# demo Published articles for demo. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Designing AI Agents as Role-Specific AI Employees DevFeed: [Designing AI Agents as Role-Specific AI Employees]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-13T14:03:10Z Content type: opinion Language: en Sources: [Behind the Craft]() Topics: [Artificial Intelligence](), [Job]() Tags: [agents](), [ai](), [ai-agents](), [business](), [ceo](), [demo](), [live]() ### AI overview The article discusses designing AI agents as AI employees with defined jobs, skills, managers, and budgets. It also describes how Brex's CEO uses AI in his personal life and business. ### Source excerpt How to think of building agents as onboarding AI employees with a specific job, skill set, manager, and budget. Plus how Brex's CEO uses AI to run his life and $5B business. ## NVIDIA Personal AI Router Distributes AI Tasks across Local Compute DevFeed: [NVIDIA Personal AI Router Distributes AI Tasks across Local Compute]() Original publisher: [Read original article]() Author: Sergio De Simone Published: 2026-09-11T15:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Local AI](), [Inference](), [GPU](), [Hardware]() Tags: [agent](), [agents](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [compute](), [demo](), [development](), [gpu](), [inference](), [large-language-models](), [local](), [ml-data-engineering](), [news](), [node](), [nvidia](), [nvidia-pair-ai-task-router](), [open-source](), [orchestration](), [performance]() ### AI overview NVIDIA has introduced PAIR in beta, a local router that distributes inference requests across compatible computers for multi-agent AI workloads. It works with local inference services such as Ollama and LM Studio and selects a node based on model and engine requirements. ### Source excerpt NVIDIA Personal AI Router (PAIR), now available in beta, lets you combine the inference capacity of multiple computers on your local network and automatically distribute AI requests among them. It is primarily designed for local multi-agent AI workloads, where multiple independent model calls can otherwise overwhelm one GPU. By Sergio De Simone ## Join our live webinars: Migrating from Atlassian to YouTrack DevFeed: [Join our live webinars: Migrating from Atlassian to YouTrack]() Original publisher: [Read original article]() Author: Elena Pishkova Published: 2026-09-09T12:51:07Z Content type: news Language: en Sources: [The JetBrains Blog]() Topics: [migration](), [AI Bots]() Tags: [ai-agents](), [atlassian](), [best-practices](), [data-center](), [demo](), [deployment](), [documentation](), [events](), [helpdesk](), [knowledge-base](), [livestreams](), [mcp](), [migration](), [project-management](), [tutorials](), [webinar](), [workflows](), [youtrack](), [youtrack-server]() ### AI overview JetBrains announces live webinars about migrating from Atlassian products to YouTrack. Sessions cover moving projects, users, and data from Jira, Confluence, and Jira Service Management, with migration demos, deployment and pricing information, customer stories, and regional sessions. ### Source excerpt Atlassian is discontinuing sales and support for Data Center products. If you're exploring alternatives, join us for a live session on September 30 to see how Jira-to-YouTrack migration works, including a demo and real customer stories. Register for the worldwide English-language webinar, hosted by the YouTrack team, or attend a regional session in Japanese hosted [...] ## What's new in Swift: August 2026 Edition DevFeed: [What's new in Swift: August 2026 Edition]() Original publisher: [Read original article]() Author: Dave Lester Published: 2026-09-04T16:30:00Z Content type: news Language: en Sources: [Swift.org]() Topics: [modern web development](), [Vapor](), [Back end]() Tags: [backend](), [cloudflare](), [demo](), [frontend](), [swift](), [wasm](), [webassembly]() ### AI overview A Swift community update highlights Elementary and ElementaryUI, projects for server-rendered and browser-based web UIs. It also describes Swift-to-WebAssembly progress and a Cloudflare full-stack demo using shared message types. ### Source excerpt Welcome to "What's new in Swift," a curated digest of releases, videos, and discussions in the Swift project and community. Here's an update from guest contributor Simon Leeb on Swift's progress as a language for web scenarios: Hi, Simon here! I am the creator of the elementary-swift project, a collection of packages born from a simple wish: I want to build web UIs in Swift and ultimately help Swift become a first-class choice for the web. This journey began after I started using Swift for backend services. The web