DEV Community: MXI.Studio

Complexity is not really the problem. Unclear complexity is. A system can be sophisticated and still remain understandable if its boundaries are intentional, its dependencies are visible, and every moving part has a reason to be there.

MXI.Studio — Thu, 01 Oct 2026 14:16:15 +0000

MXI.Studio

Sep 29

Programming Through Composition

#productivity #performance #softwareengineering #development

4 min read

The web became easier to navigate as it became harder to reach people without going through a few large discovery systems. That trade-off is shaping how independent content gets written, found, and valued.

MXI.Studio — Thu, 01 Oct 2026 14:15:35 +0000

MXI.Studio

Oct 1

How I think around the modern WEB

#web #ai #productivity #seo

4 min read

How I think around the modern WEB

MXI.Studio — Thu, 01 Oct 2026 14:15:17 +0000

It was not that the Internet got worse; it grew. This is an important point. Search engines had to deal with an enormous amount of information. Platforms had to decide what should appear first. Recommendation systems had to sort through more content than any person could realistically process.

So ranking systems became necessary. That gave us the web we have now.

Search is no longer just about matching a phrase to a page. Visibility depends on a much larger set of signals: structure, authority, engagement, links, relevance, behavior, freshness, and increasingly how easily a system can understand what a page is actually about.

If you publish anything online today, you are working inside that environment whether you like it or not. That changes how we build for the web.

You are not publishing into empty space

A webpage used to feel almost self-contained. You wrote something, indexed it, linked to it, and people eventually found it. Now every page exists inside several systems at once.

The search engine should be able to comprehend it. The social platform may choose to show a preview of it. The recommendation engine may categorize it. The AI application can gather information from it. But a person has to read it. Those are very different consumers and if the structure is weak, the content can become difficult for all of them to interpret.

That does not mean writing for robots. It means understanding that modern web content needs enough structure to survive the systems around it.

Structure has become important like never before

If a page is about something specific, make that obvious. Use a clear title. Use headings that actually describe the sections. Keep related ideas together. Link concepts where the relationship matters. Make authorship and sourcing visible. Give the page enough semantic structure that someone arriving halfway through can still understand what they are looking at.

A lot of this sounds basic. But it is easy to lose when the focus shifts entirely toward optimization. The web is full of pages that technically perform well while being surprisingly difficult to read. That is usually the wrong trade.

Work with discovery systems, not against

SEO is not the problem. Recommendation systems are not the enemy. Metadata isn't inherently broken. These are tools used to organize enormous systems. The problem starts when the content becomes subordinate to them.

You can usually feel when this happens. A title exists primarily for clicks. The introduction repeats the query five times. Every section is stretched because someone decided a certain word count performs better. Internal links exist because a metric says they should, not because the relationship is useful.

At that point, the page may be optimized for discovery while becoming worse at delivering the thing being discovered. I think the better approach is to work with the system without surrendering the structure of the content to it.

Write for the person who actually came looking for the information. Then make the topic easy to classify. make the page easy to parse. make the relationships explicit.

Build context, not just pages

The web is already full of isolated pages. What it often lacks is continuity between them. If you publish around a subject, do not treat every page as an independent object. Connect them.

If one article depends on another concept, link it. If a topic has prerequisites, make them visible. If there is a deeper technical explanation somewhere else, point to it. If a claim depends on a source, preserve that chain.

This does two things at once. It makes the content easier for people to navigate, and it gives external systems better signals about how the information fits together. In other words, the structure becomes useful on both sides.

The goal is not more content

The web is already overflowing with content, so the real challenge is making a single page contribute meaningfully to the broader topic. This shifts the focus from chasing raw exposure to ensuring a piece of writing is clear, logical, and seamlessly integrated into the larger discussion. While getting noticed remains necessary, reaching an audience means little if the material lacks the surrounding structure to give it real weight.

A page that is clear, structured, well-linked, and actually useful has a better chance of surviving changes in search, platforms, and distribution because its value is not tied to one mechanism. Content and structure are not separated. Without structure, SEO, metadata and such, there is no discovery, but without good content, there is no return. So simultaneously, we need to focus on good content while optimizing the structure for good visibility.

