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A green required check is not a merge decision. It is a signal that one job graph finished without a red X. If a pre-push hook was skipped, or a snapshot fixture was regenerated without a reason, you can still ship a lie. This article walks through a merge packet: a small JSON artifact your CI publishes next to the check. The packet records hook results, fixture drift, and required-job status. A model may write the eight-line brief. It does not get a vote. Why green still lies CI dashboards collapse many facts into one glyph. You see green. You click merge. You miss three common failures. First, someone pushed with --no-verify and skipped the hook that keeps fixture hashes honest. Second, a test helper rewrote golden files because a serializer added a field. Third, a retry job went green on the second attempt and nobody recorded that the first attempt failed. You do not need a platform rewrite to catch this. You need a contract the merge button cannot ignore. Cheap code generation makes the second failure more common. When it is easy to regenerate tests, it is easy to regenerate the fixtures those tests pin. The pin becomes a moving target. Treat unexplained fixture diffs as merge blockers, the same way you treat a failed unit job. What the merge packet contains Keep the packet boring. One file. One schema. Commit it as a CI artifact, not as a comment that can be edited after the fact. { "commit" : "REPLACE_WITH_SHA" , "generated_at" : "2026-09-03T00:00:00Z" , "hooks" : { "pre_push_fixture_guard" : "passed" }, "fixtures" : { "manifest_path" : "tests/fixtures.sha256" , "changed_paths" : [], "unexplained_paths" : [] }, "required_jobs" : [ { "name" : "unit" , "conclusion" : "success" }, { "name" : "contract" , "conclusion" : "success" } ], "merge_ready" : false , "brief" : null } merge_ready is computed by a script you own. Not by a prompt. The brief is optional prose for humans who will not open the JSON. Step 1: Pin fixtures with a manifest Pick a directory you alrea

I recently built a complete IT ticket management system in Power Apps — 9 screens, role-based access, live SLA tracking, and automatic email notifications. The part I want to actually talk about here isn't the UI, it's the SLA engine, because I built it to work without Power Automate , and the trick is simpler than it looks. The problem SLA tracking normally means: a ticket is "Critical" → 60 minute target → somebody needs to know if it's about to breach or already has. The obvious way to do this is a scheduled Power Automate flow that checks every ticket on a timer and flags the ones in trouble. I wanted this app to run on Power Apps collections only — no flow, no external data source — so a scheduled flow wasn't an option. The question was: can you get "live" SLA status without a background job? The trick: recalculate on every read, not on a timer Instead of a flow updating a SLAStatus field periodically, I recalculate it every time the app or a screen is opened, using Now() against the stored due date: \ UpdateIf( colTickets, Status <> "Resolved" && Status <> "Closed", { SLAStatus: If( Now() > DueDate, "Breached", DateDiff(Now(), DueDate, TimeUnit.Minutes) <= SLAMinutes * 0.2, "At Risk", "On Track" ) } ); UpdateIf(colTickets, Status = "Resolved" || Status = "Closed", {SLAStatus: "Met"}) \ \ This runs in App.OnStart , at the top of every screen's OnVisible , and behind a manual "Refresh SLA" button. The At Risk threshold is 20% of the SLA window remaining — so a Critical ticket (60 min target) goes At Risk with 12 minutes left; a Low ticket (1440 min / 24 hrs) goes At Risk with 4.8 hours left. The honest tradeoff: this only updates when someone has the app open. A ticket breaching at 2am with nobody looking won't trigger anything until the next visit. For a real production deployment I'd pair this with a scheduled flow for after-hours detection — but for a demo, an internal tool with regular traffic, or anything where "eventually consistent within the next visit"

How do you run an autonomous AI agent in the cloud 24/7 for just $5.70 a month? I recently wanted to build a background worker with persistent disk storage and an instant web dashboard, but I didn't want the headache of managing a virtual machine or paying a massive monthly bill. If you are building long-running agents, you know this exact cloud hosting dilemma: Standard serverless (like Cloud Run services or Lambda): When traffic stops, the container scales to zero — instantly killing your background loops and wiping your agent's active memory (RAM). On the flip side, a sudden traffic spike spins up multiple containers that can overwrite each other's state files and corrupt your data. (Note: Save state using JSON or Markdown files. Avoid SQLite, as Cloud Run volume mounts ) A regular virtual machine (like EC2 or Compute Engine): Keeps your agent running 24/7, but a standard 1-vCPU machine typically costs $15 to $25 a month even when idle. Even if you use a heavily-throttled fractional VM for $7/month, you are still stuck with the full infrastructure management overhead. Last year, I built a multi-agent Trend Spotter with ADK . It worked well, but I wanted to make it fully autonomous: a continuous, long-running agent that scans and summarizes tech feeds in the background without manual triggers or high hosting costs. Google Cloud's new Cloud Run instances primitive solves this exact problem. It gives you a single, always-on container that runs 24/7, costs $5.70 a month on a shared CPU, provides a free HTTPS endpoint, and lets you mount cloud storage like a normal local disk. Here is how to build and deploy a production long-running agent with this setup (you can follow along with the complete source code in the repo . What are we building? I want to stay up to date with what is happening in AI and agent engineering. But instead of manually opening 20 browser tabs across different websites every morning, I wanted to build my own long-running agent that updates me on

