DEV Community: 0x41414141

Don't Use AI to Do This

0x41414141 — Thu, 16 Apr 2026 11:33:35 +0000

AI is useful. I use it too. But there is one thing I really do not think you should hand over to it too early.

Do not use AI to replace your first round of thinking.

That first messy pass matters more than people admit. It is where you figure out what you actually believe, what you do not understand yet, and what the real problem even is. When you let AI jump in before you do, it is easy to end up with something that looks smart but has no real point.

I have seen this happen a lot with writing, coding, and planning.

A person asks AI to write the article, explain the bug, or map out the project. The result is usually fine on the surface. Clean. Fast. Convincing. But when they try to use it, something feels off. The ideas are too generic. The code solves the wrong problem. The plan skips the parts that actually matter.

That is the trap.

AI is best when you already have some direction. It can help you refine, speed up, and polish. It can suggest better wording, spot weak spots, and save time. But it should not be the thing that tells you what you think before you have even thought it through.

So my rule is simple.

Think first.
Write the rough draft yourself.
Break the problem down on your own.
Then bring AI in to help sharpen what you already started.

That small difference changes everything.

You stay in control of the work instead of becoming a passenger in it. And honestly, the final result usually gets better too.

AI is a tool. Not a replacement for judgment.

Source: How to Use AI for Writing Without Killing Your Own Thinking

In the AI age, Java is more relevant than ever

0x41414141 — Sat, 11 Apr 2026 19:23:16 +0000

Powerful, scalable, reliable, cost-efficient, and ready to be your next AI language, Java can help modernize critical enterprise applications.

Java is the language used throughout enterprise platforms: ERPs, your ecommerce backends, analytics, logistics, and business workflows. You have decades of code, build pipelines, deployment practices, and operational runbooks all built around the JVM. When it comes to a language for AI though, your first thought might be Python, Node.js and TypeScript, or even Go.

When you’re figuring out what AI features are useful to add to those critical enterprise systems, it may well make sense to experiment in a language like Python. But when it’s time to move from experimentation to production, Java is ready for building AI – and the AI tools that are speeding up developers across the industry are now ready for Java too.

Java is both a foundation for AI-powered systems and a first-class language for building AI applications, especially at enterprise scale.

Java is ready for AI, and AI is ready for Java

One of the reasons Java has remained so popular in the enterprise for so long is how efficient the JVM is, as well as the strength of the ecosystem around it.

Bruno Borges, Principal Product and Community Manager for Java at Microsoft, tells The New Stack, “When you look at benchmarks and compare other language runtimes, the performance and efficiency of those other runtimes, especially Python and Node.js, is very far from what runtimes like the JVM can deliver in terms of cost efficiency.”

That’s even more of an advantage when it comes to AI, where any budget spent on runtime is budget unavailable for tokens and API calls. Efficient Java runtimes also allow you to write efficient, scalable agents: something that’s going to become more important as agents become useful for many more tasks than just writing code. If you have hundreds or thousands of AI agents running in your enterprise, you want them to use as few resources as possible.

“Now that it’s easy to write code with AI, there is really no excuse to not use languages that provide the best runtime performance and great ecosystem.”

You get those same advantages for creating AI features because that Java ecosystem now includes first-class AI frameworks and SDKs for connecting to LLMs. LangChain4j and Spring AI simplify integrating AI models into Java applications and using powerful patterns like RAG while working with familiar Java frameworks; agentic frameworks like embabel add agentic flows to Spring and the JVM. Building chatbots, generating images, summarizing text or creating search services: Java is ready for generative AI as well as the machine learning and big data workloads developers are already familiar with.

Java’s traditional strength in integration is even more relevant as you start adding more AI features to applications, whether that’s MCP or large-scale event-driven architectures.

Julien Dubois, JHipster author and lead of Microsoft’s Java Developer Relations team, tells The New Stack that you need context for AI.

