to discipline the badge's internal real-time clock, although timing accuracy was limited by the hardware and BadgeOS architecture. The author also logged GPS satellite and fix-quality data and used Claude to create an interactive visualization, highlighting data visualization as a practical use of AI tools. ### Source excerpt In the midst of our AI slop revolution, traveling to the West coast for Open Sauce this past weekend was the perfect antiseptic for rising costs, summer heat, and online divisiveness. It's ironic, then, that I used Claude to vibe code my Tufty GPS Time Badge. Partly due to time constraints, and partly because I wanted to see if I could complete a personal project end-to-end, without editing a line of code, I throw my requirements at Claude and ultimately came up with this 2141-line MicroPython app for Pimoroni's Badgeware ecosystem. ## Flint: A visualization language for the AI era DevFeed: [Flint: A visualization language for the AI era]() Original publisher: [Read original article]() Author: Chenglong Wang, Alper Sarikaya, Scott Tsukamaki, Michel Galley, Jianfeng Gao Published: 2026-07-08T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Artificial Intelligence](), [Open Source](), [MCP Server](), [data](), [Server]() Tags: [agents](), [ai](), [ai-agents](), [llms](), [mcp](), [mcp-server](), [open](), [open-source](), [research-blog](), [visualization]() ### AI overview Flint is an open-source visualization intermediate language designed for AI-driven chart creation. It lets AI agents produce expressive charts from compact, human-editable specifications, using semantic data types and automatic layout decisions while targeting multiple visualization backends. ### Source excerpt Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive charts from compact, human-editable specifications. The post Flint: A visualization language for the AI era appeared first on Microsoft Research. ## Too big to draw, but yet drawable DevFeed: [Too big to draw, but yet drawable]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T09:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [ordering](), [structure]() Tags: [aboulafia](), [caustics](), [generative](), [math](), [permutation](), [permutations](), [random](), [ranking](), [recursion](), [visualization]() ### AI overview The third article in a series explains how Aboulafia's tserouf orders all permutations of a word and visualizes them by placing the permutations around a circle and connecting each word to its reversal. Because the full permutation space becomes too large to draw, the article samples chords and shows that they form recurring caustic curves visible at multiple scales. ### Source excerpt Aboulafia's Tserouf - Part 3 of 4 <- Previous: An elegant formulation, inspired by Bill Gates - Next: A wheel, the same forwards and backwards -> ## What Is Software, and Will LLMs Replace It? DevFeed: [What Is Software, and Will LLMs Replace It?]() Original publisher: [Read original article]() Author: Federico Tomassetti Published: 2026-06-23T08:56:06Z Content type: opinion Language: en Sources: [Federico Tomassetti]() Topics: [Large Language Model](), [Software](), [data](), [integrity](), [Software as a service]() Tags: [ai](), [business](), [chatbots](), [consistency](), [data](), [future-of-ai](), [future-of-software](), [integrity](), [interfaces](), [language-engineering](), [llms](), [mcp](), [opinion](), [patterns](), [reflections](), [saas](), [software](), [software-development](), [sql](), [structure](), [visualization]() ### AI overview The article argues that large language models are unlikely to replace software. Instead, they may provide more flexible interfaces while software's underlying structures--organized data, schemas, constraints, consistency rules, visualizations, and guided processes--remain essential. ### Source excerpt Software isn't being replaced by LLMs, it's being fronted by them, with the deterministic core (schemas, constraints, processes) staying as essential as ever. The post What Is Software, and Will LLMs Replace It? appeared first on Federico Tomassetti. ## The Matter upgrade you've been waiting for DevFeed: [The Matter upgrade you've been waiting for]() Original publisher: [Read original article]() Author: Ingo Fischer Published: 2026-06-23T00:00:00Z Content type: article Language: en Sources: [Home Assistant]() Topics: [Matter](), [Home Assistant](), [TypeScript](), [Open