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Nine months ago I wrote a post on here claiming my ENS identity architecture had reached "85% Knowledge Panel trigger probability." Two things happened since. Google's Knowledge Graph actually minted an entity node for me. And I learned that the 85% number was fiction — mine. This is the honest retrospective. The timeline, with receipts Date What happened Aug 2025 ookyet.com first indexed Oct 2025 Entity markup shipped: Person @graph , Dentity verification, ENS identifiers. The "85%" post Jun 2026 Search Console turned red: Q&A errors, Profile page: Invalid object type . Cleanup Jun 28, 2026 Fixed markup deployed. Then: hands off Jul 2, 2026 Knowledge Graph Search API returns a machine-minted Person node for ookyet Jul 7, 2026 Search Console fully green: ProfilePage valid, indexed pages up, zero 404s Still no Knowledge Panel. Keep reading — that part matters. What Google actually built You can reproduce this: curl "https://kgsearch.googleapis.com/v1/entities:search?query=ookyet&limit=10&key=YOUR_API_KEY" { "result" : { "@id" : "kg:/g/11z806my44" , "name" : "Qifeng Huang" , "@type" : [ "Person" ] } } Three details in that tiny response taught me more than anything I shipped in 2025. The /g/ MID is machine-minted. You can't register one, buy one, or submit one. Google's entity reconciliation creates it when enough independent sources agree that a person exists. This is the mechanical prerequisite for a Knowledge Panel — the entity has to exist in the graph before anything can be displayed about it. The node's name is my real name, not my handle. My site declares name: "ookyet" . The node says "Qifeng Huang" — pulled from the high-authority anchors (LinkedIn, ORCID), not from my self-declaration. Third-party corroboration outweighs anything you say about yourself. Expected, and honestly a relief: the graph is working as designed. The Knowledge Graph holds 8 distinct people named Qifeng Huang. Query any of them by real name and you get a crowded namespace. Query ookyet

Google's new phones could feature glowing LEDs and higher price tags.

Google's upcoming event in August will introduce new Pixel devices.

Google's next Pixel event will be August 12 at 6pm ET.

Google is hosting its next Made by Google launch event for Pixel hardware on August 12th in New York City, according to an invitation sent by Google to The Verge. Unusually, the event is taking place in the evening: It'll kick off at 6PM ET that day. The email also includes a brief animation teasing […]

Google is going to give content creators and website owners a better idea of how people find their social media profiles and YouTube content through Search. With a new feature in the Google Search Console called "platform properties," Google says that you'll be able to "easily track which search terms lead people to your Instagram, […]

Fortunately, it shouldn't take too much extra space.

PSA: A change to Google's privacy settings let it train its AI on more of your data. Here's how to opt out.

"Group project, but make it 1776." That's how a new commercial for Google Workspace opens. And things only get cringier from there. The clip imagines what it would be like if the founding fathers turned to Google's collaboration tools and Gemini to help them draft the Declaration of Independence. Ben Franklin texts Thomas Jefferson to […]

Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace?

During negotiations on Wednesday, employees voiced frustrations with what they consider an unwillingness among executives to engage meaningfully with the prospect of unionization.

Google has released A2UI v0.9, a framework-agnostic standard for AI agents to declare user interface intent across multiple platforms without arbitrary code. The update emphasizes alignment with existing design systems. It includes a new SDK for Python, improved error handling, and various transport methods. Migration guidance and evolution specifications are also provided. By Daniel Curtis

