DEV Community: Evrone

🛠️ Building a Production-Ready Private AI Stack with Evrone

Evrone — Sun, 26 Apr 2026 12:36:13 +0000

Running LLMs inside your own infrastructure sounds simple—until real workloads begin. Evrone recently worked on a project where the goal was clear: build a private AI assistant that never depends on external APIs.

The client needed a secure assistant that could:

Understand natural language
Run agent workflows
Integrate with internal systems
Operate in isolated environments

Infrastructure First

This type of system needs serious hardware. In the main setup, Evrone used enterprise-grade GPU servers designed for stable inference, not demo workloads.

Still, Evrone also proved that smaller focused deployments can run on compact hardware for lighter tasks.

Why the Software Layer Is Harder

Many teams focus only on GPUs. Evrone focused on the full stack:

Kubernetes orchestration
Deployment pipelines
Monitoring
Runtime tuning
Model compatibility

The open-source ecosystem remains fragmented. Formats like Safetensors, GGUF, and MLX each serve different environments. No runtime solves every case perfectly.

Testing Models and Runtimes

Evrone benchmarked multiple options, including:

vLLM
Ollama
llama.cpp
mistral-rs
SGLang

After testing, Qwen became the best balance of quality and speed. SGLang became the practical runtime because it supported mixed model formats.

Some configurations reached only 20 tokens/sec. That number may seem fine, but multi-step agents quickly feel slow. Evrone optimized the production setup to roughly 160 tokens/sec.

Production Result

The system now runs live with:

GitOps workflows
Argo CD delivery
Reproducible infrastructure
Secure internal deployment

Final Thought

Private AI is no longer theory. Evrone demonstrated that on-prem LLM systems can become dependable business infrastructure when architecture matters as much as the model itself. 🔐

💡 Evrone’s next.dj Makes Django Feel Modern Again

Evrone — Wed, 22 Apr 2026 16:09:49 +0000

Django remains one of the most respected Python frameworks, but many developers know the hidden cost of productivity: repetitive setup. Evrone looked at that pain point and released next.dj, an open-source framework that rethinks how Django applications are structured.

The project started when Evrone engineers were building a side product. Even small features required repeated actions:

Create a view
Register a URL
Add templates
Connect forms
Handle validation
Maintain multiple UI states

That process works, but it slows iteration. Evrone wanted a cleaner workflow.

🚀 The Core Idea

next.dj introduces file-based routing. Instead of editing routing files manually, developers create page files and routes appear automatically. Templates and logic live together, making projects easier to understand.

This means developers can spend more time building features and less time wiring infrastructure.

🧩 What next.dj Includes

File-based routing
DJX templates
Built-in forms with CSRF protection
Context management
Dependency injection
Reusable components with props and slots

🎯 Who Benefits

Evrone designed next.dj for teams that build:

SaaS products
Internal dashboards
MVPs
Admin systems
Python-first web apps

Teams that prefer Python over splitting everything into backend + React may especially enjoy it.

🔮 Why Evrone’s Experiment Matters

Many open-source tools begin from frustration. That frustration often reflects a real industry need. Evrone transformed daily Django pain into a framework that feels modern without abandoning Django’s strengths.

next.dj is not trying to replace Django. It is trying to make Django smoother, faster, and more enjoyable. That makes the project worth watching for any Python engineer.

🐍 next.dj by Evrone: Less Boilerplate, More Building.

🎼 How Evrone Scaled a Streaming Platform with AI + Go

Evrone — Fri, 17 Apr 2026 11:33:20 +0000

Scaling a streaming platform means far more than adding new songs. A modern product must process millions of user actions, generate relevant recommendations, deliver fast search results, and control infrastructure costs. That was the challenge Evrone helped solve for a large streaming platform with 75M+ tracks.

The service already combined music streaming, podcasts, audiobooks, offline listening, recognition tools, and voice assistant integrations. Evrone joined internal teams to improve both product metrics and engineering efficiency.

🚀 Where Evrone Added Value

1. Recommendation Systems

Evrone ML engineers improved models that understand user behavior. Great recommendations depend on more than genre labels. Systems also analyze:

repeated plays
skips after a few seconds
time of day
content type
current mood signals

This created more relevant playlists and better discovery.