frontend, however, still lived in a separate ecosystem, and I really wanted it to feel as ergonomic, safe, and efficient as the Swift I was writing everywhere else. That led to the creation of Elementary: a modern and efficient HTML rendering library with a familiar declarative API, built for the web. It integrates easily with frameworks like Vapor and Hummingbird, and has become a practical option for server-rendered web UIs. Around that same time, years of community work in the swift-wasm project made compiling Swift to WebAssembly increasingly viable, while Embedded Swift was taking its first experimental steps. This made me wonder: "How hard can it be to use Embedded Swift and build a state-driven web UI framework that produces tiny WebAssembly binaries?" Turns out: quite hard, actually! But it was too late. Despite my better judgment, I was in the middle of creating what is now known as ElementaryUI. Where Elementary renders HTML on the server, ElementaryUI runs in the browser itself. You can watch my talk at Swift@FOSDEM 2026 if you want to know more about the why, what, and how. To showcase where the project is heading, I recently posted a small Full-Stack Swift on Cloudflare demo. It features Swift in the browser communicating with a Swift backend on an edge worker through shared message types. I hope it gives people a concrete sense of how much the core technologies and the surrounding tooling have advanced. ElementaryUI is still young, with plen ## 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. ## How to run an internal-tools demo night: WorkOS field notes DevFeed: [How to run an internal-tools demo night: WorkOS field notes]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-28T12:52:51Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [Low-Code / Internal Tools](), [Demo](), [Tooling]() Tags: [2026](), [demo](), [how-to](), [internal-tools](), [talks]() ### AI overview WorkOS shares field notes from running two internal-tools showcases. The playbook covers a sequenced lineup of five short talks, live demonstrations, a single panel Q&A, audience mingling, and contingency planning for presenter and demo failures. ### Source excerpt Field notes from running the WorkOS internal-tools showcase twice: five talks, live demos only, one Q&A, a sequenced lineup, and a contingency for every demo. ## Hands-on with Raspberry Pi's CM5 Programming Jig DevFeed: [Hands-on with Raspberry Pi's CM5 Programming Jig]() Original publisher: [Read original article]() Author: jeff@jeffgeerling.com (Jeff Geerling) Published: 2026-08-19T07:13:00Z Content type: article Language: en Sources: [Jeff Geerling]() Topics: [Provisioning](), [Programming](), [Open Source](), [Python]() Tags: [cm5](), [compute-module](), [demo](), [flash](), [images](), [management-tools](), [open-source](), [programming-jig](), [python](), [raspberry-pi](), [raspberry-pi-os](), [testing](), [tools](), [video](), [youtube]() ### AI overview Hands-on evaluation of Raspberry Pi's CM5 Programming Jig for provisioning and flashing Raspberry Pi OS onto multiple Compute Modules. The article covers its workflow, setup, web interface, imaging tools, pricing, and comparison with Everypin's CM5 Flash Jig. ### Source excerpt In the before-times, when Raspberry Pi CM5s were (relatively) affordable, I built a number of Pi clusters (example), and one of the most annoying parts of the build was flashing Raspberry Pi OS to all the Pis. One, two, or even three Pis isn't a big deal, but once you hit 4+, the process of plugging the Compute Module into a carrier board, plugging that into a computer, managing Raspberry Pi Imager, and trying to match up details like a hostname, MAC address, and the physical Pi itself, gets annoying. ## Droidcon Orlando 2026 Recap - Tadeas Kriz DevFeed: [Droidcon Orlando 2026 Recap - Tadeas Kriz]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Kotlin Multiplatform](), [Kotlin](), [Android](), [iOS](), [Coroutines](), [Concurrency]() Tags: [2026](), [android](), [community](), [coroutines](), [demo](), [droidcon](), [experience](), [ios](), [kotlin](), [kotlin-interpreter](), [kotlin-multiplatform](), [multiplatform](), [recap](), [speaking]() ### AI overview A recap of Droidcon USA in Orlando, covering the conference venue, talks, and a demo of Kotlin Multiplatform Hot Reload. The author explains that file changes were propagated to an application running on Android and iOS, while Kotlin Interpreter work continues. ### Source excerpt Droidcon USA moved to Orlando this year. Tadeas recaps his experience attending and speaking at the new venue. ## Agent Night demo recap: How Mastra turned its issue backlog into a software factory DevFeed: [Agent Night demo