But once the app grows, the same question keeps coming back: should I use Context API or Redux Toolkit?

I've seen developers argue about this a lot. Some say Context is enough. Some say Redux is the proper way. Honestly, I don't think one answer fits every project. It depends on the size of the app, the type of state, and how often that state changes.

Context API is great when the state is simple

React Context is built into React — that's already a big advantage. No extra package, no additional setup, no separate store configuration. For simple shared data, Context works nicely: logged-in user info, the selected theme, language preference, basic app settings, small global values.

In these cases Context feels clean and lightweight. You create a provider, wrap your app, and use the value wherever you need it. Simple. And for many apps, that's enough.

But Context can become messy when the app grows

The problem starts when Context is used for everything — authentication state, cart state, filters, API data, form state, notification state, dashboard data. Suddenly you have multiple providers. State updates become harder to track. Components re-render more than expected. Debugging becomes annoying. And the app slowly becomes difficult to reason about.

That's where Context starts showing its limits. Not because Context is bad, but because it was never meant to become a full state management system for complex apps.

Redux Toolkit feels better for serious app state

Redux had a reputation for being heavy earlier — too much boilerplate, too many files, too many concepts. But Redux Toolkit changed that a lot. Now it feels much cleaner. You create slices, write reducers more easily, and manage async logic with createAsyncThunk or RTK Query. And most importantly, you get a predictable state flow.

That matters when your app gets bigger — ecommerce carts, dashboards, complex filters, user permissions, API-driven state, multi-step forms, large admin panels. In these cases Redux Toolkit feels more organized.

The biggest advantage of Redux Toolkit is structure

This is why I personally prefer Redux Toolkit for bigger applications. It gives structure. You know where state lives. You know how it changes. You can trace actions, debug with Redux DevTools, and separate logic into slices.

That makes the application easier to maintain, especially when multiple developers are working on the same project. Context can work, but Redux Toolkit gives discipline. And in real projects, discipline matters.

So which one do I prefer?

For small apps, I prefer Context API. It's simple, fast, no extra dependency, and no need to over-engineer.

But for medium or large applications, I prefer Redux Toolkit — especially when the app has multiple screens, API data, user roles, cart logic, filters, or shared state used in many places. Because once state becomes important to the product, I want something predictable, something easier to debug, something that doesn't become messy after six months. For that, Redux Toolkit feels safer.

My personal rule

I usually think about it like this:

• If the state changes rarely and is simple, Context is fine.
• If the state changes often, affects many components, or needs debugging, Redux Toolkit is better.

That simple rule avoids a lot of confusion. Because the real mistake isn't choosing Context or Redux — it's using the wrong tool for the wrong kind of state.

The short version

• Context API is best for simple shared values like theme, auth user, or language.
• Redux Toolkit is better for larger apps with complex and frequently changing state.
• Context is lightweight but can become messy when overused.
• Redux Toolkit gives better structure, debugging, and predictable state flow.
• My preference: Context for small apps, Redux Toolkit for serious production apps.

Written by the TechKis team — an AI-first engineering studio. techkis.tech

That's usually the moment the real question shows up: should I just use a database queue, or is it time to move to RabbitMQ?

I've thought about this a lot while working on backend-heavy applications. And honestly, the answer usually depends less on what's "better" technically and more on what your application actually needs. Both work. They just solve slightly different problems.

Database queues are often the easiest place to start

For many applications, database queues are enough — especially in the beginning. If you're using something like Laravel queues backed by a jobs table, setup is straightforward. No extra infrastructure, no separate message broker, no queue server to manage. Everything stays inside the application environment, which makes development fast. And that simplicity matters.

For things like sending emails, OTP delivery, notifications, invoice generation, and small background jobs, database queues usually do the job perfectly fine. We sometimes overcomplicate things too early.

But as traffic grows, the database starts doing too much

This is where things begin to feel heavier. Your database is already handling user requests, inserts, updates, joins, reporting queries, and search filters. Then queue jobs get added on top, and suddenly the same DB is also responsible for processing background tasks.

That works for a while — until the queue volume increases. Then you start noticing jobs piling up, slower processing, delayed workers, increased DB load, and retries creating even more pressure. At that point the queue system itself becomes part of the performance conversation. And that's usually where RabbitMQ enters the picture.

RabbitMQ feels built for messaging from day one

RabbitMQ approaches the problem differently. Instead of storing jobs inside the database, it acts as a dedicated message broker. Its only job is moving messages between producers and consumers efficiently.

That separation helps a lot. Your app pushes a message, RabbitMQ handles routing, and workers consume it independently. The database stays focused on data; queue processing stays focused on messages.

That separation becomes valuable when your workload gets heavier — high-volume notifications, real-time event processing, order pipelines, multiple worker services, microservice communication, and external integrations happening continuously. This is where RabbitMQ starts feeling stronger.

The trade-off: RabbitMQ gives power, but adds complexity

This part matters. RabbitMQ is powerful, but it isn't free. You're adding more infrastructure, which means server setup, deployment config, monitoring, worker management, connection handling, retry strategy, dead-letter queues, and failure recovery.

