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The Ultimate Claude Masterclass: 13 Power Features Changing the AI Game Artificial Intelligence is no longer just about asking questions and getting text answers. Anthropic’s Claude has evolved from a simple chatbot into a fully autonomous, visual, and connected AI ecosystem. If you are still using it just to draft emails, you are barely scratching the surface. Whether you are a developer, content creator, or business professional, here is your definitive guide to the 13 powerhouse features of Claude, packed with practical examples, formatted specifically for Dev.to. 🧩 Part 1: Smart Onboarding & Personalization 1. Introduction to Claude Claude stands out in the crowded AI landscape because of its advanced reasoning, high emotional intelligence, and natural, human-like writing style. From parsing complex codebases to writing creative narratives, Claude feels less like a machine and more like a brilliant colleague. 2. Import Memory From ChatGPT To Claude Switching platforms shouldn't mean losing your progress. With this feature, you can instantly migrate your entire persona, past context, and custom instructions from ChatGPT straight into Claude with a single click. Example: If ChatGPT already knows your coding style or specific brand rules, importing it means Claude hits the ground running without you having to re-explain everything. 3. Add User Preferences Tired of typing "Act as a Senior Developer" or "Keep it casual" in every single prompt? User Preferences lets you set permanent system-level instructions that Claude remembers across all new conversations. Example: You can set a preference like: I manage a technology brand. Always keep explanations direct, modular, and optimized for scalability. 🎨 Part 2: Visuals, Coding & Apps 4. Create Apps & Artifacts Using Claude For Free Claude’s Artifacts feature opens a dedicated, interactive window right next to your chat. When you ask Claude to write code, a webpage, or a game, it doesn't just show you lines of text—it re

The narrative around Artificial Intelligence and software engineering has shifted dramatically. We are no longer asking if AI will change development, but rather how we change with it. If your value as a developer is tied solely to how fast you can churn out boilerplate code, write standard API endpoints, or memorize syntax, the landscape is becoming challenging. AI can do those things in seconds. However, this isn't a death sentence for the engineering career—it is an evolution. The industry is moving away from pure "code generation" and shifting toward system architecture, integration, and governance. To remain indispensable, you need to know exactly where to direct your energy and what pitfalls to avoid. Where to Focus Your Energy To stay relevant, you must position yourself in the areas where AI struggles: high-level abstraction, complex contextual reasoning, and human leadership. 1. System Design and Enterprise Architecture AI is excellent at writing isolated functions, but it struggles with massive, interconnected systems. Focus on how components interact at scale. Understanding how to slice a monolithic application into resilient microservices, orchestrate microfrontends, or design cloud-native solutions is where the high-value work lies. 2. Code Governance and Quality Assurance With AI generating code at unprecedented speeds, codebases are expanding faster than ever. The world doesn't just need people who can create code; it needs gatekeepers who can validate it. Your role will increasingly focus on setting quality standards, establishing robust CI/CD pipelines, and ensuring that AI-generated code adheres to strict security, compliance, and performance metrics. 3. Mentorship and Team Leadership The influx of AI tools means junior engineers can produce code much earlier in their careers, but they often lack the foundational experience to spot subtle architectural flaws or security vulnerabilities. Senior developers must step up as leaders, guiding less experi

The RabbitMQ upgrade looked like a straightforward infrastructure task: move from RabbitMQ 3.X to 4.X, provision the new broker, review the client setup, confirm queues still declare correctly, restart consumers, watch the logs, and move on. But infrastructure upgrades rarely test only infrastructure. They also test the assumptions your application has been making for years. In this case, the upgrade forced a more important question: is our messaging system reliable by design, or has it simply been relying on stable conditions? That distinction matters because a message queue can appear healthy when the broker is running, the network is stable, consumers are alive, and messages are acknowledged quickly. But production systems are not judged only by how they behave when everything is fine. They are judged by how they behave during restarts, closed channels, slow handlers, bad configuration, deployment windows, and partial failure. The RabbitMQ upgrade exposed those edges. It revealed assumptions around connection lifecycle, acknowledgements, dead-letter routing, retry behavior, observability, and operational simplicity. The real lesson was not just how to upgrade RabbitMQ. The real lesson was how to build a messaging layer that is easier to operate, easier to reason about, and safer to fail. Simplicity Is an Operational Feature One of the first things the upgrade exposed was complexity. Over time, messaging code can quietly become a small internal framework. A connection helper becomes a connection manager. A consumer wrapper becomes a consumer framework. Retry helpers appear, dead-letter helpers appear, failure handlers appear, and monitoring logic gets layered on top. Each addition may have been reasonable when introduced, but during an incident, complexity has a cost. Every abstraction becomes another place to inspect. Every helper becomes another assumption to validate. Every unused file becomes a possible source of false confidence. RabbitMQ integration code doe

