# Abror Aliboyev — Full Site Content > Software Engineer. Nine years building web applications with Python, TypeScript, Go, Kubernetes, and Docker. ### Selected Engineering Wins - 14,000+ publications — built the entire platform, every integration, solo - 99.9% uptime across 20+ microservices at a US anti-fraud startup - Recovered a corrupted 11M-row database in 26 hours under exam-season pressure - 100× speed improvement — rewrote Python scrapers to Go - Designed pipelines from scripts → Airflow → Temporal for unreliable legal data --- ## Landing Page I build systems and run them in production. Nine years in, I still write code every day. Python, TypeScript, Go — whatever fits the problem. I also run what I build, so I know my way around Kubernetes and Docker. I like keeping things simple and boring where it counts. --- ## For Hiring Managers Software engineer with 9 years of professional experience. I work across the entire stack — from database schema and API design to production UI. I've been part of teams where I owned product verticals end-to-end, and I've worked on projects that ranged from cloud deployments on AWS and GCP to bare-metal Kubernetes clusters. My primary languages are TypeScript, Python, and Go. ### Experience 9 years of professional experience since 2018. Started with JavaScript and PHP, built full-stack applications with Laravel and Vue, then moved to React and the broader TypeScript ecosystem as projects demanded it. Was part of building an academic publishing platform that grew to 14,000+ publications. Helped maintain 20+ microservices with 99.9% uptime at a US-based anti-fraud startup, where I got deep into AWS infrastructure — SAM templates, CDK, and SST for automated deployments. Took initiative to migrate a code intelligence product from a legacy stack to Next.js and FastAPI. Along the way I've worked on medical AI, fintech, code intelligence, e-commerce fraud detection, academic publishing, and visual website builders. ### Projects I've worked on **Tusdi AI** — Full-Stack Engineer · Next.js, FastAPI, PostgreSQL, Python Greenfield medical AI platform. The system parsed medical documents, tracked conditions, symptoms, and visit histories so patients and doctors could have more productive interactions. I worked on both the frontend and backend, and spent a lot of time navigating the challenges of AI indeterminism in a domain where accuracy expectations were very high. **Codevalet** — Full-Stack Engineer · Next.js, FastAPI, Redis, Celery, Kubernetes, Helm Code intelligence platform for understanding and documenting repositories. The existing stack needed reworking, so I took initiative to migrate the UI and backend to Next.js and FastAPI. Also helped rebuild the AI pipeline with Celery and Redis Streams, and put together Helm charts for multi-cloud Kubernetes deployment. **XLRT** — Freelance Full-Stack Engineer · Next.js, Python, RabbitMQ, PostgreSQL Finance platform using LLMs to analyze company reports and help banks make credit approval decisions. Worked on dynamic dashboards for company-level financial data with server-driven layouts, and integrated chat and report generation via RabbitMQ. **ICOMS** — Freelance Full-Stack Engineer · Next.js, Laravel, FastAPI, Kubernetes Visual website builder in the space of Webflow and Framer. Contributed a component library for the visual editor and worked on automated custom domain assignment through Kubernetes — SSL certificates, ingress rules, and routing. Also did early experiments with GPT-3.5 for UI generation. **YoFi (BotNot.io)** — Lead Backend & DevOps · Python, Node.js, AWS, GCP, MongoDB Anti-fraud AI platform for e-commerce. I was part of the backend and DevOps side — helping manage 20+ microservices, contributing to ML data pipelines with Airflow and BigQuery, and working on serverless applications across AWS and GCP. Went through the challenges of automated deployment with SAM templates, CDK, and SST. The team maintained 99.9% uptime. **scienceweb.uz** — Lead Full-Stack Engineer · Vue.js/Nuxt, Laravel, Python, Node.js Research publishing platform for Uzbekistan. I built most of the stack — frontend, backend, and integrations with Scopus, Web of Science, ORCID, Google Scholar, and Crossref for automatic DOI resolution. Originally wrote it in Vue 2.6, later rewrote it with Vue 3 and Composition API. The platform grew to 14,000+ publications from 4,000+ researchers. ### Technical skills **Frontend:** TypeScript / JavaScript, React / Next.js / Vue / Nuxt, Tailwind CSS, State management (Zustand, Redux) **Backend:** Python (FastAPI preferred, can work with Flask & Django), Go (current focus, exploring deeply), Node.js / Express / Fastify, REST APIs / GraphQL / WebSockets **Databases:** PostgreSQL / MySQL / MongoDB / Redis, Elasticsearch (wrote custom query generation modules), Vector databases (Weaviate, Qdrant) **Infrastructure:** Kubernetes / Docker / Helm (CKA-level knowledge), AWS (SAM, CDK, SST) / GCP / Vercel, CI/CD / ArgoCD / GitOps, Monitoring (Victoria Metrics, Grafana) **AI & Data:** LLM integration & prompt engineering, Celery / RabbitMQ / Redis Streams, Apache Airflow / BigQuery, ML pipelines & data processing ### What I enjoy These days