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This is Part 8 of 10 , a bonus practice article with 70 code-output challenges . Each challenge asks you to predict the result before revealing the answer and reasoning. Complete series This Dev.to series has five core handbook articles plus five focused practice extras. Open the series page to move through the complete reading order: Part 1: JavaScript — core handbook, questions 1–120 Part 2: React — core handbook, questions 121–220 Part 3: React Native — core handbook, questions 221–420 Part 4: Performance & Architecture — core handbook, questions 421–560 Part 5: Senior & System Design — core handbook, questions 561–719 Part 6: Output-Based JavaScript Practice — bonus practice article Part 7: Coding Interview Practice — bonus practice article Part 8: Code Output Challenges — bonus practice article Part 9: Current React Native Interview Questions — new high-frequency practice article Part 10: Project & Production Interviews — senior project ownership and real-production practice How to use this challenge set Read the code, state the exact output or error, then explain the language rule. Do not run the snippet until you have committed to an answer. For React Native interviews, connect the JavaScript behavior to rendering, state updates, list handling, or the JavaScript thread when relevant. Skills tested Hoisting, scope, closures, and this Arrays, conditions, references, object behavior, and loose versus strict equality Promises, timers, async / await , and microtasks Common JavaScript patterns used in React and React Native interviews Code output challenges Challenge 1. Block-scoped counter Predict the exact output before opening the answer. let total = 0 ; for ( let i = 0 ; i < 3 ; i ++ ) { total += i ; } console . log ( total ); Answer and explanation Expected output: 3 Why: The loop adds 0, 1, and 2. Challenge 2. var callback loop Predict the exact output before opening the answer. for ( var i = 0 ; i < 3 ; i ++ ) { setTimeout (() => { console . log ( i ); }, 0

I was tired of buzzword-heavy AI projects and marginally impactful demos. Surely we can do something more inspiring with these LLMs than build another chatbot? For me, the answer is physical AI: the moment all those breakthroughs finally reach into the real world, in robots that see, move, and figure things out for themselves. I think it is the most exciting frontier in tech right now. It is also genuinely hard to break into, because it is not one field. It is about five of them stacked on top of each other: electronics, mechanics, programming, data, and AI. Eight months ago I started my own robotics journey from scratch, and I was completely overwhelmed. How do you get from blinking an LED to a humanoid that does your dishes? There are thousands of scattered tutorials out there, with no sense of what comes first, or what any of it is building toward. So I decided to build the map. Stealing the best idea from my favorite games: If you have ever played a factory-building or strategy game like Satisfactory or Civ Six, you know the feeling. You start with almost nothing, and you unlock new tech one satisfying step at a time. Those games are proof that we will happily spend hours mastering an intimidatingly complex system, as long as it is laid out as a clear tree of unlocks. So why not point that same instinct at learning something real? That is exactly what a tech tree is: a structured, visual path where each node is a skill and each connection is a prerequisite. You start at Curiosity on the far left and work your way right, through electronics, mechanics, code, data, and AI, all the way toward autonomous robots and humanoids. The idea is simple: turn gaming time into learning time. What the tree actually is Every node on the tree is a skill to learn, and the star-shaped nodes are hands-on projects where theory finally meets a soldering iron. Nodes are color-coded by discipline, so you can see at a glance whether you are in electronics, mechanics, programming, data s

The prediction market’s Panamanian operation seems odd, even for a company whose CEO had his apartment raided by FBI agents.

This is Part 7 of 10 , a bonus practice article containing 75 coding interview questions drawn from the React Native Interview Handbook. It covers the implementation tasks commonly used in JavaScript and React Native rounds, from string and array problems to hooks, FlatList , asynchronous work, caching, retries, and native modules. Complete series This Dev.to series has five core handbook articles plus five focused practice extras. Open the series page to move through the complete reading order: Part 1: JavaScript — core handbook, questions 1–120 Part 2: React — core handbook, questions 121–220 Part 3: React Native — core handbook, questions 221–420 Part 4: Performance & Architecture — core handbook, questions 421–560 Part 5: Senior & System Design — core handbook, questions 561–719 Part 6: Output-Based JavaScript Practice — bonus practice article Part 7: Coding Interview Practice — bonus practice article Part 8: Code Output Challenges — bonus practice article Part 9: Current React Native Interview Questions — new high-frequency practice article Part 10: Project & Production Interviews — senior project ownership and real-production practice How to answer coding questions Before coding, clarify inputs, output, edge cases, platform constraints, time complexity, space complexity, cancellation, and test coverage. Start with a correct readable solution, then optimize only when the constraint justifies it. Topics covered Strings, arrays, maps, sets, recursion, and algorithmic complexity Debounce, throttle, memoization, deep cloning, and polyfills Custom hooks, API state, error boundaries, and React rendering FlatList pagination, pull to refresh, search, and offline retry Promises, timeouts, concurrency limits, and exponential backoff Caching, EventEmitter, Pub/Sub, LRU design, and native module boundaries Interview coding checklist Confirm assumptions before writing code. State time and space complexity. Handle empty input, invalid input, and duplicate values deliberately

