AI 资讯

DevOps has always evolved with technology. Cloud changed how teams manage infrastructure. Containers changed how applications are deployed. CI/CD changed how software is released. Observability changed how teams monitor systems. Now AI is starting to change DevOps again. The next stage of DevOps is not only automation. It is intelligence. * DevOps Was Built on Automation * Automation is one of the strongest foundations of DevOps. DevOps teams automate: • Builds • Tests • Deployments • Infrastructure provisioning • Monitoring alerts • Rollbacks • Scaling • Security checks This has helped teams deliver software faster and more reliably. But most automation still works through fixed rules. For example: if CPU crosses a threshold, send an alert. If a build passes, deploy to staging. If a container fails, restart it. This works well for known situations. But modern systems are more complex. Microservices, cloud platforms, Kubernetes, APIs, databases, queues, and third-party dependencies create huge amounts of operational data. When something goes wrong, fixed rules are not always enough. * Why Intelligence Matters * Modern DevOps teams do not just need more automation. They need better understanding. AI can help teams identify patterns, detect unusual behavior, summarize logs, group related alerts, and suggest possible causes during incidents. This is where AIOps becomes important. AIOps means using AI for IT operations. It helps DevOps and SRE teams move from reactive operations to smarter operations. Instead of only asking, “What alert fired?” teams can start asking: • What changed recently? • Which services are aff ected? • Are these alerts connected? • Is this behavior unusual? • Has this happened before? • What is the likely root cause? This does not mean AI will replace DevOps engineers. It means AI can support engineers with faster insights. * What This Means for DevOps Engineers * DevOps engineers should pay attention to AI because their role is evolving. Traditi

I Spent Two Weeks Pitting Qwen 3 Max Against DeepSeek V4 I want to tell you about a rabbit hole I fell into recently. It started the way most of my projects do — someone on a Discord server I frequent asked a simple question: "Should I use Qwen 3 Max or DeepSeek V4 for my internal_compare workflow?" I had opinions, sure, but I wanted real numbers. So I cleared my calendar, fired up a couple of GPU instances, and started benchmarking. What I found surprised me, and it also reinforced something I've been saying for years: the open source ecosystem is winning, and the walled gardens of the proprietary AI world are starting to look pretty silly. Let me walk you through what I learned, the actual numbers I got, and why I keep coming back to open weight models with permissive licenses (looking at you, Apache 2.0 and MIT). Why I Care About This in the First Place I've been burned too many times by closed source vendors changing their pricing overnight, deprecating models without warning, or locking features behind enterprise tiers. You know the drill. The moment your application depends on a proprietary API, you're renting infrastructure you can't inspect, can't fork, and can't run on your own hardware. That's not a partnership — that's a leash. When a model ships under Apache or MIT, I can download the weights, audit the architecture, fine-tune it on my own data, and deploy it wherever I want. Nobody can rug-pull me. Nobody can raise prices because some quarterly earnings call didn't go their way. That's freedom, and freedom matters more than people think when you're building anything serious. So when I started this comparison, I was already rooting for the open weight contenders. But I wanted to be honest about the results, even if they complicated my bias. The Lineup I Tested Global API currently exposes 184 models through a single unified endpoint, which is honestly wild. I picked five that I thought represented the interesting tradeoffs between cost, capability, and o

Forward manufacturing RFQs by email, no portals required Discussion | Link

I've been working on deconvolution, a comprehensive Rust image deconvolution and restoration library. Deconvolution implements 28 different image deconvolution/restoration methods which range from practical blur removal techniques to research-grade scientific imaging algorithms. Features: - Top-level functions use image::DynamicImage and return images - Inverse filters, Wiener, Richardson-Lucy, constrained, proximal, Krylov, MLE restoration - Blind Richardson-Lucy, blind maximum likelihood, para

The only AI tool designed specifically for resume writing Discussion | Link

In this session, Sam Newman interviews Kief Morris and Adrian Mouat, both experts in their field. They explore the current reality of security in the container world, how infrastructure automation is impacted by latest trends, and whether platform teams are actually working. submitted by /u/goto-con [link] [留言]

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. These new solid-state ACs promise a cool future. Scientists aren’t so sure. After three years of record-­breaking heat and another scorcher underway, air-conditioning isn’t going anywhere. That’s good for our health,…

A proposed FCC rule would kill burner phones: phones whose accounts are not attached to a particular person. The FCC plans to do this by legally forcing the country’s telecoms to store a wealth of personal information about essentially all phone customers, including a government issued identification number and their physical address, alarming privacy advocates and civil rights activists who compare the measures to those from authoritarian countries where it can be difficult to buy a mobile phone plan without giving up your identity. The proposed change would drastically shake up how people obtain phone plans in the U.S., and have all sorts of privacy and cybersecurity knock-on effects. The FCC is proposing the data collection partly as a way to combat scammers, with telecoms being required to collect other information on business and foreign customers like the intended use case of their bulk phone plan purchase and their IP address. But the changes would mean telecoms collect data on all new and renewing customers, and the FCC provides a long list of other things that the collected data could help authorities with...