my main interests are converging around backend work in Go, building and automating Kubernetes clusters, and infrastructure tooling. I like exploring concurrency patterns, low-level protocol work (tried implementing Telegram's MTProto in Go as a learning exercise), and making systems simpler and more reliable. I enjoy the kind of work where you dig into how things actually work under the hood — not just using tools but understanding them. I care about the outcome as much as the code, and I bring that curiosity to every team I join. ### Practical details - Rate: $35–40 / hour - Availability: Available now - Work format: Prefer full-time or part-time employment. Open to contract work if the project is well-scoped and estimated by someone with software engineering expertise - Employment: Regular employee or independent consultant with NDA - Location: Uzbekistan, working remotely. Open to relocation (Malaysia, Indonesia, Middle East, Turkey) - Languages: English, Russian (confident, worked on both English and Russian-speaking teams), Uzbek (native) - Education: Corporate Governance, Tashkent State University of Economics (TSUE), graduated 2023 - Timezone: UTC+5 (Uzbekistan Standard Time) --- ## For Technical Interviewers 9 years building with TypeScript, Python, and Go. I'm not tied to specific frameworks — I've picked up new stacks, languages, and tools throughout my career as projects demanded it. I've written Python modules, worked at the protocol level, managed bare-metal servers, and debugged things most developers never touch. The frameworks I mention are what I've used most recently, but I'm comfortable adapting to whatever the problem needs. ### How I approach work **Align first, then build.** Before anyone writes code, the team should be on the same page — API protocols, data schemas, conventions. Align on how things communicate and what the contracts look like, then everyone can go build with confidence instead of discovering incompatibilities later. I'm also obsessed with efficiency — every line of code matters, and readable code beats a wall of comments. Bugs are part of development; they should be minimized, not treated as moral failures. Good architecture doesn't need shortcuts, hardcoded values, or placeholder fixes. **Understand before you touch.** At Codevalet, I joined an 80k+ line codebase spread across multiple microservices. Before writing any code, I spent a week reading, tracing, and documenting the entire architecture — every service, every connection, every implicit assumption. I wrote two documents: one explaining how it all worked, and another making the case for why we should rewrite it from scratch and take nothing from the current codebase. That investment paid off — when I started coding, I shipped my first PR in two days. I'm confident in my ability to get up to speed on existing code quickly, and I think that comes from taking documentation seriously before jumping in. **No shortcuts.** I don't like implementing solutions with quick fixes and placeholders. If I'm asked to take a shortcut, I'll still try to find a better way — because it's the developer who suffers bad code and maintenance down the road. A well-planned architecture usually doesn't require hardcoded values or temporary workarounds. When it does, that's a signal something needs rethinking, not patching. **Test what matters, not everything.** I don't enjoy writing tests for the sake of coverage numbers. Test the parts you know are critical — the business logic, the edge cases, the integration points. Leave room for things to break if they're not critical. When testing tries to cover 100% of a codebase, development becomes boring and slow. These days AI handles a lot of the tedious test writing, which honestly makes me much happier about the whole process. **If I have an opinion, I'll show you why.** When I disagree with a technical decision, I don't just argue — I explain my reasoning and, if needed, build a prototype to demonstrate the alternative. If it's beyond my scope, I'll come asking for input. But if a task is trusted to me, I expect the space to use my expertise. I've found that the best teams work this way — trust the person closest to the problem, and discuss when it affects the bigger picture. **Readability is the real documentation.** I'd rather read well-structured code with clear naming than a codebase covered in comments explaining what should be obvious from the code itself. Types help too — TypeScript strict mode with well-designed types is documentation that the compiler enforces. Comments go stale, types don't. ### The stack, in depth **Frontend:** Started with JavaScript and PHP, moved to Vue through Laravel's ecosystem, built scienceweb.uz in Vue 2.6 and later rewrote it with Vue 3 and Composition API. Moved to React for the ecosystem breadth. I know the tradeoffs — Next.js is great for most apps but starts struggling when the frontend is highly interactive; Vue handles that better performance-wise, and TanStack is a better React story than Next.js for certain use cases. I pick based on what the project actually needs, not habit. Tailwind CSS for styling, Zustand over Redux for state. I avoid component libraries