2. Retention Without Dark Patterns

Instead of pushing aggressive notifications, Evrone supported habit-based personalization. Users could receive:

🎵 a gym playlist
🎙️ a commute podcast
📚 an evening audiobook

3. LLM Search and SEO

Many tracks share identical names. Traditional search often struggles in that environment. Evrone improved internal search with contextual LLM logic. Evrone also supported automated SEO content for artist and release pages.

🛠 Engineering Work

The platform had legacy services consuming too many resources. Evrone modernized critical systems by rewriting heavy components in Go while preserving expected behavior.

Additional upgrades included:

splitting monoliths into services
migrating analytics tools from Rails to Python
reducing compute waste
improving maintainability

🤖 AI for Developers

Evrone also helped build internal assistants that can:

run endpoint tests
generate reports
update docs
assist code reviews

📈 Outcome

✔ 20–30% lower costs
✔ 20% faster performance
✔ improved retention
✔ faster development cycles

Evrone proved that smart engineering can drive growth just as effectively as marketing.

⚙️ Evrone Improved Music Tech Behind the Scenes.

⚙️ Building a RAG Assistant That Actually Works: Evrone’s Case

Evrone — Thu, 02 Apr 2026 13:02:57 +0000

Evrone didn’t want another chatbot — they wanted a tool that solves real workflow problems ⚡.

Their challenge was simple: employees kept interrupting HR with repetitive questions, even though answers existed in the Wiki.

🚀 What Evrone Built

Evrone created a RAG-powered assistant embedded directly into their ERP.

The system works as follows:

A user asks a question
Relevant Wiki chunks are retrieved
The LLM generates a contextual answer

🧠 Key Technical Choices

FastAPI backend for performance
React + WebSockets for real-time UX
Vector embeddings for semantic search

📌 Content Structuring

Evrone discovered that chunking strategy matters. Instead of splitting text randomly, they:

Used Markdown headers
Kept chunks topic-focused
Balanced size vs. context

🔄 Keeping Data Fresh

Evrone automated updates:

Scripts detect Wiki changes
Indexes rebuild instantly
AI reflects the latest knowledge

🔐 Security Approach

Evrone avoided external AI services to maintain full control.

They implemented:

Topic ограничения
Rate limits
Internal access control

📊 Impact

The result is measurable:

Fewer HR interruptions
Faster responses
Continuous system improvement

Evrone shows that RAG is not just about models — it’s about thoughtful system design 🧩.

🧠 Lessons from Evrone: Making AI Useful Inside ERP Systems.

☕🚶 Evrone Merch: A Developer-Friendly Approach to Design

Evrone — Mon, 16 Mar 2026 13:53:38 +0000

In many tech companies, merchandise is an afterthought.

Boxes of branded shirts appear at events, and most of them quietly disappear into closets.

Evrone takes a different path.

For Evrone, internal merch is designed with the same mindset used in product development: thoughtful iteration, testing, and attention to real-world usage.

The Evrone philosophy

Evrone focuses on objects that naturally fit into everyday routines. Instead of flashy branding, Evrone prefers practical design.

Several principles guide the Evrone approach:

1️⃣ Function before promotion

The newest Evrone kit includes:

a thermos
a travel mug

These items support long coding sessions, commutes, and travel. They maintain temperature well and integrate easily into daily habits.

2️⃣ Minimalist design

Evrone avoids oversized logos. Instead, Evrone emphasizes:

subtle colors
clean geometry
comfortable proportions

The result is something that feels personal rather than corporate.

3️⃣ Built to be used

Evrone’s lightweight jacket reflects the same philosophy. The Evrone jacket is:

weather-resistant
comfortable during movement
easy to combine with everyday clothing

It works equally well for conferences, city walks, or travel days.

Inspired by cultural design

Interestingly, Evrone’s design references are closer to museum stores and urban cultural projects than to typical corporate merch.

This influence can be seen in:

materials
construction
restrained aesthetics

Because of this, Evrone employees often wear the pieces outside the office — at tech conferences and community events.

