标签：#p

Let’s be honest, the tech we use today is amazing, but it can also be complex. It’s only natural that teams want to build platforms that hide this complexity to improve productivity, avoid mistakes, and reduce cognitive load. But they may be misled to believe that the more complexity they hide, the better their platform is. Instead, they end up creating dangerous illusions! This talk reflects on two decades of building complex distributed systems, highlighting where abstractions helped and where illusions led to major disappointments. submitted by /u/goto-con [link] [留言]

Rocket Lab will own satellites connecting the most remote corners of the Earth.

Ending any story is hard, but that's especially true of mystery-packed TV shows. Series like Lost initially hook viewers with constantly building secrets and questions, to the point that they can often seem incomprehensible. But the promise is that it will all pay off in the end - a feat that few shows ultimately manage. […]

In this episode, Heroku co-founder and Ink & Switch founder Adam Wiggins argues for a 'local-first' architecture that reconciles cloud-based collaboration with the performance and data ownership of local software. He explores the role of CRDTs and version control primitives in non-code domains, and examines how a hybrid AI future might leverage local models for core productivity tasks. By Adam Wiggins

This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows. By Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic

A sneaky rule change has the potential to blow up scientific research in the United States. But there's still time to fight it. On May 29th, the Office of Management and Budget (OMB) issued a 412-page proposal to revise federal financial assistance. The language is a combination of distinctly Trumpian attacks on "woke" policies and […]

We’ve taken one small step towards robot police officers: a drone capable of disarming a suspect: In a June 22 video posted on the Sacramento County Sheriff’s Office’s Instagram page, an officer wearing goggles can be seen operating a drone to retrieve a knife from an armed suspect hiding inside a cluttered house. “After not responding to negotiators, a drone was deployed inside the residence,” the post says. “Drone pilots located the suspect hiding in a corner of a garage” and then used a high-powered magnet attached to the drone to grab the knife out of the suspect’s hand. In the video ­ which is soundtracked by the “Mission: Impossible” theme song—the intercepted knife can be seen spinning around in the air as the drone carries it back to the deputies...

With AI-powered noise reduction, an automatic speech-focusing system, and a simple, effective hearing test, it’s a shame these aids don't come with a better app.

Climate activist Will Lawrence cofounded the Sunrise Movement. Now, he has shifted his focus in his attempt to compete for a swing-district seat by calling for a data center moratorium.

Europe’s pro-competition proposals could see Google Search and Android systems opened up. The company claims there are serious privacy flaws.

FIFA says hydration breaks protect players from heat. They also create new annoying commercial breaks—and fans are calling foul.

submitted by /u/joladev [link] [留言]

You know what you want from a JSON document. You just don't want to memorize whether it's $[?(@.age > 18)] or $..users[?(@.active)] . Plain English in. JSONPath out. JSONPath Assistant on FormatList lets you paste JSON, describe what you need in natural language, and get a validated JSONPath expression plus the actual results — all in your browser. No account, no API key, no data sent to a server. How it works Paste your JSON — an API response, config file, or test fixture. Describe what you need — e.g. "get all user names" or "find products with tag tech". Generate — the assistant reads your JSON structure and maps your query to JSONPath. Validate & copy — the expression runs against your data immediately. Copy the path or the matched values in one click. If the first attempt returns no matches, the tool retries with a simpler variation automatically. Example queries You type Generated JSONPath Get all user names $.users[*].name Find users older than 18 $.users[?(@.age > 18)] Get names of active users $.users[?(@.active == true)].name Find products with tag tech $.products[?(@.tags.indexOf('tech') >= 0)] Get all order prices $.orders[*].price Get the first user's email $.users[0].email Get all pod names {.items[*].metadata.name} Get all pod IP addresses {.items[*].status.podIP} The tool ships with one-click examples for each of these — load one, hit Generate, and see how it works before trying your own JSON. What kinds of queries it understands Property access — "get all names", "list order prices" Numeric filters — "older than 18", "price under $10", "greater than 100" Boolean filters — "active users", "enabled devices" Tag / category searches — "with tag tech", "beauty category" Multi-condition — "both tech and mobile tags" Quantifiers — "the first user", "the last item" Kubernetes — "get all pod names", "pod IP addresses" It analyzes field names in your JSON — so users , products , orders , or whatever keys you actually have — and builds paths that match your sc