On paper, the Honor Magic V6 sounds like a tremendous leap forward for foldable phones: It's the thinnest one yet, with the biggest battery, and the best water-resistance ever. In practice, only the bigger battery feels like a meaningful improvement. The other upgrades are only fractionally superior to what came before. This isn't entirely Honor's […]

In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery pipelines, balancing security, compliance, and developer productivity without relying on manual processes. By Dave Ward

Martin Kleppmann, an associate professor at Cambridge and author of Designing Data-Intensive Applications, discusses the evolution of data systems over the last decade, mainly the shift from monolithic databases to modular building blocks. Kleppmann underlines the importance of moving from cloud-centric data storage systems to decentralised data storage similar to Bluesky’s AT protocol. By Martin Kleppmann

Both kratom and one of its active components, 7-OH, have opioid-like effects and are widely available across the US. As health secretary RFK Jr. aims to get 7-OH banned, proponents of both are fighting.

Ahead of the G7 conference in France, Donald Trump is once again threatening massive tariffs on France over its digital tax.

Turn your analytics into beautiful monthly email reports Discussion | Link

Vos tests API passent en local. Le vrai enjeu est de les exécuter automatiquement à chaque pull request, fusion et build nocturne, sans clic manuel. Pour cela, vous avez besoin d’un exécuteur CLI : il lance vos tests en mode headless, retourne un code de sortie exploitable par la CI et génère un rapport lisible par votre pipeline. Essayez Apidog aujourd'hui Deux outils reviennent souvent pour ce cas d’usage : le CLI Bruno et le CLI Apidog . Les deux exécutent des tests API depuis GitHub Actions, GitLab CI, Jenkins ou tout environnement Node.js. Les deux font échouer la build lorsqu’un test échoue. La différence principale se situe avant l’exécution : où vivent les tests, comment ils sont créés et comment la CI y accède. Cet article compare les deux outils au niveau commande, avec des exemples directement intégrables dans un pipeline. En bref CLI Bruno ( @usebruno/cli , binaire bru ) exécute des fichiers .bru présents dans votre dépôt Git. Il est open source, fonctionne hors ligne et ne nécessite ni compte ni jeton. CLI Apidog ( apidog-cli , binaire apidog ) exécute des scénarios de test créés visuellement dans Apidog, récupérés par ID avec un jeton d’accès. Les deux génèrent des rapports JUnit, JSON et HTML. Les deux retournent un code non nul en cas d’échec, ce qui permet à la CI de bloquer une fusion ou un déploiement. Choisissez Bruno si vous voulez des tests versionnés comme du code, dans le dépôt. Choisissez Apidog si vous voulez créer, chaîner et exécuter des scénarios visuels sans maintenir manuellement des fichiers de test. Le problème : des tests qui existent mais ne tournent pas Un test API lancé manuellement finit souvent par devenir obsolète. Il a été écrit, validé une fois, puis oublié pendant que l’API évoluait. La solution n’est pas seulement d’ajouter plus de tests. Il faut les exécuter automatiquement à chaque changement avec un signal clair : succès ou échec ; rapport exploitable ; code de sortie lisible par la CI. Un exécuteur CLI doit donc rempli

As AI systems become more powerful, the conversation is shifting. The biggest challenge is no longer whether AI can write code, solve problems, or accelerate scientific discovery. The real question is: How do we safely govern systems that may eventually become more capable than the institutions built to regulate them? This is where my research on Universal Biometric Identification (UBID) and Universal Digital Credits (UDC) becomes interesting. The Problem Modern AI systems operate in a world where identity is increasingly difficult to verify. A powerful AI model can be accessed through: Anonymous accounts Disposable email addresses VPNs Automated bot networks Fake identities As AI capabilities increase, this creates a growing governance challenge. If a future AI system could discover software vulnerabilities, design advanced technologies, or perform high-impact research, how would organizations determine who should have access? Today, they largely cannot. The internet was designed around connectivity, not verified human identity. What Is UBID? In my paper, I propose Universal Biometric Identification (UBID), a framework where every person receives a globally unique identity based on multiple biometric factors such as: Fingerprints Facial recognition Iris patterns Voice recognition Behavioral characteristics These biometric signals are combined with cryptographic security and distributed ledger technologies to create a secure digital identity framework. The goal is not surveillance. The goal is to create a trusted proof-of-personhood system. A system capable of answering a simple question: Is this a real, verified human? What Is UDC? Universal Digital Credits (UDC) extend this identity layer into a global transaction framework. Instead of relying entirely on traditional banking systems, transactions can be linked directly to verified digital identities. This creates: Reduced fraud Better accountability Financial inclusion Transparent transaction records Global access