That is probably the practical lesson of the modern web. We have to work with the systems that organize information now. But we should not confuse those systems with the purpose of the content itself.

The better the web gets at ranking information, the more important it becomes to build information that is actually worth ranking.

This is the topic of my original article The Modern Landscape of the Web

AI may be most useful in software not as an architect, but as an integration accelerator. If it becomes easier to understand and connect mature tools, compositional architectures become much more practical.

MXI.Studio — Wed, 30 Sep 2026 13:46:37 +0000

MXI.Studio

Sep 29

Programming Through Composition

#productivity #performance #softwareengineering #development

4 min read

There is a point where a framework stops simplifying the project and the project starts reorganizing itself around the framework. That is usually when the abstraction has become more expensive than the problem it was meant to solve. Read the article

MXI.Studio — Tue, 29 Sep 2026 23:31:44 +0000

MXI.Studio

Sep 29

Programming Through Composition

#productivity #performance #softwareengineering #development

4 min read

Programming Through Composition

MXI.Studio — Tue, 29 Sep 2026 12:42:30 +0000

> There is a point where a framework stops simplifying the project and the project starts reorganizing itself around the framework. That is usually when the abstraction has become more expensive than the problem it was meant to solve.

I’ve built enough software at this point to notice a pattern. A lot of the time, I don’t actually need a huge framework. I need a few things that work well.

A parser. A networking layer. A renderer. Some math. A bit of concurrency. Maybe a native layer where performance matters. That’s usually it. And those are usually readily available in forms that are optimized, lightweight and sometimes even cross platforms by default.

The problem starts when I go looking for a “complete solution” and end up pulling in something that solves far more than I actually need or just solves a lot of things I don’t need. Then the project starts growing around the framework.

Now I need the framework’s lifecycle. Its abstractions. Its configuration system. Its plugin model. Its dependency tree. And when I finally hit something the framework does not fit cleanly, I add a wrapper.

Then another library. Then some glue code. Then a workaround. Somewhere along the way, I’m no longer solving the original problem.

I’m solving the framework against my needs in order to be able to use it to solve my initial problem.

It changed my entire approach to building systems. Start with capabilities, not with frameworks. The question I now ask myself is straightforward; What does this project actually need to do? Not; what framework should I use? But rather what capabilities are required? This difference is tiny, but the result is monumental.

If I need JSON parsing, I can use a mature JSON library. If I need networking, I can use a focused networking library. If I need linear algebra, I can use a math package that already does it well. If I need rendering, I can pick a renderer that does not also try to own my application.

The goal is not to avoid dependencies. It is to choose dependencies that have a clear job. Stitch the pieces together yourself. Once the core capabilities are there, I build my application around them.

I would rather write a relatively small amount of glue code that I fully understand than adopt a large abstraction that forces the project into a shape I did not choose. In doing so, I gain something that is very important to me: architectural ownership.

I know what each component does. I know why it is there. I know where the boundaries are. And if something breaks, I have a much better idea of where to look. That makes debugging, refactoring, and optimization much easier. It makes sliding in error management a lot easier.

But don’t rewrite everything either. This is not a “build everything from scratch” argument. That usually makes no sense. If a mature library solves the problem well, use it. And there is a lot of powerful libraries out there waiting to be used.

The interesting part is knowing when the abstraction has become larger than the problem. Sometimes using a giant generalized system means spending more time adapting it than it would take to build the exact behavior needed from a few smaller pieces. It also means taking the risk to not be able to fix issues, if these issues are unrelated to your code.

That is usually the point where I reconsider the architecture.

We need to ask ourselves if we are saving time by using this tool, or are we now maintaining a translation layer between the tool and my project? If the answer is the second one, composition often starts looking a lot better.

Separate contexts when it helps

Another thing I stopped assuming is that the whole application needs to live in one environment. Some parts are better low-level. Performance-sensitive logic, simulation, rendering, memory-heavy work, networking internals.
Other parts are better high-level. Scripting, orchestration, experimentation, interfaces, automation. So, a perfectly reasonable setup might be:

C++ for the performance-critical core
Python for orchestration
bindings between the two

The binding layer is not a trick. It is an architectural element. You get control where we need it and speed of development where iteration matters.

