标签：#r

Cloud spending is on track to pass a trillion dollars a year, and most of it is wasted. Industry data puts idle resources, over-provisioned instances, and missed commitment discounts at 25 to 35% of the average cloud bill. For an early-stage company where hosting can eat 6 to 12% of revenue, that waste is not a rounding error. It is runway. The good news is that cloud cost optimization rarely requires a painful re-architecture. The biggest wins come from a few low-risk moves: switching off what nobody is using, rightsizing what is over-provisioned, and buying commitments for the baseline you will run anyway. The discipline that ties these together is called FinOps, and you do not need a dedicated team to practise it. You need visibility into where the money goes, a short list of high-leverage actions, and the habit of reviewing the bill before it reviews you. This playbook walks through exactly that, in the order we apply it for the startups we work with. Find the waste before you cut it You cannot optimize what you cannot see. Before touching a single instance, make your spend legible. That starts with cost allocation tags, a small enforced set like env , team , service , and customer , applied to every resource. Untagged spend is where waste hides, so treat an untagged resource as a bug to be fixed, not a footnote. With tags in place, the native tools do most of the heavy lifting. AWS Cost Explorer (and its equivalents on GCP and Azure) will show you the trend line, the biggest line items, and the resources sitting idle. Set budget alerts at the account and per-environment level so a runaway job pings you on day two, not on the invoice. The most important shift is what you measure. Don't stop at "we spent $14k on EC2." Tie cost to a unit of business value: cost per customer, per active user, or per thousand requests. That single number turns an abstract bill into a metric you can defend in a board meeting and optimize against deliberately. The number that matters

Next.js gives you almost everything you need to rank well out of the box, and most teams still ship sites that Google struggles to read. The framework is not the problem. The problem is that SEO gets treated as a final checkbox instead of an architectural decision, so metadata ends up scattered, content renders on the client, and the structured data never gets written. The App Router changed how all of this works. The generateMetadata function, file-based conventions for sitemap.ts and robots.ts , and Server Components as the default each remove a class of SEO bug that used to be common in the Pages Router. But they only help if you use them deliberately. This is the playbook we follow when we build a Next.js site that has to rank, the same approach behind this site. It is opinionated and concrete: where to put metadata, which files to ship, how to handle structured data and multiple languages, and why Core Web Vitals is an SEO feature rather than a performance afterthought. None of it requires a plugin. Render on the server so Google sees real HTML The single biggest SEO win in Next.js is also the easiest to get wrong: make sure your indexable content is in the HTML on the first byte. Googlebot will execute JavaScript, but it does so on a delay and with no guarantees. Content that depends on a client-side fetch can be missed, indexed late, or indexed empty. Server Components are the default in the App Router, so this is mostly about not opting out. Keep 'use client' at the leaves of your tree, on the button that needs an onClick , not on the page that holds your copy. Fetch your data in the Server Component and pass the rendered result down. If you can view the page source and read your headline and body text without JavaScript, you are in good shape. Master the Metadata API instead of next/head In the App Router you never touch next/head . Every route exports either a static metadata object or a dynamic generateMetadata function, and Next.js merges and d

Next.js 16 is the biggest release since the App Router landed, and the upgrade is not a one-line bump. The caching model changed shape, params and searchParams are now promises everywhere, Turbopack runs your builds by default, and middleware.ts is on its way out in favour of proxy.ts . None of that is hard on its own. The trouble is that the changes touch almost every dynamic route in a real app at once, so a rushed upgrade tends to fail in a dozen small places rather than one obvious one. We run this site on Next.js 16, and we have moved client projects across the same gap. The pattern that works is boring and reliable: read the codemod output, fix the async APIs first, decide your caching strategy deliberately instead of letting the old implicit behaviour leak back in, then clean up the renamed files. This guide walks through that order, with the specific gotchas that cost the most time. If you are still on Next.js 13 or 14, the same steps apply, you just have more of them to work through. Run the codemod, then read what it could not fix Start with the official upgrade command. It pulls the right versions of next , react , and react-dom , and runs the codemods that handle the mechanical rewrites for you. npx @next/codemod@latest upgrade latest The codemod is good, but it is not magic. It will happily wrap your params access in await where the shape is obvious, and skip anything indirect, a params object passed into a helper, destructured two functions deep, or read inside a generateMetadata you wrote by hand. Treat the codemod as the first 80%, not the finish line. Once it has run, do a clean install and a type check before you touch anything else. With typescript.ignoreBuildErrors set, as it is on many projects, the build will not catch these for you, so run the type checker yourself. rm -rf node_modules .next && npm install && npx tsc --noEmit The errors that come back are your real to-do list. Most of them will be the async API change, which is the next sectio

For a decade the advice was simple: put everything in the cloud and never look back. In 2026 that consensus is cracking. A Barclays survey found 83% of enterprises plan to repatriate at least some workloads from public cloud to private infrastructure, and IDC puts the share expecting to move compute or storage within the year near 80%. The most-cited example is still 37signals, the team behind Basecamp, who left the public cloud and reported saving roughly $7 million over five years. It is tempting to read those numbers as "cloud was a mistake." It was not. The cloud is still the right home for spiky, unpredictable, early-stage workloads where you are buying speed and optionality. What changed is that a lot of companies have now run the same steady, predictable workload on rented hardware for years, paying a premium for flexibility they stopped using. Repatriation is not a reversal of cloud strategy. It is the correction that comes after the bill gets big enough to read carefully. The question worth answering is not "should we leave the cloud" but "which specific workloads no longer earn their cloud premium," and that is a question you can answer with numbers. What is actually driving the move Cost is the headline, and it is real. Organizations that repatriate the right workloads commonly report 30 to 60% lower infrastructure spend for those workloads, because on-demand cloud pricing carries a large convenience margin that only makes sense when your usage is genuinely variable. Run a database at a steady 60% utilization every day for three years and you are paying a premium for elasticity you never touch. But cost is not the only force. Just over half of organizations name data security and privacy as a top driver, and in Europe the regulatory pressure is sharper than the cost case. Frameworks like DORA are already enforceable, and regulators increasingly want evidence of control over where data physically lives, not just a contractual promise from a hyperscaler. Fo

Follow-up to my 512 MB Spring Boot experiment: I tried the same application on a 256 MB Alpine VPS. JDK 21 struggled badly at this size, but with JDK 25 and a tuned 80 MB heap, the app plus lightweight monitoring completed a one-hour run without restarts or OOM kills. Still not something I’d recommend for normal production, but the difference was interesting. submitted by /u/fykup [link] [留言]

Meta fixes AI glasses to stop recording any time users cover up the safety light.

Retro, a friend-focused photo-sharing app built by former Instagram employees, has raised more than $21 million in Series A funding.

Your phone should be able to still sound your morning alarm when it's in silent mode. If it's not, here are some settings to check.

You know the person blocking your view of the concert because their phone is swaying in the air, recording the entire thing? Get ready for the unfolded version of it. This week on The Vergecast, we dive right into the big week of Apple news. First, it's the refreshed Mac Mini and Mac Studio, as […]

https://twitter.com/Zai_org/status/2093354097122455713 https://z.ai/blog/glm-5.3