标签：#ui

The feature was supposed to be the easy one. NotebookLM generates flashcards. My extension reads them...

Okay. This one is going to feel weird at first. I'm going to warn you in advance so you don't think...

Overview Open WebUI exposed a real Ota lifecycle boundary. This was not mainly a parsing or contract-shape repo. The contract was already strong enough to model: source-checkout verification packaged native runtime through uv run open-webui serve frontend development runtime default Docker Compose runtime What the repo exposed was operational truth after proof: a successful native proof still left a host workload alive the first cleanup fix then widened too far and treated a Compose-owned runtime as the same class of host workload That made Open WebUI a valuable pressure repo. It forced Ota to get more precise about cleanup ownership instead of treating all successful runtime proof as one generic teardown problem. The current pressure contract pins released Ota v1.6.24 . Its latest green matrix run proves the release surface at the exact contract and workflow revision linked below. What Open WebUI exposed in Ota This repo exposed four meaningful weaknesses. 1. proof success was weaker than it looked The first issue was not that runtime proof failed. It was that runtime proof succeeded and still left the native workload alive afterward. In this repo, the packaged native workflow launches: serve:native : launch : kind : command exe : uv args : - run - open-webui - serve - --host - 0.0.0.0 - --port - " 8080" Ota proved that workflow, but the launched process tree was still alive after proof completed. In GitHub Actions, that surfaced through setup-uv post-job cleanup, which blocked while the uv cache was still in use. That was an Ota gap. If proof succeeds but leaves behind repo-owned runtime state that later breaks CI cleanup, the proof surface is still incomplete. 2. native service cleanup widened past its real ownership boundary The first core fix made Ota clean selected native service workloads after successful proof. That was directionally correct, but Open WebUI immediately exposed the next boundary. The Docker workflow uses a native task shape to launch Compose:

From monsters to kittens to strategy games, these sets will liven things up on nights when everyone is tired of screens.

Most people working in offices never think about why the temperature stays comfortable throughout the day. The cooling adjusts automatically. Fresh air increases when occupancy rises. Fans start and stop without anyone touching a switch. Behind all of this is a device that most building occupants have never heard of: the DDC Controller. The Hidden Computer Inside Every Modern Building Walk into a mechanical room and you'll find equipment everywhere: Air Handling Units (AHUs) Chillers Pumps Cooling Towers VAV Boxes All these systems need coordination. If the supply air temperature rises above its target, something has to react. If occupancy increases, fresh air must increase. If a fan trips, alarms must be generated. This is where a DDC controller comes in. Think of it as a small industrial computer dedicated to one job: keeping a building running efficiently. A Typical Day in the Life of a DDC Controller Imagine an AHU supplying air to an office floor. At 9:00 AM employees begin arriving. The return air temperature starts increasing. The DDC controller notices this through a temperature sensor. Within seconds it: Reads the sensor value Compares it against the setpoint Calculates the cooling demand Adjusts the chilled water valve Verifies fan operation Repeats the process No operator is required. No manual intervention is needed. The controller quietly performs these calculations all day. Why Not Just Use a PLC? This is one of the most common questions from engineers entering building automation. PLCs and DDC controllers are both programmable devices. However, they were designed for different worlds. A PLC excels at: Manufacturing lines Packaging machines Process control High-speed sequencing A DDC controller excels at: HVAC control Energy optimization Occupancy schedules Comfort management BACnet communication Both can control equipment. The difference is what they were originally built for. The Four Signals Every BMS Engineer Learns First If you're new to building

