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I remember when coding interviews happened at a whiteboard, no computer, no internet, just a marker and whatever you could hold in your head. When AI tools arrived, using one in an interview was the red flag. Now the red flag is the candidate who doesn't use AI enough. In about two years, the same tool went from forbidden to expected. And it only gets faster. Every few months another wave rolls in, a model out of some lab, a tool that does something it couldn't last year, and it resets what counts as normal. You ride it, or you let it break over you. Plenty of people imagine a third option, waiting on the beach with their arms crossed until the water goes calm, but the water never goes calm, and it was never going to wait for them. all in I've decided to ride it, in the most literal way I have. All of my code is written by AI now (is it mine at this point?), and somewhere along the way I stopped treating that as a threat to be managed and started treating it as leverage to be spent. I know the engineers who went the other way, who made a personality out of dismissing the tools, and the tools got better anyway while they just fell further behind. Going all in wasn't one decision; it's one I make again every few weeks. A better model ships, and I rebuild a working agent on top of it instead of staying on the old version, because the result comes out better and cheaper. Relying on older models would have been easier, and most people do. Riding means doing that over and over, long after the novelty wears off. I get why people stop trying to keep up. The volume is genuinely insane, more launches in a week than you could try in a month, and it isn't only engineers feeling it now, it's anyone whose work runs on a keyboard. People are overwhelmed and often giving up. But you don't have to keep up with everything; you just can't ignore it all. keep the thinking It means handing a lot of work to AI, and I hand over more every month. It writes code, drafts the first version of

Capture the ringleader first. The rest will scatter on their own. — The 36 Stratagems, Capture the...

A few months ago, I was searching for a backend engineering job. Every day looked the same: Open LinkedIn Open Naukri Search for Python jobs Open dozens of tabs Read every job description Apply Repeat The frustrating part wasn't finding jobs. It was finding the right jobs . I kept getting recommendations for roles that technically matched my resume because they contained words like Python , Backend , or API , but after reading the description I'd realize they wanted a completely different skill set. I started wondering: Why are job boards still matching keywords instead of understanding what a developer actually knows? That question eventually turned into a side project called Jobspiq . The Problem Imagine these two jobs: Job A Python FastAPI PostgreSQL Redis Job B Java Spring Boot Oracle Kafka Both are "Backend Engineer" roles. A keyword-based system often treats them as similar. As developers, we know they're not. What I Built Instead of matching keywords, I built a system that compares a developer's profile with a job description to understand how well they actually fit. The platform: Collects jobs from multiple sources. Removes duplicate postings. Scores every job based on how closely it matches your profile. Sends alerts only for high-quality matches. Helps track applications in one place. The goal isn't to show more jobs. It's to show fewer, better ones. What I Learned Building the product taught me something interesting. Writing the software was the easier part. Helping people discover it is much harder. That's why I'm starting to build in public and share what I'm learning along the way. If you've ever built search systems, recommendation engines, or developer tools, I'd love to hear your thoughts. You can check out the project here: https://jobspiq.in Feedback is always welcome.

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

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,

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 .

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

How I went from toggling a single GPIO pin to deploying intelligent, low-power firmware on the edge — and the lessons that shaped me along the way. The first program I ever ran on a microcontroller did exactly one thing: it blinked an LED. No operating system. No framework. No safety net. Just my code, a register, and a clock ticking a few million times a second. When that LED finally blinked at the rate I intended — not too fast, not stuck on — I felt something I hadn't felt writing software before. On a bare-metal system, nothing happens unless you make it happen. There's no runtime quietly cleaning up after you. That mix of total control and total responsibility is what pulled me into embedded systems, and it's the same thread that eventually led me to running machine learning models on the edge. This is the story of that journey — from a single blinking pin to intelligent devices that sense, decide, and act on their own. The Bare-Metal Beginning Bare-metal firmware is where you learn what a computer actually is. When you write to a memory-mapped register to toggle a GPIO, or configure a UART peripheral one bit at a time, there's no abstraction hiding the hardware from you. You read the datasheet. You read the reference manual. You get the clock configuration wrong, and nothing works — no error message, just silence. Then you fix it, and suddenly bytes are streaming out of a pin at exactly the baud rate you configured. Most of my early growth happened writing low-level peripheral drivers — UART, SPI, I2C, GPIO, ADC — on ARM Cortex-M platforms. These are the unglamorous building blocks, but they teach you the discipline embedded work demands: Every byte and every milliwatt matters. On a resource-constrained MCU, you don't get to be careless with memory or power. Timing is a first-class citizen. An interrupt that fires 50 microseconds late can break the whole system. The hardware is always right. If your code and the oscilloscope disagree, the oscilloscope wins. Th