AI makes this easier than it used to be

This is also the area in which I have found AI to be of genuine use to me. Not in making decisions about the architecture. That still needs my judgment. But AI is superbly well suited for minimizing the annoying stuff that surrounds composition. Understanding an unfamiliar API. Writing a binding. Explaining a dependency. Generating boilerplate. Finding the right integration point. Translating documentation into something immediately useful.

Those tasks used to add enough friction that reaching for one large ecosystem was often easier. That trade-off is changing. It is becoming much more practical to assemble a system from focused, mature components and still move quickly.

At this point, I prefer to start small. Find the exact capabilities the system needs. Pick proven components for those capabilities. Build the project-specific layer myself. If needed rebuild some layers and own them. Keep the boundaries visible. Use multiple languages when they actually help. And avoid adopting complexity just because it comes bundled with something popular.

That is what I mean by programming through composition.

Not fewer tools. Better-chosen tools. Not less complexity at all costs. Complexity that I can actually see, understand, and control. That is the version I keep coming back to now.

The license is rarely the real cost. The real cost is everything around it: integrations, support, security, dependencies, maintenance, migration, and the complexity added to every future change. That weight accumulates quietly.

MXI.Studio — Thu, 24 Sep 2026 19:08:03 +0000

MXI.Studio

Sep 23

When Enough Is Not Enough: The Work-Enablement Problem

#productivity #tooling #performance #softwareengineering

5 min read

A system can work and still need to be decommissioned. That’s the part we often miss. “Stable” does not always mean useful, and “still used” does not always mean necessary. I explored that idea in the full article "When Enough Is Not Enough"

MXI.Studio — Thu, 24 Sep 2026 19:06:39 +0000

MXI.Studio

Sep 23

When Enough Is Not Enough: The Work-Enablement Problem

#productivity #tooling #performance #softwareengineering

5 min read

Most companies don’t have a technology shortage. They have an accumulation problem. One more platform, integration, exception. Eventually, the stack becomes harder to understand than the problem it was meant to solve. That’s the work-enablement problem

MXI.Studio — Thu, 24 Sep 2026 19:05:13 +0000

MXI.Studio

Sep 23

When Enough Is Not Enough: The Work-Enablement Problem

#productivity #tooling #performance #softwareengineering

5 min read

A technology can work perfectly fine and still no longer deserve its place. No incidents. No outages. Users still use it. The system is “stable.” And yet it may be costing the organization far more than anyone realizes. The problem, it is redundant.

MXI.Studio — Wed, 23 Sep 2026 13:03:56 +0000

MXI.Studio

Sep 23

When Enough Is Not Enough: The Work-Enablement Problem

#productivity #tooling #performance #softwareengineering

5 min read

When Enough Is Not Enough: The Work-Enablement Problem

MXI.Studio — Wed, 23 Sep 2026 12:47:30 +0000

We often have a fairly simple reaction when a problem appears in an enterprise: we need a new tool.
A need comes up, we look for a solution, choose a platform, implement it, connect it to the rest, and then move on. A few months later, another need appears, so we add another tool. Then another platform because the one we already have does not do exactly what we want. Then a local solution because the central system is too slow. Then a new technology that promises to simplify everything.

At some point, we end up with a lot of things.

Applications, platforms, systems, cloud services, integrations, databases, specialized tools, legacy solutions, and temporary solutions that somehow became permanent.
And the question I keep coming back to is this: does all of this actually help us work better?
This is where the work-enablement problem starts for me.
I am not talking about individual productivity or how teams organize their work. I am talking about something more fundamental: the capabilities the enterprise makes available so that work can actually happen.

Do we have the right tools? Are they useful? Are they relevant to the real needs? Can people use them properly? Do they work together? Are they reliable? Can they grow with the enterprise? And does what they cost still make sense compared with what they provide?

The harder question is whether we still need everything we have accumulated.
A system can be technically excellent and still provide very little value. A platform can be officially adopted and barely used. Two tools can have different vendors and architectures while providing almost the same capability. A system that was relevant five years ago can become a constraint today, even if it still works perfectly.
This is where I think we often look at the wrong part of the problem.
We talk a lot about digital transformation, architecture, cloud, AI, modernization, and new platforms. What we talk about much less is what all of those decisions leave behind. Enterprises are usually very good at adding technology. Removing it is much harder.