Compared to database queues, that's definitely more to manage. So if your application only sends the occasional background email, RabbitMQ may actually be unnecessary overhead. And that's okay — not every project needs distributed messaging infrastructure.

So which one would I choose?

If I'm building a small to medium application, I'd usually start with database queues. Fast setup, easy maintenance, works well, and less infrastructure to think about.

If I'm building a high-volume system with heavy background processing, I'd lean toward RabbitMQ — especially when thousands of jobs are processed regularly, multiple services communicate with each other, queue throughput matters, the database load is already high, and reliable async messaging is becoming core to the platform.

My personal take

I don't see RabbitMQ and database queues as competitors. I see them as stages. Database queues are a great starting point. RabbitMQ becomes valuable when scale and complexity demand it.

A lot of teams jump to advanced infrastructure too early, and sometimes the simpler solution is the better engineering choice. At the same time, once your queue load becomes part of your bottleneck, a dedicated message broker can make a huge difference.

So for me the decision usually comes down to one question: is the queue just supporting the app, or is async processing becoming part of the system itself? That answer usually makes the choice clear.

The short version

• Database queues are simple, fast to implement, and great for many applications.
• RabbitMQ is built specifically for messaging and handles high-volume async processing better.
• Database queues work well for emails, notifications, and smaller background tasks.
• RabbitMQ becomes useful when scale, throughput, and service-to-service communication grow.
• If your current queue isn't causing problems yet, database queues may still be the right choice.

Written by the TechKis team — an AI-first engineering studio. techkis.tech

So when I came across Google Antigravity, I was curious. At first I assumed it was just another editor with AI autocomplete built in. But the more I explored it, the more it felt like Google is thinking about development in a slightly different way. And that's what caught my attention.

(Quick context: Antigravity launched in public preview, free for individuals, and runs on macOS, Windows and Linux. It's built around Gemini 3 Pro but also supports Anthropic's Claude Sonnet 4.5 and OpenAI's GPT-OSS.)

It doesn't feel like just another IDE

Most IDEs still work the same way. You open files, write code, run terminal commands, debug something, fix it, repeat. AI usually sits beside that workflow as an assistant — maybe it completes code, maybe it writes a function, maybe it explains an error.

Antigravity feels like it's trying to move AI deeper into the workflow itself. Not just helping with code, but helping with the entire task. That difference sounds small, but the more you read into it, the bigger it gets.

The most interesting part: agent-first development

This is the part that stood out most to me. Google describes Antigravity as agent-first development, and honestly that explains it pretty well. Instead of AI helping with a line of code, the idea feels closer to AI helping with the job you're trying to complete.

For example: creating a new feature, updating multiple files together, debugging a failing implementation, running checks, and reviewing the generated output. There's even a dedicated "Manager" surface where you can spawn and watch several agents work in parallel, each with direct access to the editor, terminal and browser.

It feels less like autocomplete and more like having another developer inside the project with context. That's a pretty interesting direction.

It still feels familiar, which I like

One thing I noticed immediately: Google doesn't seem to be forcing developers into a completely new way of working. And that matters, because most developers already have workflows they're comfortable with — editor, terminal, Git, browser, docs, local environment. That workflow already works.

Antigravity feels more like an extra layer on top of that rather than asking you to replace everything. Personally, I think that makes adoption much easier. Good developer tools usually fit into your workflow. They don't fight against it.

The real test will be production projects

This is where I think every AI coding tool eventually gets tested. Demos always look clean. Real codebases usually aren't.

Production projects are messy. There's old code, temporary fixes, unclear naming, large folders nobody wants to touch, and half-finished documentation. That's where tools like this either become genuinely useful — or become something you open once and never come back to.

What I'm curious about is how Antigravity handles those real-world environments, because that's where developers actually spend their time.

My take after exploring it

I don't think Antigravity is trying to replace developers. And honestly, I don't think that's what most developers want anyway.

What feels more realistic is this: AI handles more of the repetitive movement around coding, and developers spend more time on decisions — architecture, reviewing, thinking through product and implementation. Less context switching, less jumping between tabs, less repetitive setup work. More actual building. That's the part I find interesting.

The short version

• Google Antigravity feels bigger than a normal AI code editor.
• It's focused on agent-first development, not only autocomplete.
• The goal seems to be helping developers complete tasks, not just write code.
• It still feels close to existing workflows, which makes it easier to approach.
• The real test will be how useful it becomes inside real production codebases.

Written by the TechKis team — an AI-first engineering studio. techkis.tech

And honestly, I understand why. AI tools have improved fast, much faster than most people expected. You type a prompt and it writes code. You paste an error and it explains the issue. You ask for a component and it builds one, sometimes surprisingly well. Enough to make people stop and think: if AI can already do this, what happens next?

My short answer? No, but things are definitely changing

I don't think AI is replacing developers anytime soon. But I do think it's changing how development happens, and that's an important difference.

Because writing code is only one part of software development. Real development includes a lot more than syntax: understanding product requirements, deciding architecture, debugging strange production issues, handling trade-offs, thinking about performance, working with teams, and understanding what users actually need.

Most of that isn't "write code." It's judgment, context, experience, and sometimes even instinct. That part is much harder to automate.