There are beginner mistakes. Then there are "I spent three days doing the wrong thing" mistakes. This is one of mine. When I first joined a company as a trainee, I was excited... and completely unprepared for real-world software development. Back in college, our web development classes mostly covered HTML and CSS. We did touch a bit of programming, but if I'm being honest, many of our projects involved copying code, tweaking a few lines, and hoping everything still worked. I graduated knowing how to make things look nice , but not necessarily how professional development teams solved problems. Reality hit pretty quickly. Our First Task A few days into training, my friend and I were assigned our first real task by our senior team lead. He gave us an Excel file. It had two sheets , each with roughly 1,000 rows of data. Every row had around 7 to 10 columns . Then he said something like: "Import all of this into the database." Simple enough... right? Well... Neither of us had ever imported data into a database before. Asking the Wrong Person Instead of asking our team lead for clarification, we asked another trainee who happened to be our former classmate. His advice? "Just make an INSERT INTO script." Perfect. Say no more. Without questioning it, my friend and I started generating SQL INSERT statements. One row. After another. After another. And another. By the end, we had spent almost three days creating what felt like an endless wall of SQL. At the time, we were actually proud of ourselves. "Look at us. Future software engineers." Looking back... We were basically expensive copy-paste machines. The Reality Check Our senior team lead eventually came over to check our progress. He looked at our SQL file for a few seconds. Then smiled and said: "You tricked me, huh? That's not what I meant." My friend and I just stared at him. Confused. We thought, "But... the data is going into the database. Mission accomplished, right?" Wrong. What he actually wanted was a CakePHP scr

Hey DEV, I'm Tobore. Let's actually connect. I've been on here for a while now, mostly writing and...

The Quest Begins (The "Why") I still remember the first time I walked out of a coding interview feeling like I’d just lost a lightsaber duel. The problem was a simple array‑rotation task, but I dove straight into typing, eyes glued to the screen, and barely said a word. When the interviewer asked, “What are you thinking right now?” I froze, mumbled something about “just trying to get it done,” and watched the seconds tick away. The feedback later? “Great coding skills, but we couldn’t follow your thought process.” That moment stung because I knew I could solve the problem—I just hadn’t learned how to show my thinking. After a few more silent attempts, I realized the interview isn’t a solo boss fight; it’s a co‑op mission where the interviewer wants to see how you navigate the terrain. If they can’t hear your internal monologue, they have no way to gauge your problem‑solving instincts, communication style, or ability to catch mistakes early. I went on a quest for a repeatable, low‑effort way to narrate my thinking without turning the interview into a monologue. What I found was a three‑step verbal framework that felt like unlocking a new Force power—simple, repeatable, and surprisingly effective. The Revelation (The Insight) The technique I now swear by is State → Plan → Execute . At each stage you say out loud exactly what you’re doing, using a tight, repeatable script. It’s not about over‑explaining; it’s about giving the interviewer a clear map of your mind. Here’s the exact wording I use, broken down by phase: State – Clarify the problem, assumptions, and constraints. “Okay, so we need to rotate an array to the right by k steps. I’m assuming k can be larger than the array length, so I’ll use modulo to normalize it. The array can contain any integers, and we should aim for O(n) time and O(1) extra space.” Plan – Outline the high‑level approach before writing a line of code. “My plan is to use the three‑step reversal algorithm: reverse the whole array, then reverse