recap: How Mastra turned its issue backlog into a software factory]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T19:04:08Z Content type: news Language: en Sources: [WorkOS Blog]() Topics: [Development](), [Open Source](), [coding](), [Software](), [Command-line interface]() Tags: [agent](), [agentic](), [agentic-development](), [agents](), [coding](), [demo](), [development](), [harness](), [mastra](), [memory](), [open-source](), [open-source-software](), [recap](), [software](), [terminal]() ### AI overview The article recaps Abhi Aiyer's Agent Night demonstration of Mastra's open source software factory. It describes observational memory, Mastra Code, and the AgentController, which supports building interactive agent applications with modes, models, storage, workspaces, approvals, subagents, and channels. ### Source excerpt Abhi Aiyer, co-founder and CTO of Mastra, demoed the company's open source software factory at Agent Night: memory, harness, a rules engine around work. ## Agent Night recap: Airlock and intent-based access control DevFeed: [Agent Night recap: Airlock and intent-based access control]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T19:04:02Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Access Control](), [Authorization](), [Software Engineering]() Tags: [agent](), [demo](), [engineering](), [least-privilege](), [recap]() ### AI overview A recap of Agent Night presents Airlock, WorkOS's proposed intent-based access control model, and argues that conventional RBAC and least-privilege approaches do not adequately fit agent permissions. It also discusses why software engineering has been an early area of agent adoption. ### Source excerpt Recap of Agent Night at the Regency Ballroom: why agent permissions break RBAC and least privilege, and a live demo of Airlock, our intent-based access control. ## Agent Night demo recap: exe.dev -- your agent can rewrite the software you use DevFeed: [Agent Night demo recap: exe.dev -- your agent can rewrite the software you use]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T18:34:20Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Development](), [Open Source](), [coding](), [systemd]() Tags: [agent](), [code](), [coding](), [configuration](), [demo](), [dev](), [interface](), [open-source](), [recap](), [systemd](), [tool]() ### AI overview David Crawshaw of exe.dev demonstrated a two-prompt workflow in which an agent customizes an open-source program and periodically rebases those changes onto the latest upstream version. ### Source excerpt David Crawshaw of exe.dev showed how two prompts let an agent customize any open source tool you use, then keep those changes rebased on upstream nightly. ## Agent Night demo recap: Brian Douglas's self-healing Pokémon agent DevFeed: [Agent Night demo recap: Brian Douglas's self-healing Pokémon agent]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T18:34:12Z Content type: news Language: en Sources: [WorkOS Blog]() Topics: [Demo](), [observability ai agents](), [telemetry](), [Claude Code](), [Python](), [DuckDB](), [Kafka](), [Code](), [Open Source]() Tags: [agent](), [claude-code](), [code](), [coding](), [demo](), [duckdb](), [kafka](), [open-source](), [python](), [self-healing](), [telemetry](), [traces]() ### AI overview A recap of Brian Douglas's Agent Night demo shows how Paper Compute ran Pokémon as a self-healing agent for six months. The setup uses Claude Code, a headless Python emulator, and traces from each session and turn. The article explains how labeled traces and per-session reflections can make the agent's history searchable and useful for improvement. ### Source excerpt Brian Douglas of Paper Compute closed Agent Night with six months of running Pokémon as a self-healing agent: traces, anomalies, dreams, and inceptions. ## WorkOS Applied AI Showcase July 28 2026 - Moving to an AI-native Organization DevFeed: [WorkOS Applied AI Showcase July 28 2026 - Moving to an AI-native Organization]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-14T13:36:57Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Low-Code / Internal Tools](), [AI-assisted coding](), [Containers](), [MCP](), [Command-line interface](), [incident](), [Security](), [datadog](), [Pull Request](), [Slack](), [GitHub]() Tags: [agent](), [agents](), [ai](), [applied-ai](), [cli](), [coding](), [coding-agents](), [containers](), [datadog](), [demo](), [event](), [github](), [incident](), [internal-tools](), [mcp](), [organization](), [pull-request](), [slack](), [talks](), [team]() ### AI overview WorkOS's Applied AI Showcase in New York presented five internal tools--Horizon, Blog Bot, the CLI, Wallaby, and Atlas--through live