when I can — custom components are easier to evolve. **Backend & Languages:** Python, TypeScript, and Go — not just at the framework level. I've written Python modules (custom Elasticsearch query generation, GitHub Linguist port from Ruby), understand asyncio deeply and know when async/await actually helps vs when it's unnecessary overhead. I can explain the difference between multiprocessing parallelism, multithreaded concurrency, and cooperative multitasking — many developers conflate these. FastAPI is my preferred Python framework; Flask is too minimal, Django too heavy, but I can work with both. Node.js and Express/Fastify for TypeScript backends. Go is my current focus — I love its concurrency model and tried implementing Telegram's MTProto protocol as a learning exercise. **Databases:** PostgreSQL is my default — it handles most use cases well. I've also worked with MongoDB (including running self-managed clusters on EC2), MySQL (recovered a corrupted 11M-row database by hand), and Redis. Wrote a Python module to generate Elasticsearch queries and understand its internals well enough to reason about scoring and analyzers. For AI projects I've used vector databases — Weaviate and Qdrant — for semantic search and embeddings. **Infrastructure:** Kubernetes, Docker, Helm, Nginx, Linux, Git — the full picture around software delivery, not just the code. Went through CKA exam preparation and run a 9-node cluster at home with Calico CNI and eBPF — including debugging real issues like ARM and eBPF networking incompatibilities. On cloud, I worked intensively with AWS at YoFi — SAM templates, CDK, SST, Lambda, DynamoDB, Neptune. Wrote SAM templates to spawn and auto-scale MongoDB clusters on raw EC2. Ansible for provisioning, ArgoCD for GitOps, Traefik for ingress, Victoria Metrics and Grafana for observability. **Tooling & Process:** Biome over ESLint + Prettier — one tool, faster, opinionated. Bun as package manager for speed. Conventional commits for readable git history. Code reviews focused on architecture and correctness, not style (that's what formatters are for). I write tests for behavior, not implementation — integration tests over unit tests for most business logic. ### Hard problems I've dealt with **Recovering a corrupted MySQL database under exam pressure** A university Moodle LMS platform I maintained at I-Edu Group went down right before exam season. The sysadmin ran a distro update and rebooted the server mid-write, corrupting parts of a MySQL database with over 11 million rows across 100+ tables. The worst hit was the answers table — the one holding test questions and student responses. I spent 26 hours straight tracking down corrupted rows from over a million entries, manually recovering what I could. In the end, we got back about 99% of the data. It was one of the most stressful experiences I've had, but the platform came back online and exams went ahead. **AWS Neptune bottleneck and MongoDB migration at YoFi** At YoFi, our data ingestion pipeline hit a wall with AWS Neptune — a serverless graph database that became the main bottleneck. No matter how much we scaled compute and RAM, it degraded past 100 orders per second, and we were processing 200k+ orders with the volume growing exponentially. RDS was also becoming unsustainably expensive, and SQL wasn't a natural fit for Shopify's data structure. We migrated to MongoDB, which solved the cost and flexibility problems, but Atlas pricing was still too high. I ended up writing SAM templates and scripts to spawn and auto-scale MongoDB clusters on EC2 instances — not Kubernetes, just raw EC2 with MongoDB's own replication. Getting replication stable on that setup had its own set of challenges, but we got it working and the cost savings were significant. **Building reliable legal data pipelines for Lexpert** For the Lexpert agent in Asy AI, I needed to build a stable data pipeline over legal data from lex.uz — which is not a reliable data platform by any measure. I built and rewrote the pipeline several times. First attempt was simple Python scripts, but when scale and observability demanded more, I adopted Airflow and RabbitMQ. That helped with flow but added Kubernetes deployment complexity — Airflow's experience on Kubernetes is not the smoothest, and developing workflows on it was error-prone. When I switched to Temporal, everything clicked — the flexibility to run workflows at scale and speed made a real difference. I also rewrote the scrapers from Python and Scrapy to Go, which gave roughly 100x speed improvement since Scrapy tasks would sometimes get stuck on different response formats. Scraping lex.uz reliably is still one of the hardest data challenges I've worked on. ### Beyond work I build side projects to explore ideas and learn new things. Tried implementing Telegram's MTProto in Go, run a homelab cluster that I constantly tinker with, and built this site as an experiment — a portfolio that adapts to its audience rather than presenting a one-size-fits-all resume. Open source contributions, personal tools, and technical writing are how I stay curious and give back to the community. --- ## Projects Things I've built, shipped, or contributed to. **Tusdi