Source](), [Network](), [Homebridge]() Tags: [2026](), [announcements](), [border-router](), [bugs](), [hardware](), [homebridge](), [matter](), [open-source](), [recovery](), [speed](), [standards](), [thread](), [typescript](), [upgrade](), [visualization]() ### AI overview Home Assistant introduces a new Matter implementation built on matter.js, an open source TypeScript implementation of the Matter standard. After beta testing and community contributions, the update aims to improve stability, reduce bugs, and accelerate startup and recovery. It also adds network visualization, upgrades to Matter 1.5.1, and updates the OpenThread Border Router app for Thread 1.4 support. ### Source excerpt It started as a passion project. Yet in a few years matter.js has grown beyond my wildest expectations, becoming the backbone of the open source Matter ecosystem, and powering everything from Homebridge to openHAB, and even some commercial products. Ten months ago, I joined forces with the Open Home Foundation to work full time as Lead Developer of Matter. Shortly after, I donated matter.js to the foundation - giving it a new home where it would be safeguarded for the future. Together we announced our plans to take Matter support in Home Assistant in a completely new direction: we set out to rebuild the Open Home Foundation Matter Server - the engine that powers Matter in Home Assistant - on matter.js. After four months of beta testing, with our community fixing bugs and contributing new features along the way, today we're thrilled to present the newest version of Matter for Home Assistant! 🎉 The Open Home Foundation invests in important technology to make open alternatives possible, and our work is funded in part by everyone who subscribes to Home Assistant Cloud and buys official hardware from our commercial partners. The launch of a new Matter experience in Home Assistant shows what's possible with your support. 💪 Presenting the matter.js plans at the Connectivity Standards Alliance (CSA) Member Meeting in November 2025 (right), and returning in March 2026 as an Outstanding Contributor Award recipient (left). Serving up more with matter.js For those new to matter.js, it's an open source TypeScript implementation of the Matter standard. I built it to give developers and power users more speed and flexibility, and to make Matter better suited for open source development across the millions of homes that use Home Assistant. By running Matter in Home Assistant on matter.js, all of those benefits now flow directly into the platform. This major update brings greater stability for Matter in Home Assistant, fewer bugs, and faster start-up and recovery. It also comes with ## What's new in Postgres Managed by ClickHouse: RBAC, Terraform, ClickPipes, extensions, and more DevFeed: [What's new in Postgres Managed by ClickHouse: RBAC, Terraform, ClickPipes, extensions, and more]() Original publisher: [Read original article]() Author: Kaushik Iska Published: 2026-06-18T16:10:49Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Authorization](), [Terraform](), [API](), [Model Context Protocol](), [Monitoring](), [Replication](), [stripe](), [draggable]() Tags: [api](), [clickhouse](), [draggable](), [management](), [mcp](), [metrics](), [monitoring](), [postgres](), [replication](), [rest-api](), [stripe](), [terraform](), [update](), [visualization]() ### AI overview This update covers new capabilities for Postgres managed by ClickHouse, including fine-grained per-service access control, a redesigned console, Terraform support, MCP support, Stripe integration, monitoring improvements, and a read-replica topology view. ### Source excerpt Postgres managed by ClickHouse's first major update since public beta, covering fine-grained access control, a redesigned console, Terraform support, MCP integration, Stripe provisioning, migrations, billing, and capacity reservations -- all shipped in und ## Powering self-driving vehicle analytics at Avride with ClickHouse Cloud DevFeed: [Powering self-driving vehicle analytics at Avride with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-11T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [data](), [Apache Iceberg](), [telemetry](), [Grafana]() Tags: [analytics](), [apache-iceberg](), [autonomous](), [aws](), [clickhouse](), [cloud](), [data-infrastructure](), [driving](), [grafana](), [latency](), [lidar](), [metrics](), [robots](), [storage](), [streams](), [telemetry](), [tooling](), [visualization]() ### AI