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

Every esports team captain has done this by hand at least once: open Discord, scroll through a dozen "I can play Thursday after 8" messages, cross-reference them against who plays Tank versus DPS, remember that one of your DPS is actually a sub, and try to assemble a starting five that can actually scrim tonight. It takes fifteen minutes, you get it slightly wrong, and you do it again the next day. I build Supatimer , a free Discord bot for competitive gaming teams, and "generate the lineup for me" was the single most requested feature. This post is about how the lineup optimizer actually works, why it is genuinely AI (and not in the marketing sense), and where a large language model fits in versus where it absolutely does not. "AI" is doing a lot of work in this industry Half the Discord bots on the market slapped "AI" on their landing page the week ChatGPT launched. Usually it means there is a chatbot command somewhere that proxies to an LLM. That is fine, but it is not what your team needs when it is 7:45pm and you have a scrim at 8. There are two honest definitions of AI worth separating: Search and optimization - the classical branch. Constraint satisfaction, combinatorial optimization, planning. This is the part of AI that solves "given these rules and these resources, find the best valid arrangement." Machine learning / LLMs - the statistical branch. Pattern recognition, generation, extraction from unstructured text. The lineup problem is squarely a problem for the first kind. So that is what I built first. The lineup problem, stated precisely Strip away the gaming context and a lineup is a constrained assignment problem: You have N players , each with a set of roles they can fill (Tank, DPS, Support, IGL, and so on). Each player has an availability signal for a given time block (available, maybe, unavailable). Each player has a roster status (starter, substitute, trial). The game defines a required composition : Overwatch 2 wants 1 Tank, 2 DPS, 2 Support. Va

Usar IA no dia a dia não é nenhuma novidade, e praticamente TODO O MUNDO já sabe disso. E hoje temos diversos "sabores" de IA, inclusive para todos os gostos, e escolher um só é difícil, porque cada um tem seu molho especial em alguma tarefa específica. Eu, como não é novidade, acabei adotando como meu "favorito das últimas semanas" o Kiro...e confesso, muito no começo pelo ícone de fantasma que acho muito massa rss, mas conforme fui usando melhor, comecei a entender a sua estrutura, e principalmente o spec-driven, aí ele me conquistou de uma maneira meio que irreversível. E engraçado que hoje mais cedo quando estava dirigindo a caminho do supermercado, pensando nas minhas demandas e atividades, comecei já a estruturar meu "steering" mentalmente de uma nova task que precisaria fazer, e nisso me veio um pensamento...Caraca! Estou usando o Kiro para praticamente tudo! É isso que quero compartilhar com vocês hoje, um pouco de não só como estou usando, mas onde e para que! Trabalho nosso de todo dia Esse é meio que óbvio né? Seja um desenvolvedor, vibe-coder, arquiteto, engenheiro e tal, todo mundo está na onda de usar a IA para acelerar seu trabalho. Como comentei acima, o spec-driven do Kiro me conquistou porque eu não saio simplesmente "curando código gerado pela IA", eu realmente troco uma ideia com a IA e estruturo realmente a arquitetura daquilo que quero construir. A geração de código virou somente a consequência de toda essa sólida estrutura que conseguimos criar antes, e nesse ponto deixei de ser apenas um curador para assumir realmente a posição de arquiteto da feature que estou desenvolvendo. Conteúdo para comunidade Inclusive, esse artigo é fruto de um pouco disso! Não é sobre ser preguiçoso e deixar a IA gerar o conteúdo (o que eu acho errado também!), mas sim em "se transformar" em uma skill, onde você produz o conteúdo (VOCÊ criando o conteúdo!) e passa por essa skill para correção de gramática, compreensão, internacionalização e regionalização...uso muit

What I wanted I'm building a system where all my daily records live in Notion, so I can point an AI at it and get feedback. Goals, tasks, daily logs, finances — those are all manual entry, and that's fine. But one day it hit me that weight would be nice to sync automatically. The requirements were simple: Every morning, my weight and body fat percentage get appended to a Notion database as one row No manual typing That's it. My scale is a Withings Body Smart. The design I picked first This one: Scale → vendor app → Apple Health → iOS Shortcut → Notion API I chose Apple Health as the hub for these reasons: It doesn't depend on the scale model. As long as the data lands in Health, the same implementation works for any vendor. No server required. A time-based Shortcuts automation handles it end to end — no always-on machine, no cron. Free. No extra subscription. Extensible later. Anything that's already in Health — steps, sleep, heart rate — could be added the same way (if I ever wanted to). Generic, zero cost, extensible. The design looked sound to me. Implementation Here's what the Shortcut looks like: 1. Find Health Samples [Weight] latest, limit 1 2. Get Details of Health Sample [Value] → variable Kg 3. Get Details of Health Sample [Start Date] → variable SampleDate 4. Format Date yyyy-MM-dd → variable Ymd 5. If Ymd == today 6. Text ← build the JSON 7. Get Contents of URL ← POST to the Notion API Step 5 matters. Without it, on a day you don't step on the scale, yesterday's weight gets appended under today's date . Here's the JSON built in step 6: { "parent" : { "database_id" : "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" }, "properties" : { "Date" : { "title" : [ { "text" : { "content" : "@@YMD@@" } } ] }, "Measured" : { "date" : { "start" : "@@YMD@@" } }, "Weight kg" : { "number" : @@KG@@ }, "Body fat %" : { "number" : @@FAT@@ } } } (My real database uses Japanese property names. What matters is that they match your database exactly.) I write this as a plain string in a