The design doc is fourteen pages. Clean service boundaries, thoughtful API contracts, a deployment story that handles rollback without incident. Six months of work. The team is proud of it, and the work is genuinely good. Three questions will tell more about this system than all fourteen pages. Not questions about implementation details or technology choices. Questions about how the system was designed to age, to fail, and to be understood by someone who didn’t build it. Most architecture conversations never reach these questions, which is part of why the gap between a good system and a great one is often invisible until it is suddenly very visible. What does this make hard? Good architecture conversations focus on what a design enables: faster deployments, independent scaling, and clearer ownership. The question that separates architectural thinking from implementation thinking is the inverse . What does this make hard? Every decision forecloses options. The service boundary that gives teams autonomy makes cross-service transactions expensive. The data model that reads cleanly under expected load makes certain write patterns awkward. The abstraction that simplifies onboarding makes some categories of refactoring nearly invisible as possibilities. These are not arguments against the decisions. They are the other side of every decision, and that other side exists whether or not anyone names it. The design doc that holds up over time is not the one where nothing is difficult. It is the one where the difficult things are named. “This approach makes distributed transactions impossible, and here is why we have decided to accept that constraint.” That sentence, or something close to it, belongs in every significant architecture document. Its absence is not a sign that the constraint wasn’t considered. Often it was. But without the name, the constraint becomes invisible to everyone who wasn’t in the room, which eventually includes the original team. When joining a system s

submitted by /u/Successful_Bowl2564 [link] [留言]

You export a CSV from a UK system and double-click it in US Excel — 28/05/2026 suddenly looks like May 28, or something even stranger. The file isn't corrupt. Excel is guessing your date format based on your computer's locale. This short guide covers why that happens, how to import CSV correctly, and how to batch-fix dates that are already wrong. For the full walkthrough with examples, see the official guide: How to Fix Excel CSV Date Import Problems . Why Excel breaks CSV dates A CSV is plain text. It does not store whether a value is a date or a string. When you double-click to open, Excel parses using your regional settings: US Excel: MM/DD/YYYY UK / Europe: DD/MM/YYYY Dates like 03/04/2025 are the most dangerous — both parts are ≤ 12. US Excel may read April 3; UK Excel reads March 4. Excel won't warn you. Other common traps: Dates exported as serial numbers (e.g. 44927 ) Two-digit years triggering century guesses Re-saving as CSV destroys formats a second time The right way: don't double-click the CSV Open Excel first — don't double-click the .csv file Data → Get Data from Text/CSV (older Excel: Data → From Text) Set date columns to Text if you need to preserve the original string Confirm the source region (US / UK), then convert to a single format For team data exchange, agree on ISO 8601: YYYY-MM-DD in your schema — unambiguous, sorts correctly as text, and works in JSON APIs and databases. Ambiguous value US reads as UK reads as Safe format (ISO) 03/04/2025 2025-04-03 2025-03-04 Confirm source region 28/05/2026 — 2026-05-28 2026-05-28 05/28/2026 2026-05-28 — 2026-05-28 Already broken? Fix dates in the browser (no server upload) I built a free tool cluster on FormatList — everything runs locally in your browser : 1. Date Format Fixer — bulk repair Paste a date column or upload .csv / .txt Ambiguous rows highlighted; optional US / UK preference Export ISO / US / UK or download a fixed CSV Best for: normalizing a whole column to ISO before a database or API imp

Contact Form 7 runs on millions of sites for a good reason: it's free, light, and gets out of your way. I shipped it on client sites for years. The problem isn't that CF7 is bad — it's that it answers exactly one question ("did the form submit?") and stays completely silent on the one that actually matters in production: did the notification arrive? Here's the call every developer who maintains WP sites has taken at least once: "I filled in your contact form last week and never heard back." You check. The form is fine. JavaScript fires, the success message shows, no console errors. CF7 did its job — it handed the message to wp_mail() and forgot it ever existed. There's no record the submission happened, and no log of whether the email was delivered, bounced, or quietly dropped by the host's unauthenticated sendmail. The lead is just gone, and you have nothing to debug with. The three gaps that bite in production No submissions database. CF7 sends an email and discards the data. If the email fails or lands in spam, the submission never existed. (Flamingo helps, but it's a bolt-on — separate screen, no filtering or export out of the box, not tied to your form config.) No delivery log. You can't tell whether mail was sent, rejected, or bounced. "I never got it" has no audit trail to check against. No native block. CF7 is still a shortcode — [contact-form-7 id="123"] . You can't drop it into a block template, control its layout with block spacing, or edit it inline in Gutenberg. You paste a shortcode and hope. None of these are dealbreakers for a throwaway contact form. All three are dealbreakers when a missed submission is a missed sale. Migrating without rebuilding by hand The reason most people put off switching isn't the feature gap — it's the thought of rebuilding every form field by field. That's the part I wanted to skip. The migration path I use reads CF7's stored form definitions directly and recreates them as native forms. What comes across automatically: All

