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Your backend returned 200. The mobile app showed an error. The user tapped "Pay" three times. Three pending charges hit their account. One order was placed. Their balance was short. And your incident log showed zero failures. Every engineer on the team did their job. Nobody solved the problem. This is the most common way engineering teams fail, not through incompetence, but through excellent execution of the wrong unit of work. And until you recognise the difference between completing a task and solving a business problem , you will keep shipping systems that work perfectly and experiences that don't. The Ticket-Thinker vs. The System-Owner Most engineers early in their careers think in tickets. Ticket assigned → code written → tests pass → PR merged → ticket closed. Done. This is fine when you're learning. It's a liability when you're trying to grow. The engineer who closes tickets is useful. The engineer who asks "what problem does this ticket actually solve, and am I solving it in the right place?" that engineer is dangerous in the best way. Here's the distinction in practice. The backend engineer builds a payment endpoint. It processes charges correctly, returns the right status codes, has proper error handling. 100% test coverage. Ticket closed. The mobile engineer builds the payment screen. It calls the endpoint, handles the response, shows confirmation or error. Smooth UI. Ticket closed. The problem nobody owned: what happens when the network drops after the backend processes the charge but before the mobile app receives the confirmation? The backend: charge processed. No error. The mobile: timeout. Shows "Payment failed." User retries. The user: charged twice. Both engineers solved their assigned problem correctly. The business problem — charge the user once and confirm it reliably — went unsolved. Because that problem lived in the space between their tickets, and nobody was watching that space. Real Scenario 1: The Payment That Worked and Failed at the Same

Most "compress your image" websites upload your photo to a server. You don't need one. The browser's own canvas can re-encode an image at any quality — I built a drag-and-drop compressor in about 30 lines , and your photo never leaves your machine. 🗜️ Try it (drop a photo): https://dev48v.infy.uk/solve/day9-image-compressor.html 1. Catch the dropped file — locally drop . addEventListener ( " drop " , e => { e . preventDefault (); const file = e . dataTransfer . files [ 0 ]; // stays in the tab, 0 bytes uploaded loadImage ( file ); }); For sensitive images (IDs, screenshots), "never uploaded" is a real feature, not just a nicety. 2. Decode it into an <img> A dropped file is just bytes. Load it via a local blob: URL: const img = new Image (); img . src = URL . createObjectURL ( file ); await img . decode (); 3. Draw it onto a canvas Now the browser holds the raw pixels, detached from the original file format: canvas . width = img . naturalWidth ; canvas . height = img . naturalHeight ; canvas . getContext ( " 2d " ). drawImage ( img , 0 , 0 ); 4. Re-encode at a quality (this IS the compression) canvas . toBlob ( blob => { preview . src = URL . createObjectURL ( blob ); showSize ( blob . size ); }, " image/jpeg " , 0.7 ); // 0.7 = 70% quality JPEG and WebP are lossy — they discard detail the eye barely notices. That third argument is the entire compression dial; a small quality drop often halves the file size. 5. Hand the result back as a download link . href = URL . createObjectURL ( blob ); link . download = " compressed.jpg " ; // the browser saves it, no server The takeaway FileReader → Image → Canvas → toBlob is a surprisingly powerful local image pipeline. The same four steps do resizing, format conversion, cropping, watermarking — all client-side, all private. A whole category of "image tools" needs no backend at all. Open it and drop a photo.

The Toy Story franchise began with a story about a vintage doll feeling threatened by the arrival of an electronic action figure. Woody and Buzz's rivalry embodied a shift that was happening in the '90s as children's toys were becoming more technologically sophisticated, and while toys have gotten even more tech-focused in the years since, […]

Turbo Start Aisle is our agentic e-commerce starter on Shopify and Sanity. Instead of filters...

Arriving six years after Google’s last smart speaker, the new HomePod-style device was redesigned to play host to Gemini’s chatbot.