Most "AI email" demos never think about deletion. The agent reads, replies, files things away, and the inbox just grows. That's fine in a demo. It is a problem the first time a real person emails your agent, because the moment that mailbox holds someone else's name, address, order history, or support complaint, you've taken on a data-protection obligation — and "we kept everything forever" is not a defensible retention policy. An Agent Account on Nylas accumulates personal data you have to be able to purge. It's a mailbox the agent owns — support@yourcompany.com answering to a model instead of a human — and every inbound message lands in it. Under GDPR that data needs two things you can prove: a retention window so it doesn't live forever, and an erasure path so you can delete a specific person's mail when they ask. This post builds both, with the curl and the CLI for each step. A quick, honest caveat before any of it: this is a docs-and-demo walkthrough, not legal advice. The Nylas primitives below cover the mail held in the mailbox . Any derived copy you made — rows in your own database, lines in your application logs, a vector store you embedded the message into — is yours to purge separately. The API can delete the message; it can't reach into your Postgres. Keep that in mind throughout. What the platform gives you Nothing new to learn on the data plane. An Agent Account is just a grant with a grant_id , so everything you already know about Messages and Threads applies directly — listing, reading, and deleting mail run against the same grant-scoped endpoints any other Nylas integration uses. Retention and erasure split cleanly into two layers: Retention is a control-plane setting. It lives on a policy — an application-scoped resource that bundles limits and spam settings — attached to the workspace your Agent Account belongs to. Two fields cap how long mail survives: limit_inbox_retention_period and limit_spam_retention_period . Set them once and Nylas deletes a

Spam traps are the failure mode nobody puts in the demo. A bounce is loud — you get a 5.x.x back, your code logs it, you move on. A complaint at least gives you a webhook. A spam trap gives you nothing . The message gets accepted, no error comes back, and somewhere a mailbox provider quietly writes your domain down as a spammer. By the time you notice, your inbox placement has already cratered and you have no single bounce to point at. That's the trap, literally. And it's the one that bites autonomous agents the hardest, because the whole appeal of an agent is that it acts without a human watching every send. Point a model at a list it scraped, let it loop, and it'll happily mail a recycled address that's been a trap for two years. The agent never sees a problem. You only see the aftermath in your deliverability dashboard a week later. I work on the Nylas CLI, so the terminal commands below are the exact ones I reach for when I'm wiring up an Agent Account to not do this. The good news is that an Agent Account is just a grant — a grant_id that works with every grant-scoped endpoint you already know — so there's nothing new to learn on the data plane. The defense is mostly discipline: validate before you send, honor every complaint, and age out the addresses that never wanted to hear from you. What a spam trap actually is, and why it's not a bounce It's worth being precise here, because the three things people lump together behave completely differently. A bounce is a rejected delivery. The receiving server tells you the address is bad, you get a message.bounced event, and you stop. Bounce handling is a solved problem — you listen, you suppress, you're done. A complaint is a recipient hitting "report spam." The mailbox provider relays that back as a feedback loop, and you get a message.complaint event. The address is real and reachable; the human just doesn't want your mail. If you keep mailing them, you're training the provider to filter you. A spam trap is neither.

This is a submission for Weekend Challenge: Passion Edition What I Built Loyalty Ledger — a fan loyalty tracker where your check-in streak, badges, and history live on Solana instead of some app's database. Live app: https://loyalty-ledger-blond.vercel.app Here's the problem I kept coming back to. Every sports app wants you to check in, engage, "prove your loyalty" — collect points, build a streak, unlock a badge. Cool. Except every single one of them throws that history away the second you stop opening the app. Switch apps and your streak resets to zero. Get banned, or the app shuts down, or they just quietly decide to wipe inactive accounts one day — and your history is just... gone. Because it was never actually yours. It was a number sitting in someone else's database, and they could reset it, inflate it, or delete it whenever they felt like it. You had zero say in it. And that bugged me way more than it probably should have. Like — we figured out how to make ownership portable for money, for domain names, for digital art. But "I've supported Argentina since 2019" 🇦🇷 still lives and dies inside one company's backend, and nobody's really questioned that. So I kept the weekend scope deliberately small: prove one fan's loyalty to one team, for real, end to end — instead of sketching ten features that are all half-fake. You connect a wallet, pick a sport and team, and check in. FIFA World Cup is the fully working path here ⚽ — that check-in sends an actual transaction that creates or updates a program-owned account, not a row in my database somewhere. Your streak count, your badge tier, the actual badge tokens — none of it exists anywhere I control. Which honestly felt a little weird to build, in a good way. Once that core loop worked, I built the rest of the identity around it: a Fan Passport that shows your streak, a derived "Fan Score," your tier (Rookie → Devoted → Veteran → Legend 🏆), a progress bar toward the next tier, an achievements grid with locked/unlocke

