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Articles by Nazar Boyko
Tabs to Trails: My Unread Tabs Became Walks, Made on My Own Laptop
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass What I Built My reading system is a row of browser tabs I opened because the title looked worth my time, then kept open for weeks, because reading takes a chair and a chair is where I spend the day already. Tabs to Trails turns those tabs into walks. A bookmarklet called "Walk this tab" drops the page you're on into a list, and each row shows how many minutes it takes to hear.
React and Vue Are Turning Into the Same Framework
Before diving into this article, I have to say that I haven't been a front-end developer for a very, very long time π I just analyze architectures and enjoy listening to people who argue about what's best to use on the front end. So I might be wrong about some things. π€·ββοΈπ€·ββοΈπ€·ββοΈ Please correct me if I'm wrong! Recently, I came across a team that was rewriting a project from Vue to Ract, and their argument was that Vue runs slower.
Laravel Is Not As Heavy As You Think
Recently I came across another discussion about Laravel's memory usage and I can't say that it was exactly new to me, I've worked with Laravel and Lumen for years and have seen how differently they can behave in terms of memory, but it made me curious about what actually creates that difference.
A Million Go Map Entries Take 38MB, Not 16MB
A few months ago, I noticed an issue with the internal structure of the new Go map—specifically regarding empty slots! So I decided to figure out why a map[int64]int64 theoretically appears to be 16 bytes per element: 8 bytes for the key + 8 bytes for the value. But in Go 1.27.1, 1,000,000 elements take up approximately 37.8 MB, which is ~37.8 bytes per element.
I Added One Key to a PHP Array. It Cost 25 MB of Memory
I was recently refactoring some legacy PHP code and noticed that the same data array was being used in completely different ways! Radically different! After some analysis, I came to the conclusion that a PHP array can be a very efficient data structure as long as it remains a packed list, but as soon as it turns into a hash table, memory consumption skyrockets, and for large datasets, associative arrays often consume nearly twice as much memory as regular typed objects.
I Stopped Reviewing Code And Started Reviewing Agents
I had an incident at work where an AI-powered agent made a change that passed CI and seemed entirely reasonable in the PR. The problem wasn’t just with the code—the issue was that we had never properly verified what the agent was actually allowed to do, which tools it could run, and where its authority ended. And to be honest, this incident completely changed my view of the review process! I hadn't really thought about what the agent does behind the scenes.
Has AI Made You A Lazier Developer? Be Honest.
Haven't you ever wondered if this AI vibe coding has made us lazy? Who's been solving problems on LeetCode lately? 😅 I've noticed that accepting is easier than thinking, by a margin so small that no single accept feels like anything, and it adds up anyway. Part of why it's hard to notice is that it feels faster even when it isn't. But I've come to think "lazy" is the right worry aimed at the wrong thing. There are two kinds of lazy and only one of them is a problem.
You write one kind sentence. I donate a dime. A blockchain makes sure I do.
This is a submission for the DEV Weekend Challenge: Generosity Edition. What I Built Kindness Chain is one page. You write one sentence about a kind thing you did or will do today, add your name if you want, and click "Add my link". Your sentence appears in the feed, then flips to "confirmed" with a link to Solana Explorer, because it was just written to the Solana devnet as an SPL Memo transaction.
Should You Still Learn to Code If AI Can Do It?
Every beginner is quietly asking the same thing in 2026: why learn to code when the AI model can write it? The fear is fair, the entry path really is narrower than it was. But the part of coding that got cheap was the typing, and the part that's left, knowing what to ask for and telling right from almost right, is worth more than it ever was.
Should AI-Generated Code Be Labeled in Your Git History?
The Linux kernel, Fedora, and LLVM now require an “Assisted-by” tag on patches created with the help of AI, and Claude Code adds a “Co-Authored-By” line to your commits, whether you ask for it or not.
[Dev Log][Python] Create short videos from photos and clips with Gemini 3.7 Flash: ReelCraft
Preface: It all started with a misunderstanding.