overview Avride uses ClickHouse Cloud as the data backbone for its autonomous vehicles and delivery robots, supporting ride-data indexing, metrics, analytics, and internal tooling. Its migration from Apache Iceberg reduced index lookup and ingestion latency. ### Source excerpt Avride replaced Apache Iceberg with ClickHouse Cloud, cutting index lookup latency from 20 seconds to under 100ms and ingestion from hours to seconds. ## AI-generated synthetic neurons speed up brain mapping DevFeed: [AI-generated synthetic neurons speed up brain mapping]() Original publisher: [Read original article]() Published: 2026-04-16T12:18:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [AI Models](), [Google](), [Point cloud](), [Training AI Models](), [datasets](), [neuron](), [data]() Tags: [3d](), [accelerate](), [ai](), [classification](), [errors](), [general-science](), [generation](), [google](), [health-bioscience](), [iclr](), [iclr-2026](), [images](), [machine-intelligence](), [mapping](), [model](), [models](), [neuron](), [partners](), [research](), [scale](), [science](), [synthetic](), [visualization]() ### AI overview Google Research describes how MoGen generates synthetic neuronal shapes to improve AI models that reconstruct brain wiring maps. Adding synthetic training examples reduced reconstruction errors by 4.4%, potentially saving 157 person-years of manual proofreading for a complete mouse brain. ### Source excerpt General Science ## ClickHouse at FOSDEM 2026 DevFeed: [ClickHouse at FOSDEM 2026]() Original publisher: [Read original article]() Author: Tyler Hannan Published: 2026-04-08T18:00:47Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data-processing](), [real-time](), [debugging](), [LLVM](), [log management](), [Shell](), [SQL](), [dashboards](), [observability](), [Parser](), [Tooling]() Tags: [bugs](), [clickhouse](), [community](), [conference](), [data](), [debugging](), [llvm](), [logging](), [performance](), [real-time](), [sql](), [visualization]() ### AI overview ClickHouse at FOSDEM 2026 recaps the ClickHouse community dinner and technical talks on real-time visualization, production hotpatching with LLVM XRay, and inverted database indexes. ### Source excerpt FOSDEM 2026 took place on 31 January and 1 February in Brussels, and it was a great weekend for the ClickHouse community, both inside the conference rooms and out. ## Modernizing Kubeflow Pipelines UI DevFeed: [Modernizing Kubeflow Pipelines UI]() Original publisher: [Read original article]() Author: Manaswini Das Published: 2026-03-31T05:00:00Z Content type: article Language: en Sources: [Kubeflow]() Topics: [React](), [Front end](), [ui](), [Accessibility](), [Web](), [Testing]() Tags: [accessibility](), [frontend](), [metrics](), [migration](), [navigation](), [performance](), [pipelines](), [react](), [ui](), [visualization](), [web]() ### AI overview Kubeflow Pipelines' web interface was upgraded from React 16 to React 19. The frontend modernization improves responsiveness, pipeline graph navigation, charts, accessibility, and testing foundations while keeping the production bundle size unchanged and preserving user workflows, APIs, backend behavior, pipeline execution, and artifact storage. ### Source excerpt The Kubeflow Pipelines web interface has been upgraded from React 16 to React 19 -- a modernization effort that touches every layer of the frontend stack. Whether you use the UI to manage pipelines day-to-day or contribute to the codebase, here is what this means for you. ## Vibe Coding XR: Accelerating AI + XR prototyping with XR Blocks and Gemini DevFeed: [Vibe Coding XR: Accelerating AI + XR prototyping with XR Blocks and Gemini]() Original publisher: [Read original article]() Published: 2026-03-25T09:14:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Vibe coding](), [Android XR](), [WebXR](), [3D web development](), [Human-Computer Interaction and Visualization](), [long-context](), [Artificial Intelligence](), [creative technology](), [Google]() Tags: [3d](), [3d-web-development](), [ai](), [android-xr](), [gemini](), [google](), [human-computer-interaction](), [human-computer-interaction-and-visualization](), [language-models](), [long-context](), [machine-intelligence](), [prototyping](), [vibe-coding](), [visualization]() ### AI overview Google Research presents Vibe