There is no single "right" framework in Go. And that's perfectly fine.
Some need a lightweight router with nothing extra. Others need a heavy MVC with ORM, authorization, and code generation. The choice depends not on trends but on the task: microservices, highload, monolith, MVP, or enterprise.
A Go framework doesn't make code "magically better." It just takes care of the routine: routing, JSON handling, validation, logging. That saves development hours and reduces the number of bugs. Plus, a new developer will get up to speed faster if the project uses a popular tool.
Next up β a quick overview of the most common options. What works for which task, how they differ, and where they might shine or just get in the way. A detailed breakdown is available in the article on our website: https://evrone.com/blog/best-golang-frameworks-2025
Some need a lightweight router with nothing extra. Others need a heavy MVC with ORM, authorization, and code generation. The choice depends not on trends but on the task: microservices, highload, monolith, MVP, or enterprise.
A Go framework doesn't make code "magically better." It just takes care of the routine: routing, JSON handling, validation, logging. That saves development hours and reduces the number of bugs. Plus, a new developer will get up to speed faster if the project uses a popular tool.
Next up β a quick overview of the most common options. What works for which task, how they differ, and where they might shine or just get in the way. A detailed breakdown is available in the article on our website: https://evrone.com/blog/best-golang-frameworks-2025
π₯4π3β€1
Large streaming services have one particular characteristic: you almost can't just "rewrite" them properly. While developers change the architecture, the product keeps living β people watch shows, streams go live, new releases come out, marketing runs special projects. Everything has to keep working simultaneously.
Our work with mΠΎre tv and CTC's platforms wasn't a "we built a cool new service from scratch" story. It was about a rather painful transition from a heavy legacy system to an architecture that could be properly maintained going forward.
The project had a lot of what typically accumulates over the years at large media platforms: a Rails monolith, tightly coupled services, shared databases, a large number of integrations, and infrastructure that was already difficult to change without risking breaking something.
Projects like this always highlight the gap between "ideal" architecture and real-world development. In theory, everyone loves rewriting from scratch. In practice, you're more often carefully disassembling a functioning airplane mid-flight: https://evrone.com/cases/ctc-moretv
Our work with mΠΎre tv and CTC's platforms wasn't a "we built a cool new service from scratch" story. It was about a rather painful transition from a heavy legacy system to an architecture that could be properly maintained going forward.
The project had a lot of what typically accumulates over the years at large media platforms: a Rails monolith, tightly coupled services, shared databases, a large number of integrations, and infrastructure that was already difficult to change without risking breaking something.
Projects like this always highlight the gap between "ideal" architecture and real-world development. In theory, everyone loves rewriting from scratch. In practice, you're more often carefully disassembling a functioning airplane mid-flight: https://evrone.com/cases/ctc-moretv
π6π₯6β€5
Flutter or native? Each approach has its fans who are ready to argue until they're blue in the face. But outside of internet battles, the choice usually comes down to three things: how much time to launch, how much money for maintenance, and what kind of experience users need.
In the cards β our version of the pros and cons, without trying to declare a winner.
And now a question for those who actually write code or make decisions: what do you choose for new projects and why? Not abstractly "in general," but in practice. Your actual opinion is what matters.
In the cards β our version of the pros and cons, without trying to declare a winner.
And now a question for those who actually write code or make decisions: what do you choose for new projects and why? Not abstractly "in general," but in practice. Your actual opinion is what matters.
β€5π₯4π3
Sometimes the best redesign is the one users barely notice.
When the Mindbox team started updating their product, it quickly became clear: they needed to change more than just the screens.
As the platform grew, it became increasingly difficult to maintain and develop. So the work began with the frontend architecture.
Evrone developers joined the Mindbox team and took part in building a new product structure. Individual parts of the system gained the ability to evolve independently, without constant intersections and limitations.
In parallel, the team reworked key user scenarios:
β reports;
β the customer data page;
β recommendation management tools.
This case clearly shows that the most impactful improvements for a business often start where users never look.
With architectural decisions.
Those decisions ultimately determine how fast the product can continue to grow.
π Full case study: https://evrone.com/cases/mindbox
When the Mindbox team started updating their product, it quickly became clear: they needed to change more than just the screens.
As the platform grew, it became increasingly difficult to maintain and develop. So the work began with the frontend architecture.
Evrone developers joined the Mindbox team and took part in building a new product structure. Individual parts of the system gained the ability to evolve independently, without constant intersections and limitations.
In parallel, the team reworked key user scenarios:
β reports;
β the customer data page;
β recommendation management tools.
