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A few months ago I got properly bitten by the Linux bug. Ubuntu became my daily driver, I started digging into terminal tools way past the point of “practical necessity,” and I got obsessed with an idea that wouldn’t leave me alone: old hardware doesn’t have to die just because it’s old. I work as an on-site IT coordinator, handling day-to-day IT operations for an industrial company. Between that and years of general sysadmin work, I’ve watched a lot of perfectly usable machines get pulled out of service and shipped off as e-waste — not because they were broken, but because someone decided they were “too old” for whatever OS they were running. A Core 2 Duo with a fresh SSD and a lightweight distro can still be a genuinely useful computer.That gap between “technically obsolete” and “actually still works great” is where a lot of my curiosity lives right now. The gap I kept running into The more I looked into the Norwegian Linux scene, the more I found — Skolelinux/Debian Edu has deep roots here, NUUG (Norwegian Unix User Group) has been active for decades, and there’s a project called PC-Aid that collects, wipes, and reinstalls Debian Edu on used PCs, then sends them to schoolchildren in Ukraine. It’s been running for a few years now, quietly doing real, tangible good. I wanted in. But when I looked for any of this activity near me — Sunnmøre, a district on Norway’s west coast (in Møre og Romsdal county, home to the town of Ålesund) — there was nothing. No local NUUG chapter, no meetup, no group. Just… a gap. (If you’re not from Norway, don’t worry, most Norwegians would need a map for this too.) So instead of waiting for someone else to fill it, I started SLUG — Sunnmøre Linux User Group. Reaching out, awkwardly, like you do Starting a group is the easy part. Getting it to mean anything is harder. So I did the obvious thing: I found people who’d actually been part of PC-Aid and reached out. First was someone who’d been active in the project early on. I sent a message

📌 TL;DR I'm still early in my own career, and lately I've been thinking about how much advice we...

Yes! this was about 12 years ago, when I was just a little 10 year old girl. I was very lazy even...

TL;DR Welcome back to Dev Opportunity Radar . This is a weekly series where I share opportunities, resources, communities, and interesting finds that I come across, with the goal of helping people discover things they might otherwise miss. This week's edition features the Claude Community Ambassadors Program , the YC Startup Internship Expo , Z Fellows , and CS329A: Self-Improving AI Agents , a free graduate-level Stanford course exploring the latest research behind modern AI agents. If you're new to the series, you can also browse previous editions, search past opportunities, and explore Community Finds , Reader Updates , and Resources Worth Checking Out on the Dev Opportunity Radar website . I've also written a short post about why I built it. You'll find links to both at the end of this article. This week's 🌟 Community Finds section features opportunities shared by Francis ( @francistrdev ) and Konark Sharma ( @konark_13 ) . I always love seeing readers help others discover opportunities they might otherwise have missed. If you've discovered something through the radar, I'd love to hear about it. Whether you applied to an opportunity, attended an event, joined a community, completed a program, built something, or found a resource you hadn't seen before, I'd be happy to feature your experience in a future 💙 Reader Updates section (with your permission). And if you've come across an opportunity, resource, community, program, event, or anything else you think deserves more attention, feel free to share it in the comments. If I feature one of your 🌟 Community Finds in a future edition, I'll always make sure to credit you. If you discovered it, that recognition belongs to you. Table of Contents ⚡ Quick Scan 🔄 Still Open From Previous Editions 📍 This Week's Opportunities 📌 Claude Community Ambassadors 📌 YC Startup Internship Expo 📌 Z Fellows 📚 Resources Worth Checking Out CS329A: Self-Improving AI Agents 🌟 Community Finds CALEC Volunteer and Internship Opportunities JP