Most remote job listings are ghosts — already filled, never opened, or posted just to farm résumés. As a developer, that annoyed me enough to solve it with code instead of complaining about it on X . So I built Remoty.work , which grades every listing A–F on how likely it is to be real . Here's how the detection actually works under the hood. The problem, from an engineering angle Job boards are an endless scrape loop. Board A scrapes Board B, which scraped Board C. The same dead listing propagates across a dozen sites, and none of them verify anything. There's no signal for "is this real," so the noise compounds until every board looks identical. I didn't want to build board number thirteen in that loop. I wanted a layer on top that answers one question every listing should have to: is anyone actually going to read my application? The architecture Everything runs on a single VPS — Postgres, scrapers, and the scoring jobs, supervised on a schedule. Deliberately boring. High level: Ingestion: scheduled scrapers pull from source boards and company ATS feeds into Postgres. Each raw listing is deduplicated by a fingerprint (title + company + normalized URL) so the same job reposted across five boards collapses into one row with a repost_count . Scoring engine: every listing gets a ghost-risk score from a handful of signals, then mapped to an A–F grade so it's human-readable, not a black-box number. The "rant" signal: I cross-reference what people say about companies in places like r/recruitinghell and hiring threads. That's where the truth about a company's hiring leaks out, and it turns out to be a strong predictor. Agents: I use DeepSeek to classify and summarize the messy text job descriptions, company chatter. DeepSeek because running this over thousands of listings every night on GPT-4-class models would have killed the unit economics before I had a single user. One infra detail I didn't expect to spend a weekend on: the frontend is on Cloudflare's edge, but the ed

When the enemy occupies favorable terrain, don't attack head-on. Let them think they're safe, let...

Hey everyone! I don't want to steal @hemapriya_kanagala series. This is more of a DEV opportunity...

What I learned at Zone01 Kisumu's IP training Introduction Yesterday, I attended an Intellectual Property training at Zone01 Kisumu, and it fundamentally changed how I think about the code I write. As developers, we spend countless hours building software, but how many of us truly understand the value of what we create,and how to protect it? In Kenya's rapidly growing tech ecosystem, understanding IP isn't just a legal nicety, it's a competitive advantage. Whether you're building an app, contributing to open source, or launching a startup, knowing your rights can mean the difference between owning your work and losing it. This article breaks down the essential IP frameworks every software developer should know. What is Intellectual Property in Software? Intellectual Property (IP) in software encompasses legal protections designed to safeguard the rights of creators and developers. The four primary types of IP protection relevant to software are: patents, copyrights, trademarks, and trade secrets . Copyright: Protecting Your Code Copyright is the most immediate form of protection for software developers. In Kenya, copyright protection arises automatically at the moment of creation,registration is not mandatory . This means that when you write code, you automatically own the copyright to that expression, provided the work is fixed in a tangible medium . However, a crucial distinction exists: copyright protects the expression of ideas, not the ideas themselves . This principle was reinforced in the Kenyan case of Solut Technology Limited v Safaricom Limited, where the court confirmed that without access to source code, it's difficult to prove infringement because the "expression" (the code itself) wasn't shared . Key Insight: Registering your copyright with KECOBO in Kenya provides prima facie evidence of ownership and can make enforcement faster if someone copies your work . Patents: Protecting Functionality While copyright protects the expression of code, patents pro

A bug showed up in my personal project last month. Nothing dramatic - a value wasn't updating the way...

When I started learning programming, I believed my future as a developer depended on one thing: How...

When the enemy occupies favorable terrain, don't attack head-on. Use a decoy to lure the tiger down the mountain. Then take the mountain. — The 36 Stratagems, Lure the Tiger Down the Mountain Previously on this series: #2: Derek Shaw Walked Into Another AI Promise. The Pipeline Had a Better Plan. — Derek lost the Finova contract at QualiGuard. In the parking lot, Lena turned back before getting in her car: "Next time you put together a proposal — make sure your boss knows what you're doing out there." That line followed him. At MediSys he nearly made the same mistake. Fixed the ETL pipeline instead of the model, beat OmniDx with their own white paper. But VP Morgan still saw through him: he still hadn't told his boss. #8: Alex Watched an AI Dashboard Take Over. He Kept the Keys Under the Table. — MedTech signed a seven-figure AI operations monitoring system. Alex was assigned as training lead. Under everyone's noses, he built a second monitoring panel labeled "training environment." Three weeks later the vendor dashboard went down. Alex's hidden panel was the only one still running. The first time Alex and Derek really talked, and it wasn't in a working group. At the FHIR standards committee quarterly meeting, they sat in the same row, three seats apart, and voted against the same proposal. They knew each other's names. That was it. Two months later, Derek was fixing a partition config in the staging environment. MediSys had just signed a major hospital; the AI diagnostic validation platform was in integration testing. He found an unrotated log directory in /etc/logrotate.d/ with a prefix that didn't match MediSys's naming convention. He traced it upstream. One server. Labeled "temporary data exchange node." MedTech's supply chain order stream and MediSys's diagnostic validation records were sitting in the same directory. Write permissions hadn't been restricted. The hospital's integration spec had a line saying "both parties are recommended to complete data alignme