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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

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

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

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

Turn any website into LLM-ready Markdown Discussion | Link

Had the hacks used conventional methods, someone would likely go to prison.

submitted by /u/fagnerbrack [link] [留言]

“What on earth is Google doing?” Misinformation fears spur walk-back of AI tool.

We spotted a great deal on Tile trackers earlier this week that’s still live, but if you’re an iPhone owner, we ultimately recommend Apple’s latest AirTag. Right now, you can pick up a four-pack for $89 ($10 off) at Amazon and Target, matching the bundle’s all-time low price. If you’re a member, Costco also has […]

Google has shut down Google Earth feature it launched Thursday that allowed users to edit satellite images with text prompts using AI. The tool essentially let users create AI deepfakes of the real world using text prompts; Digital Digging's Henk van Ess, for example, intentionally generated images adding things like refugees near the Mexican border […]

Find the best spot for your air purifier in seconds Discussion | Link

LemonLime’s CEO got “carried away” with tattoo gimmick.

When I joined Entire, I noticed my boss spending a chunk of time every week writing detailed release notes, called Dispatches at Entire. It looked like a painful process. Each Dispatch had to cover changes across several repositories, explain why those changes mattered, credit external contributors, and carefully avoid leaking anything that was not public yet. I offered to take it over. I had solved a similar problem before, so I figured it would be an easy win. I built something similar and simpler at Block While I was at Block, I built a release notes generator for goose . It ran in GitHub Actions after a release workflow completed, checked out the new tag, compared it against the previous one, and handed goose a recipe to inspect the commit diff. Goose organized those commits into features, bug fixes, improvements, and documentation. Each entry got a short description and a PR link. The workflow then updated the GitHub release and posted the announcement to Discord, opening a thread if the notes exceeded the message limit. It was clean and effective, but it solved a very clean problem: one repository, one new release tag, public commit history, and concise output. So when I looked at Entire’s Dispatches, I assumed I could reuse the same playbook. Gather changes, run goose, post the draft. That assumption did not survive contact with reality. But a Dispatch turned out to be more complex A Dispatch spans multiple projects: the Entire CLI, entire.io, EntireDB, external agent integrations, and open source libraries like go-git, go-nuts, git-sync, and ForgeMark. Every project also ships on a different cadence. Some push to main and deploy continuously. Others bundle work into scheduled releases. The CLI maintains separate stable and nightly channels, which means a feature can be available to testers without being part of the latest stable tag. Then there are feature flags. Finding changes was not the hard part because GitHub APIs handle that easily. The hard part was

New research from the U.K.’s Imperial College and France’s Emlyon Business School mapped out how Silicon Valley founders commit fraud — and the role investors play.

The Situation Our team's CI/CD pipeline on Azure DevOps was taking 15 minutes to complete on every push to develop. You'd merge a PR, grab a coffee, come back — and it was still running. A 15-minute feedback loop breaks flow state — by the time the pipeline finishes, you've already switched context twice and forgotten what you were checking. I spent an afternoon digging into the Azure DevOps logs. Here's what I found. The Numbers (Before) Artifact content (uncompressed): 1,218 MB (1.2 GB) Artifact downloaded (compressed): 614 MB Download time: 3-4 min Pipeline breakdown: Build stage: ~5 min (Docker build + artifact) Download artifact: ~3 min (614 MB over the wire) Configure App Service: 2m54s (5 Azure API calls) Deploy (AzureWebApp@1): ~1 min Validate: 2m07s (sleep 30 + 3×30s probes) ───────────────────────────────── Total: ~15 min Root Cause #1: Ignoring output: 'standalone' next.config.js had this: const nextConfig = { output : ' standalone ' , // ← was there the whole time ... }; output: 'standalone' tells Next.js to produce .next/standalone/ — a self-contained directory with only what's needed at runtime. Trimmed node_modules . Auto-generated server.js . No source files. No dev dependencies. But the pipeline was ignoring it: # Old pipeline — copies everything from Docker docker cp deployImage:/app/node_modules . # 600 MB 😱 docker cp deployImage:/app/src . docker cp deployImage:/app/.next . docker cp deployImage:/app/server.js . # ... more files /bin/zip -r deploy.zip .env .next public node_modules package.json \ next.config.js jsconfig.json postcss.config.mjs decs.d.ts src server.js # Then published the ENTIRE working directory as the artifact - task : PublishPipelineArtifact@0 inputs : targetPath : ' $(System.DefaultWorkingDirectory)' # 1.2 GB of loose files + zip Azure DevOps compressed this to 614 MB for transfer. The deploy stage downloaded 614 MB to use a 24 MB zip buried inside it. The fix: # New pipeline — standalone only docker cp deployImage:/app/.next/