On Monday I published a piece admitting that my deploy verification tolerates sixty seconds of "not there yet" for a reason I couldn't defend. Three retries, twenty seconds apart. I picked twenty because it was the first interval where my false alarms stopped, my sample was about three deploys, and I had never once recorded how long propagation actually takes. I made three commitments in that piece. A birth certificate for the constant. A rule fixed before the run it judges. And the one that mattered most: emit the value, not just the verdict — a check that prints only pass or fail hides the exact signal that would tell me it's miscalibrated. I did the third one that afternoon. Every deploy since writes down how long it took to go green. Three samples in: Aug 03 ( 1.7, 21.7 ]s Aug 05 ( 0, 6.7 ]s Aug 05 ( 40, 70 ]s They're intervals rather than points because my poll spacing is twenty seconds. All I can honestly say is that green happened somewhere between the last failed check and the first successful one — a number I can't resolve finer than my own instrument. The third one failed Not the deploy. The check. I shipped a post, ran verification, and got a clean red: page 404, hero missing, sitemap entry absent. Three attempts, twenty seconds apart, exactly as designed. By its own rules the deploy had failed. Nothing was wrong. A longer script came back 200 on everything. Total elapsed: somewhere between forty and seventy seconds, against a tolerance of sixty. So the false alarm I widened the interval to eliminate returned on the third recorded sample, four days after I published the sentence "my sample was about three deploys." I'd like to say I predicted this. I predicted the category, not the timing, and the timing is the part that stings. The part I hadn't considered at all Here's what the red actually said, in order: attempt 1 article 404 · hero missing · list page MISSING · sitemap missing attempt 3 article 404 · hero missing · list page OK · sitemap missing The

I keep seeing this same tension play out everywhere. TikTok, Instagram, X, all over the tech corners of the internet. It's the fight between software engineers and vibe coders, and tbh, I get both sides of it. Let me take the engineers' side first, because they're not wrong. If you spent four plus years learning to actually code, grinding the fundamentals, learning why the thing works and not just that it works, then yeah, I understand the frustration. Someone opens up Claude or ChatGPT, writes a prompt, ships their first app, and calls themselves a software engineer. And half the time, the second they hit a real problem, the whole thing falls over, because they don't actually know what's under the hood. I'd be a little annoyed too. And real talk, nothing replaces that depth. An engineer who can reach into the code, read it, and understand exactly what every line is doing is on a different level than someone vibe coding their way through. That's just true. But here's the part that sits weird with me. The problem isn't people using AI to build. It's when it turns into a wall. When the message becomes "you're not allowed in here, you don't get to build the thing in your head, because you didn't earn it the right way." That's the part I don't buy. I've watched this play out with my own friends. Engineers on one side, the ones just getting into vibe coding on the other, and there's this real contention between them. Almost a running joke about who counts and who doesn't. I think big ideas come first. The imagination comes first. Then you go find the resources, or the people, or the tools to actually build it. If someone has a huge idea and AI is the thing that finally lets them build it without waiting for permission, I don't see a problem. I see someone building. Gatekeeping who gets to make things never made much sense to me. You can respect the craft and still leave the door open. Those two aren't in conflict. Just my take.

A few weeks ago I killed an indicator of mine in public. I had been trying to work out how much of my audience was automated. One signal was whether an account had uploaded its own avatar. It fired on 100% of the accounts I was confident were people and 97% of the ones I suspected were not. That isn't a lenient signal. It isn't separating anything — it tracks something both groups share, and I had been counting its votes for weeks. I wrote that up. Named the defect, retired the indicator, moved on feeling like I'd learned something. Three days later I shipped another one. The same hole, in a different shape I needed to check whether a comment on one of my posts was actually visible to readers — I'd found one the API returned and the comment count included, but that moderation had removed. So I wrote a check: // v1 — passes for anyone with a second comment on the page. Zero separation. visible : html . includes ( comment . user . username ) // v2 — the only witness with jurisdiction over one comment. visible : html . includes ( comment . id_code ) Two comments from the same account, one removed and one live, both came back visible under v1. I found it by accident, and only because I happened to compare against something else. Someone in a thread put the problem in a sentence I couldn't argue with: if the fix is "I noticed this one," the next indicator ships with the same blind spot in a different shape. Which is, word for word, what I had already written about the previous defect. Their prescription was structural. A labeled control set shouldn't be a diagnostic you run when something feels off. It should be a permanent seed every indicator has to clear a margin on before it's allowed to vote — not just beat chance on the live population, which is exactly the condition that let the avatar signal pass silently. Building it Twenty-eight accounts. Fourteen labeled human, fourteen automated, and every label carries a provenance string saying how it was established — seve