Design engineers increasingly work across two systems: the visual language of a product and the implementation details that make it usable. Skills for Design Engineers from ibelick focuses on that overlap, packaging practical guidance for building interfaces with stronger visual quality, clearer interaction patterns, and more consistent engineering decisions. The project is attracting attention, with +46 stars today . That momentum makes sense: design-focused AI workflows are moving quickly, but many generated interfaces still need human judgment around spacing, typography, responsive behavior, accessibility, and component reuse. The useful way to approach this project is not as a drop-in framework. Treat it as a reference layer for your development workflow. Read the relevant skill instructions, adapt them to your stack, and keep the resulting guidance close to the codebase so it can be applied consistently during implementation and review. A lightweight local setup might look like this: mkdir -p .ai/skills/design-engineering curl -L https://github.com/sponsors/ibelick \ -o .ai/skills/design-engineering/reference.html For a real team workflow, I would convert the useful parts into a checked-in Markdown file: .ai/ └── skills/ └── design-engineering/ ├── interface-quality.md ├── responsive-layouts.md └── review-checklist.md This keeps the process portable across editors and AI assistants instead of tying it to one tool. It also makes design decisions reviewable in pull requests, which is more valuable than keeping them inside an undocumented prompt. Before using the approach in production, watch for: Context drift: generic design guidance can conflict with an existing design system, so define project-specific tokens and component rules first. AI overconfidence: generated UI still requires manual checks for accessibility, keyboard navigation, mobile behavior, and performance. The strongest ROI comes from using these skills as repeatable engineering standards—not as a

Welcome back to our series on building the ultimate AI research assistant for our AI agent podcast! In our first post, we built a fantastic agent that could search the web to find the latest AI agent news for the agent factory podcast . But what if we want to add more specialized skills, like getting the real pulse from developer communities on Reddit? To do that, we need to upgrade our agent's design. In this guide, we are going to level up our skills and refactor our simple agent into a powerful multi-agent system . We will build a "Manager" agent that directs a team of specialists, including one with a custom-built Reddit tool , to gather richer, more diverse insights. By the end of this post, you'll have an even more powerful Trend Spotter agent that gets information from multiple sources. More importantly, you will learn the advanced skills needed to build complex agents with ADK . You will know how to: Build a scalable multi-agent system. Build a custom tool from any Python function (like our new Reddit tool). Create an orchestrator agent that delegates tasks to a team of specialists. Write advanced prompts to manage a multi-step, multi-tool workflow. Debug a multi-agent system using the ADK's powerful Trace view. This architecture is the key to unlocking your agent's full potential. Let's get started! Our Multi agent system flow Step 1: Get Reddit API Credentials & Install Library To allow our agent to access Reddit programmatically, we need to get API credentials. This is free and only takes a minute. Navigate to Reddit Apps: Log in to your Reddit account and go to the app preferences page: https://www.reddit.com/prefs/apps . Create a New App: Scroll to the bottom and click the button that says "are you a developer? create an app…" . Fill out the form: name: Trend Spotter Agent Select the script option for the application type. about url: You can leave this blank. redirect url: You must enter http://localhost:8080 for this field. Click create app . You will

Sony showed off expansions for Crimson Desert and Ghost of Yōtei, Grand Theft Auto 6 controllers and much more.

OpenAI's ChatGPT, xAI's Grok, and Anthropic's Claude are all experiencing issues. At around 11AM ET, ChatGPT started returning error messages for users trying to use the chatbot, with its status page saying there are currently "elevated errors across ChatGPT and Codex." In addition to preventing users from having conversations with ChatGPT, the outage is also […]

WeatherNext 3 is the latest wave of a sea change in meteorology brought out by deep learning techniques. Google says it will start feeding into weather information users see in search, Google Maps, and Gemini.

After expanding its health and beauty lineup earlier this week with a camera-equipped electric toothbrush, Dyson is also introducing two new hair care products this month. The $329 Corrale CoolShine straightener upgrades the original Corrale that launched in 2020 with active cooling technology designed to "reduce residual heatexposure" so hair feels smoother, softer, and cooler […]

Google is rolling out an updated AI weather model that's supposed to be more accurate, especially when it comes to predicting rain and snowfall. In the announcement today, the company says it's now able to make forecasts with "unprecedented resolution" using its new WeatherNext 3 AI model. It can produce a global picture that's five […]

The detailed Lego replica of the first PlayStation console also features two miniature dioramas of classic PlayStation games.

Hohem has combined its gimbal tech with a wearable camera unit.

SwitchBot has launched a new retrofit smart lock that gives owners multiple ways to enter European homes without a traditional key. Announced at IFA today, the SwitchBot Lock Ultra Max Vision Pro Combo provides multi-user entry and up to 19 ways to unlock doors, including fingerprint scans, passcodes, NFC card access, contactless authentication via facial […]

Today, I’m talking with Tom Conrad, the CEO of Sonos. Tom and I have known each other for a long time — he was the chief technology officer of Pandora, VP of product at Snap, and the chief product officer of Quibi. He was also on the board at Sonos during its disastrous 2024 app […]

Hohem is trying something new with its Eyepic handheld stabilized camera to differentiate it from existing offerings from companies like DJI and Insta360. At its core, the camera is functionally similar to devices like the Osmo Pocket 4 and Insta360 Luna Ultra with a rotating preview screen and a small camera module stabilized by a […]

Nvidia says Hugging Face will stay open even as the chipmaker takes control of a key AI hub.

OpenAI introduces Daybreak for Frontline Defenders. A $1 billion commitment expands access to frontier cyber AI, training, and support for essential services.

DJI has announced a second generation of its Romo robovac. The Romo 2 series is quieter, more powerful, and better at climbing over small steps and other obstacles. But more importantly for DJI, a new local-only data mode should go some way to reassuring anyone concerned by the first Romo's serious security flaws. There are […]

GitHub热门项目 | A cross-platform desktop app to manage Agent Skills in one place and sync them to multiple AI coding tools’ global skills directories — “Install once, sync everywhere”. | Stars: 1,550 | 43 stars today | 语言: Rust

GitHub热门项目 | A Privacy-first, Cross-platform Text Expander written in Rust | Stars: 14,407 | 13 stars today | 语言: Rust