If you build with AI, three stories this week rhyme into one theme: the hype is colliding with the bill. Here's the builder's read on each — and what I'd actually do about it. 1. Most of a new TikTok feed is now AI slop A Kapwing study reported by Tubefilter hand-checked 10,742 videos across 20 categories and found that 59% of what a brand-new TikTok account sees is AI-generated . Kids content was the worst — 57% slop, with the #CartoonKids tag hitting 97% — and TikTok serves roughly 3x more slop than YouTube. Why builders should care: generation is now free and infinite, so volume is worthless as a moat. The scarce thing is taste and verification. If your product or content can be faked by a feed of bots, it will be. Polish, point of view, and "a human clearly did this" are the new differentiators. 2. Databricks grew 80% — but agents are eating its margins Per CNBC , Databricks' annualized revenue jumped about 80% to ~$6.9B, and its AI products now bring in $1.7B (up from $1.4B). The catch: the CEO says gross margin "will go lower" as customers run more agents. Why builders should care: this is the quiet tax of agentic software. An agent that loops, retries, and calls tools burns far more tokens than a single API call. If you're shipping agents, budget for inference at scale , not the sticker price on the pricing page. Profitability now lives in prompt efficiency, caching, and knowing when not to call the model. 3. 60% of US consumers are turned off by "AI" branding A WordPress VIP survey of 2,000 people, covered by TechCrunch , found that 60% reject "AI" in brand messaging , while 86% still want to check the original sources behind a claim. Why builders should care: "Now with AI!" is starting to read like a warning label. Sell the outcome, not the technology — "2x faster," "fewer errors," "your data stays private" — and cite where your results come from. Trust is becoming a feature you ship, not a slogan you bolt on. The takeaway Feeds are flooded, agents are cost

Lars Faye's Agentic Coding Is a Trap — published Sunday, May 3, picked up on Hacker News at 398 points and 316 comments — is the best single compendium of the cognitive-debt evidence base anyone has put together in 2026. It catalogues the studies. It names the trade-offs. It lands on a personal-discipline conclusion. The receipts are now collected; the careful reader will have spent the weekend nodding through them. Buried in Faye's second paragraph, almost in passing, is the line that does the actual analytical work. Faye describes the agentic workflow as a process in which "someone defines the project's requirements ... generates a plan, and then pulls the slot machine lever over and over, iterating and reiterating with often multiple agent instances until it's done." The link goes to a March post by Quentin Rousseau, CTO and co-founder of Rootly, titled One More Prompt: The Dopamine Trap of Agentic Coding. The metaphor isn't Faye's. Rousseau got there first, in clinical language: the workflow runs on "variable ratio reinforcement — the same psychological mechanism that makes slot machines the most addictive form of gambling" . That is the framing the rest of Faye's piece is downstream of, and it is the framing this article is about. What the receipts add up to Faye's catalogue, briefly. Anthropic's own research note on internal use names what it calls the "paradox of supervision" : effective use of Claude requires the very skills that sustained Claude use atrophies. MIT Media Lab's Your Brain on ChatGPT measured the cognitive impact and labelled it cognitive debt . A Microsoft study covered by 404 Media reached parallel findings for knowledge workers more broadly. A separate Anthropic study on coding skills reported a 47% drop-off in debugging skills among engineers leaning heavily on AI-assisted workflows. Sandor Nyako, the LinkedIn engineering director who oversees fifty engineers, has reportedly asked his team not to use these tools for "tasks that require cri

Most developers run git pull dozens of times a week without thinking about it. And most of the time, it works. Then one day you open a PR and the reviewer says "can you clean up the merge commits?" You look at your branch and see three "Merge branch 'main' into feature/login" commits scattered through history. The feature itself is 5 commits. The log is a mess. That mess comes from one decision: using git pull instead of git pull --rebase . Here's what's actually happening, and why the rebase variant produces cleaner history for teams. The setup: diverged history You're working on feature/login . You commit two changes locally ( X , Y ). Meanwhile, your teammate pushes two commits to main ( C , D ). Your branch and main have now diverged . Neither is a strict superset of the other. Git needs to reconcile them when you pull. Shared history: A → B Your local: A → B → X → Y (you added X, Y) Remote main: A → B → C → D (teammate added C, D) Git has two strategies for this reconciliation. Strategy 1: git pull (merge) A plain git pull creates a merge commit that joins your local history with the remote. Your commits and the remote's commits both appear in the log, connected by a merge node. The git log reads: M Merge branch 'main' into feature/login D fix: timeout on slow connections Y feat: client-side validation C chore: upgrade eslint X feat: login form B (shared) A (shared) This is honest history — it records exactly what happened: parallel development that was joined at a specific point. But it's also noisy history — the merge commit has no meaningful changes, and the log interleaves commits that weren't conceptually related. Strategy 2: git pull --rebase With --rebase , Git takes a different approach. It: Temporarily sets aside your local commits ( X , Y ) Fast-forwards your branch to the tip of the remote ( D ) Replays your commits on top, one by one, creating new commits ( X' , Y' ) The git log reads: Y' feat: client-side validation X' feat: login form D fix: timeo