This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . What I Built Preparing for technical interviews can be overwhelming. I wanted to build a tool that doesn't just give generic questions, but actually analyzes my specific resume to challenge my unique skill set. This led me to build a multi-agent system using the ADK. This multi-agent system, will take user resume and extracts their profile for generating Interview Questions specialized to the candidate profile. The agents communicate in a sequential loop: The Profiler extracts data -> The Interviewer generates questions -> The Judge validates them. If the Judge rejects a question, the Interviewer re-drafts it, ensuring only high-quality, resume-relevant questions make it to the user. Cloud Run Embed Your Agents Profiler Receives a resume PDF GCS path, downloads it in-memory, parses the text content, and builds a summary of skills. Interviewer Reads the candidate summary and drafts 3 technical interview questions designed to test the boundaries of their experience. Analyzes the drafted questions. Passes the iteration if they are resume-specific; rejects/fails them if they are too generic. Key Learnings This project was a fantastic weekend challenge. Working through the Google Codelab gave me a solid grasp of agent-based architectures, specifically implementing Agent, LoopAgent, and SequentialAgent to create a robust workflow. A few key technical takeaways included: Managing Statelessness : Learning to handle agent sessions in a Cloud Run environment was a great lesson in explicit session lifecycle management. Cloud Integration : Integrating Google Cloud Storage for file handling taught me how to bridge in-memory document processing with persistent cloud storage efficiently. Deployment Architecture : Mastering the transition from local development to a containerized, production-ready Cloud Run deployment provided deep insights into modern backend orchestration. Check the Code from

Monitoring is expensive and labor intensive. But it helps public health officials stop outbreaks.

Keep your phone, laptop, handheld gaming console, and other electronics running with these travel-friendly power banks.

Get those lost minerals back with the help of our top electrolyte powders, tablets, drops, and chews for athletes, partiers, and everyone in between.

A few months ago, I was searching for a backend engineering job. Every day looked the same: Open LinkedIn Open Naukri Search for Python jobs Open dozens of tabs Read every job description Apply Repeat The frustrating part wasn't finding jobs. It was finding the right jobs . I kept getting recommendations for roles that technically matched my resume because they contained words like Python , Backend , or API , but after reading the description I'd realize they wanted a completely different skill set. I started wondering: Why are job boards still matching keywords instead of understanding what a developer actually knows? That question eventually turned into a side project called Jobspiq . The Problem Imagine these two jobs: Job A Python FastAPI PostgreSQL Redis Job B Java Spring Boot Oracle Kafka Both are "Backend Engineer" roles. A keyword-based system often treats them as similar. As developers, we know they're not. What I Built Instead of matching keywords, I built a system that compares a developer's profile with a job description to understand how well they actually fit. The platform: Collects jobs from multiple sources. Removes duplicate postings. Scores every job based on how closely it matches your profile. Sends alerts only for high-quality matches. Helps track applications in one place. The goal isn't to show more jobs. It's to show fewer, better ones. What I Learned Building the product taught me something interesting. Writing the software was the easier part. Helping people discover it is much harder. That's why I'm starting to build in public and share what I'm learning along the way. If you've ever built search systems, recommendation engines, or developer tools, I'd love to hear your thoughts. You can check out the project here: https://jobspiq.in Feedback is always welcome.

Our remote team clocked serious hours walking, working, and sometimes jogging to find the best under-desk treadmills for home offices and small spaces.

If you prefer not to have cameras in and around your home, try one of these more private, WIRED-tested security devices.

Get ready to crush your coursework or scoot to class in style with these back-to-school gear picks.

I'm not an engineer. I built a prompt-structuring tool anyway, using Claude Code — here's what actually went wrong 🤔 The problem I don't write code. I'm not an engineer, day to day — but I use AI chatbots constantly, and I kept running into the same annoying pattern. I'd type something lazy into ChatGPT or Claude — half a sentence, no context, whatever came to mind first — and get back a mediocre answer. Then, later, I'd realize: if I'd just written a slightly better prompt, I probably would've gotten a much better answer on the first try. Instead I'd burned a chunk of my monthly quota (sometimes on a paid plan) on something forgettable. I figured other people had to be doing the same thing. So I built something for it. 💡 What I built It's called Deep Prompt Studio . You paste in a rough, unpolished prompt — the kind you'd type without thinking too hard — and it hands back a detailed version that pulls in whichever pieces actually matter for that request: role, task, constraints, output format, and more. It's not tuned for one specific chatbot; it's meant to work well whether you're pasting the result into ChatGPT, Claude, Gemini, or something else. https://deep-prompt-studio.vercel.app 🛠 Tech stack Next.js (App Router, Turbopack) + TypeScript Tailwind CSS Anthropic Claude API for the actual prompt enhancement Stripe for payments Upstash Redis for storage Deployed on Vercel ⚙️ How it works Tone selector — Default, Professional, Casual & friendly, Concise, Direct Target-model optimization — Generic, ChatGPT, Claude, Gemini, Midjourney Snippets — save reusable context/tone/role info and toggle them on or off per enhance (free: up to 3, Pro: unlimited) Variable fill-in — template placeholders like {{product name}} you can reuse Refine mode — if your prompt is too vague, it asks a couple of follow-up questions before enhancing, instead of guessing Template gallery — writing, coding, business, learning, SEO, customer support Library — saved enhanced prompts (Pro) Free ti