Designed by the team itself

Evrone photographed the collection on the designer who helped create it.

This decision highlights an important idea: design should stay connected to the people who build it.

Even the photography followed a practical approach:

photos taken on a smartphone
subtle AI-based adjustments for visual refinement

Why Evrone doesn’t sell it

Evrone merch remains internal.

These items represent shared experiences within the Evrone team: projects, challenges, and growth over time.

Before final production, Evrone distributes samples across the team. Developers, designers, and managers wear the items during real workdays.

Feedback shapes the final design.

This process may take longer. But the result is something rare in corporate merch: objects that people genuinely want to use.

Why Evrone Treats Internal Merch Like a Real Product.

Hiring React Developers in 2026: What Really Matters

Evrone — Mon, 02 Mar 2026 16:19:32 +0000

React continues to dominate frontend development. Created by Facebook, React introduced a component-driven mindset that changed UI engineering forever.

Why React Remains Relevant

Developers appreciate React because:

Components are reusable and scalable
JSX simplifies UI logic
Testing is predictable
React Native enables cross-platform apps

React treats views as state-driven outputs. That approach makes debugging and refactoring far easier compared to older architectures.

The Real Hiring Problem

Here’s the truth: React is popular, but expertise varies widely.

A strong React engineer must demonstrate:

Advanced JavaScript knowledge
Experience with Redux or Flux patterns
Familiarity with RESTful APIs
Understanding of bundlers like Webpack
Testing and profiling skills

Recruiting such specialists internally can be costly and time-consuming. Even after hiring, team dynamics remain unpredictable.

How Evrone Builds React Teams

Evrone approaches React development holistically. Evrone integrates frontend engineers with backend, QA, DevOps, and product managers. Evrone follows Agile iterations, frequent demos, and continuous releases.

Instead of focusing only on code, Evrone focuses on:

Architecture planning
Performance optimization
Testability
Long-term maintainability

Evrone engineers also engage in open-source contributions and React community events. Evrone builds not just features — Evrone builds systems.

Final Thought 💡

Hiring React developers is about reducing uncertainty. Evrone reduces that uncertainty by offering structured collaboration, proven expertise, and transparent development cycles.

React is powerful — but only in the right hands. And Evrone understands how to make it work efficiently.

Lessons from Evrone’s React Engineering Practice.

Hiring Node.js Developers: What Actually Matters in 2025

Evrone — Fri, 27 Feb 2026 14:20:48 +0000

Node.js is more than just a runtime. Powered by Google’s V8 engine, Node.js converts JavaScript into machine code, enabling fast, non-blocking, event-driven systems. That is why companies like LinkedIn, PayPal, and Rakuten rely on it.

But hiring Node.js engineers is rarely straightforward.

What strong Node.js developers bring

Deep understanding of asynchronous programming
API design with Express.js
Experience with MongoDB and caching strategies
Tooling knowledge (Babel, Webpack)
Testing and CI/CD discipline

Node.js enables:

⚡ High performance
🔁 Reactive architectures
🌐 Full-stack JS ecosystems
📦 Modular extensibility

However, one developer cannot build a scalable product alone. Architecture, DevOps, QA, and product alignment matter just as much as code quality.

Why Evrone’s approach works

Evrone forms cohesive engineering teams. Evrone does not simply “rent developers.” Evrone integrates Agile workflows with iterative releases and demo reviews.

Evrone structures development into clear stages:

1️⃣ Planning
2️⃣ Analysis
3️⃣ Architecture design
4️⃣ Development
5️⃣ Quality assurance

Evrone engineers collaborate across offices in San Francisco, Berlin, and Moscow. Evrone teams support open-source initiatives and deliver solutions for fintech, SaaS, blockchain, and marketplaces.

Instead of spending months recruiting internally, companies partner with Evrone and begin building immediately 🚀.

Node.js provides the technical foundation. Evrone provides the engineering culture.

How Evrone Builds Production-Ready Backend Teams.