The new Thread Networks Diagnostics Tools app from Thread Group, the standards body behind the wireless IoT protocol, officially launches in beta today. The app, which arrives on iOS and has been available on Android in alpha for a few weeks, is the first dedicated tool to provide visibility into your Thread-based smart home network. […]

You change one value in a JSON config, run git diff , and get a wall of red and green — because a formatter reflowed the file, or the serializer reordered the keys, or the indentation shifted by two spaces. The one thing you actually changed is buried in there somewhere. Good luck finding it in review. The problem is that git diff and diff work on lines . JSON isn't lines — it's a tree. So I built jdelta : it compares the data , ignores key order and whitespace entirely, and tells you exactly which values changed, addressed by path. Zero dependencies, no network. What it looks like $ jdelta config.before.json config.after.json Added (1) + features.darkMode true Changed (2) ~ auth.required true → false ~ server.port 8080 → 3000 +1 -0 ~2 auth.required flipped to false and you can see it instantly — no scrolling past 200 lines of reindented braces. Why not git diff / diff ? Because they diff text. Run a formatter, sort your keys, change two spaces of indent, and a line differ lights up the whole file while reporting zero semantic change. jdelta parses both sides and walks the trees: Object keys show as user.profile.age ; array elements as items[2].price . Odd keys fall back to quoted brackets ( ["order-id"] ). A key on only one side is added / removed ; a key on both with a different value is changed . A type change ( number → string , object → array ) is one changed entry tagged with the kinds — not a confusing add + remove. Reordered keys and whitespace produce nothing , because they aren't data changes. Great for reviewing config changes, API-response snapshots, tsconfig / settings.json , lockfile-adjacent files, and test fixtures. In scripts and CI jdelta a.json b.json --json # machine-readable: {added, removed, changed, summary} jdelta a.json b.json --quiet # just the +a -r ~c line jdelta a.json b.json --exit-code # exit 1 if they differ — gate a pipeline on it Install npx jdelta a.json b.json # Node build (npm) pip install jdelta # Python build — same behavior Tw

TL;DR — UUID v7 trie 13× plus vite que v4 en simulation B-tree (1M entrées), expose une empreinte mémoire identique, mais révèle son timestamp d'émission. UUID v4 reste le choix "zéro réflexion" pour les identifiants isolés. Le reste de cet article vous donnera les données pour décider. Introduction Les UUIDs sont omniprésents dans les systèmes modernes : clés primaires de bases de données, identifiants de sessions, tokens de traçabilité. Pourtant, le choix de la version impacte directement les performances en écriture et en lecture, la fragmentation des index, et — dans certains contextes — la confidentialité des données. UUID v4 (RFC 4122, 2005) est aujourd'hui la version par défaut de presque tous les ORM et frameworks. UUID v7 (RFC 9562, 2024) est son successeur moderne, conçu pour corriger son principal défaut : le désordre lexicographique. Dans cet article, nous allons mettre les deux en face avec des benchmarks réels sur des volumes de 100 000 à 10 millions d'UUIDs , analyser leur structure bit par bit, et vous donner une grille de décision claire. Structure interne : ce que contiennent ces 128 bits UUID v4 xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx Bits Contenu 0–47 Aléatoire 48–51 Version (0100 = 4) 52–63 Aléatoire 64–65 Variant (10) 66–127 Aléatoire 122 bits d'entropie pure. Aucune information temporelle. Chaque UUID est statistiquement indépendant des autres. UUID v7 019ed5c8-2a2f-7974-91f2-6ba1f313dcfa └──────────────┘ 48 bits = timestamp Unix en millisecondes Bits Contenu 0–47 Timestamp Unix (ms) 48–51 Version (0111 = 7) 52–63 Aléatoire (sub-ms ou compteur) 64–65 Variant (10) 66–127 Aléatoire 74 bits d'entropie + 48 bits de temps. Naturellement monotone : deux UUIDs générés dans la même milliseconde sont toujours distincts et ordonnés de façon cohérente. Lecture du timestamp (Python) : import uuid6 u = uuid6 . uuid7 () b = u . bytes ts_ms = int . from_bytes ( b [: 6 ], ' big ' ) # → 1781703125553 (ms depuis epoch Unix) Depuis la sortie de nos benchmarks : [00

