Writing linear-gradient(135deg, #667eea 0%, #764ba2 100%) from scratch every time is tedious. Remembering the syntax for radial and conic gradients is even worse. I added a free CSS gradient generator to PaletteCSS that handles all three gradient types with a live visual preview. What it supports linear-gradient — any angle, drag the dial or type degrees radial-gradient — circular and elliptical conic-gradient — pie-chart style, great for progress rings and color wheels Up to 5 color stops with draggable positions Instant copy in 3 formats CSS background : linear-gradient ( 135 deg , #667 eea 0 %, #764 ba2 100 %); SCSS $gradient-primary : linear-gradient ( 135deg , #667eea 0% , #764ba2 100% ); Tailwind style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%)" Try it free 👉 https://palettecss.com/css-gradient-generator No signup. The site also has a browsable gradient library if you want inspiration rather than building from scratch. Any gradient types or export formats you'd want added? Drop a comment.

A product search, a filter list that kept growing, and a status code I hadn't seen in years. Filters went in the query string, the way they always do. That held up fine until someone saved a "filter set" with a few hundred SKUs in it and the endpoint started answering with 414. I rebuilt a small version of it to find the exact wall. Same search, filters as repeated ?sku= values, count going up in steps of a hundred: 1) GET with filters in the URL 100 filters | request line 1534 bytes | 200 OK 200 filters | request line 3034 bytes | 200 OK 300 filters | request line 4534 bytes | 200 OK 400 filters | request line 6034 bytes | 200 OK 500 filters | request line 7534 bytes | 200 OK 600 filters | request line 9034 bytes | 414 RequestUriTooLong Kestrel's default max request line is 8 KB, and the request line is the method plus the URL plus the HTTP version. Somewhere between 500 and 600 filters, my URL stopped being a URL. Every fix I knew was a compromise. A body on GET is undefined by spec and some proxies quietly drop it. POST works, but POST announces "this might change something", so caches skip it, gateways won't auto-retry it, and anyone reading your API docs has to guess whether POST /search is actually a search. Cramming the filters into a header is the kind of idea that sounds clever for about a day. The method that was missing RFC 10008 defines QUERY , and it's exactly the thing that spot in the matrix was waiting for. The body carries the query. The method is safe and idempotent, so it can be retried after a dropped connection without anyone panicking. Responses are cacheable, and the spec is explicit that the cache key has to be built from "the request content and related metadata". There's also a nice touch on the response side: Content-Location can point at a URL where those exact results can be fetched with a plain GET. The one-line version I keep giving people: it's a GET with a body, and that's the entire point. Wiring it up in ASP.NET Core 10 .NET 10 shi

I got tired of Googling basic course info, so I made a free directory of every US course Google filters for golf course results are terrible so I built a better way to browse courses using OSM as the backbone.... https://golfcoursebrowser.com/ It's a work in progress and mostly US for now, but I want to expand to the rest of North America, the EU, and the rest of the world ASAP. It's free, no ads, no login, no bs. If you spot anything wrong (bad info, a missing course, wrong scorecard), you can

The graph is free to explore and requires no registration. SynapTale builds a model of a story as a temporal graph made up of nodes (entities) and edges (their actions and relationships). The graph is not a visualization of the wiki. The wiki, timelines, relationship histories, and analytics are projections of the graph. The current demo contains 232 entities, 1,852 edges, and a snapshot of the story’s state at every chapter. By chapter 100, it still remembers a promise made in chapter 8 and tur

Reviewers requested additional experiments. In table format, I fear the results would not be as digestible as in a figure/plot. Links are "technically" not allowed as per the official website, but for those with experience, can/should I still go ahead and link my plots/figures ? If this goes badly, will this be a slap on the wrist, or outright rejection? Has anyone taken a chance with this in the past? How did it turn out? IMO openreview should really start supporting more modern markdown to allow figure embeds. submitted by /u/confirm-jannati [link] [留言]

A running look — in reverse chronological order — at the bigger tech companies that have announced significant layoffs this year with AI as a stated factor.

Evidence First, Answer Second: Building an Observable Industrial AI Agent with SigNoz Most AI systems are designed to give an answer. That is useful in a chatbot. On a factory floor, it can be dangerous. While building Industrial IoT Anomaly Control , I kept coming back to one question: What should an AI agent do when it detects a real problem but does not have enough evidence to explain the cause safely? My answer was simple: it should stop, show what it knows, and send the case to a human. This project is a real-time monitoring system for a simulated water-treatment plant. It streams live sensor data from six industrial assets, detects unusual behaviour, searches a knowledge base for similar incidents, and then chooses between two paths: recommend a safe action when the evidence is strong; escalate for human review when the evidence is weak. SigNoz is what makes this decision process visible. Instead of seeing only the final AI response, I can inspect the full path from the incoming sensor reading to anomaly detection, knowledge retrieval, policy checks, agent explanation, and recovery. The problem with traditional alarms Factories already collect large amounts of telemetry such as vibration, temperature, humidity, sequence numbers, and timestamps. The problem is not missing data. The problem is turning that data into a useful decision. A traditional threshold alarm may say: Vibration is above the configured limit. That still leaves the operator with several questions: Is the machine actually failing? Is the sensor or gateway sending bad data? Has this pattern happened before? Is there enough evidence to recommend maintenance? Why did the AI reach this conclusion? Repeated threshold alerts can also create alarm fatigue. If one fault produces dozens of alerts, operators may start treating them as noise. I wanted the system to create one investigation instead of another flood of alarms. What I built The demo represents six assets in a water-treatment plant. A TCP si