Part 2 — Search, palette, and settings Level: Intermediate · Time: ~35 minutes · Builds on: Part 1 — Contacts app Part 1 got you shipping. This one gets you productive . We'll take the Contacts app and give it the ergonomics real users expect: an adaptive sidebar that becomes a tab bar on iPhone, a command palette on ⌘K, honest loading states while data comes in, and a proper settings screen. Zero #if os guards. Zero re-rolled controls. What we're adding An adaptive shell — DFSidebar on regular width, DFTabBar on compact. A search field at the top of the list, filtering as you type. A ⌘K command palette exposing every action in the app. Skeleton loaders for a simulated slow fetch. A settings screen — notifications toggle, density picker, sync-interval slider, pinned-since date picker, beta-features checkbox. Per-component token overrides on the settings screen, without forking the theme. 1. Shell: sidebar on wide, tab bar on narrow The routing decision — sidebar vs tab bar — should be data, not a view hierarchy. Enumerate your sections once, then feed the two components the shapes they want. API note. DFSidebar uses Binding<String?> and is just the sidebar view — you compose the detail pane yourself (naturally via NavigationSplitView ). DFTabBar uses Binding<String> (non-optional) and does take a content builder that receives the selected ID. Both use plain String IDs, so we keep a simple Section enum and pass rawValue at the boundary. enum Section : String , CaseIterable , Identifiable , Hashable { case contacts , favorites , archive , settings var id : String { rawValue } var label : String { switch self { case . contacts : "Contacts" case . favorites : "Favorites" case . archive : "Archive" case . settings : "Settings" } } var icon : String { switch self { case . contacts : "person.2.fill" case . favorites : "star.fill" case . archive : "archivebox.fill" case . settings : "gear" } } static func from ( _ id : String ?) -> Section { id . flatMap ( Section . init (

Lots of articles about this . As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

Apple filed a trade secrets lawsuit against OpenAI last Friday, and it’s not messing around. The complaint alleges a pattern of misconduct reaching all the way up to OpenAI’s chief hardware officer and claims more than 400 former Apple employees now work at the company. OpenAI’s response so far has been carefully hedged, and the timing couldn’t be worse with the company reportedly eyeing an IPO […]

I tested REI’s best new and revamped outdoor gear of 2026. Here’s what’s worth buying, from tents and sleeping bags to coolers and bikes.

I run a pipeline that generates explainer articles. LLM in the middle, structured output, published in several languages. It had been running for a while and the articles looked good: clean layout, a chart, and near the top of each one a confident little box with a statistic. Something in the shape of "68% of people never change the default." A number, a source, an authoritative ring to it. Not one of those numbers had been researched. The pipeline had never looked up a single statistic in its life. It asked the model for a number and printed whatever came back. I did not find this through a clever eval. I found it while cleaning up something unrelated and actually reading the prompt. The field that forced a lie The output schema had a required field. statistic.text and statistic.source , described in the prompt as an "eye-catching stat" for the top of the article. Required. Every article had to have one. The prompt also, helpfully, told the model what to do when it did not have a real number. It said to round to a safe order of magnitude. And it said to strip the year off the source, so the article would look evergreen instead of dated. Read that back slowly. The instructions were: always produce a statistic, make up a plausible magnitude if you have to, and remove the one piece of metadata that would let anyone check it. That is not a prompt that occasionally allows a hallucination. That is a prompt that requires one, every single time the model does not happen to know a real figure. So it produced them, confidently, in every language, each wearing a real-sounding source: a named institute, an industry association, a government statistics office. None of it had been looked up when it was written. This was content people actually act on, which is exactly the category where being wrong is not a rounding error. There was a second engine doing the same thing in the chart code. The block that generated the data visualization asked the model for "actual statistics from

We consulted preparedness experts and WIRED’s team of testers for the essential gear to keep on hand in case of wildfires, earthquakes, and lord knows what else.