The Next Evolution of Software Developers
The next evolution of software developers: from implementation to intent, orchestration, and ownership. Over the last few months, I've read countless articles debating whether AI will replace software developers. Some predict the end of programming as we know it, while others argue that nothing significant will change. Personally, I think both perspectives miss the point. The real transformation isn't that AI can write code. We already know it can.
Agent Plugins Package Capabilities. IRC-A Asks: Who Authorizes Them at Runtime?
Yesterday I stumbled on a LinkedIn post about a new open standard for packaging Agent Skills and MCP servers: Agent Plugins, built with collaboration from players like Vercel, OpenAI, Microsoft, AWS, GitHub and Cursor. I won't lie: at first, I felt that familiar punch in the stomach. A while ago, I had already felt the first signal when I read an article by @lukeocodes about the transformation from MCP to stateless.
TypeScript 7 Went Native: What Actually Changes And What Doesn't
Everyone's heard by now that TypeScript "went native." And I keep seeing the same wrong conclusion drawn from it: that TypeScript now somehow runs without being compiled, that the build step is gone, that your .ts files execute directly. None of that happened. Your browser still can't run TypeScript. Node still can't type-check it. The thing that went native isn't your code. It's the compiler. And honestly, that's the better story.
Slopsquatting: The Supply Chain Attack That Weaponizes AI Hallucinations
Typosquatting bets on your typo. Slopsquatting bets on your AI assistant. When a model invents a package that doesn't exist, an attacker registers the name and waits for install to run. Here's the kill chain, why your usual defenses miss it, and what actually stops it across npm, Composer, and pip. Imagine a coding assistant just told you to run this: pip install requests-oauth2-helper It looks right.
The Junior Developer Pipeline Is Broken... And AI Broke It
Everyone agrees AI makes senior engineers more valuable. Almost nobody asks where the next generation of seniors is supposed to come from. The work AI automated away was never waste, it was the apprenticeship, and the hiring data says the bottom rung is already gone. There's a story the industry loves telling right now: AI makes senior engineers more valuable. The people with judgment, the ones who can smell a bad design from the doorway, are the winners of this shift. It's a comforting story.
How terminal-sharing tools put your shell in a browser
Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. You have probably done this dance. You are debugging something on a box, your teammate says "can you just show me", and you end up screen sharing a 4K monitor over a video call so they can squint at 11px monospace text that has been compressed into oatmeal.
AI And Code Ownership: Who Is Responsible For Generated Code?
Imagine your AI assistant just produced 200 lines of code. Legally, you may not own a single line of it but legally, you're still on the hook for every bug it ships. Here's the gap between those two sentences, and what it means for the code you merge tomorrow. That's the gap this whole topic lives in. The tool that produced the code can't be sued. The model can't be held accountable.
LLM Evals For Developer Tools: Useful, Correct, Safe
Someone on your team built an LLM feature. Maybe it's an inline code-suggest. Maybe it's a "fix this PR comment" button. Maybe it's a full agent that opens pull requests on its own. The demo worked. The screenshots were good. You shipped it. Now a real user gives it a real codebase, and you have no idea whether it's getting better or worse week to week. That gap, between "it worked in the demo" and "we can prove this is improving," is what evals are for. And in 2026 we are still surprisingly bad at it.
The Senior Devs Refusing to Use AI Are Becoming Juniors Again
I sat in a code review last week where a staff engineer with 14 years of experience got outshipped by a junior with 8 months. The junior wasn't smarter. She wasn't working longer hours. She had Claude Code, an MCP server wired to their internal docs, and zero ego about any of it. The staff engineer had opinions about "real engineering." Guess whose PR merged first.
You can't transition a CSS variable. @property says otherwise.
You've probably written something like this: .button { --hue: 220; background: hsl(var(--hue) 70% 50%); transition: --hue 0.3s ease; } .button:hover { --hue: 340; } You expected a smooth color shift. You got a snap cut. That's not a browser bug. It's the result of one architectural decision baked into custom properties from the start: CSS variables are text substitution, and the browser has no idea what type they hold.