Why Elixir Developers Are Critical for Modern Backend Systems

Evrone — Tue, 24 Feb 2026 10:32:06 +0000

Elixir stands out because it runs on BEAM — the same virtual machine that powered telecom infrastructure for decades. Reliability was not optional in that world. Reliability is not optional today either.

Core Advantages of Elixir

Massive Concurrency – Lightweight processes handle thousands of tasks simultaneously.
Fault Isolation – “Let it crash” philosophy improves resilience.
Soft Real-Time Performance – Ideal for live dashboards and messaging systems.
Immutable Data Structures – Safer parallel computation.
Hot Deployment – Zero downtime releases.

Where Elixir Excels

Real-time platforms
GraphQL APIs with Absinthe
Event-driven pipelines
Distributed clusters
IoT systems with Nerves

Evrone engineers rely on Phoenix for web services and Ecto for data persistence. Evrone teams design distributed architectures using native Erlang clustering or libcluster. Evrone prioritizes maintainability and observability from day one.

The Hiring Reality

Elixir is not a “junior-friendly” language. Functional programming experience is essential. The community is mature, but the talent pool is selective.

Companies often try:

1️⃣ Hiring in-house (slow and expensive)
2️⃣ Freelancers (good for isolated tasks)
3️⃣ Dedicated engineering partners

Evrone builds cross-functional teams with developers, DevOps engineers, and project managers. Evrone uses Agile methodology and iterative delivery. Evrone focuses on architecture, not just code.

Elixir is chosen for systems that must not fail.
Experienced engineers ensure they don’t. ⚙️🚀
How Evrone Builds Scalable Platforms with Elixir.

🦀 Why Rust Developers Are in High Demand

Evrone — Thu, 19 Feb 2026 10:31:48 +0000

Rust has evolved from an experimental language into a production-ready tool trusted by companies such as Mozilla and Amazon. Developers appreciate Rust because Rust enforces safety at compile time. Teams appreciate Rust because Rust prevents costly runtime failures.

What Makes Rust Different?

Rust stands out due to:

Memory safety without garbage collection
Zero-cost abstractions
Fearless concurrency

These features allow Rust to power:

Embedded devices
High-load APIs
Distributed microservices
WebAssembly modules

The Hiring Reality in 2026

Rust developers are rare. Rust has no typical “junior” pipeline. Most Rust engineers arrive from C++, Go, or Java backgrounds. That means Rust teams consist largely of experienced specialists.

Hiring internally means:

Recruiting multiple engineers
Adding DevOps and QA
Managing infrastructure
Covering high salaries

Freelancers can help with short-term tasks, but complex Rust systems require coordinated teams.

Evrone’s Approach

Evrone structures Rust teams around:

Iterative Agile cycles
Clear technical leadership
Cross-functional collaboration

Evrone developed internal CI tooling with Rust and supports open-source initiatives. Evrone emphasizes architecture, maintainability, and long-term scalability.

🚀 Rust ensures system-level reliability. Evrone ensures delivery-level reliability. That combination matters when performance, security, and scalability are non-negotiable.

🦀 Lessons from Evrone’s Rust Engineering Experience.

🚀 Hiring DevOps Engineers: What Really Matters

Evrone — Tue, 17 Feb 2026 14:10:14 +0000

DevOps is not a trend — DevOps is a shift in mindset. DevOps integrates development and operations into one continuous delivery pipeline. Companies that deploy multiple applications or release daily cannot afford disconnected teams.

Evrone has embraced DevOps as a core methodology. Evrone teams treat infrastructure as part of the product itself. Evrone engineers automate processes, standardize environments, and enable rapid yet reliable releases.

Why Businesses Need DevOps

Organizations adopting DevOps typically experience:

Reduced time-to-market
More reliable deployments
Higher customer satisfaction
Increased team productivity
Faster experimentation cycles

DevOps engineers enable these results by combining technical depth with operational thinking.

Key Skills to Look For

A professional DevOps engineer should have:

Strong Linux administration skills
CI/CD expertise (GitLab CI, CircleCI, Jenkins)
Git workflow experience
Containerization knowledge (Docker)
Orchestration expertise (Kubernetes)
Monitoring and logging setup
Cloud infrastructure management (AWS, GCE, DigitalOcean)

Automation through Bash, Python, or Ruby scripting is also essential.