IaC tools built for single-team deployments fail structurally at 200 accounts because the failure modes are architectural, not configurational. Why 200 Accounts Is Where IaC Tools Break IaC tools built for single-team deployments fail structurally at 200 accounts because the failure modes are architectural, not configurational. Scale Threshold State Management Provider Auth Overhead Execution Time Impact ~10 accounts One backend bucket, one workspace; quirks are routable Not a meaningful bottleneck Sequential plan/apply is manageable ~50 accounts Sequential execution still viable; blast radius contained Per-account latency exists but tolerable Below threshold where parallelism is required 200+ accounts 200 separate plan operations per shared-module refactor 3-sec per-account auth × 200 = 10 min added per plan cycle Sequential Terraform applies measured at 4.1 hours end-to-end A 10-account environment forgives sloppy state management. One backend bucket, one workspace convention, one pipeline. Engineers learn the tool's quirks and route around them. At 200 accounts, those same quirks compound. Why the threshold is 200 State lock contention, cross-account provider authentication chains, and module resolution latency stack on top of each other. The result is not slower deploys. It is non-deterministic deploys, which is operationally worse. The specific threshold matters. Below roughly 50 accounts, most teams run plan and apply sequentially without parallelism because the blast radius of a runaway apply is contained. Above 200 accounts, sequential execution becomes untenable. We measured a 200-account org where sequential Terraform applies across all accounts took 4.1 hours end-to-end. That latency made emergency remediation impossible inside a standard incident window. Three compounding failure modes State file proliferation. Each account carries its own state file, and each state file is a consistency boundary. At 200 accounts, a single refactor touching a shared modu

They’re much more recyclable than the current plastic ones, and they will still probably be locked away behind that anti-theft plexiglass.

Let's be real: There's no shortage of Zelda-style games to play right now. That's true even if you've exhausted all there is to see in Tears of the Kingdom or Echoes of Wisdom. Indie developers keep finding new ways to refresh the formula, whether it's fusing it with a Soulslike, making it cozier, or simply […]

I'll admit it. I was wrong. Wildly colorful lighting is delightful for your smart home - well, outdoors, at least. Smart lighting is one of my favorite features of the smart home - it combines convenience with ambiance, letting you change the entire look of your room with just a press of a button. But, […]

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

It’s like Big Brother without any limits, or broadcast standards. WIRED goes on location—and on camera—with the cult hit.