demos and talks. The article explains how the Applied AI team helps non-engineering functions build AI-powered workflows and systems. ### Source excerpt WorkOS is becoming an AI-native organization. The Applied AI Showcase in NYC on July 28th 2026 showed exactly how with live demos and talks from the Applied AI team. ## What 21 five-minute demos looked like at WorkOS Demo Night DevFeed: [What 21 five-minute demos looked like at WorkOS Demo Night]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Demo](), [Agent Harness](), [Artificial Intelligence]() Tags: [agent](), [agent-harness](), [demo](), [mac](), [mac-os](), [recap]() ### AI overview A recap of WorkOS Demo Night in San Francisco, featuring 21 live five-minute demos. The article highlights Carbon Code, an agent harness running on emulated Mac OS 9 that edited its own source and added an effort selector during the event. ### Source excerpt A recap of the August 5 Demo Night at our San Francisco office: 21 live five-minute demos, no slides, no pitches, and an agent harness running on Mac OS 9. ## OpenTelemetry Demo 3.0 introduces breaking changes to attributes, dashboards, and local deployment DevFeed: [OpenTelemetry Demo 3.0 introduces breaking changes to attributes, dashboards, and local deployment]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-07-28T10:15:19Z Content type: opinion Language: en Sources: [Blog on OpenTelemetry]() Topics: [Demo](), [OpenTelemetry](), [Maintainability](), [dashboards](), [Kafka]() Tags: [changes](), [dashboards](), [demo](), [maintainability](), [opentelemetry](), [services]() ### AI overview This article explains intentional breaking changes in OpenTelemetry Demo 3.0, including renamed custom telemetry attributes, broken dashboards and queries, and a revised local-run configuration split across multiple files. The changes aim to improve maintainability and make forks easier to manage. ### Source excerpt If you've been running the Demo for some time, you may have seen a couple of structural changes lately, and you may even have gotten mad about things not working as expected. We feel your pain and we totally understand it. Unfortunately there was no better way to change things without breaking the existing flow. Some of what you knew is gone. New services were added. Attribute names have changed. If you had dashboards with custom metrics, everything is broken now. ## Preventing Cypress Test Failures When Reloaded Elements Detach from the DOM DevFeed: [Preventing Cypress Test Failures When Reloaded Elements Detach from the DOM]() Original publisher: [Read original article]() Author: Gleb Bahmutov Published: 2026-06-17T04:00:00Z Content type: tutorial Language: en Sources: [Gleb Bahmutov]() Topics: [Cypress](), [Document Object Model (DOM)](), [navigation]() Tags: [blog-post](), [component](), [cypress](), [demo](), [how-to](), [navigation](), [products](), [repo](), [testing](), [verify]() ### AI overview This tutorial explains why Cypress end-to-end tests can fail when client-side navigation updates the page after a command has found an element. It demonstrates how delayed rendering can leave an old element attached to the test subject and recommends waiting for the expected component or DOM state before continuing the command chain. ### Source excerpt Have you ever encountered end-to-end test error "... failed because the page updated as a result of this command, but you tried to c ## ScyllaDB Customer Experience Spotlight: Faisal Saeed DevFeed: [ScyllaDB Customer Experience Spotlight: Faisal Saeed]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-06-11T12:01:56Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [ScyllaDB Cloud](), [data-architecture](), [migration](), [Automation](), [Database](), [on-prem](), [MariaDB]() Tags: [app](), [automation](), [blog](), [cloud](), [community](), [customer](), [cx-profiles](), [data-modeling](), [database](), [demo](), [india](), [migration](), [on-prem](), [scale](), [scylladb](), [scylladb-cloud](), [testing](), [tool]() ### AI overview Faisal Saeed, Principal Customer Engineer at ScyllaDB, describes his work supporting existing customers, evaluating deployments, data modeling, and migrations to ScyllaDB Enterprise or ScyllaDB Cloud. He also discusses the ScyllaDB Automation Framework, which automates cluster operations, workload execution, and stress testing, and highlights ScyllaDB Cloud and a large-scale customer use case in India. ### Source excerpt Meet Faisal Saeed, Principal Customer Engineer on the Customer Experience team here at ScyllaDB. ## SR-MPLS over Unnumbered Interfaces DevFeed: [SR-MPLS over Unnumbered Interfaces]() Original publisher: [Read original article]() Published: 