Everything looked perfect. I had mcporter 0.7.3 configured with the Exa MCP server: mcporter list exa # ✅ exa (2 tools) — "Search the web for any topic..." Healthy. Ready. Then I made the first real call: mcporter call "exa.web_search_exa(query: \" ollama cloud models\ ", numResults: 5)" JSON parse error at position 1. Every. Single. Time. I tried every quoting trick known to PowerShell: Backslash escaping --% stop-parsing operator cmd /c wrapper Single-quoted outer strings Same error. The shell was eating my quotes before mcporter ever saw them. This is the full story of how I debugged it, verified on Windows PowerShell 5.1 on July 31, 2026. Chapter 1: The Root Cause - PowerShell 5.1's Dirty Secret PowerShell 5.1 strips ALL embedded double-quotes at the native-argument boundary when passing args to external programs. There is no $PSNativeCommandArgumentPassing in 5.1. That is a PowerShell 7.3+ feature. So this: mcporter call --args '{"query":"test"}' Literally becomes this before Node.js even starts: { query:test } The JSON is destroyed. No shell-level trick can fix it. Stop fighting the shell. Chapter 2: The Hero - A 10-Line Node.js Spawn Wrapper The fix is to bypass the shell entirely with spawn(..., { shell: false }) . Node passes a real argv array, no re-quoting happens. Create mcporter_exa.js : // mcporter_exa.js - The hero const { spawn } = require ( ' node:child_process ' ); const args = process . argv . slice ( 2 ); // --tool <tool> <base64Json> mode, or default web_search_exa const tool = args [ 0 ] === ' --tool ' ? args [ 1 ] : ' exa.web_search_exa ' ; const payload = args [ 0 ] === ' --tool ' ? args [ 2 ] : JSON . stringify ({ query : args [ 0 ], numResults : Number ( args [ 1 ] || 5 ) }); const child = spawn ( process . execPath , [ require . resolve ( ' mcporter/dist/cli.js ' ), ' call ' , tool , ' --args ' , payload ], { shell : false , stdio : ' inherit ' }); child . on ( ' exit ' , ( code ) => process . exit ( code ?? 0 )); Usage: # Web search - que

Reddit advances lawsuit accusing Perplexity AI of conspiring with web scraper.

Most dev-to content about longevity comes from people who scaled one product for a decade. My version of 18 years is different: 235+ separate small projects, each with a different client, budget, and expectation. That produces a completely different set of lessons. Every project restarts the trust clock. In a startup, trust compounds — the team, the codebase, the client relationship all carry forward. In agency work for small businesses, you start from zero credibility on every single engagement. The client has no idea if you're competent until you prove it, usually within the first draft. That reality shaped how we scope: front-load a visible win early, even a small one, rather than saving the "impressive part" for the end. Most client requests aren't really about the website. "Can we change the homepage headline" is often actually "I'm nervous this won't generate leads" or "my business partner didn't like it." Treating every request as a literal design brief instead of what it's actually about leads to a lot of pointless revision cycles. Asking one clarifying question — "what's this in response to?" — before touching the page has cut our revision count more than any process change. Consistency beats innovation for this client base. A small business owner doesn't want a novel UX pattern. They want their site to look like the successful competitor's site, load fast, and not embarrass them. Chasing design trends for this audience is optimizing for the wrong judge — they're not evaluating craft, they're evaluating "does this look like it'll work." The real skill is saying no to the wrong project, not saying yes to more of them. Early on I took every lead. Now the highest-leverage thing I do is a 10-minute pre-call that filters out projects where the client's expectations and budget don't match — before either of us spends real time on it. That single filter has done more for margin than any pricing change. None of this shows up in a portfolio. But if you're a develope

Interesting, to say the least, is how I qualify the articles that we have for this week's review. It's fascinating to know what's possible and reading articles that challenge your reality is something that you and I can put in our autobiography sometime in the future. I mean, wow. Just wow. I bet you'll feel the same when you read each article that I have for you. Now, what are you waiting for? Let's get started. Malicious sites use JavaScript to build malware in browser memory Just when you think that you have seen it all. You read something like this. What's the end goal? Avoiding detection. But, building malware in browser memory? I need to do more research on this. For now, read the excerpt below. After building the final malware executable, the fake download page hands it to the service worker at the beginning of the process and triggers a same-origin download path. "From the browser’s point of view, the user is downloading an executable from the landing page domain," MedusaHVNC Malware Uses Hidden Windows Desktops to Evade Detection It's invisible to the naked eye. The way to detect this malware? During data exfiltration that goes through the network. Then, it might be too late. From the article: The hidden desktop allows the attacker to take full advantage of legitimate Windows tools without being observed by the user. The C2 is hardcoded into the malware but is relatively safe from observation. The result is a stealthy and persistent RAT. The only obvious mitigation is detection of unexpected data exfiltration. For Some, So-Called ‘Skynet Day’ Came too Close to Sci-Fi After a Rogue Agent Hacked Into a Startup The difference between reality and Sci-Fi might appear far away. However, with the recent incidents at OpenAI—where an AI model escaped its sandbox and attacked Hugging Face—and Anthropic—where Claude breached three organizations and uploaded a PyPi malware during tests—you can say that it's only a matter of time. From the article: Generative AI is grow

India's app market generated a record $345 million in Q2.

The Squid is a new scientific machine : One of the technological breakthroughs was the onboard use of a spinning wheel confocal microscope, nicknamed the Squid, which uses lasers to scan microscopic details of how organisms are put together. “That opens up a whole new world of exploring. We could see cells interacting with each other, exchanging material and building skeletons. And we could do that live on the ship, when usually it takes a couple of weeks of staining and mounting to see anything,” Osborn said. The expedition discovered thirty-one new marine species in two weeks. The article doesn’t say if any of them were new species of squid...

Article URL: https://www.ycombinator.com/companies/loops/jobs/zqUnwqB-product-educator-technical-content-creator Comments URL: https://news.ycombinator.com/item?id=49128462 Points: 0 # Comments: 0