“You want tools, you want databases, you want MCP servers, and Java is great for that because Java has always been great for integrating with third-party solutions,” Dubois says.

The language constructs and the ecosystem of libraries and frameworks for Java make it a good fit for AI, he argues: “it’s not at all difficult for developers to add intelligent capabilities to their existing applications.”

Harder to write, easier to read

Java’s explicitness and verbosity turn into a strength when it comes to using AI code assistants, because it’s easier to read and understand the Java code they suggest adding to your critical, highly optimized enterprise apps.

When an AI agent is doing most of the typing, language choice should come down to readability, argues Borges: “not the shortest, smallest piece of code.”

“AI writes the code, the developer can understand and read their code, and the runtime runs the best performance possible for that particular code with an amazing ecosystem around it.”

Java’s popularity and the convergence around a small set of frameworks have given LLMs plenty of open-source Java code to learn from. The latest versions of AI coding tools like GitHub Copilot, Claude Code, and Cursor are extremely good at writing Java code, Dubois notes. “If you’re a Java developer, you’re probably using frameworks such as Spring Boot, Hibernate, or Elasticsearch: because of the available training data, GitHub Copilot will be excellent at writing this code for you.”

That’s not just useful for adding AI features. The combination of efficient coding assistants and code that developers can quickly understand and review makes it far less expensive to modernize older Java applications you want to update and migrate to the cloud. “Big enterprises have a lot of older Java applications, which have been complicated to update as they require large budgets, and developer motivation is quite low on those projects. AI can drastically reduce that effort and make those projects possible,” Dubois says.

Why AI is replacing some jobs faster than others #AI

0x41414141 — Tue, 07 Apr 2026 20:22:58 +0000

Data-rich industries are the most prone to being disrupted by AI.
Data-poor industries are scrabbling to digitize in order to enjoy the benefits of AI – but experiencing greater friction with established practices.
Employees and job-seekers must focus on opportunities that combine tech capabilities with human judgement and business needs.

Everyone talks about how AI is going to take jobs, and that's somewhat true. We're worried about AI job takeover – yet we don't fully understand how it's going to happen, when it's going to occur or how much time we have to prepare.

Most people believe that task complexity determines whether AI adoption will be fast or slow. This assumption is completely wrong. As the Sun Tzu saying goes: "Know yourself and your enemies and you would be ever victorious." So you need to understand the following dynamics.

How AI works

The mechanics of AI are still somewhat unclear, even to experts. However, we do know one important thing: it learns.

Have you ever gotten shocked while fiddling with electronics? I have, and that experience taught me never to do it again. We humans learn from experience.

AI models learn from data. An AI model with limited data is like a toddler. In contrast, one with extensive data is like an experienced grandfather.

The data paradox

Which is harder: driving a car or writing code? Most would say coding. Yet in AI development, the opposite seems true.

Large Language Models (LLMs) are relatively new. Before ChatGPT, few associated AI with chatbots – more likely the Terminator. The LLM era began around 2013-2014 with neural networks like word2vec. Autonomous driving, on the other hand, began in the 1980s. In 1987, Ernst Dickmanns’ team had a Mercedes-Benz van drive itself at 96 km/h on a German highway using computer vision.

Despite this massive head start, autonomous vehicles still lag behind LLMs. While ChatGPT performs reliably across countless scenarios, AI drivers remain inconsistent.

But why? Companies like Tesla and Waymo have invested billions. Yet if a new company wanted to enter the space – even with brilliant engineers and unlimited funding – they’d still need thousands of hours of diverse driving data. Some accident types are so rare they’re nearly impossible to train for.

Meanwhile, LLMs train on the entire internet – a data-rich playground. Hence, AI is more likely to replace coders than drivers not because coding is easier, but because the data is easier to come by.

AI is basically like that kid in college who had access to all the old exams and study guides. Of course they're going to crush the test compared to someone scrambling with incomplete notes from a few lectures.