AI is very good at generating code

That part is obvious now. Need a React component? AI can generate one. Need help with a SQL query? Usually fast. Need a boilerplate API structure? Done in seconds. Need test cases? Also possible.

For repetitive work, AI is genuinely useful — sometimes very useful. And if I'm honest, I use it too. A lot of developers do now. Not because we can't code without it, but because it speeds things up.

But generating code isn't the same as building software

This is where the conversation usually gets oversimplified, because code generation looks impressive. But software engineering isn't just output.

Real projects are messy. Codebases are messy. Requirements change halfway through development. Clients change priorities. Legacy code exists. Production bugs happen only on one server at 2 AM. Naming conventions make no sense. Documentation is incomplete. And business logic lives partly in code... and partly inside someone's head.

That's real software development. And AI still struggles there, because context matters more than code.

The role of developers is shifting

If anything, AI is changing where developers spend their time. Less time writing repetitive code. More time reviewing. More time designing systems. More time asking better questions. More time making decisions.

AI can generate options. Developers still decide which option is correct — and often which one is safest, fastest, cleanest, or easiest to maintain six months later.

That decision-making layer is still deeply human.

What I think actually happens next

My guess? AI becomes part of every developer workflow — kind of like Git, or Stack Overflow, or package managers. At first optional, then normal, then expected.

Developers who learn to use AI well will probably move faster, prototype faster, debug faster, and ship faster. Not because AI replaces them, but because it removes some of the repetitive friction around development. And the human part — thinking, deciding, building — becomes even more important.

The short version

• No, I don't think AI will replace developers anytime soon.
• AI is very good at generating code, especially repetitive code.
• But software engineering is much more than writing syntax.
• Context, architecture, debugging, business logic, and decision-making still need developers.
• AI will likely become a daily development tool, not a replacement for engineers.

Written by the TechKis team — an AI-first engineering studio. techkis.tech

That changed fast.

In 2026, AI tools aren't sitting on the side anymore. They're part of the daily workflow. And honestly, even if someone tells you they don't use AI while coding, there's a good chance they already have without thinking much about it. Autocomplete, debugging help, test generation, documentation, code review... AI has quietly become part of software development itself.

It started with autocomplete, but it's no longer just that

For a lot of developers, the entry point was simple. GitHub Copilot finishing a function. ChatGPT helping debug an error. Cursor rewriting some repetitive code. At first it felt like productivity support: useful, but optional.

Now it feels bigger. AI tools have moved well beyond "suggest the next line." They help developers generate components, write API routes, explain unfamiliar code, refactor old files, write unit tests, generate SQL queries, review pull requests, and summarize documentation.

And increasingly, they help complete entire tasks, not just snippets. That's a very different shift.

Developers spend less time typing and more time thinking

This is probably the biggest change I've noticed. Coding used to involve a lot of repetition: writing similar API controllers, repeating validation rules, creating DTOs, building CRUD pages, setting up routes, writing test scaffolding. Useful work, but repetitive.

AI handles a surprising amount of that now. So developers spend more time on the things that actually need a human: architecture decisions, reviewing logic, debugging edge cases, thinking through product requirements, and deciding what should be built in the first place.

Less typing. More decision-making. And honestly, that feels closer to real engineering.

The tools are improving faster than most people expected

A year ago, AI could write code... sometimes, and often with mistakes. Now tools like Cursor, GitHub Copilot, Claude Code, Windsurf, and Google Antigravity feel far more capable.

Not perfect. They still get things wrong and still need review. But they're noticeably more useful than before, especially when they understand your project's context. And that context changes everything, because generating code is easy. Generating code that actually fits your project is the hard part. That's exactly where the newer tools are improving quickly.

But AI hasn't replaced developers, and probably won't anytime soon

This question comes up constantly: "Will AI replace software engineers?" Personally, I don't think that's the right question.

AI is very good at generating. Developers are still needed for deciding. And software is full of decisions: trade-offs, business logic, edge cases, system design, security, performance, understanding users. AI can help with the code, but it still doesn't understand product thinking the way an experienced developer does. At least not yet.

Right now it feels more like this: AI speeds up development, developers still guide it. And that distinction matters.

My personal take

I think AI coding tools are becoming like Git, or Stack Overflow, or package managers. At one point each of those felt optional. Then they became normal, the kind of thing you just expect every developer to use.

AI coding tools are heading the same way. Not because developers are being replaced, but because the workflow is changing. The developers who adapt will probably move faster, ship faster, debug faster, and learn faster, while spending less time stuck on repetitive work.

That doesn't make software development easier. It just removes some of the friction around building software. And honestly, that's a pretty exciting shift to watch.

The short version

• AI coding tools are now part of the daily developer workflow.
• They've moved beyond autocomplete into debugging, testing, refactoring, and full task completion.
• Developers spend less time writing repetitive code and more time thinking through architecture and logic.
• Tools like Copilot, Cursor, Claude Code, and Google Antigravity are evolving very quickly.
• AI isn't replacing developers, but it's definitely changing how software gets built.

Written by the TechKis team — an AI-first engineering studio. techkis.tech