2026-06-01T06:03:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [SR-MPLS](), [IS-IS](), [networking](), [Protocol (disambiguation)]() Tags: [container](), [demo](), [examples](), [github](), [implementation](), [installation](), [interfaces](), [ipv4](), [is-is](), [mpls](), [netlab](), [network](), [protocol](), [routing](), [sr-mpls](), [traffic-engineering]() ### AI overview This tutorial demonstrates SR-MPLS over unnumbered IPv4 point-to-point interfaces using netlab and IS-IS. It explains the resulting SR-MPLS and IS-IS advertisements, including adjacency and prefix segments, and provides steps for trying the lab through GitHub Codespaces and an Arista cEOS container. ### Source excerpt After the simple SR-MPLS demo and the dual-stack SR-MPLS setup, it was time for the next obvious question: Does SR-MPLS work over unnumbered IPv4 interfaces1, assuming the implementation of the underlying routing protocol supports them? Of course it does; let's go through the details, using the same topology I used throughout the Segment Routing workshop @ ITNOG10. Read more ... ## Creating an Arduino Demo With No Coding Experience Using AI DevFeed: [Creating an Arduino Demo With No Coding Experience Using AI]() Original publisher: [Read original article]() Author: John Lee Published: 2026-05-21T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [Arduino](), [ESP32-C5](), [Artificial Intelligence](), [debugging](), [Embedded Systems](), [ide]() Tags: [ai](), [arduino](), [arduino-ide](), [blog](), [coding](), [debugging](), [demo](), [embedded-systems](), [esp32-c5](), [hardware]() ### AI overview A first-time maker describes using AI to build an Arduino temperature indicator with an ESP32-C5, encountering generated-code errors, silent failures, serial-monitor confusion, and wiring-related problems before the device began working. ### Source excerpt A first-person account of building an ESP32-C5 Arduino temperature indicator with AI assistance, from generated code and confusing errors to serial debugging, wiring issues, and the moment the LED finally changed color. ## Cup and Counter Demo now Availiable DevFeed: [Cup and Counter Demo now Availiable]() Original publisher: [Read original article]() Author: Gus Published: 2026-05-15T00:26:02Z Content type: news Language: en Sources: [Pi My Life Up]() Topics: [Game Development](), [systems]() Tags: [bugs](), [cup-and-counter](), [demo](), [development](), [game-development](), [release](), [support]() ### AI overview An announcement of the Cup and Counter demo's release on Steam. The work-in-progress demo showcases core cafe gameplay, while player feedback will guide further polish ahead of the planned full release later this year. ### Source excerpt We are excited to announce that the demo for Cup and Counter is now available to play on Steam. The post Cup and Counter Demo now Availiable appeared first on Pi My Life Up. ## Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform DevFeed: [Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform]() Original publisher: [Read original article]() Author: Anna Lushnikova Published: 2026-04-13T16:59:45Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Documentation](), [AI Agent](), [AI Platform](), [Prompt Engineering](), [Retrieval Augmented Generation (RAG)](), [CI/CD](), [Digital Ocean]() Tags: [agent](), [agents](), [ai](), [ai-platform](), [ci-cd](), [demo](), [documentation](), [engineering](), [llm](), [prompt-engineering](), [rag]() ### AI overview DigitalOcean describes how it built and shipped a production documentation agent on Gradient AI Platform. The article covers the agent architecture, knowledge-base and retrieval setup, prompt engineering, golden datasets, LLM-judged evaluations, and CI/CD-gated prompt iteration used to improve and monitor answer quality. ### Source excerpt At DigitalOcean, documentation has always been a priority. Developers come to our docs to get unstuck, and the faster they find what they need, the better. Traditional docs pages work, but they require users to know which page to visit, scan for the relevant section, and map generic instructions to their specific setup. That process takes minutes (or longer) when it could take seconds. So we built an AI documentation assistant. Ask a question in plain language, get an answer with working links and ready-to-use commands. Simple enough as a demo. Getting it production-ready was a different story. It took us several iterations to reach an agent we were confident enough to ship. The LLM could generate plausible-sounding answers from day one. Knowing whether those answers were grounded, and keeping them grounded after every model update and prompt change -- that's where we spent most of our time. This post covers what we built, how we validated it, and the specific decisions that moved our metrics from "not great" to "ready for launch." We'll walk through prompt engineering, evaluation pipelines, and the CI/CD glue that holds it all together. Throughout, we relied on DigitalOcean's Agentic Inference Cloud so we could focus on product behavior instead of stitching together inference, RAG, and evaluation tooling ourselves -- one place to run agents, attach knowledge bases, and measure quality, with scale when we need it, and straightforward operational patterns. Architecture Gradient™ AI Platform is the control plane and runtime for standing up production AI agents without assembling pieces by hand. In one place you can attach a knowledge base, define an agent, and tune how it behaves: pick an LLM (managed or open-source), set temperature and top P, choose retrieval behavior, and edit the system prompt. The goal is to get from an empty project to a working agent in minutes, not to wire up inference, RAG, and evaluation from scratch. A Gradient AI Agent is the concrete thing ## Test Database Resilience with the CockroachDB Fault Tolerance Demo DevFeed: [Test Database Resilience with the CockroachDB Fault Tolerance Demo]() Original publisher: [Read original article]() Author: David Bressler,Ayushi Jain Published: 2026-04-08T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Resilience](), [Availability](), [Demo](), [distributed-systems](), [consistency](), [Cloud]() Tags: [availability](), [cloud](), [cockroachdb](), [consistency](), [demo](), [failover](), [how-to](), [production](), [resilience](), [verify]() ### AI overview This article explains how to use CockroachDB's Fault Tolerance Demo to trigger a real availability zone failure and observe availability, performance, consistency, failover, and replica rebalancing. The Public Preview demo is available to CockroachDB Cloud Advanced customers through the Cloud Console and should be run on a dedicated test or staging cluster. ### Source excerpt The CockroachDB database fault tolerance demo lets you trigger a real availability zone (AZ) failure within your cluster. ## Evil Trout + DEG DevFeed: [Evil Trout + DEG]() Original publisher: [Read original article]() Published: 2026-03-31T00:00:00Z Content type: article Language: en Sources: [Robin Ward]() Topics: [Demo]() Tags: [2026](), [deal](), [demo](), [developers](), [games](), [puzzles](), [steam]() ### AI overview Evil Trout founder Robin Ward describes investing in Doseo's puzzle game DEG after playing its demo during Steam Next Fest. The funding will support the game's improvement and marketing; a full release is planned for later in 2026. ### Source excerpt During the last Steam Next Fest, I played a demo of a puzzle game called DEG that was getting a lot of buzz. The game is made for people who like grid puzzles like Sudoku, or Nurikabe, with many additional unique concepts and puzzle ideas that are tutorialized silently to the player. Needless to say, I was hooked. I reached out to Doseo, its creator, and inquired about investing in the game. We discussed how he would use additional money to improve and market the game, and a deal was struck. ## A New Framework for Evaluating Voice Agents (EVA) DevFeed: [A New Framework for Evaluating Voice Agents (EVA)]() Original publisher: [Read original article]() Author: Tara Bogavelli; Gabrielle Gauthier Melancon; Katrina Stankiewicz; Nifemi Bamgbose; Hoang Nguyen; Raghav Mehndiratta; Hari Subramani; Fanny Riols Published: 2026-03-24T02:01:52Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [voice ai](), [Benchmark](), [Bot](), [dataset](), [speech-to-speech](), [asr](), [Large Language Model]() Tags: [agent](), [agents](), [architecture](), [audio](), [benchmark](), [code](), [demo](), [eval](), [evaluation](), [llm](), [models](), [speech](), [speech-to-speech](), [transcription](), [voice]() ### AI overview EVA is an end-to-end framework for evaluating conversational voice agents across both task accuracy and conversational experience. It scores complete multi-turn spoken conversations, includes an airline dataset of 50 scenarios, and reports benchmark results for cascade and audio-native systems. The article highlights a recurring tradeoff: stronger task completion can coincide with worse user experience. ### Source excerpt Conversational voice agents present a distinct evaluation challenge: they must simultaneously satisfy two objectives -- accuracy (completing the user's task correctly and faithfully) and conversational experience (doing so naturally, concisely, and in a way appropriate for spoken interaction). [Next page]()