Every Node.js developer has been burned by this at least once: const port = parseInt ( process . env . PORT ); // NaN if PORT is missing const db = process . env . DATABASE_URL ; // string | undefined — not safe! Your app starts fine locally, then crashes in production because someone forgot to set an env var. The error shows up 3 hours later, not at startup. The solution I built @harmand66/typesafe-env — a tiny, zero-dependency library that validates and types your environment variables at boot time. import { createEnv } from ' @harmand66/typesafe-env ' ; const env = createEnv ({ PORT : { type : ' number ' , default : 3000 }, DATABASE_URL : { type : ' string ' , required : true }, DEBUG : { type : ' boolean ' , default : false }, }); // ✅ TypeScript knows PORT is a number env . PORT + 1 // 3001 — not "30001" env . DATABASE_URL // string — guaranteed, never undefined If anything is missing or wrong, your app fails immediately at startup with a clear message: All errors at once — no more fixing them one by one. Why not Zod? Zod is great but it's 57kb and requires a lot of boilerplate for this specific use case. @harmand66/typesafe-env is zero dependencies and does one thing well. Try it npm install @harmand66/typesafe-env GitHub: https://github.com/giannielloemmanuele-lgtm/typesafe-env npm: https://www.npmjs.com/package/@harmand66/typesafe-env Would love any feedback or contributions! 🙏

Industry 4.0 data collection sounds simple until you look closely at the factory floor. In theory, the flow is clean: machine → gateway → cloud → dashboard In practice, it is usually less tidy. Factories may have PLCs, CNC machines, sensors, meters, inspection systems, production lines, and older equipment all working together. Some devices use Ethernet. Some still rely on serial interfaces. Some data is useful every second. Some data only matters when a machine changes state, crosses a threshold, or triggers an alarm. This is where an industrial edge gateway becomes useful. A gateway such as Robustel EG5120 can sit between factory equipment and upper-layer systems, helping collect selected machine or PLC data, handle it locally where needed, and forward useful information toward cloud or enterprise platforms. That does not mean the gateway replaces PLCs, SCADA, MES, or the cloud. It simply means factory data often needs a practical middle layer before it becomes useful somewhere else. Factory data is not one clean data stream One thing that gets underestimated in Industry 4.0 projects is how mixed the data sources can be. A PLC may provide equipment status, alarms, and process values. A CNC machine may expose cycle information or maintenance indicators. Sensors and meters may generate temperature, vibration, energy, or environmental data. Inspection systems may produce quality-related events or selected result data. A production line may generate throughput signals, downtime events, or operating states. These are all “factory data,” but they do not behave the same way. A machine fault may need quick attention. An energy reading may only need periodic reporting. A repeated sensor value may not need to be sent upstream every time. A quality inspection output may be useful as metadata, but not every raw file is practical to upload continuously.So the first question is not only: Can we connect this machine? A better question is: What data do we actually need, where sho

Today, we’re excited to launch UIAble v1.0, an open-source component library built for developers, by developers. We explored a lot of UI libraries built on Shadcn. Most of them feel nearly identical — same structure, same aesthetic, same tradeoffs. That’s what pushed us to build something different. Not another library that looks like Shadcn with a coat of paint, but a design system with its own identity and a clearer sense of what it’s actually for. Why UIAble exists After enough frontend projects, one thing becomes obvious: the same UI patterns get rebuilt again and again. Inputs. Dialogs. Tables. Alerts. Dropdowns. OTP fields. Form validation states. Not because they’re hard to build, but because most existing libraries never quite fit real project requirements. Some are too opinionated. Some pile on unnecessary abstraction. Some become rigid after initial setup. And some make simple UI unnecessarily complicated. That friction is what led to UIAble. Not to launch another oversized library, just to build a cleaner, more practical foundation for modern frontend development. What UIAble actually is UIAble is a free, open-source UI component library built with Tailwind CSS , Shadcn-style architecture , and Base UI principles . The idea is straightforward: reusable components should stay flexible, readable, and easy to maintain. Instead of pulling projects into a rigid ecosystem, UIAble gives you components you can copy directly into your codebase, edit freely, and scale without fighting the library. No lock-in. No unnecessary abstraction. No dependency trap. What makes it different in practice A few things actually matter here. You get the code. UIAble doesn’t hide logic behind layers of packaging. You see the component. You edit it. You ship it. That alone changes how teams work with UI. It’s built for real product UI, not showcase pages. A lot of UI kits look great in demos and fall apart in production. UIAble focuses on the unglamorous stuff, forms that don’t bre