OpenAI has reportedly found evidence of additional agent misbehavior as it looks into the incident that occurred with Hugging Face.

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The company appears to have built new laptops and a 2-in-1 for the Googlebook initiative.

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Also has big plans beyond the TM-B. The startup mostly refers to itself as a "vehicle" company, and has plans to make four-wheel pedal-assist cargo vehicles for Amazon.

AI tools have democratized the opportunity to build, shortening the timelines of success and enabling more young people to start successful companies without stepping foot inside a Big Tech company.

The term "Milwaukee Shoulder Syndrome" was coined in 1981, based on cases in four women.

There's a discussion running on Product Hunt right now about where an AI agent should stop and hand a decision back to a person. The framing that stuck with me: "can this be undone?" is the wrong gate. It over-fires on things nobody cares about, like writing a log line, and under-fires on the ones that actually hurt. A mass email. A migration you can only roll back with downtime. The proposed replacement is reach. Not "is this safe" but "how far does this go if I'm wrong." And the sharpest point in that thread is that the agent is the worst possible judge of its own reach, because it doesn't know there are 50,000 people on the list. I've been building a browser with an agent in it, and the browser case makes that concrete in a way the terminal case doesn't. A coding agent's blast radius is usually one person: you, reading a diff. A browser agent doesn't start there. It inherits every session you're already signed into. The reach of a click isn't a property of the agent's plan, it's a property of the cookie jar it's holding. The same "click the blue button" step is harmless on a docs site and irreversible on a payments dashboard, and nothing in the agent's own summary distinguishes them. So I stopped trying to make the agent classify risk. Here's what I do instead, with the caveat that this is alpha and I've been wrong about it before. Some things never reach the model. Credential fields are filtered out of the page snapshot before the agent sees it. That means password inputs, and anything with cc-number , cc-csc , cc-exp , or one-time-code autocomplete semantics. A fill targeting a password field refuses outright. The agent cannot misjudge the reach of a control it was never shown, and that's a stronger guarantee than any classifier, because it doesn't depend on getting a judgment right. The model's judgment is a floor, never a ceiling. For everything that does reach it, the agent can say "this needs approval" and be believed. It cannot say "this is routine" and be

Hi Dev.to community! 👋 As an aspiring technical writer and a Computer Science student, I've been working hard on building a series of deep-dive articles about AI, Python, and software concepts. I just published a guide on Natural Language Processing (NLP) over on my Hashnode blog, featuring text preprocessing pipelines and a Python code example using spaCy! I would love for you to check it out and share your feedback—especially if you have tips on how I can improve my technical explanations: 👉 [ https://hilda-biende.hashnode.dev/natural-language-processing-nlp-explained-how-computers-understand-human-language ] Looking forward to connecting with fellow devs and writers here!

Kubernetes Interviews Are Broken When Trivia Matters More Than Real Skill Kubernetes interviews often fail when they test whether a candidate can recall obscure implementation details instead of showing how that person diagnoses failures, reasons through tradeoffs, and learns under pressure. Certifications can prove useful baseline knowledge, but neither a certificate nor a perfect whiteboard answer reliably proves that someone can operate a production cluster. The frustration becomes obvious when an interview demands a kernel level explanation of what happens when traffic reaches an ingress controller in a Cilium based, proxyless setup, while the actual role may involve changing a CPU request from 500m to 550m. The contrast is funny because it feels painfully familiar. Candidates prepare for architecture, networking, controllers, scheduling, and troubleshooting, then get judged on a detail they could verify in seconds during real work. That does not mean deep technical knowledge is useless. Some roles genuinely require it. The problem begins when interview difficulty becomes disconnected from job difficulty, and when memorization is treated as a shortcut for measuring engineering judgment. Why Kubernetes interview questions feel disconnected from the job The strongest complaint in the discussion was not that Kubernetes is too difficult. It was that many interview questions appear designed to establish superiority rather than measure readiness for the role. One example captured the problem perfectly: the interview asks for a detailed explanation of kernel behavior, ingress traffic, Cilium, eBPF, and proxyless networking. The work itself turns out to be a minor resource adjustment. That gap creates distrust because candidates are being filtered through a standard that the daily job may never require. A technical interview should reflect the decisions the engineer will actually make. If the job involves operating clusters, useful questions might examine how the candid