This case clearly shows that the most impactful improvements for a business often start where users never look.
With architectural decisions.
Those decisions ultimately determine how fast the product can continue to grow.
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π₯5π3π1
Rust in 2026: rewrite or not?
This question doesn't come up during a retro, and it's not a joke. It comes up when the pain becomes too obvious. When production crashes again due to a segfault. When you've spent three days hunting down yet another concurrency bug. When a new developer looks at the codebase and says: "I have no idea what's going on here."
Rust solves specific problems. Memory safety, data races, low-level performance. That's why Amazon, Google, and Microsoft choose it. Not because it's trendy, but because it works.
But migrating to it only makes sense in one case: when you have a real problem that Rust can solve and your current stack can't.
Segfaults, memory leaks, complex multithreaded code, legacy that's impossible to maintain β that's when it's worth considering.
If your project is stable, brings in money, is written in something familiar, and your team doesn't know Rust β don't invent a problem that doesn't exist.
Rust is a tool for systems programming, not a silver bullet.
Anyone already made the switch? How did it go? Worth it or not?
And if you want to dive deeper β together with Evrone's DevRel Grigory Petrov, we wrote an article breaking down exactly when migrating to Rust is truly justified, and when it's better to leave well enough alone. Link here: https://evrone.com/blog/migrating-to-rust-in-2025
This question doesn't come up during a retro, and it's not a joke. It comes up when the pain becomes too obvious. When production crashes again due to a segfault. When you've spent three days hunting down yet another concurrency bug. When a new developer looks at the codebase and says: "I have no idea what's going on here."
Rust solves specific problems. Memory safety, data races, low-level performance. That's why Amazon, Google, and Microsoft choose it. Not because it's trendy, but because it works.
But migrating to it only makes sense in one case: when you have a real problem that Rust can solve and your current stack can't.
Segfaults, memory leaks, complex multithreaded code, legacy that's impossible to maintain β that's when it's worth considering.
If your project is stable, brings in money, is written in something familiar, and your team doesn't know Rust β don't invent a problem that doesn't exist.
Rust is a tool for systems programming, not a silver bullet.
Anyone already made the switch? How did it go? Worth it or not?
And if you want to dive deeper β together with Evrone's DevRel Grigory Petrov, we wrote an article breaking down exactly when migrating to Rust is truly justified, and when it's better to leave well enough alone. Link here: https://evrone.com/blog/migrating-to-rust-in-2025
π₯3β€1π1
We're probably all a little tired of posts about AI writing code.
So here's a case study about a different kind of use.
In a music streaming service with a multi-million user base, we integrated AI into the development process. It helps with code review, documentation, automated testing, and other routine tasks that usually eat up the team's time.
At the same time, we optimized the platform's most resource-intensive services. As a result, infrastructure resource consumption dropped by roughly 25%, while performance increased by nearly 20%.
This is what mature AI adoption looks like: not instead of engineers, but alongside them β automating the routine and freeing up time for truly complex challenges.
Case study: https://evrone.com/cases/tune
So here's a case study about a different kind of use.
In a music streaming service with a multi-million user base, we integrated AI into the development process. It helps with code review, documentation, automated testing, and other routine tasks that usually eat up the team's time.
At the same time, we optimized the platform's most resource-intensive services. As a result, infrastructure resource consumption dropped by roughly 25%, while performance increased by nearly 20%.
This is what mature AI adoption looks like: not instead of engineers, but alongside them β automating the routine and freeing up time for truly complex challenges.
Case study: https://evrone.com/cases/tune
π₯3π€©3π2
An internal AI assistant doesn't have to be a separate application.
We built Mimir β an assistant that lives in Telegram and helps with everyday tasks: answering questions, searching for information, maintaining conversation context, writing Python scripts for automation, and using them to handle routine operations.
In the article, we share how Mimir grew from an internal tool into a full-fledged AI assistant powered by OpenClaw, how much its infrastructure costs, what tasks it already handles, and why a familiar messenger turned out to be more convenient than a new interface.
If you're interested in practical applications of AI agents β not just another demo β take a look: https://evrone.com/blog/mimir
We built Mimir β an assistant that lives in Telegram and helps with everyday tasks: answering questions, searching for information, maintaining conversation context, writing Python scripts for automation, and using them to handle routine operations.
In the article, we share how Mimir grew from an internal tool into a full-fledged AI assistant powered by OpenClaw, how much its infrastructure costs, what tasks it already handles, and why a familiar messenger turned out to be more convenient than a new interface.