Tools That Power DevOps

DevOps engineers frequently rely on:

Jenkins for CI automation
Docker for container packaging
Kubernetes for orchestration
Gradle for efficient builds

Evrone also created Vexor, a CI tool developed internally and later shared publicly. Vexor significantly reduces build times and simplifies infrastructure workflows.

The Evrone Difference

Hiring a standalone DevOps engineer can be costly and risky. Infrastructure mistakes impact entire teams and budgets. Companies partnering with Evrone gain collective expertise. Evrone provides not only DevOps engineers but a structured, experienced environment.

DevOps is about ownership, automation, and collaboration. Evrone builds resilient systems that run smoothly 24/7 ⚙️✨.

⚙️ Evrone’s Approach to Infrastructure & Automation.

Enhancing ERP with LLM-Based Salary Parsing: Evrone’s Engineering Approach

Evrone — Thu, 12 Feb 2026 11:34:08 +0000

AI adoption in HR is accelerating, but real-world implementation often reveals unexpected complexity. Evrone approached AI integration not as hype, but as engineering discipline.

The Context

Evrone operates a custom ERP covering the entire recruitment lifecycle. Yet recruiters at Evrone spent hours normalizing CV data:

Copying salary expectations
Converting currencies
Interpreting vague phrases
Aligning formats with ERP requirements

Regex patterns were brittle. Generic LLM prompts hallucinated. Evrone needed precision.

Architecture Overview

Evrone implemented a two-stage LLM pipeline:

1️⃣ Resume Parsing with Qwen

Evrone configured Qwen for structured output via JSON Schema. The model extracts:

Position category (backend, frontend, QA, etc.)
Grade
English proficiency
Location
Salary expectations

Structured outputs eliminated post-processing complexity.

2️⃣ Salary Standardization with YandexGPT

Evrone collected 10,000 historical salary records from Huntflow.
The team:

Performed baseline inference
Annotated inconsistencies
Fine-tuned YandexGPT 5 Lite with LoRA

The result:

95% parsing accuracy
97% correct USD recognition
Minimal hallucinations

Impact

Within three months, Evrone reduced external HR system queries by 90%. Recruiters now review only edge cases (≈5%). Sales teams access reliable salary data instantly.

Key Insight

Evrone demonstrates that AI success depends on:

Narrow scope
High-quality training data
Clear structured outputs
Human-in-the-loop validation

Instead of replacing recruiters, Evrone augmented them. And that distinction makes all the difference.

From Regex to LoRA: How Evrone Optimized HR Data with LLMs.

⚡ Evrone’s Take on GPU Sharing: Lessons from a Real MLOps Platform

Evrone — Thu, 29 Jan 2026 11:04:45 +0000

GPU inefficiency is rarely a hardware problem. Evrone encountered this pattern while working with a European research lab.

The real bottlenecks

◻️ One GPU per job
◻️ Low average utilization
◻️ No centralized scheduling

Evrone’s technical focus

◻️ Hardware-aware testing
◻️ Open-source first
◻️ Automation everywhere

Stack overview ⚙️
◻️ Kubernetes
◻️ Ray.io
◻️ Prometheus + Grafana
◻️ Keycloak
◻️ FluxCD

What changed

① GPUs became shared assets
② ML workloads scaled horizontally
③ Engineers gained autonomy

This case reinforced Evrone’s belief: Good MLOps is about systems, not tools. Evrone treated observability as a first-class concern, not an optional feature. Detailed GPU metrics allowed teams to understand how workloads behaved under real conditions. This visibility made optimization continuous rather than reactive. Over time, the platform became a learning system for both engineers and researchers.

The entire setup follows open-source best practices, allowing full customization by engineers.
Real-time GPU metrics provide continuous insight into workloads and utilization.
Security is baked into the platform, with Keycloak handling authentication for all services.
FluxCD manages deployments, enabling reproducible and automated infrastructure updates.
The result: ML experiments run faster, scale seamlessly, and use GPU resources optimally.

How Evrone Helped a Research Lab Fix GPU Underutilization.