That's exactly what's happening in the job market. Some industries are drowning in useful data that AI can learn from. Others? They're working with scraps. The numbers are pretty stark. Industries with tons of good data could have AI adoption rates around 60-70%. Meanwhile, sectors without much data might struggle with less than 25%.

How to adapt

For job-seekers, the key insight is that industry boundaries are blurring faster than job categories are crystallizing. Rather than focusing solely on traditional career paths, look for roles that bridge domains – positions that combine human judgement with AI capabilities, or that translate between technical systems and business needs.

Reframe your skills inventory around adaptability, not just expertise. Instead of listing what you've done, identify how you learn, solve problems and work with new systems. Employers increasingly value people who can navigate uncertainty and integrate new tools into existing workflows. Your ability to successfully adopt the last major change at your workplace may matter more than your proficiency with any specific software.

Target the friction points. Every organization implementing AI faces the same challenge: how to make sophisticated technology work within messy human systems. Look for roles in charge of management, training or process optimization within AI-adopting companies. These positions often don't require deep technical knowledge, but do need people who understand how organizations actually function when theory meets practice.

Consider the “last mile” opportunities in your current industry. While tech hubs generate headlines, every sector needs people who can bridge the gap between AI capabilities and local implementation. Healthcare systems need someone who understands both patient care and data analytics. Manufacturing plants need operators who can work alongside automated systems. Often, your existing industry knowledge combined with basic AI literacy creates more opportunities than starting from scratch in a completely new field.

[Axios Hacked] How .npmrc Can Protect Your Node.js Projects from Supply Chain Attacks??

0x41414141 — Wed, 01 Apr 2026 19:29:25 +0000

The .npmrc file is a configuration file used by the npm (Node Package Manager) command-line tool. It allows you to customize various settings related to how npm behaves while managing packages and dependencies for your Node.js projects. This file is usually placed in your project's root directory and can contain various configuration options. Here are some common use cases and configurations that can be set in the .npmrc file:

Registry Configuration: You can use the .npmrc file to specify the registry where npm should fetch packages from. For example, you might want to use a private registry or a mirror of the default registry.

Scoped Package Configuration: If you’re using scoped packages (packages with a name that starts with @scope/), you can set configuration options specific to those packages.

Authentication: You can use the .npmrc file to store authentication tokens or credentials for private registries or services.

Proxy Configuration: If you’re behind a corporate proxy, you can configure npm to work through the proxy by setting proxy-related options in the .npmrc file.

Cache Control: You can control how npm caches packages by specifying cache-related settings in the .npmrc file.

Global vs. Local Configuration: You can have different .npmrc files for global and local settings. Global settings are applied to all projects on your system, while local settings are specific to the project's directory.

Package Installation Behavior: You can configure npm to save packages as dependencies or devDependencies by default when you run npm install. You can also control whether npm automatically saves packages to your package.json file.

Security & Supply Chain Protection (Recent Axios Incident): In light of recent incidents like the axios ecosystem concerns and broader supply chain attacks, the .npmrc file can help mitigate risks by enforcing stricter controls. For example, you can lock your registry to trusted sources, disable scripts using:

ignore-scripts=true

or enforce exact versions:

save-exact=true

This reduces the chances of pulling compromised or tampered packages and ensures more predictable and secure installs.

Here’s an example of what a simple .npmrc file might look like:

registry=https://registry.npmjs.org/
loglevel=warn
save-exact=true

In this example, the file sets the default registry to the npm public registry, sets the log level to “warn,” and configures npm to save exact versions of packages.

Keep in mind that some options set in the .npmrc file might be overridden by command-line arguments when using npm. You can find a comprehensive list of configuration options in the npm documentation.

It’s important to handle sensitive information like authentication tokens carefully, as the .npmrc file is often stored in version control systems alongside your code. You should consider using environment variables or other secure methods to manage sensitive information.