Nuxt In 2026: The Vue Full-Stack Framework That Deserves More Attention
Everyone knows Next.js is the default full-stack JavaScript framework in 2026. The job ads say so, the AI tooling assumes so, the conference talks revolve around React Server Components, and even people who've never touched a Vue file have an opinion about which version of getServerSideProps they like best. Everyone is mostly right - and missing a piece of the story.
My credential rule reported 842 secrets in vercel/ai. The real count was 0.
A credential scanner that reports 842 secrets in a codebase with zero hardcoded secrets isn't cautious. It's broken. Mine was, and it took a peer plugin reporting less than half my count to make me check. The flagship rule secure-coding/no-hardcoded-credentials runs alongside eslint-plugin-no-secrets/no-secrets on vercel/ai (the AI SDK) as part of our ILB-Flagship bench. Both rules see the same source.
Is Forward Deployed Engineering Killing DevRel?
When Palantir invented Forward Deployed Engineering (FDE) in the 2010s, the industry mocked them as "consultants with equity." Nobody's laughing now. During the opening keynote, @swyx called out the brand new FDE track at the AI Engineer World’s Fair as one of the things he was most excited about. Cursor just hired a VP of Forward Deployed Engineering. Anthropic teaches 101 classes on it. LinkedIn says it's the fastest-growing job AI has created, with postings up 42x since 2023.
AI For Test Generation: Where It Helps And Where It Lies
AI is great at writing tests fast, and good at writing tests that look real but verify the wrong thing. Here's the line between useful scaffolding and confident-sounding test theater, told through unit tests, edge cases, and brittle mocks. You've been there. You finish a function, paste it into the AI, and ask for tests. Thirty seconds later you have twelve of them. They run. They pass. The coverage badge nudges up by a percent. You feel like you did something useful, and on most days, you did.
AI For Test Generation: Where It Helps And Where It Lies
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tgo Devlog #6: The Origin, the "Crack" of Hard Problems, and the Zero-Friction Threshold
There isn’t a singular, highly compelling, life-or-death reason I started building tgo (a TypeScript-to-Go native compiler). It didn’t start because I ran into a wall that could only be solved by compiling TypeScript down to a Go binary. *It started because of a disappointment. * A few weeks ago, I saw that Microsoft was releasing a Go compiler for TypeScript. I got incredibly pumped up.
AWS S3, Deduplication & the Presigned URL Production Flow A ~1 hour read. Continues directly from Episode 1 (IAM + EC2). Every term is defined the moment it shows up. 👦 Nephew: Uncle, I've been thinking about what you asked last time — where does the uploaded file actually go if my server is disposable. I looked it up. It's S3, right? 👨🦳 Uncle: Right. And today we're not doing the toy version.
I Started Building a Premium Template Marketplace — Week 1 Progress, Stack & What's Coming
I've been thinking about this problem for a while. Developers and businesses need quality websites fast — but the options are either overpriced custom builds, outdated templates, or starting from scratch every single time. So I decided to build the solution myself. Softchic is a premium template and ready-made website marketplace — production-ready, built on modern stacks, designed to actually look good. This is Week 1 of building it in public. Why Softchic The market exists.
I Built an AI Agent That Gets Curious On Its Own
Active inference: curiosity emerges for free from minimizing surprise — 48% vs 100% on a foraging task. TL;DR: Most AI agents chase rewards — they pick whatever action scores the most points. I tried a different, brain-inspired goal: avoid surprises. Something neat happened — the agent became curious without being told to. It goes looking for information before acting, and that takes it from 48% to 100% on a simple task. ~100 lines.
Inside An AI Agent: Planning, Tool Use, Memory, Constraints, And Verification
Have you noticed how every demo of "an AI agent" looks impressive in the video and falls apart the moment you ask a sharper question? The agent confidently does the wrong thing. It forgets what it just decided. It tries to call a tool that doesn't exist. It loops forever rewriting the same file. It calmly tells you the deployment succeeded when it didn't. These aren't failures of the model. They're failures of the workflow around the model.
