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Managing payload cms lexical tables in a content-heavy site means enabling EXPERIMENTAL_TableFeature — but the real trap is the markdown import that strips tables without warning. We lost a whole batch of production blog posts to this exact hole before we found the fix. Here’s why it happens and the step-by-step configuration that keeps your tables intact. The Silent Table Eater: Payload CMS Lexical Tables and Markdown Conversion The default markdown-to-Lexical conversion helper completely ignores your editor’s feature list. So even when you’ve added the table feature to your editor config, every GFM table in imported markdown is silently dropped. Here’s the code that ate our data: import { editorConfigFactory , defaultFeatures } from ' @payloadcms/richtext-lexical ' // ❌ This uses a plain config that doesn’t know about tables const mdConverter = editorConfigFactory . default ({ features : defaultFeatures , }) const lexicalData = mdConverter . parse ( ' # Hello \n\n | A | B | \n |---|---| \n | 1 | 2 | ' ) // result: { root: … } — no table node anywhere The problem: editorConfigFactory.default builds a conversion pipeline from a static feature set, not from your actual editor config. Any experimental or custom feature you’ve wired into the editor simply isn’t there during markdown parsing. Fix It: Wire EXPERIMENTAL_TableFeature Into the Conversion Config Switch to editorConfigFactory.fromFeatures , which actually reads the feature array you provide. Include the table feature alongside the defaults, and the markdown converter will start producing proper Lexical table nodes. import { editorConfigFactory , defaultFeatures , EXPERIMENTAL_TableFeature , } from ' @payloadcms/richtext-lexical ' const mdConverter = editorConfigFactory . fromFeatures ({ features : [... defaultFeatures , EXPERIMENTAL_TableFeature ()], }) Takeaway: You must add EXPERIMENTAL_TableFeature() to both your editor’s features array and to every markdown conversion config. Missing one side silently eat

When you wire up a payload cms live preview , you’re not just plumbing a URL—you’re building a contract between the admin panel and your Next.js App Router. The goal is keystrokes-ago fidelity: editors click Preview, land on your front end, and see exactly what’s in the draft, even when the public site is cached to the hilt. Our implementation at techpotions settled on one preview route, one shared secret, and one shared URL builder. Here’s every decision that made it work. One /next/preview route for the entire site The admin panel’s preview button doesn’t need to know about your page structure. It calls a single /next/preview route with a secret query param and a slug search param that points to the document being previewed. // app/(payload)/next/preview/route.ts import { draftMode } from ' next/headers ' import { redirect } from ' next/navigation ' export async function GET ( request : Request ) { const { searchParams } = new URL ( request . url ) const secret = searchParams . get ( ' secret ' ) const slug = searchParams . get ( ' slug ' ) if ( secret !== process . env . PREVIEW_SECRET ) { return new Response ( ' Invalid token ' , { status : 401 }) } const draft = await draftMode () draft . enable () redirect ( slug ?? ' / ' ) } That’s the entire route. No collection-specific logic, no second-guessing which page type is involved. The redirect lands on the actual page, which reads draftMode().isEnabled and fetches accordingly. This is the pattern the Payload CMS preview documentation expects: a function that resolves to a string with additional URL parameters pointing to your app. The preview-URL builder lives in one shared lib Here’s where most implementations drift apart. The admin config, the preview route, and each page component all need to agree on how a preview URL is constructed. Store that logic in one place—a single getPreviewUrl utility imported everywhere—or you’ll be chasing 404s in production when someone renames a collection slug. // lib/getPreviewU

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. China’s AI models have Trump’s AI world at war with itself Last weekend, several current and former advisers to President Donald Trump on AI publicly lobbed insults at the country’s leading…

These WIRED-tested wearables reduce your reliance on a phone while keeping you connected.

GitLab has introduced a new approach to Green DevOps, demonstrating how software engineering teams can measure the carbon emissions generated by their CI/CD pipelines. By Craig Risi

Orchestrate agents at scale while reducing cost Discussion | Link

It’s a lot : According to information obtained by The Tech , MIT is spending over $3 million on more than 500 AI surveillance cameras in academic buildings, residence halls, and outdoor areas along Memorial Drive. Installation of the new cameras, along with the wiring and infrastructure that will support them, began November 2025 and will likely continue until September 2026. Technical specifications for the cameras suggest that they will be capable of collecting real-time face and object classification data, including detection of motion, loitering, crowds, face masks, and camera tampering. Individuals can also be automatically classified on the basis of clothing color, gender, and age, up to a distance of 35 feet (11 meters) from the camera. According to a statement from MIT spokesperson Kimberly Allen, any collected data is “retained up to 30 days,” unless an exception is granted...

These locks, lights, and other smart home upgrades let you add automation without messing up your home’s vibe.