Your GitOps Hub Will Become the Bottleneck Long Before Cluster Count Tells You GitOps hub bottlenecks are usually predicted more accurately by watched object volume, reconcile queue depth, and controller memory growth than by cluster count alone. Large-scale testing described in the discussion showed Argo CD application controllers hitting out-of-memory failures around 15,000 to 20,000 cached objects per hub, while sharding and tuning delayed the limit without removing the underlying memory cost. The most important lesson was uncomfortable because it challenged the usual instinct to keep tuning the existing platform. At very large scale, architecture mattered more than configuration. Hydrated manifests helped. More replicas helped. Dynamic sharding helped. None of them changed the fact that a centralized reconciliation model still had to hold and process a huge amount of state. The testing was not presented as a universal benchmark or as proof that one tool always beats another. It was a record of one setup, built through dozens of iterations over several months, and the failures were as valuable as the successful runs. That is exactly why the results matter. They show where teams should look before the hub becomes the thing taking the fleet down. Cluster count is the wrong first metric A fleet with 1,000 tiny clusters may place less pressure on a GitOps control plane than a much smaller fleet containing thousands of applications and deeply expanded resource trees. The number of managed clusters is visible and easy to report, but it does not describe the controller’s actual workload. The more useful mental model is objects over clusters. Each application contributes desired state, live state, cached trees, reconciliation work, and queue activity. A cluster that runs a few small addons may be cheap to manage. Another cluster with many applications and large manifest sets may consume far more memory and reconciliation time. That means two fleets with the same cluster

This is a submission for Frontend Challenge: Comfort Food Edition , CSS Art: Comfort Food. What I Built A pure CSS art scene of the ultimate Hong Kong comfort food: a steaming bowl of wonton noodle soup (雲吞麵) at a late-night dai pai dong. Nothing says "home" to me like a midnight bowl of wonton noodles under a glowing paper lantern — so I recreated that feeling entirely in CSS: no images, no SVG, just divs, gradients, border-radius tricks, and keyframe animations. The scene includes: 🥣 A classic HK porcelain bowl with the iconic blue rim stripe pattern (repeating-linear-gradient) 🍜 Golden broth with a noodle nest built from repeating-radial-gradient concentric arcs 🥟 Four pleated wontons, half-submerged at the broth line 🥢 Wooden chopsticks resting across the rim (tapered with clip-path) ♨️ Soft, organic steam wisps — blurred gradient blobs on staggered transform/opacity loops 🏮 A swaying red paper lantern casting a warm light cone 🌙 Moon, twinkling stars, bokeh lights, a flickering pink neon 雲吞麵 sign, chili oil saucer, and a cup of tea Demo zsp67x2nfnudg.kimi.page 👆 Live full-screen demo — watch the steam rise, the lantern sway, and the neon sign flicker. View page source to see the full CSS — every technique is commented! Journey Design goal: I wanted the warmth of the lantern light to contrast against the cool indigo night, with a subtle purple dusk at the horizon — the exact feeling of sitting at a Hong Kong street stall at 1am. Techniques I'm proud of: The steam was the hardest part. Thin wisps disappeared against the sky, so I layered blurred radial-gradient blobs (13% wide, filter: blur) with keyframes that hold a long visible opacity plateau (0 → .7 → .65 → .38 → 0). Four wisps run on two different periods (6s / 7.2s) with delays locked 25% of a cycle apart, so at least one wisp is always near peak — the bowl never stops steaming. The bowl is a single div with border-radius: 0 0 50% 50% / 0 0 100% 100% for the porcelain body; the broth ellipse's own border d

Someone in Cuba or Iran can keep installing APKs with no new restrictions, but devs will suffer.

How to Verify a SHA-256 Checksum on Windows, macOS, and Linux You download an ISO, installer, archive, or release binary. The publisher provides a long value such as: 9f86d081884c7d659a2feaa0c55ad015 a3bf4f1b2b0b822cd15d6c15b0f00a08 That value is a checksum, usually generated with SHA-256. Verifying it answers one practical question: Does the file you downloaded have exactly the same contents as the file the publisher hashed? A checksum mismatch can indicate a damaged download, an incomplete transfer, the wrong file version, or modified contents. Before verifying anything Get the expected checksum from a source you trust. Ideally, use the software publisher’s official website, release page, package repository, or signed checksum file. A matching checksum confirms that your file matches the data represented by the expected hash. It does not prove that the original publisher or website was trustworthy. If an attacker can replace both the download and the displayed checksum, they can make the two values match. For stronger authenticity verification, use a signed release when the publisher provides one. Verify SHA-256 on Windows Open PowerShell in the folder containing the downloaded file. Run: Get-FileHash ".\filename.iso" -Algorithm SHA256 Example: Get-FileHash ".\ubuntu.iso" -Algorithm SHA256 PowerShell returns something similar to: Algorithm : SHA256 Hash : 4A1F... Path : C:\Users\You\Downloads\ubuntu.iso Compare the value beside Hash with the checksum published by the download provider. Uppercase and lowercase letters do not matter in hexadecimal hashes. The characters themselves must otherwise match exactly. Compare automatically in PowerShell Instead of comparing two 64-character values manually, store the expected checksum and let PowerShell compare them: $expected = "PASTE_EXPECTED_SHA256_HERE" $actual = ( Get-FileHash ".\filename.iso" -Algorithm SHA256 ) . Hash if ( $actual -eq $expected ) { Write-Host "Checksum matches" } else { Write-Host "Checksum does not