Note: I am a developer available for freelance work. If you need help building secure, high-performance web tools or optimizing your development workflow, feel free to reach out. I'd be happy to work with you.

The Illusion of the One-Day Build: Why I Deleted Half My AI-Generated Landing Page

0x41414141 — Tue, 31 Mar 2026 09:33:06 +0000

I built a SaaS landing page. Then I deleted half of it.

Not because it was ugly. It wasn't. It had all the classics: scrolling logo clouds, "10,000+ brands served", glowing testimonials from Sarah Chen ("This changed everything for our team!"), a pricing table with a Free tier and an Enterprise plan, urgency banners, floating CTAs, and a "Loved by builders worldwide" section.

The problem? Every single one of those was made up.

The Trap of Plausible Fiction

I built Mayasura, an open-source brand-building platform, using AI sub-agents to go from zero to shipped in a day. The sub-agents did exactly what I asked: build a professional SaaS app. They used standard SaaS landing page templates. They filled in plausible-looking social proof. They made the numbers sound reasonable.

The app was real. The code worked. The landing page was pure fiction.

After the sprint, I ran a principles audit and created a strict rule: No fake data, anywhere. Not in the landing page, not in demo content, and not as placeholder analytics.

Then I went in to enforce it.

Auditing the Artificial: What I Actually Deleted

Here is exactly what I had to strip out from a single landing page:

6 AI-generated testimonials: Complete with names, job titles, and photos (they were icons, but the intent was clear).
4 fake stats: Claims like "10,000+ brands", "50,000+ products", "1,000,000+ visitors", and "99% satisfaction".
The LogoCloud component: A row of made-up company logos.
The SocialProof component: A generic "brands worldwide" counter.
The BeforeAfter cost comparison: Pitted against competitors whose pricing I had never even checked.
The ComparisonTable: A feature list versus "Competitor A" and "Competitor B" with completely fabricated checkmarks.
Pricing tiers: A Free, Pro, and Enterprise layout for a project that has absolutely no monetization plan.
The UrgencyBanner: Screaming "Limited early access!" for a product with no waitlist.
Excessive CTAs: FloatingCTA, ScrollCTAModal, and StickyMobileCTA resulting in three variants of "Start Free Trial" for a tool that doesn't have a trial.
Fake avatar row: Sitting right in the hero section.
"No credit card required": A completely unnecessary banner for a product you self-host.

Total: 1,462 lines deleted from the landing page alone.

Need a developer who values clean architecture and honest design?
If you are looking for a high-performance website, an SEO-optimized platform, or a custom multi-tool web application built with modern frameworks like React.js, Next.js, Node.js, Astro, Svelte, Tailwind CSS, TypeScript, and wordpress let's talk. [Contact me to create a better website today.]

The Underlying Issue with AI Seed Data

Here is the actual lesson. When you use AI to build a product fast, it will fill every blank with plausible fiction. That is exactly what you are asking it to do.

The AI doesn't know you have zero users yet. It models "professional SaaS product" and generates professional SaaS copy to match. It isn't lying; it is just pattern-matching. The problem is that the established pattern includes social proof, and social proof is made up by definition when you are on day one.

The dangerous part is how convincing it looks. The testimonials were not obviously fake. "Sarah Chen, Brand Manager at Elevate Creative" sounds real. The stats used round numbers exactly like real statistics do.

If I had deployed this without the audit, I would have shipped a technically real product paired with a fraudulent pitch page.

Replacing Fabrication with Fact

The new rule was simple: only put things on the page that are true.

The massive numbers became 16 templates, 34 fonts, 16 color palettes, and 7 consumer channels. Those are exact numbers pulled directly from the codebase. I can defend each one.

The pricing section became a self-hosting guide. If there is no pricing, don't pretend there is. Show a quick-start terminal block instead.

The testimonials became nothing. There are no testimonials yet, and an empty section is infinitely more honest than a fake one.