This is Part 6 of 10 , a bonus practice article containing 191 output-based JavaScript interview questions . It includes 111 questions drawn from the React Native Interview Handbook plus 80 additional questions on the JavaScript behavior interviewers commonly test in React Native rounds: hoisting, scope, closures, arrays, objects, functions, coercion, conditions, Promises, and the event loop. Complete series This Dev.to series has five core handbook articles plus five focused practice extras. Open the series page to move through the complete reading order: Part 1: JavaScript — core handbook, questions 1–120 Part 2: React — core handbook, questions 121–220 Part 3: React Native — core handbook, questions 221–420 Part 4: Performance & Architecture — core handbook, questions 421–560 Part 5: Senior & System Design — core handbook, questions 561–719 Part 6: Output-Based JavaScript Practice — bonus practice article Part 7: Coding Interview Practice — bonus practice article Part 8: Code Output Challenges — bonus practice article Part 9: Current React Native Interview Questions — new high-frequency practice article Part 10: Project & Production Interviews — senior project ownership and real-production practice How to use this guide Before opening an answer, state the exact output first. Then explain the rule that causes it: evaluation order, scope, coercion, reference identity, prototype lookup, or microtask scheduling. Run the snippet only after committing to an answer. Topics covered Hoisting, Temporal Dead Zone, var , let , const , and function declarations Scope, closures, this , arrow functions, call , apply , and bind Arrays, sparse arrays, map , reduce , sort , slice , splice , and mutation Objects, references, shallow copies, prototypes, getters, and property lookup Conditions, truthiness, equality, nullish coalescing, and type coercion Promises, async / await , microtasks, timers, and error recovery React and React Native rendering behavior, state updates, effects,

Camera upgrades in the Z Flip 8, Fold 8 and Fold 8 Ultra appear to be slim.

Designed for year-round comfort, the Dyson Hot+Cool HF1 combines quiet operation and simple controls with Dyson's signature bladeless design.

A newly renewed, $25 million-per-year contract with a subsidiary of Thompson Reuters further expands the power of ICE under the Trump administration.

Toss out a brick to lure a jade gem. — The 36 Stratagems, Throw Out a Brick to Get a...

The cost of writing code dropped; the cost of owning it didn't. A framework for deciding which changes are actually cheap in the AI era. The post The cost of saying yes has changed appeared first on The GitHub Blog .

As parents look for alternatives to unrestricted smartphones, a growing number of companies are building phones designed specifically for kids, from feature-limited mobile devices to minimalist home phones.

Everyone recommends ByteByteGo and The Pragmatic Engineer. Don't get me wrong, they're great... but the best engineering writing of the last two years is coming from newer publications nobody's put on a list yet. Here's what survived my filter. I have a rule: if I haven't opened a newsletter in three weeks, I unsubscribe. No guilt, no "maybe later" folder. It's the only way to keep email useful when every engineering team, indie hacker, and AI startup on the planet is running a Substack. That rule has consequences. Over the past couple of years it has killed off almost every famous-name newsletter in my inbox — not because they got worse, but because they got comfortable. Meanwhile, a new generation of engineering publications launched around 2023–2024 started earning their slot every single week. They're smaller, sharper, and written by people still close to the work. The other thing my rule revealed: AI engineering quietly became its own discipline. Not "AI news" — there are a thousand newsletters rehashing model launches. I mean the craft of building production systems on top of LLMs: agents, evals, brownfield integration, governance, cost. That coverage barely existed two years ago. Now it's the most valuable section of my inbox, which is why it leads this list. So here's what survived. Twenty-six newsletters, organized by topic, heavy on publications you haven't seen on every listicle. Steal the whole list. 🤖 AI Engineering & Production AI Two years ago this category didn't exist. Today it's the most important one here, because building with LLMs in production is genuinely different work — different failure modes, different economics, different skills — and general engineering newsletters mostly aren't covering it. Latent Space — swyx & Alessio Fanelli. swyx literally coined "AI engineering" as a discipline, and this is its watering hole: podcast, essays, and the AINews digest covering frontier models, agents, and the career path itself. The anchor of the categ

Desktops, laptops, phones, CPUs, GPUs, NAS—if you know this stuff, come work for us!