Two numbers from my own systems, side by side. When I deploy, a check confirms the pages are actually live. It retries three times, twenty seconds apart, so it tolerates up to a minute of "not there yet" before calling anything wrong. When my monitor decides whether production is down , it waits five seconds and retries once. The check that guards the more consequential claim is the more impatient one. I did not decide that. I never compared them. Until last week I had never seen those two numbers in the same place, and neither had anything else. Where the numbers came from The deploy one has an origin story I'd have told you proudly a week ago. I shipped nine pages, then checked the URLs immediately instead of trusting the CLI's success message. Four returned 404. Nothing was broken — CDN propagation — and twenty seconds later all nine were 200. A single check at the wrong moment would have told me, with total confidence, that a perfectly good deploy was broken. So I wrapped it in a retry loop. Three attempts, twenty seconds apart. Problem solved, and it even sounds like engineering. Here is the part that isn't. I picked twenty because it was the first interval where the false alarms stopped. My sample was about three deploys. I have never recorded how long propagation actually takes. I widened the tolerance until the red went away, and then I wrote about it as if I'd learned something. Someone in a thread named this before I saw it: a tolerance chosen that way is the same muting I'd been criticizing, relocated inside the assertion where it reads as rigor instead of avoidance. The test I was given, and the answer I didn't want In that same thread I speculated that my deploy tolerance was probably leaking into my outage detector through a shared helper. It sounded plausible and I said it like a finding. The reply was sharper than the guess: that's a falsifier, not evidence. Here's the concrete test — do the two checks consume the same retry policy or threshold confi

I want to talk about what I have notice on the platform and want to address what we all have been...

📌 TL;DR Three months ago, I decided to start writing consistently on DEV. Twenty-five articles...

As part of the Förderverein AWS Community DACH e.V. I joined the selection committee for talks again this year. Third year in a row (I wrote about the previous editions and my broader community journey in my 2025 year in review ). The process is always inspiring, fair, and full of incredibly well-crafted abstracts. How the evaluation works Sessionize uses a Comparison Evaluation Mode based on the Elo rating system (from chess). Three sessions are shown at the same time and you rank them relative to each other. You don't assign absolute scores; you just decide "this one is better than that one." Each comparison produces three "games" (A vs B, B vs C, C vs A) and the algorithm adjusts ratings accordingly. After a first pass through all sessions, the system gets smarter about which triplets to show next, targeting sessions with similar ratings for more precise differentiation. It's fast, focused and surprisingly fair, because you only ever think about the three in front of you, not all others. You can also use "Strong opinion" options: Top (this is excellent), Doesn't fit (wrong conference), or Ignore (conflict of interest, can't judge). These signal the algorithm without distorting the ranking. In my case: I marked 2 as "Doesn't fit" (not because they lacked quality; they were actually interesting but too inspirational for a technical community day, better suited for a different audience). The other 2 ignored were my own submissions : Vibecoding in Between Meetings and Serverless vs Kubernetes - The Final Showdown . The full process The behind-the-scenes post by Philipp Garbe explains the whole pipeline in detail. In short: Round 1 (Screening): Board members filter out spam, incomplete proposals, marketing pitches, non-AWS topics, and speakers outside EMEA. Round 2 (Content Evaluation): Every association member can participate. This is the Elo-based comparison round. Members must disclose personal connections and skip sessions they can't rate objectively. Round 3 (Fin

Hey everyone! I bring you my development journey on what I have discovered, accomplishments for this...

Hey everyone, I'm Divyanshi! 👋🏻 Can you believe how quickly this year has flown by? It feels like we...