Michigan has been at the epicenter of the wildfire smoke and explosive diarrhea outbreaks. The progressive Democrat talks to WIRED about how he would tackle the forces behind them if he’s elected.

The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…

Last month, more than a hundred Stanford students left their own graduation to protest Google’s military contracts and deals with ICE. Two organizers, Amanda Campos and Eva Jones, tell us why.

Bhavuk Jain discusses translating foundational AI into scalable mobile products. He shares the engineering challenges behind AI Wallpapers and Circle to Search, detailing how to implement robust runtime guardrails, fine-tuning, and seamless OS integration. For engineering leaders, he explains balancing UX constraints with model latency and infrastructure cost to deliver safe, reliable AI. By Bhavuk Jain

How does AI make you feel? Are you excited to “vibe-code” your smart home? Or anxious about all the added pollution and billions of gallons of water used by data centers? Dig a little deeper and you’ll start to question the actual value of the GPUs that underpin all the leaps and promises of generative […]

Dealerships installed alarms in millions of vehicles—and left them in even if the buyer didn’t want them. Now researchers warn they can be hacked to unlock, track, and disable cars.

Everyone is talking about the reading crisis. But inside the “Mississippi Miracle,” it’s all about comprehension.

Gritt is coming out of stealth with $34 million and plan to automate the hardest tasks on construction sites.

Yelp has launched Training Orchestrator. This new internal framework replaces individual team Spark training scripts. Now, it uses a configuration-driven, DAG-based execution model. By Claudio Masolo

Simple computers for agent w/ full VMs Discussion | Link

You have built an AI agent harness. It calls tools, routes requests, and returns results. Your team trusts its telemetry to tell you which tool was selected and why. That trust is a liability. An agent's own logs are self-reported. They tell you what the agent thinks it did, not what actually happened. A hallucinated tool name, a misrouted parameter, a silent fallback to a different function — none of these surface in the agent's own trace. You need an external witness. Here is how to build one. The Problem: Self-Reported Truth Is Not Truth Most teams validate agent behavior by reading the agent's own output. They check the tool_calls field in the response, match it against an expected schema, and call it done. This works until it doesn't. Consider a common failure mode: the agent decides to call search_knowledge_base but the LLM formats the tool name as searchKnowledgeBase . The routing layer silently normalizes it, the call succeeds, and the agent logs search_knowledge_base . Your test passes. The actual execution path was different from what you verified. Another pattern: the agent selects the correct tool but passes a parameter that the tool silently coerces. A date string gets parsed into a different timezone. A user ID gets truncated. The tool returns a result, the agent logs success, and your test never catches the drift. The root cause is the same. You are testing the agent's intent , not its execution . Intent is cheap to fake. Execution leaves fingerprints. The Solution: An External Observer You need a layer that sits between the agent and the tools it calls. This observer records every invocation — tool name, parameters, response, latency — without the agent knowing it is being watched. The observer does not trust the agent's logs. It trusts what it sees on the wire. Here is the architecture at a high level: Intercept every outbound call from the agent to a tool. Record the raw request before any normalization or routing. Compare the recorded call against

How I Built a Full-Stack Quality Skill for AI Coding Agents AI coding agents are getting very good at writing code. But I kept running into the same problem: They can move fast, but without strong project rules they can also create messy architecture, duplicate utilities, inconsistent APIs, weak security checks, and frontend components that slowly drift away from the design system. So I built Full-Stack Quality Skill . It is a reusable AI coding skill for full-stack audits, architecture guidance, long-term project memory, and CI quality gates. Repo: https://github.com/lablnet/full-stack-quality-skill Website: https://skills.lablnet.com Why I Built It When I use AI agents like Cursor, Codex, Claude Code, Antigravity, or similar tools, I do not only want them to "write code". I want them to think like a careful senior engineer: Is the database normalized correctly? Are backend layers clean? Is business logic leaking into controllers? Are frontend components consistent? Are Vue components using composables? Are React components using hooks correctly? Are HTTP methods and status codes right? Is GraphQL safe from N+1 problems? Are security and privacy risks checked? Are tests missing for critical paths? Is documentation still matching the code? That is a lot to remember every time. So instead of repeating the same instructions in prompts, I turned them into a reusable skill. What It Covers The skill includes audit areas for: Database Backend Frontend Mobile HTTP APIs GraphQL Security Privacy Accessibility i18n Analytics Background jobs Infrastructure Testing Performance Observability Delivery / CI Multi-tenancy Payments Notifications Data import/export API compatibility Developer experience AI/LLM safety It also includes examples for common stacks: Node.js / TypeScript Python Django Laravel Java / Spring C# / ASP.NET Core Go Ruby on Rails React Next.js Vue Angular SvelteKit Flutter React Native Kotlin / Android Swift / iOS SQL GraphQL Read-Only Audits by Default One impo