token-warden is a thrifty office manager for your AI assistants. It does four things: Keeps the receipts. Every time the AI finishes a task, it quietly notes how much that cost — like saving every taxi receipt in a drawer. Notices waste. When a task costs far more than usual, it asks a cheap junior AI: "Why was that so expensive? What habit would've made it cheaper?" — and writes down a suggested habit, e.g. "search for the right file before opening files at random." Tests the habit for real — this is the important part. It doesn't just trust the suggestion. It keeps a fixed set of practice tasks (like a standardized test that never changes), and runs them twice: once with the new habit, once without. Now it has hard numbers on whether the habit actually saved money, instead of a hunch. Keeps only what pays off. A habit takes up room in the AI's memory, and that room itself costs a little every single time. So the rule is strict: a habit must save at least twice what it costs to keep, or it's thrown out. Winners get written into the AI's permanent memory so it uses them automatically forever after; losers are discarded (but remembered as "tried it, didn't work" so the same bad idea won't come back). vukkt / token-warden Claude Code plugin that makes coding agents measurably cheaper over time: collect token costs, distill candidate rules, benchmark them on a frozen golden suite, and keep only rules that earn their context rent. token-warden A Claude Code plugin that makes coding agents measurably cheaper over time. Most "agent memory" accumulates advice nobody ever verifies. token-warden treats agent memory as an engineering problem: every rule that wants space in an agent's context must prove, on a fixed benchmark, that it saves more tokens than it costs — or it gets evicted. The result is a per-agent memory file containing only rules with measured positive return. Measured, not vibes — every rule carries a token delta from real benchmark runs Self-funding — rules m

We Built ARK Because Our Customer Support Was Spread Across 4 Apps The Problem A few months ago, our small team was drowning. Not in customers (well, a little) — but in tabs. WhatsApp open in one window. Instagram DMs in another. A live chat widget buried in a third. Email in a fourth. Every time a customer reached out, someone had to figure out: which channel did this come from, has anyone replied already, and what was the context of the last conversation? The result was predictable: slower replies, repeated questions to customers, and a support workflow that didn't scale past a handful of conversations a day. Why Existing Tools Didn't Fit We looked at the usual suspects — Intercom, Zendesk, Front. They're solid products, but they're built for large support teams with big budgets and dedicated admins. We needed something simpler: a single inbox, AI doing the repetitive work, and a setup that doesn't take weeks to configure. What We Built ARK pulls every customer conversation — WhatsApp, Instagram, Messenger, email, live chat — into one inbox. On top of that, AI handles three things: Drafting replies based on conversation history and context Summarizing long threads so anyone on the team can jump in without reading 40 messages Routing conversations to the right person automatically based on topic or channel The goal wasn't to replace human support — it was to remove the busywork so the team can focus on actually helping people. Where We Are Now ARK is live with a 7-day free trial (auto-renews after that). We're still early, and we're shaping the roadmap based on real feedback from teams managing support across multiple channels. If you're dealing with the same multichannel chaos we were, I'd love to hear how you're handling it — and what's still missing from the tools you've tried. 🔗 https://byark.ai/

Hey developers! I have been working on a side project to help people discover the best AI tools in one place. It is a curated directory designed to be clean, fast, and user-friendly. You can check it out live here: GetNexusAI Tech Stack Used: Next.js / React Tailwind CSS Vercel for hosting Why I Built This: Finding the right AI tool among thousands of options can be overwhelming. I wanted to create a simple dashboard where users can easily filter and find exactly what they need without the clutter. I Need Your Help! Since I just launched it, I would love to get your honest feedback: How is the loading speed and UI/UX? What features should I add next (e.g., user reviews, bookmarking tools)? If you have built an AI tool, let me know so I can feature it! Check the website here: https://getnexusai.tech

Check this out: i run four monetization channels side by side. Sponsored posts, display ads, YouTube ad revenue, and affiliate links. After eighteen months of tracking every dollar in a spreadsheet I built myself, I can tell you with brutal honesty: affiliate income is the only one that scales without me having to constantly produce more content or chase the next brand deal. But the math only works if you pick the right program. Most affiliates I know are promoting garbage with terrible retention, and they have no idea they're burning their audience's trust for a $9 one-time payout. Let me walk you through how I evaluate affiliate programs, what I've learned from running real funnels, and why the AI API category has quietly become the most lucrative vertical for tech creators in 2026. My Monetization Stack After 18 Months of Testing Here's a snapshot of my monthly revenue from a tech newsletter with around 34,000 subscribers and a YouTube channel sitting at 88,000 subscribers: Sponsored posts: $2,100 per placement, but I can only land maybe 2-3 per month without annoying my list Display ads: $1,800 per month from Mediavine, but this number barely moves regardless of how hard I work YouTube ad revenue: $2,400 per month, capped by watch time and RPMs Affiliate income: $6,800 per month, and it grows every single month even when I publish nothing That last number is what got my attention. Affiliate income compounds. When I published a tutorial in February recommending a tool, that single piece of content still earned me $340 in May because users stayed subscribed. No other channel behaves like that. No other channel lets a piece of content from four months ago keep paying you. But here's the catch that took me a while to figure out: not all affiliate programs are built the same way. And the difference between a good program and a bad one can be 10x in lifetime earnings per referred user. # # How I Score an Affiliate Program (The Growth Hacker Scorecard) Before I promote