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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

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.

submitted by /u/shintoist [link] [留言]

Recently submitted my abstract and the submission number is 32xxx. With still a day to go, I just wonder where are we heading. Hope these conferences at least start making the reviews and names public for the withdrawn/rejected papers. So that people atleast take that accountability submitted by /u/Fantastic-Nerve-4056 [link] [留言]

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

submitted by /u/joladev [link] [留言]