A data analyst describes how AI tools enabled a nontechnical product manager to build complex, client‐ready dashboards spanning hundreds of data sources. After validating the results and finding few errors, the author realizes that core analytics tasks once thought AI‐proof are now automated.

• A nontechnical PM used an AI tool to build a client-ready dashboard across hundreds of data sources with minimal hallucinations, in minutes instead of weeks.
• The data analyst's role shifted from hands-on querying/dashboard-building to validation, governance, schema design, and writing guardrails.
• Core analytics tasks once assumed automation-proof (writing queries, wrangling data, building visualizations) are now being handled by AI.
• Remaining human value lies in strategy: choosing metrics, designing experiments, prompt design, bias detection, and communicating results.

This page catalogs community-built apps, games and UI resources. It covers templates and starters for landing pages, dashboards, blogs, e-commerce, AI, animations, design systems and more.

• v0.app organizes its community template library into 13 categories, ranging from dashboards and e-commerce to AI chat UIs and prebuilt agents.
• Every template links back to its original community creator, offering live demos and instant access to source code.

Claude Code now generates live, interactive web pages—artifacts—from your session context, codebase, and connected tools. These auto-updating, versioned pages share via a single link for incident reports, PR walkthroughs, dashboards, and more, with org-level access controls and no extra infrastructure.

• Claude Code can now turn a session's context (code, logs, tool connections) into a live, shareable web page instead of a static chat answer.
• Artifacts auto-update and version at the same URL, so an incident report or dashboard stays current without re-sharing links.
• Access is locked to org accounts with role-based permissions, retention policies, and compliance API access—nothing public by default.
• Rolling out in beta to Claude Team/Enterprise via CLI or desktop app, with prebuilt use cases spanning legal, security, FinOps, design, and PM workflows.

Claude’s Cowork feature now builds live artifacts—dashboards and trackers—that link directly to your apps and files. Whenever you open one, it auto-refreshes to show current data without any manual steps.

• Claude's Cowork feature now generates live artifacts (dashboards/trackers) that auto-refresh with current data each time they're opened, instead of static snapshots
• Users just point Claude at data sources (Google Sheets, SQL databases, project management tools) and it builds the dashboard, handling authentication and refresh automatically
• Teams sharing one Cowork link all see the same live view, eliminating stale slides or outdated CSVs and reducing manual data wrangling

The article argues that AI can now generate and manage design systems and dashboards better than humans, making manual frameworks and large UI teams obsolete. It predicts a shift from uniform, high-cognitive-load interfaces to conversational, intent-driven experiences that deliver only the insights users need.

• AI can now generate and customize full design systems on demand, undercutting the need for large design teams and paid seats in tools like Figma
• Design systems have become their own worst enemy—organizations spend more time managing them than they save, producing generic, uninspired interfaces
• Dashboards demand too much cognitive effort and assume users already know what to look for, making them a poor fit for actual decision-making
• AI chatbots that query databases directly and generate charts on the fly are replacing fixed dashboards, delivering precise answers without manual analysis

The article outlines five steps to turn AI-generated dashboards from eye candy into actionable tools. It covers defining clear questions, matching chart types to those questions, intentional design (colors, layout, context), narrative flow, and thoughtful interactivity. It also shares prompt examples and tips to enforce these rules in any AI charting tool.

• Before prompting an AI for charts, nail down who's looking, what decision they'll make, and the one key takeaway—otherwise you get eye candy that tells you nothing.
• Match chart type to question: line charts for trends, bar charts for rankings, scatter plots for correlations, and skip pie charts with many slices, 3D effects, dual axes, and spaghetti charts.
• Limit dashboards to five meaningful colors, start bar axes at zero, and add context markers like a WHO guideline line or a COVID-lockdown marker.
• In the WHO air quality example, 93% of cities exceed safe PM2.5 limits, and framing the dashboard as normal levels → problem → improvements (e.g., China's Blue Sky Policy) → next steps turns it into a narrative rather than just charts.

This article examines the high rate of unused and broken dashboards in organizations, highlighting how they often fail to provide lasting value. It discusses the disconnect between dashboard creation and actual usage, driven by shifting priorities and limited attention spans within teams. The piece also touches on the implications of this phenomenon for organizational behavior and project management.

• About half of an org's dashboards audited turned out to be broken or ignored, raising the question of why they were built at all.
• Dashboards act as "grave markers" for past priorities—built for a moment of attention that fades as new projects compete for focus.
• Letting teams self-manage their own dashboards helped somewhat, but didn't solve the core problem since attention still shifts away as new initiatives emerge.
• Organizational attention is a genuinely limited resource (with academic grounding), so strategic planning that overcommits people guarantees old dashboards get abandoned.