Are we living through a small car renaissance? There's the Slate Truck, Amble's dune buggy, and the Fiat Topolino, as well as a whole galaxy of kei cars and trucks from Japan that have their own built-in fan base. While microcar sales in the US are still as tiny as the vehicles themselves, there are […]

If someone asked me ten years ago what it takes to break into cloud, I would have said "get certified and hope someone gives you a chance." I was wrong. And I watched dozens of freshers follow that exact advice, collect a certification, then sit in interviews unable to explain why they chose one architecture over another. The problem was never knowledge. It was proof. Proof that you can gather requirements from a confused client, design something that works, and actually build it with your own hands. AWS just launched something that helps close that gap. And two of these credentials cost nothing. Table of Contents What AWS SimuLearn Badges Actually Are Why This Matters More Than Another Certification The 12 Badges Available Right Now The Free Starting Path I Would Follow Today The LinkedIn Advantage Nobody Is Talking About For Career Switchers: Your Existing Skills Are the Cheat Code My Honest Take After 10 Years in Cloud What AWS SimuLearn Badges Actually Are SimuLearn is not another video course. Not another multiple-choice exam. You sit in a simulated client meeting powered by generative AI. A virtual customer explains their business problem. You ask questions, uncover requirements, handle objections, and propose an architecture. The AI evaluates your communication, your technical accuracy, and your decision-making in real time. Then you build the solution. In a live AWS environment. Not a sandbox with three buttons. The real console. After that, an automated validation confirms your solution actually works. Complete every assignment in a learning plan, and AWS issues you a badge through Credly. Automatically. No exam booking. No proctored test. Just demonstrated capability across the full workflow. Each badge represents the entire journey: customer conversations, architecture design, hands-on building, and validated outcomes. Not a single quiz. Not one lab. The whole thing. Why This Matters More Than Another Certification I hold 7 AWS certifications. Let me tell

I've been tuning Codenames AI , a small web game where an LLM plays Codenames with you. Clue generation is tightly constrained: one word, a count, optional intended targets, JSON on the wire, then deterministic validation before anything reaches the board. As the project started attracting regular players, I wanted to improve the gameplay experience without blowing out costs. Moving one model generation from gpt-4o-mini to gpt-5-mini was my first instinct. The default reasoning setting made responses an order of magnitude slower for this workload. Minimal reasoning looked like the obvious compromise: newer model, responsive gameplay. I expected to compare clue quality, latency, and cost while the surrounding prompt, validator, and consumer contracts stayed put. That last part was wrong. The experiment stopped behaving like an A/B test What showed up was structural, and it showed up in places that had been stable for months. Validation failures started rising. Retries started rising. Entire candidate batches started failing before the game ever saw a clue. The sharpest signal came from a clue-selection path that had run untouched for months, and it hard-failed for the first time. They weren't latency regressions so much as architectural ones. It is easy to read that as "minimal reasoning made the model worse." More often, the failures were exposing gaps in contracts that had looked fine under the previous model. What each failure actually invalidated Eventually every failure traced back to one of three layers: Prompt contracts ask for exactly count targets and, in batch mode, several distinct candidates. Deterministic validators reject target/count mismatches and filter invalid candidates before anything downstream runs. Downstream consumers only see survivors. Empty batches retry with rejection feedback, then fall back if needed. Those layers share one job: enforce the same invariants. The failures below cut across all three rather than mapping one to one. Side comm

Just getting this concept is like money in the bank! I'm too excited for words! At age 67, I have never coded a thing in my life. All of the sudden in 2026, I find that I've become a "vibe" coder with the help of Grok and Gemini. Both in segregated project silos are acting as my PMs and doing a damn fine job of it. As CEO of a brand new company, I'm taking on the entire scope of development for The Avinoam Group, LLC. My business partner Eyal and I have a very cool vision for what we intend to accomplish. And while it is beyond the scope of this post, only 2 crazy non-dev guys would be this bold, with all due respect to the young guns here on DEV. Our core principles incorporate transparency and authenticity. I can think of no better business idea as 4 separate cornerstones (we have 4 separate projects) than the brilliant concept-commitment to #build in public. So, I'm looking forward to telling everyone what we're doing and how we're doing it. We have nothing to hide except a little "secret sauce" that we can't really talk about without shooting ourselves in the foot. Other than that, I want to "show & tell" just like when I was in kindergarten in 1964 and brought "Meet The Beatles" in to Miss Shreiner's class and all the little kids danced! Right now, I'm reading how to get the most out of DEV and being here. At my age, I like to give back. What can an old geezer whose never coded before offer? My life experience and business knowledge. For example, we are developing freepaycalc (Google it if you want) to help devs, makers and freelancers plug the leaks in their "money buckets." and how to kill off that dreaded "scope creep" that so many independent consultants and contractors get trapped in. I don't want this post to be too long. I just wanted to reinforce the value of building in public. If you want to punch through each and every challenge of a complex project, then why not tell brilliant dev colleagues what you're up to? Whatever problem you're facing has a so

Patreon is strengthening its defenses against AI scraping by working with Cloudflare to block bots that train AI models on creators’ content without permission. The move marks a shift away from relying on websites using robots.txt alone to actively block unauthorized AI training.

I tested REI’s best new and revamped outdoor gear of 2026. Here’s what’s worth buying, from tents and sleeping bags to coolers and bikes.

Remarkable's Paper Pure replaces Remarkable 2, and it is quite good.