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 – 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 – 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 – 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

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 – automating the routine and freeing up time for truly complex challenges.

Case study: https://evrone.com/cases/tune

An internal AI assistant doesn't have to be a separate application.

We built Mimir – 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 – not just another demo – take a look: https://evrone.com/blog/mimir

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:

• the differences between MIG and time-slicing;

• how we built an MLOps platform on Kubernetes with GitOps;

• 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

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 25–30 seconds.

The solution wasn’t 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 250–300 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/

Can you publish 5–6 videos a week without a team of scriptwriters?

We decided to find out.

That's why we built Screenwriter – an AI-powered assistant that analyzes massive amounts of information, identifies the most relevant topics, and helps create scripts and interview briefs.

In its first two months, Screenwriter helped us produce around 25 video episodes. One of our new shows also surpassed 240,000 views in less than six weeks.

But AI isn't replacing creators. It takes care of the most time-consuming parts of pre-production, while every script is still reviewed, refined, and finalized by the host.

In our latest article, we explain how Screenwriter works, why we built it around multiple AI models, and how it has transformed the economics of content production.

Read the full story here: https://evrone.com/blog/screenwriter

How do you migrate dozens of services to a new infrastructure without taking down production?

In the Wayo* case study, we had one month to do it.
The task wasn't just about moving infrastructure from an old Kubernetes cluster to a new one. The services also had to be adapted to an updated internal engineering platform.

The most interesting part was the dependencies. Each service had its own set of configurations, databases, Redis and other KV stores, brokers, internal services, and external APIs. Moreover, some connections weren't obvious from the original configuration and were only discovered during analysis.

So for each service, we defined a unified preparation process and a set of readiness criteria: startup, health checks, accessibility within the cluster, connections to storage systems and APIs, logs, and key user scenarios.

In production, the old and new versions ran in parallel for some time. First we validated the new version, then switched traffic over to it, and only after a monitoring period did we decommission the old one. If something went wrong, we had the option to roll back.

By the end of the fourth week, all services scheduled for migration had been moved to the new platform and launched in the new cloud environment. The user-facing application continued running without interruption.

We covered the dependency audit, the migration process, and the traffic switchover in detail in our case study: https://evrone.com/cases/wayo 

And what stage of migration usually turns out to be the trickiest for you?

*The company name and certain identifying details have been changed because the project was completed under an NDA.