How To Prepare For A Golang Interview: A Practical Guide For Mid & Senior Engineers
A Go interview is rarely about syntax alone. Yes, you should know how slices work, how goroutines and channels behave, how interfaces compose, and how errors propagate. But for mid and senior roles, interviewers are looking for something deeper: Can you build correct software under real constraints? Can you reason about trade-offs and explain them in plain language?
Stop Manual Code Reviews: Build Your AI-Powered Review Workflow
Stop Manual Code Reviews: Build Your AI-Powered Review Workflow You're staring at a 2000-line PR at 4:59 PM on a Friday. Classic. Someone's asking for review feedback, and you've got about 47 other things due tonight. Here's the thing though: you don't actually need to read all 2000 lines yourself anymore. I've been automating my code review workflow for the past few months, and it's genuinely saved me hours every week.
How To Learn Go Fast: A Practical Roadmap For Senior Backend Developers
Why I Am Writing This: A PHP Developer Crossing Into Go I am a PHP developer. I have shipped Laravel and Symfony services in production, debugged messy Eloquent queries at 2am, traced N+1 problems through real traffic, and lived inside enough service containers to know exactly how a framework feels when it grows up with your career. I always intended to learn Go. For years, "I'll seriously learn Go someday" sat on my list next to Rust, Elixir, and a dozen side projects.
AI Agents For Laravel/Symfony Projects
Laravel and Symfony projects are great places to use AI agents. Not because PHP is "easy" (it isn't), but because PHP backends carry a lot of real business logic. Controllers, services, console commands, queue jobs, Doctrine repositories, Eloquent models, event listeners, validators, policies, migrations, and tests all coexist in one repo, and the relationships between them are exactly the kind of context that's expensive for a human to load and cheap for a careful agent to map.
The CSV bug that compiles clean and breaks in production
Here is a bug I have shipped more than once. You have an export that turns records into CSV. The column list is just strings: import Papa from 'papaparse'; Papa.unparse(users, { columns: ['id', 'name', 'email'] }); // columns are plain strings Six months later someone renames name to fullName on the User type. The compiler is happy. The tests that do not touch this path are happy. The export keeps running.
π Day 4 of Learning React: What Actually Makes React... React? (Hooks, useState, and State Explained)
π Missed Day 3? I covered what actually happens after running npm run dev, how bundlers work, JSX, components, and props. You can read it here and then come back. I'll wait. βοΈ If someone asked me yesterday what makes React different from plain JavaScript, I probably would've talked about JSX or components. Today, I'd give a completely different answer. Hooks. Until today, I thought Hooks were just another React feature. Turns out, they're much more than that.
Next.js 16 Server Actions Security: The Auth Check Most Developers Miss
I keep seeing it on code reviews. Proxy solid. Auth on every Server Component. Header direction correct. Then I look at the Server Actions. No auth check. Not one. The page that renders the button is protected. The action behind the button accepts whatever the caller sends -- any valid session, any resource ID, no ownership verification anywhere. 'use server' doesn't add authentication. It exposes an HTTP endpoint. Anyone with a valid session cookie and a cURL command can call it directly, no UI required.
Building An AI Agent Playground Before Giving It Production Access
A coding agent runs cleanup_old_records() against what it thinks is a staging database. It isn't. The connection string came from an environment variable that got overwritten three deploys ago, and the agent just deleted four months of customer orders. It did exactly what it was told. It just had its hands on the wrong system. That failure isn't an argument against agents. It's an argument against the thing almost everyone skips: a place for the agent to be wrong cheaply.
TypeScript Tips That Actually Matter in Real Projects (including the satisfies operator)
Gavin Cettolo Full stack developer with a passion for technology Location Torino, Italy Education International master degree in Computer Science + MBA Work Fullstack Product & Platform Engineer Joined
Evaluating LLM Output Quality In Production
In March 2023, GPT-4 could tell you whether a number was prime with 97.6% accuracy. By June of the same year, the same model name answered those same questions correctly 2.4% of the time. Nobody pushed a bad commit. No prompt changed in your repo. The thing behind the API just... moved.