Every trader I know starts journaling in a spreadsheet, and every one of them abandons it within a month. Not because journaling is useless, but because the spreadsheet can't answer the questions that actually matter: "which of my rules costs me the most money?" or "am I cutting winners too early?" A flat sheet of rows can't tell you. Tools like RizeTrade solve this with a proper data model, and in this post I'll walk through how to design one yourself. The problem is almost always the data model. If you design the schema well up front, the hard reports fall out of it for free. Here's how I'd structure it. * Start with the trade, but don't overload it * The instinct is to make one giant trades table with fifty columns. Resist it. A trade has a small core of facts: CREATE TABLE trades ( id BIGSERIAL PRIMARY KEY , symbol TEXT NOT NULL , side TEXT NOT NULL CHECK ( side IN ( 'long' , 'short' )), entry_price NUMERIC NOT NULL , exit_price NUMERIC , quantity NUMERIC NOT NULL , entry_at TIMESTAMPTZ NOT NULL , exit_at TIMESTAMPTZ , stop_loss NUMERIC , take_profit NUMERIC , commission NUMERIC DEFAULT 0 , strategy_id BIGINT REFERENCES strategies ( id ) ); Everything else (emotions, notes, screenshots, rule checks) hangs off this in separate tables. That separation is what lets you slice the data later without schema migrations every time you want a new report. * R multiples belong in the model, not the UI * The single most useful number in trading isn't dollar P&L. It's the R multiple: profit or loss expressed in units of the risk you planned to take. A +2R win and a +2R win are comparable across a $500 account and a $50,000 account; two dollar figures aren't. Planned risk is defined the moment you enter, by your stop: planned_risk_per_unit = |entry_price - stop_loss| planned_R = (exit_price - entry_price) / planned_risk_per_unit (for longs) Store the stop at entry time. If you only record the realized exit, you lose the ability to detect stop loss violations, which is the who

The best developer tools share one quality: you forget you are using them. Think about it. Your IDE fades into the background when you are in flow. Your terminal becomes muscle memory. These tools are invisible because they match your mental model so perfectly that there is zero friction between thought and action. This is exactly what online developer utilities should aspire to. The Problem with Fragmented DevTools Most developers have a bookmarks folder full of single-purpose websites: One for JSON formatting One for timestamp conversion One for Base64 encoding One for URL decoding Every switch between these tabs is a context loss. Every tool has a slightly different UI, different copy-paste format, different quirks. The Invisible Toolkit Opennomos Json (opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y) consolidates timestamp conversion, JSON formatting, and Base64 encoding into a single workspace. No install. No accounts. No pricing tiers. Just open a tab and work. This is the north star for developer tools: make them so simple that the user never has to think about the tool itself — they only think about their actual task. The Trend Is Clear We have seen this pattern across the dev ecosystem: GitHub Codespaces made local IDE setup invisible Vercel made deployment invisible Replit made runtime environment invisible The next frontier is utility tools. The sooner they become invisible, the better. Try it: opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y Part of the Nomos Build-in-Public series.

Roleplaying games are often defined by excess. Storylines that span dozens of hours, side quests so big they could be their own game, massive worlds that require complex maps to explore, and casts so big you start forgetting character names. That's part of what makes these games feel like epic adventures, but it can also […]

The AquaSense X brings self-cleaning technology to pool robots for the first time, but is it worth nearly twice the price of Beatbot’s flagship cleaner?

There’s a new fleet of TVs using new mini and micro RBG display tech, and Samsung’s R95H model isn’t as impressive as it should be.