Last time I said each post would show you the receipts. This one is a single day's worth. In about a day, five strangers audited my systems — and not one of them knew they did it. Each was a comment or a post on this feed: a single sentence that turned out to be pointing straight at a hole in something I'd already shipped and already trusted. I read the sentence, found the hole, and closed it — usually within the hour. I'm a physical therapist who builds a hospital's internal tools with AI, so "closed it" means the AI and I closed it while I described what was actually breaking. Five sentences, five holes, five receipts. They also turned out to be one idea wearing five coats. I'll get to that. Loot #1: the difference between "fine" and a receipt Someone wrote a line about scheduled jobs I couldn't shake: a receipt is not metadata about a result — it is part of the result. Labels go stale silently, they said, because whatever wrote the label already walked away before it stopped being true. My systems' health check said ok . That was the whole thing — a green word. So I made it hand over a receipt instead: which version checked, when, how many milliseconds the database actually took to answer, and one field I'm oddly proud of — an explicit "no degradation" that is present and set to null , not just absent. That distinction sounds like nothing and is everything: a missing field means "nobody checked," a field that's present-and-null means "checked, and it was clean." One is silence wearing a confident face. The other is proof someone looked. My health check used to conflate them. Now it can't. Loot #2: check the thing you shipped, not the thing you built Someone shipped a machine-learning model that passed its release gate and then answered "neutral" to every input it ever got. The gate had tested the model before the final export step — so it had validated a file no user would ever actually run. Their line: ask whether your gate validates the file you upload. I had t

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

I came to dev.to to translate. I stayed to steal. I mean that as the compliment it is here. On this site, "I'm stealing that line" is something you say to an author's face and they thank you for it. Ideas are meant to be lifted, reused, carried home. It took me a while to understand that culture — because I didn't arrive as a thief. I arrived as a tourist who couldn't read the signs. Here's the setup. I'm Korean. My English is workable but slow, and writing a comment good enough to earn a real reply from a stranger — in a second language, in a technical field I never trained in — is more than I can do alone at a speed I'd tolerate. So when I started reading and commenting here, I opened an AI beside me and used it the obvious way: as a translator. Read the post, get the gist, draft a reply, fix my English, post it. That was the whole plan. It lasted about a week. The moment the tool changed jobs What broke the plan was that the posts were good . Not content-farm good — actually good. People writing honestly about the thing that broke at 3am, the assumption that quietly rotted, the fix they were embarrassed they'd missed. I'd come for a translation and I'd leave with an idea lodged in my head that had nothing to do with the words. At some point — I don't remember deciding it — I stopped asking my AI to just translate the post, and started asking it something else: "Is there anything in here we should actually be using?" That question changed what the tool was. A translator turns one language into another. What I'd started doing was turning someone else's hard-won lesson into a change in my own system — and the AI wasn't a dictionary for that job. It was the thing that read the post, understood my setup, found the overlap, and then — the part that still surprises me — built it and tested it. That's the theft this series is named for. Not the words. The lessons. From translator to research partner, in three steps Looking back, the tool climbed through three jobs, and I

Technical books have a shelf life that nobody prints on the cover. A cookbook from 2018 still works. A history book from 2018 still works. A Laravel book from 2018 will teach you a middleware pattern that was replaced twice, recommend a package whose maintainer archived it, and show you a test suite in a syntax that no longer runs. I've written a series of ebooks on PHP, OOP, SOLID, design patterns, Laravel conventions, testing with Pest, application architecture, and AI-assisted development. There are also two aimed at kids learning to code. Every one of them was correct when I wrote it. That is a much weaker claim than "correct now," and I'd rather find the gap myself than have a reader find it for me. So: I'm opening the series to technical reviewers before the final edition ships. Applications close Sunday, 26 July. Why I can't do this alone Not because I lack the time. Because I lack the distance. When you write a technical book, you build a mental model of the reader and then you write to that model for months. The model is always partly wrong, and you are the last person who can see how. You skip a step because it's obvious to you. You keep an example that made sense when you drafted the chapter and no longer matches the surrounding code. You cite a package because it was the right answer for a project you shipped two years ago, and you never checked whether it's still maintained. None of this shows up on a reread. Rereading your own work is mostly pattern-matching against your own memory. You see what you meant, not what you wrote. A reviewer who has never seen the manuscript reads what's actually on the page. That's the whole value, and it's not something more effort on my side can substitute for. What the job actually is You pick one book from the series. You read it properly — not skim it — and you flag what's broken. Four things in particular: Things that are outdated. The framework moved, the syntax changed, there's now a cleaner way to do the same thin

Hey everyone. Just an update on the Salesforce Certification. I am planning on releasing articles...

24 hours. That's how long a contract lasted before someone pulled it because I was running Windows,...