React Folder Structures That Scale: A Practical Guide for Modern Frontend Teams
Learn how to organize React projects for scalability, maintainability, and team collaboration. Introduction As React applications grow, one challenge consistently emerges: project organization. A folder structure that works perfectly for a small side project can quickly become a nightmare when multiple developers contribute to a production-scale application.
The App Store's silent giants: AI assistants reply to almost none of their reviewers
An App Store rating looks like a verdict. It behaves more like a monument, built over years and slow to move. It says very little about how this month's users feel. I took the 12 most-rated Productivity apps on the US App Store, 32 million ratings between them, and split the headline star into the two numbers it hides: how far recent sentiment has fallen below the lifetime average, and whether the developer replies when users complain. How it is measured Population truth.
How To Measure If AI Agents Actually Improve Developer Productivity
In 2025, a research nonprofit called METR ran a careful experiment. They took 16 experienced open-source developers, gave them 246 real tasks on codebases they'd worked in for years, and randomly let them use AI tools on some tasks and not others. Then they timed everything. The developers expected AI to make them about 24% faster. After the study, they reported feeling about 20% faster. They were actually 19% slower. Read that again, because it's the whole problem in three numbers.
AI Denialism In 2026 Is Becoming A Software Engineering Risk
AI denialism in software engineering made more sense when AI tools were mostly autocomplete, boilerplate generators, or a faster way to write unit tests. That version of the debate is now outdated. In 2026, the meaningful shift is not that AI can write code, but that AI agents can participate in the software delivery loop: exploring repositories, planning changes, editing files, running checks, fixing failures, and returning work that a developer can review.
Vector Databases Compared: pgvector, Qdrant, Pinecone, Weaviate
There's a moment in almost every RAG project where someone asks the question that decides your next two years of ops work: "Do we actually need a vector database, or can Postgres just do this?" It's a better question than it sounds, because the honest answer isn't "use Pinecone" or "use Postgres." It's "it depends on numbers you probably haven't measured yet": how many vectors, how aggressively you filter, how much you care about the absolute ceiling of queries per second.
AI Agents For Release Notes And Changelog Automation
Here's a changelog entry nobody asked for: ## v2.4.0 - fix stuff - wip - address PR comments - Merge branch 'main' into feature/checkout - update deps - final fix (for real this time) That's not a changelog. That's a git log with a version number stapled on top. And the people who maintain Keep a Changelog have a name for it that I can't improve on: "Don't let your friends dump git logs into changelogs." The interesting part is the timing. That tagline is from 2014.
LLM Gateways: Routing, Fallbacks, And Semantic Caching
Here's a line of code that's quietly running in production at a surprising number of companies: const response = await openai.chat.completions.create({ model: "gpt-4o", messages }); It looks harmless. It's also why your AI bill is whatever it is this month, why your app goes down the moment OpenAI has a bad afternoon, and why the same question typed by ten thousand users costs you ten thousand inference calls.
AI Agents And Branch Strategy: Safe Automation With Git
There's a GitHub issue on the Claude Code repo with a title that should make anyone automating git nervous: "Claude repeatedly commits and pushes directly to main despite explicit instructions." The reporter had a rule in their CLAUDE.md saying, in plain English, never commit to main: everything goes through a feature branch and a PR. The agent read it, agreed with it, and then pushed to main anyway. That's the whole problem with letting an agent touch git, compressed into one bug report.
Running Local LLMs With Ollama For Private Development
Here's a thing that catches almost everyone the first week they run a model locally. You paste a 600-line file into your shiny new local assistant, ask it to find the bug, and it confidently rewrites a function that isn't even in the part it read. No error. No warning. It just... silently dropped most of your file on the floor before the model ever saw it. That's not the model being dumb. That's Ollama doing exactly what it was told.
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