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

Cross-posted from the HTML to Image blog , where the original lives. Browsershot is the package most Laravel developers reach for when they need to turn HTML into an image. It wraps Puppeteer, drives real Chrome and produces pixel-accurate output. On your machine it works first time. Then you deploy, and the first render throws Failed to launch the browser process! . The problem is not Browsershot's code. The problem is what it demands from the machine it runs on. What Browsershot actually asks of your server Browsershot is a PHP package with a second runtime hiding inside it. To run it in production you need Node.js, the Puppeteer npm package, a Chrome or Chromium binary, the long tail of shared libraries Chrome links against ( libnss3 , libatk , libgbm and friends on a slim Debian image) and a font set wide enough to cover whatever your templates contain, emoji included. That is manageable on a full VPS you control. It falls apart in the places Laravel apps increasingly run: Laravel Vapor and serverless. The PHP Lambda runtime ships neither Node nor Chrome, and you cannot apt-get your way out of a Lambda. The Puppeteer on Lambda guide covers just how deep that particular hole goes. Shared and managed hosting. No root, no system packages, no browser binary. Browsershot is simply off the table. Slim Docker images. php:8.3-fpm-alpine carries none of Chrome's dependencies. Adding Chromium, its libraries and fonts costs a few hundred megabytes and a permanent maintenance line in your Dockerfile. CI pipelines , where every job downloads a browser before your test suite can touch a render. The dependency does not stay contained either. Even Spatie's newer packages inherit it: spatie/laravel-og-image renders through laravel-screenshot , which drives Browsershot underneath, so the Node and Chrome requirement follows the whole family wherever it goes. The usual workarounds The first workaround is the fat container: bake Chromium, the shared libraries and a font stack into y

A container's writable layer feels like a filesystem, and that's exactly the trap. Write a database into it, remove the container, and the data is gone — no warning, no recovery. If you want anything to survive docker rm , it has to live outside the container, and Docker gives you three ways to do that: named volumes, bind mounts, and tmpfs. Knowing which one to reach for is most of the battle. Why the writable layer betrays you Every running container gets a thin read-write layer stacked on top of its image layers. It looks persistent because you can docker exec in and see your files. But that layer is bound to the container's lifecycle. docker run --name scratch alpine sh -c 'echo hello > /data.txt; cat /data.txt' # hello docker rm scratch # the layer — and /data.txt — no longer exists There's no "oops." The writable layer is discarded with the container. Persistence is not a default you get; it's a decision you make. That decision is a volume, a bind mount, or tmpfs. Named volumes: the default for state A named volume is storage that Docker creates and manages for you. You give it a name, Docker keeps the actual bytes under its own directory, and you never have to care where that is. docker volume create pgdata docker run -d --name db \ --mount type = volume,source = pgdata,target = /var/lib/postgresql/data \ postgres:16 The container writes to /var/lib/postgresql/data , but those bytes land in a Docker-managed location on the host. Remove and recreate the container against the same volume and the data is still there. docker rm -f db docker run -d --name db \ --mount type = volume,source = pgdata,target = /var/lib/postgresql/data \ postgres:16 # same data, new container Where do the bytes actually live? Under Docker's data root, typically /var/lib/docker/volumes/<name>/_data : docker volume inspect pgdata --format '{{ .Mountpoint }}' # /var/lib/docker/volumes/pgdata/_data The point is that you're not supposed to reach into that path directly — Docker owns it. You

The Story: Picture this: It is 10 PM. I was eating my dinner while adding one final touch to my social media app, Vlox. It should just say "Processing..." while generating a card. Simple, right? See the nightmare. 🔥 The Nightmare Scenario 📉 Suddenly, my "Download Card" button broke. htmlToImage started spitting out completely empty 0b images. The hunt was on. Failed Mission Log 🛰️ Attempt 1: Swap to html2canvas 🔄 Result: Error stating the element was not found in the cloned iframe. Verdict: The parent container was completely lost. Attempt 2: Use CoolAlertJS Toast 🍞 Result: It looked ugly and meant loading two different alert libraries for the same job? Not my game. Verdict: Total waste of bundle size. Attempt 3: Append to body + display: none 🙈 Result: The canvas process failed entirely. Verdict: Canvas snapshot engines completely ignore hidden elements. The "Aha!" Moment 💡 Why did the element vanish? Because the card generator lives entirely inside a Swal popup. When you click the download/confirm button, Swal instantly destroys that entire popup DOM tree. You cannot snapshot an element that no longer exists. 👻 The Ultimate Fix 🚀 Inside the new "Processing" popup, I appended the #card-generator-image-preview directly into the new alert container. The element stays alive in the active DOM. The snapshot succeeds perfectly. Clean code. Happy developer. Delicious dinner. Want to see the exact JavaScript code block that fixed it? Drop a comment below or check Vlox on Github .