Coding XR, a rapid-prototyping workflow that combines Gemini Canvas with the open-source XR Blocks framework to turn natural-language prompts into interactive, physics-aware WebXR applications for Android XR. The workflow supports desktop simulation in Chrome and enables creators to test spatial interactions before deploying to headsets. ### Source excerpt Human-Computer Interaction and Visualization ## How We Built Time Series: Configuration-Driven Visualization in Tinybird Forward DevFeed: [How We Built Time Series: Configuration-Driven Visualization in Tinybird Forward]() Original publisher: [Read original article]() Author: Julia Vallina Published: 2026-02-09T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [Time Series](), [clickhouse](), [SQL](), [configuration](), [debug]() Tags: [clickhouse](), [configuration](), [debug](), [engineering-excellence](), [sql](), [time-series](), [visualization]() ### AI overview An account of how Tinybird rebuilt its Time Series feature, focusing on the SQL problems encountered, the ClickHouse patterns used, and its use for debugging alerts. ### Source excerpt How we rebuilt Time Series: the SQL problems we hit, the ClickHouse patterns we used, and how we use it to debug our own alerts. ## Index and Search Every File on Your Homelab Server using Sist2 DevFeed: [Index and Search Every File on Your Homelab Server using Sist2]() Original publisher: [Read original article]() Author: Jeremy Published: 2026-01-29T12:25:00Z Content type: article Language: en Sources: [Noted]() Topics: [Homelab](), [Docker Compose](), [Docker](), [Web](), [Front end](), [Back end](), [Server]() Tags: [backend](), [compose](), [docker](), [homelab](), [self-hosted](), [self-hosted-file-indexing-and-searching](), [server](), [visualization](), [web](), [web-interface]() ### AI overview A practical guide to using Sist2 as a self-hosted file indexing and search engine on a homelab server. It covers deploying Sist2 with Docker Compose, mounting directories for indexing, creating scan jobs, and searching filenames and document contents, including PDFs processed with OCR. ### Source excerpt Setup your own self-hosted file search engine using sist2. ## Giving Agents a Visual Voice: MCP Apps Support in VS Code DevFeed: [Giving Agents a Visual Voice: MCP Apps Support in VS Code]() Original publisher: [Read original article]() Author: Harald Kirschner, Connor Peet Published: 2026-01-26T00:00:00Z Content type: article Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [Model Context Protocol](), [Visual Studio Code](), [AI-assisted coding]() Tags: [agents](), [blog](), [forms](), [mcp](), [ui](), [visualization](), [vs-code]() ### AI overview VS Code supports MCP Apps, allowing AI coding agents to return interactive UI components such as dashboards, forms, visualizations, and multi-step workflows directly in conversations. The article demonstrates how these interfaces improve collaboration through visual list reordering, interactive performance profiling, and feature flag selection. ### Source excerpt VS Code now supports MCP Apps, enabling AI agents to display interactive UIs for richer developer workflows. Read the full article ## Own Your Observability: Supabase Metrics API DevFeed: [Own Your Observability: Supabase Metrics API]() Original publisher: [Read original article]() Author: Steven Eubank Published: 2025-12-16T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [API](), [telemetry](), [observability](), [Prometheus](), [Grafana](), [Grafana Cloud](), [datadog agent](), [Amazon Web Services](), [Self-hosted]() Tags: [api](), [aws](), [datadog-agent](), [grafana](), [grafana-cloud](), [metrics](), [monitoring](), [observability](), [prometheus](), [replication](), [self-hosted](), [storage](), [telemetry](), [visualization]() ### AI overview Supabase's Metrics API streams roughly 200 Postgres performance and health metrics in Prometheus exposition format, enabling integration with Grafana Cloud, Datadog, AWS Managed Prometheus, self-hosted Grafana, and other Prometheus-compatible backends. The article explains how this unifies database telemetry with existing observability stacks for monitoring, visualization, alerting, and troubleshooting. ### Source excerpt Stream your Supabase database telemetry into any Prometheus-compatible observability stack with the Metrics API. Full control over monitoring, visualization, and alerting. [Next page]()