Alexander Lyutov

Technical chaos
into working systems.

AI-native engineer, technical fixer, backend and infrastructure architect, author of Savage Code.

I solve hard engineering problems where legacy systems, unclear requirements, business pressure, databases, infrastructure, rendering pipelines, and deadlines collide.

Send me the deadlock Read Savage Code
Backend APIs, services, data flows, integrations, legacy rescue.
Infrastructure Linux, Docker, Kubernetes, monitoring, deployment, reliability.
Data PostgreSQL, migrations, performance, history, operational safety.
AI-native Intent extraction, fast synthesis, verification, tool generation.

01. What I Do

Technical Rescue

I enter projects that are stuck, fragile, late, politically complicated, or blocked by expert dependency — and turn them into working systems.

Backend & Integration Architecture

APIs, distributed service interaction, data synchronization, import pipelines, business logic boundaries, and pragmatic system design.

Infrastructure & Reliability

Linux servers, Docker, Kubernetes, PostgreSQL, S3-like storage, monitoring, deployment pipelines, failover thinking, and production diagnostics.

Internal Engineering Tools

Debuggers, analyzers, automation utilities, diagnostic consoles, migration helpers, and ugly but powerful tools that make teams faster.

02. Where I Am Useful

  • A business-critical technical problem has been sitting unresolved for weeks or months.
  • The team is blocked by legacy code, unclear ownership, weak requirements, or a single irreplaceable expert.
  • You need a prototype, proof of concept, diagnostic tool, or working implementation before the usual process even warms up.
  • Your system spans backend, database, infrastructure, storage, integrations, and business logic at the same time.
  • You need someone who can ask hard questions, reduce ambiguity, and move from chaos to execution.

03. Savage Code

My AI-native engineering methodology for impossible deadlines and technical deadlocks.

Savage Code combines intent extraction, executable specifications, AI-assisted synthesis, brutal verification, and disposable architecture.

It is not chaos. It is not blind prompting. It is not skipping requirements. It is a controlled loop for turning vague business pain into working software fast.

Read the manifesto

04. Operating Principles

The bottleneck is rarely typing code. The bottleneck is turning intent into verified capability.

Intent before implementation

A vague request is not a task. I extract constraints, edge cases, failure modes, and success criteria before generating solutions.

Verification over aesthetics

Code is judged by behavior, tests, benchmarks, logs, integration stability, and production reality — not by architectural theater.

AI as leverage, not autopilot

AI accelerates research, scaffolding, explanation, variants, and implementation. The engineer remains responsible for judgment.

Tools should appear when needed

If a diagnostic tool can save hours every week, it should not require a separate project, budget cycle, and committee.

05. Selected Cases

Low-level Rust DLL / Render pipeline

A critical render integration was trapped behind expert dependency, unstable behavior, crashes, hangs, and years of painful negotiations.

Result: first working version in minutes, production-ready direction in two days, DLL reduced from roughly 50 MB to 3 MB, faster rendering, more predictable behavior.

Cross-platform PDF engine

A traditional implementation attempt burned nearly two months, produced a half-working result, depended on a problematic library, and was abandoned for a year.

Result: fully working prototype in one day, three days to optimization, around 4× faster output and 3× smaller result size.

Multi-system debugging tool

Manual investigation across several systems took tens of minutes, sometimes hours, and depended on experienced people reconstructing events by hand.

Result: an internal diagnostic tool created in the background, ugly as a battlefield instrument, but able to locate problems in seconds.

Some details are intentionally abstracted because the work involves proprietary systems, internal infrastructure, and commercial software.

06. Background

I work with complex B2B software systems: CAD, rendering, catalogs, backend services, integrations, infrastructure, PostgreSQL, S3-like storage, Linux servers, deployment pipelines, monitoring, and internal automation.

My natural zone is the uncomfortable middle: where product logic, legacy code, database behavior, infrastructure constraints, business deadlines, and human decision-making all collide.

I am not interested in engineering theater. I am interested in systems that work.

07. Contact

If you have a hard technical problem, a failed tool, a legacy bottleneck, or a deadline that makes normal process useless — send it.

alexander.lyutov@gmail.com Savage Code