Lately, I've been doing some deep personal reflection. I'm talking about myself, I hope no one misunderstands, on how pervasive the use of AI has become in my daily development workflow. Through a bit of self-analysis, I've discovered some interesting dynamics. Dependencies often arise from the desire to fill a void. But what kind of void does an experienced developer like me face? As a professional, I have the skills. Sure, AI helps me get things done faster, but the final product is always the translation of my vision; if I don't fully understand the solution, I discard it. I'm not looking for "magic," I'm looking for efficiency. Yet, I realize I've used AI to fill a specific void: the need for discussion. Software development is inherently solitary. The satisfaction of a successful "execution" after hours of discussions, refinements, and clashes over an architecture is an experience I miss today. The chat interface is always there, ready to respond. But there's a problem: it's a "yes-man." Even when I force it to be critical or provocative via the system's prompts, I know it's just reciting a script to please me. There's no conviction, no risk of error, none of the friction that arises when a colleague courageously defends their vision, perhaps one that conflicts with mine. We are part of a huge community, but debate often remains superficial. One might argue that posts and comments are enough, but anyone who has tried knows it doesn't work very well: a debate is truly alive only when there is no latency. In comments, the time between thinking, writing, and waiting for a response diminishes the energy of the exchange, turning it into a series of monologues rather than a dialogue. Why don't we try creating "virtual tables" where we can discuss projects, architectures, and technical choices with the natural rhythm of a conversation? Direct, real-time discussions, in person or remotely, where the exchange of ideas can spark sparks, without the filter (and delay) of

Markdown to HTML: The Fastest Way to Convert Markdown Online Markdown is one of the easiest ways to write documentation, blog posts, README files, and notes. The only problem is that many platforms require HTML instead of Markdown. Instead of installing software or using complicated editors, you can convert Markdown directly in your browser. I built MDConvertHub to make this simple. It lets you: Convert Markdown to HTML instantly Preview the output before copying Work completely in your browser No signup required Free to use I started building MDConvertHub because I wanted a collection of small Markdown tools in one place instead of visiting different websites for every task. The project now includes multiple Markdown utilities, and I'm continuously adding new tools based on real use cases. If you'd like to try it, I'd love your feedback. 👉 https://mdconverthub.com/markdown-to-html What Markdown tool do you use most often? Feedback and suggestions are always welcome. I'm building MDConvertHub one tool at a time.

Finding a co-founder is one of the hardest parts of building a startup, and most platforms weren't built for it. LinkedIn is a professional directory, not a matching network. Reddit threads are noisy and unstructured. Cold outreach is a gamble. FoundrGeeks is built specifically for this problem . It's an AI-powered co-founder and team matching platform that connects builders based on what they're building, what skills they bring, and what gaps they need to fill, not just their job title or who they already know. The problem with finding a co-founder most builders looking for a co-founder face the same wall: the people they need aren't in their network, and the platforms that exist weren't designed for this specific search. You're not just looking for someone with the right skills. You need someone at the same stage, with the same intensity, who fills exactly the gaps you have right now. And you need to know that before spending three hours on discovery calls. That's the gap FoundrGeeks fills. How FoundrGeeks works When you create a profile, you describe what you're building, what you bring to the table, and what you need. You set your stage, idea, MVP, or funded, and your weekly availability. From there, the AI takes over. It surfaces people whose strengths complement your gaps, scores each match as Strong, Good, or Potential, and generates a plain-English explanation of why each person fits what you're building right now. Three features stand out at launch: Complementary matching: the engine looks for people who fill your gaps, not mirror your background Scored matches with explanations, every match tells you exactly why, before you reach out Stage-aware feeds, as you move from idea to MVP to funded, your matches reshuffle automatically You also control your visibility, go public and let talent find you, or stay private and let the AI work quietly on your behalf. Why we built this This platform exists because of a project that never got finished. I had an idea I wa

This is a submission for Weekend Challenge: Passion Edition What I Built Loyalty Ledger —...

Hot showers, like electricity, are a luxury that's easy to take for granted. That all changes after a few nights camping at a music festival, a week toiling at a backcountry job site, or overlanding all summer in the great unknown. An itchy scalp and the vague smell of warm clams suddenly make the idea […]