Google added two new models to the Gemini API today: Nano Banana 2 Lite (image generation) and Gemini Omni Flash (video generation + editing). Neither is the Gemini 3.5 Pro release people have been waiting for, so it's easy to miss. Here's what's actually in them. TL;DR Nano Banana 2 Lite: gemini-3.1-flash-lite-image = text-to-image in ~4s, $0.034/1K images Gemini Omni Flash: gemini-omni-flash-preview = video gen + conversational editing, $0.10/sec Both are built to be chained: generate an image fast, then animate it into video Neither model is positioned as a quality upgrade = both are cost/speed plays Nano Banana 2 Lite Model ID: gemini-3.1-flash-lite-image Text-to-image output in about 4 seconds $0.034 per 1K-resolution image Positioned as the direct replacement for the original Nano Banana ( gemini-2.5-flash-image ) - if you're on that model, this is a drop-in upgrade Available in Google AI Studio, Gemini API, Gemini Enterprise Agent Platform, and consumer surfaces (Search AI Mode, Gemini app, Photos, NotebookLM, Flow, Google Ads) Gemini Omni Flash Model ID: gemini-omni-flash-preview Public preview in Google AI Studio and the Gemini API Conversational editing - refine a generated video using plain-language instructions instead of re-prompting from zero Multimodal referencing - combine text, image, and video inputs to keep a scene consistent $0.10 per second of video output (same rate as Veo 3.1 Fast) Known limitations right now Generations capped at 10 seconds No audio reference uploads yet No scene extension yet Video references under 3 seconds are accepted by the API schema but not correctly processed yet Character consistency across scene changes/pans still has rough edges Google says longer durations are coming. The part worth paying attention to: chaining them Generate an image with Nano Banana 2 Lite (fast, cheap) Pass that image as a reference into Omni Flash Omni Flash animates it into a video Both models are optimized for throughput and cost, not for to

AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.

The EU went after Google for the practice of bundling its search engine and browser with Android.

Even the most advanced enterprise systems are tethered to a costly paradox: manual bottlenecks that introduce critical errors, security risks, and slow innovation. These hidden operational anchors are the friction preventing your organization from realizing its full potential. The Challenge: Manual Bottlenecks in Modern Enterprise Operations In an era defined by cloud-native architectures, microservices, and declarative infrastructure, a persistent and costly paradox remains at the heart of enterprise operations. We have built systems capable of immense scale and resilience, yet they are often tethered to manual, human-driven processes that act as operational anchors. These bottlenecks aren't just minor inefficiencies; they are critical points of failure, introducing latency, human error, and security vulnerabilities into our most important workflows. They represent the friction that slows down innovation, drains resources, and prevents organizations from realizing the full potential of their digital investments. Before we can orchestrate an autonomous workspace, we must first dissect the anatomy of these manual constraints. Identifying the High Cost of Manual Invoice Reconciliation To ground this challenge in reality, consider a ubiquitous and deceptively complex business process: accounts payable invoice reconciliation. On the surface, it seems simple. In practice, it's a classic example of a high-friction, manual workflow that silently bleeds enterprise resources. The typical process is a gauntlet of context-switching and swivel-chair integration: An invoice arrives, often as a PDF attached to an email, with no standardized format. A finance professional must manually open the document and visually identify key data points: invoice number, date, vendor, line items, and total amount. They then pivot to an ERP system like SAP or NetSuite to find the corresponding Purchase Order (PO). Next, they might need to access a separate logistics or warehouse management syste

Google tries balancing AI data center emissions with clean energy efforts.

Apple chose Google Cloud to run Private Cloud Compute outside its own data centers for the first time, using NVIDIA Blackwell GPUs, Intel TDX, and Google's Titan chip. Apple maintains an independent append-only hardware ledger and dual-vendor attestation roots. AWS and Azure are not part of the collaboration. By Steef-Jan Wiggers

When I checked Google Search Console after a month, only 2 of my 8 sites were indexed. The other 6 had zero pages in Google's eyes. No penalty, no error banner. Just silence. The bug My build script generated the sitemap by mapping over page objects. Somewhere a URL field was an object, not a string. So the sitemap shipped lines like: <url><loc> https://example.com/[object Object] </loc></url> Google fetched the sitemap, saw garbage URLs, and quietly skipped the whole file. No crawl, no index. How I caught it GSC > Sitemaps > it said "Success" but "Discovered pages: 0". That mismatch is the tell. I opened the raw sitemap.xml in the browser and searched for [object . There it was. Root cause: url: page.url where page.url was itself { path, params } , not a string. The fix // before loc : page . url // -> [object Object] // after loc : `https://livephotokit.com ${ page . path } ` Redeployed, resubmitted the sitemap, and requested indexing on the core pages. Pages started landing in the index within a couple of days. Takeaway A "Success" status on your sitemap does not mean Google read your URLs. Always open the raw XML and eyeball it. One bad [object Object] can silently sink an entire site. I'm building LivePhotoKit and a handful of other small tools solo with AI. Sharing the real bugs as I hit them.