The competitor table became a FAQ with honest answers. For example, "Is this production-ready?" is answered with, "It's an open-source tool at v3.2. You can run it in production if you're comfortable self-hosting and maintaining it."

The urgency banners disappeared entirely because there is no urgency.

Cleaning Up the App Architecture

While I was under the hood, I applied the exact same principle to the application itself:

Random numbers in analytics charts: Replaced with deterministic fallbacks clearly labeled "Sample Data". If you don't have real data yet, say so.
Random view counts on blog posts: Removed. Blog views show zero until real people read them.
Lighthouse scores: Labeled "Estimated" because they were run in a local dev environment rather than production.
Analytics "realtime visitors": Labeled "(estimated)" because the number is approximated from session tracking rather than a pixel-perfect count.

Labeling estimates as estimates, showing zeroes when you have no data, and deleting claims you cannot back up isn't just about ethics. It is about maintenance. Every fake testimonial is a lie you have to remember. Every fake stat is a number you will eventually have to update or quietly leave stale. The longer you wait to clean it up, the more the technical debt compounds.

Catching Silent Bugs: The Slug Collision Fix

The audit also caught something non-obvious: there was no slug collision protection.

If two users created brands with the exact same name, they would get the identical slug. A path like /site/alpine-coffee would become ambiguous, and the last write would silently overwrite the previous one.

The fix was straightforward:

export async function generateUniqueSlug(
  base: string,
  existingIds: string[] = []
): Promise<string> {
  const sanitized = sanitizeSlug(base);

  // Check reserved slugs
  if (isReservedSlug(sanitized)) {
    return generateUniqueSlug(`${sanitized}-brand`, existingIds);
  }

  // Check for collisions, append -2, -3, etc.
  let candidate = sanitized;
  let counter = 2;
  while (await slugExists(candidate, existingIds)) {
    candidate = `${sanitized}-${counter}`;
    counter++;
  }
  return candidate;
}

I also added a reserved slug list: admin, api, dashboard, site, shop, blog, chat, login, signup, health. These are all real routing paths that someone could easily accidentally claim as a brand slug.

Leaving this unchecked would have caused real bugs, not just embarrassment.

Lessons for Prompting and Shipping

What would I do differently next time? I would tell the AI up front: "This is a new project with no users. Do not generate social proof, testimonials, pricing, or fake statistics. Use real numbers from the codebase only."

That single prompt constraint would have saved a 2.25-hour cleanup session. The AI follows rules if you give it rules. The mistake was letting it fill in the blanks with default SaaS tropes.

The flip side is that AI-generated fake data is highly predictable and easy to find. It follows clear patterns like round numbers ending in 000 or identically structured testimonials. You can simply grep for them, delete them, and replace them with reality.

The audited version of the site ships smaller, loads faster, and I am not nervous about anyone reading it carefully.

The Final Product: Quiet, Short, and True

After the great deletion, the landing page features:

A hero section with real copy explaining exactly what the platform does.
Six feature cards describing tools that actually exist in the code.
A "How It Works" section detailing three verifiable steps.
A template showcase displaying real screenshots of included templates.
A "Deploy Anywhere" section listing verified platforms like Railway, Vercel, Docker, and standard self-hosting.
A FAQ with honest answers.
A footer with a GitHub link and an MIT license badge.

No pricing. No testimonials. No urgency. No fake logos.

It is shorter. It is quieter. Everything on it is true. Mayasura is an open-source brand-building platform. The code is on GitHub under MIT. There is no waitlist, no pricing, and no enterprise tier. It is just a Next.js app you can self-host.

Need a developer who values clean architecture and honest design?
If you are looking for a high-performance website, an SEO-optimized platform, or a custom multi-tool web application built with modern frameworks like Astro, Svelte, Tailwind CSS, and TypeScript, let's talk. [Contact me to create a better website today.]