If you're interested in practical applications of AI agents β not just another demo β take a look: https://evrone.com/blog/mimir
π₯4β€2π2
A powerful GPU cluster doesn't guarantee efficient use of compute resources.
In our new case study, we share how we helped a research lab solve the challenge of sharing GPUs for ML workloads.
One of the most interesting aspects of the project was the choice between MIG and time-slicing. Both approaches allow multiple tasks to run on a single GPU, but they work differently and aren't suitable for every type of hardware.
In this case study, we cover:
β’ the differences between MIG and time-slicing;
β’ how we built an MLOps platform on Kubernetes with GitOps;
β’ why we chose open source and avoided vendor lock-in.
If you work with Kubernetes, ML infrastructure, or GPU clusters, this breakdown might be useful: https://evrone.com/cases/relab
In our new case study, we share how we helped a research lab solve the challenge of sharing GPUs for ML workloads.
One of the most interesting aspects of the project was the choice between MIG and time-slicing. Both approaches allow multiple tasks to run on a single GPU, but they work differently and aren't suitable for every type of hardware.
In this case study, we cover:
β’ the differences between MIG and time-slicing;
β’ how we built an MLOps platform on Kubernetes with GitOps;
β’ why we chose open source and avoided vendor lock-in.
If you work with Kubernetes, ML infrastructure, or GPU clusters, this breakdown might be useful: https://evrone.com/cases/relab
π4π₯2
Vibe coding: a revolution in development or a new way to accumulate technical debt?
Not long ago, putting together a working MVP required a team of developers and weeks of work.
Today, all you need is an idea, an AI tool, and a couple of evenings.
But that's where things get interesting.
AI writes great code. Sometimes even really great code. But who's responsible for architecture? Security? Scalability? Making sure that six months from now, this project doesn't need to be rewritten from scratch?
Vibe coding is reshaping the very approach to development:
βͺοΈ developers become more like architects and reviewers;
βͺοΈ prototyping speed increases manifold;
βͺοΈ the line between an "idea" and a "working product" becomes thinner.
But the big question remains open: where does acceleration end and technical debt begin?
We've broken down how development is changing in the age of AI and why vibe coding isn't just a trend β it's a new phase in the way humans and code interact.
Article: https://evrone.com/blog/vibecoding-in-2026
What do you think? Is vibe coding the future of development, or just a shiny wrapper for old problems? Share your thoughts in the comments.
Not long ago, putting together a working MVP required a team of developers and weeks of work.
Today, all you need is an idea, an AI tool, and a couple of evenings.
But that's where things get interesting.
AI writes great code. Sometimes even really great code. But who's responsible for architecture? Security? Scalability? Making sure that six months from now, this project doesn't need to be rewritten from scratch?
Vibe coding is reshaping the very approach to development:
But the big question remains open: where does acceleration end and technical debt begin?
We've broken down how development is changing in the age of AI and why vibe coding isn't just a trend β it's a new phase in the way humans and code interact.
Article: https://evrone.com/blog/vibecoding-in-2026
What do you think? Is vibe coding the future of development, or just a shiny wrapper for old problems? Share your thoughts in the comments.
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π₯7π4
When a monolith becomes a bottleneck, starting from scratch is not always the answer.
One of the hardest challenges in software development is modernizing a system that is already running at scale and has become critical to the business.
In this case, we worked with a platform processing millions of orders for a large restaurant network. As data volumes grew, performance issues started to appear: searching through order history could take 25β30 seconds.
The solution wasnβt a complete rewrite. Instead, we evolved the system step by step: optimized data processing, moved search capabilities to Elasticsearch, started breaking down the monolith, and built a more flexible architecture for future scaling.
The result: search response time dropped to 250β300 milliseconds, while the platform gained the foundation needed for further growth.
A good example of why legacy systems are not always something you need to replace. Sometimes, they are systems you need to evolve in the right direction.
More technical case studies and engineering stories:
https://evrone.com/
One of the hardest challenges in software development is modernizing a system that is already running at scale and has become critical to the business.
In this case, we worked with a platform processing millions of orders for a large restaurant network. As data volumes grew, performance issues started to appear: searching through order history could take 25β30 seconds.
The solution wasnβt a complete rewrite. Instead, we evolved the system step by step: optimized data processing, moved search capabilities to Elasticsearch, started breaking down the monolith, and built a more flexible architecture for future scaling.
The result: search response time dropped to 250β300 milliseconds, while the platform gained the foundation needed for further growth.
A good example of why legacy systems are not always something you need to replace. Sometimes, they are systems you need to evolve in the right direction.
More technical case studies and engineering stories:
https://evrone.com/
π₯4π3