Every new capability gets added to something that already exists. Every exception creates another dependency. Every temporary solution has a chance of becoming permanent. Every specialized tool looks reasonable at the moment it is introduced.
The problem appears later, in the accumulation.

The problem of accumulation

This is what interests me in this topic: not simply the technology itself, but the technological weight of the enterprise. Everything it has to maintain, connect, secure, finance, document, support, and evolve simply because those technologies are now part of the environment.

Take a very ordinary example.

An enterprise already has three solutions covering roughly the same family of features. None of them is completely bad. None of them is totally useless. Each team has its reasons. One is historical, another is more modern, and the third was chosen because it handled one particular need very well.
On paper, this can still look acceptable.

But behind it, the enterprise is paying for three contracts, maintaining three integrations, managing three security models, supporting three environments, and following three different product roadmaps. Every change becomes a little more complicated.
So, the cost is not only the licenses.
The cost is also the enterprise's ability to carry all of that complexity. This is a form of debt.

Debt is not only technical

We usually talk about technical debt in relation to old code, aging architectures, or technical decisions that become expensive over time. I think the view has to be broader than that.
There is also debt created by platform accumulation, duplicate capabilities, integrations, exceptions, and technologies that we keep because they are still used "somewhere."

This type of debt does not always look dramatic. Sometimes it is an annual invoice. Sometimes it is an architecture that has become difficult to understand. Sometimes it is a project that needs five technical teams instead of two, or a migration that takes eighteen months because new dependencies keep appearing.
Sometimes it is simply a change that should have been easy and somehow is not anymore.

Over-engineering creates a similar problem.
We often associate a more sophisticated solution with a better solution. More features, more flexibility, more configuration, more integrations, more possibilities. But additional capability is not always useful capability. Sometimes we build something extremely powerful for a problem that only needed something reliable and good enough. And once that solution is in place, we still have to maintain everything that came with it.

This becomes even more important now because adding technology has become so easy. With SaaS, cloud, and now AI, a new capability can sometimes be introduced almost immediately. That is a good thing. It gives teams more options and makes experimentation much easier. But it also makes accumulation easier.

One solution for one need. Another because it does one part better. Another because one team prefers it. Another because it is already used somewhere else.
Eventually, several products provide capabilities that are close enough that we have to ask why we still have all of them.
I do not think the answer is simply to reduce the number of tools.

Some redundancy is useful. Some technological diversity is necessary. Some complex architectures are justified.
The point is to understand whether the complexity still makes sense.

That means asking whether a technology is still useful, relevant, used, integrated properly, reliable enough, scalable enough, and worth what it costs. It also means asking something enterprises are not always comfortable asking: Do we still need to keep it?

We are usually good at evaluating how a technology enters the environment. We evaluate vendors, capabilities, costs, security, integration, and implementation.
We know how to buy, deploy, connect, and migrate. Rationalizing and retiring technologies is harder, especially when every system still seems to serve something.

Where we are going

I want to look at technical debt, over-engineering, platform accumulation, redundant solutions, hidden integration costs, underused technologies, standardization problems, aging systems, reliability, security, scalability, and technology retirement.
The idea is not that enterprises should have less technology.
Some need more. Some technologies are essential. Some redundancy is justified. Some complexity is unavoidable.
What matters is whether that complexity stays proportional to the value it enables.

That is where I place work-enablement.
Not in the quantity of technology we have, or how modern the architecture looks, but in whether the overall environment gives the enterprise what it actually needs in a way that is useful, integrated, reliable, and sustainable.
The question is not simply whether we have enough technology.
It is whether we still have the right technology environment to enable the work.

And sometimes, the answer is to add something.
Sometimes, it is to remove something.

To follow more content, read the original article.

the original article. When Enough Is Not Enough.

Productivity is often perceived as just doing more. Literature says otherwise. I wrote about it in my article. What do you think productivity refers to?

MXI.Studio — Fri, 18 Sep 2026 22:27:19 +0000

MXI.Studio

Sep 9

Productivity Is Not What We Think It Is

#productivity #performance #development #management

3 min read