AI 资讯

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The inevitable weakness of metrics There are plenty of useful things a metric can reveal. There are even more that it can obscure or corrupt. Like a lot of people bitten…

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

We (at DEV and MLH) are covering AI Engineer's World Fair by printing a physical newspaper called...

Comcast has announced plans to separate itself into two publicly traded companies, spinning off its NBCUniversal and Sky broadcasting arms. The shake up aims to protect the media conglomerate's profitable broadband and wireless brand, which will retain the "Comcast" company name, as its media and entertainment business - now collectively named "NBCUniversal" - faces increasing […]

Google was forced to cap Meta's use of Gemini AI due to a lack of capacity.

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 […]

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

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

The 1970s porn actress turned New York City late night public access queen says censorship and a lack of star quality among modern adult entertainers turned her off of the industry.

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.

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

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

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

An accessibility audit is not only a compliance activity. For engineering teams, it is also a quality review of how real users interact with the product. If you are preparing for a WCAG 2.2 AA audit, the biggest mistake is waiting for the auditor to tell you what information is missing. You can make the process much smoother by preparing the right workflows, accounts, test data, and remediation owners upfront. Scope the product by user flow Do not start with only a list of URLs. URLs matter, but accessibility bugs often appear inside stateful interactions: Form validation Custom dropdowns Modal dialogs Keyboard focus management Error recovery Dynamic tables Authenticated dashboards Document downloads Instead of asking, "Which pages should we test?" ask: What tasks must users be able to complete? That usually gives you a better audit scope. Prepare accounts and stable data If a workflow requires authentication, roles, or sample records, prepare them before the audit starts. Useful prep includes: Admin, standard user, and limited-role accounts Stable sample records Forms with prefilled data where needed Test payment or transaction flows if applicable Known feature flags Environment notes This avoids spending audit time debugging access problems. Confirm the standards WCAG 2.2 AA may be the target, but the report may also need to reference WCAG 2.1 AA, Section 508, EN 301 549, GIGW, or IS 17802. Engineering teams should know this early because it affects reporting language and remediation priority. Make evidence developer-friendly A useful issue should be reproducible. Good audit findings usually include: Affected URL or screen Component or selector Steps to reproduce User impact WCAG success criterion Expected behavior Screenshot or notes This helps teams move from report to ticket without guessing. Plan remediation ownership Accessibility issues do not always map cleanly to one discipline. Examples: Missing form label: engineering Confusing error copy: content and pr