Hello Dev Community! 👋 It is officially Day 39 of my non-stop run toward full-stack MERN engineering! Yesterday, I mapped out basic HTTP verbs like GET and POST. Today, I advanced into Prashant Sir's (Complete Coding) backend masterclass to tackle one of the most critical core operations: Handling Data Streams and Body Parsing . When a user submits a form or uploads data, the server doesn't receive the file all at once. It arrives as an asynchronous stream of network data chunks. Today, I learned how to collect and decode those packets natively! 🧠 Key Learnings From Node.js Lecture 7 (Streams & Buffers) Node.js is designed to be non-blocking and memory-efficient. Here is the technical breakdown of how it intercepts client payloads: 1. Inbound Streams & Data Chunks I learned that incoming POST data is treated as a Readable Stream . Instead of loading a massive data file into the server memory instantly, Node transmits the payload in tiny pieces called Chunks (hexadecimal binary data). 2. Event Listeners for Requests ( req.on ) Natively, we don't have an instant req.body object. We have to listen to the network events on the incoming request stream: req.on("data", (chunk) => { ... }) : Fires every single time a fresh chunk of binary data arrives at the network interface. We push these raw chunks into a temporary array. req.on("end", () => { ... }) : Fires automatically once the stream concludes and all chunks have arrived safely. javascript if (req.url === "/submit" && req.method === "POST") { let body = []; req.on("data", (chunk) => { body.push(chunk); // Collecting raw binary chunks }); req.on("end", () => { // Concatenating and converting hexadecimal binary buffers into a readable string layout let parsedBody = Buffer.concat(body).toString(); console.log("Received Form Payload:", parsedBody); res.writeHead(200, { "Content-Type": "text/plain" }); res.end("Data received and parsed successfully!"); }); }

Hello Dev Community! 👋 It is officially Day 38 of my unbroken streak toward mastering the MERN stack! Yesterday, I learned how to extract query strings from the URL bar. Today, I took a massive step forward into full-stack backend architecture by diving into Prashant Sir's (Complete Coding) roadmap to master HTTP Request Methods . Up until now, our server treated every incoming request the same way. Today, I learned how to make the backend execute completely different actions based on the "intent" (HTTP Verb) of the user! 🧠 Key Learnings From Node.js Lecture 6 (HTTP Verbs) An endpoint is no longer static when you map request methods against it. Here is the technical breakdown of what I locked down today: 1. Cracking req.method I discovered that the incoming Request object holds a crucial property called req.method . This tells the server exactly what action the client wants to perform. 2. The Big Four Core Methods GET: Used when the user simply wants to read or fetch data from the server (e.g., viewing a product page). POST: Used when the user wants to securely send or create new data on the server (e.g., submitting a signup form). PUT/PATCH: Used to update existing data records. DELETE: Used to wipe a specific data entry from the server storage. javascript const http = require("http"); const server = http.createServer((req, res) => { if (req.url === "/api/data") { if (req.method === "GET") { res.writeHead(200, { "Content-Type": "text/plain" }); res.end("Fetching and reading secure database records..."); } else if (req.method === "POST") { res.writeHead(201, { "Content-Type": "text/plain" }); res.end("Securely creating and injecting new data into the server!"); } } else { res.end("Standard Route"); } }); server.listen(8000);

Ask a language model to score a developer portfolio out of 100 and you get a confident number back. Hand it a near-empty page with a name and a broken avatar, and it will often still tell you something like 92. Nice layout. Strong personal branding. The model is being polite, not accurate. That was the first wall I hit building Leon, the reviewer inside getfolio. If the score is not trustworthy, nothing downstream matters: the critique, the suggestions, and the fix button all hang off a number the model invented to sound encouraging. This is the build log of how I stopped letting the model hold the pen. Short version: a deterministic rules engine owns the score, and the language model only owns the words around it. The failure mode: an LLM judge wants to be liked If you have shipped anything with an LLM evaluator you have probably seen this. You hand it a rubric, a JSON schema, even worked examples, and it still drifts upward. Empty inputs get encouraging scores. Weak inputs get the benefit of the doubt. Strong inputs land in the same band as the weak ones, just with longer praise. A few reasons, roughly in order of how much they hurt: Tuning rewards a helpful, encouraging tone. Harsh scoring reads as unhelpful, so the model softens it. The model has no ground truth for what a 70 versus an 85 looks like in your specific domain. It is scoring on vibes. Scoring and explaining are entangled. The model writes the kind explanation first, then picks a number to match the nice things it just said. Run it twice on the same input and you get two different numbers. There is no anchor. For a portfolio reviewer that real recruiters and developers would act on, that was a non-starter. If Leon says 64, an empty page should not be able to reach 64 by accident, and a strong portfolio should not get talked down to it either. The number has to mean something. The fix: rules engine owns the score, model owns the language The architecture splits responsibilities hard. A deterministic e