AI** — Next.js, FastAPI, PostgreSQL, Python, TypeScript A medical AI platform that parsed medical documents, tracked conditions, symptoms, allergies, and visit histories. Patients could report how they feel and the system would track their state for more productive doctor visits. I worked on both frontend and backend with Next.js and FastAPI. **Codevalet** — Next.js, FastAPI, Redis, Celery, Kubernetes, Helm A code intelligence platform for understanding and documenting repositories. Helped migrate the frontend and backend to Next.js and FastAPI, reworked the AI pipeline with Celery and Redis Streams, and set up Helm charts for multi-cloud Kubernetes deployment. **XLRT** — Next.js, TypeScript, Python, RabbitMQ, PostgreSQL A finance platform using LLMs to analyze company reports and help banks make credit approval decisions. Worked on dynamic dashboards for company-level financial data with server-driven layouts, and integrated chat and report generation via RabbitMQ. **ICOMS** — Next.js, Laravel, FastAPI, Kubernetes, TypeScript A visual website builder in the space of Webflow and Framer. Contributed a component library for the visual editor and worked on automated custom domain assignment through Kubernetes — SSL certificates, ingress rules, and routing. Also did early experiments with GPT-3.5 for UI generation. **Asy AI** — FastAPI, Next.js, PostgreSQL, Python A personal project — an early AI agent platform with a visual workflow editor, like an AI-enabled Zapier. Built a shopping agent, a legal research agent, and a Gmail integration agent. Made it to the final phase of the Prezident Tech Awards. **YoFi (BotNot.io)** — Python, Node.js, AWS, GCP, MongoDB, React An anti-fraud AI platform for e-commerce merchants. Was part of the team managing 20+ microservices across AWS and GCP, contributed to ML data pipelines with Airflow and BigQuery, and worked on serverless applications for Shopify integration. **scienceweb.uz** — Nuxt.js, Laravel, Python, Node.js, Docker A research publishing platform for Uzbekistan. I built most of the stack — frontend, backend, and integrations with Scopus, Web of Science, ORCID, Google Scholar, and Crossref. The platform grew to 14,000+ publications from 4,000+ researchers. --- ## Career Timeline ### Jan 2026 — Building my own things No clients, no deadlines, no legacy. Just me, a few interesting ideas, and the freedom of building without constraints. After years of freelancing, startups, and navigating other people's architectural decisions, I'm finally spending time on my own projects. Exploring ideas I've been sitting on, building at my own pace, and enjoying the process without the pressure of demos or investor timelines. Also keeping my eyes open for the right opportunity — something where I can build things that matter with people who care about how they're built. Status: Active — building. ### Jul 2025 — Full-Stack Engineer at Tusdi AI Technologies: Next.js, FastAPI, PostgreSQL, Python, TypeScript, Tailwind CSS A new chapter that freed me from legacy code. A greenfield medical AI platform — built from scratch, no compromises on the stack, no inherited mess to untangle. We agreed on Next.js and FastAPI. We built a complex platform on both frontend and backend. The idea was an AI system that could parse medical documents, visit histories, and appointment records so users could track their and their family members' entire medical life. Conditions, symptoms, allergies, active supplements, scheduled visits, current medical situation — all extracted and displayed on a UI where they could report how they feel, letting the system react and track their state so doctor visits would be more productive. Doctors had their own cabinet where they could see everything that happened to a patient and assist them better. The challenging part was the lack of medical knowledge among developers and AI's indeterminism — we spent more time tuning prompts than writing actual code. LLMs were expected to be 100% accurate in all cases — which is technically impossible — and that was treated as the developer's problem. Left: Project stopped receiving financing from January 2026. The expectations for AI accuracy were ahead of what the technology could reliably deliver. ### Jan 2025 — Started working on Codevalet Technologies: Next.js, FastAPI, PostgreSQL, Python, TypeScript, Redis, Celery, Kubernetes, Helm, Docker A great opportunity — both financially and professionally. An interesting project in a difficult state, a tight schedule, and a field of work I genuinely cared about. Codevalet's vision was deterministic code understanding — code should be properly read, parsed, learned, documented, maintained, and developed further. LLMs were used for chatting with humans, but analyzing code was not a big burner of tokens. They had a complex phased pipeline — pulling code from GitHub, GitLab, or ZIP files, running semantic analysis, counting stats. On my first day, I saw the stack and couldn't agree with the approach — the frontend and backend were built with a custom framework that rendered UI components written in Python using py-script. I started reimplementing the UI and backend from scratch with Next.js, FastAPI, and PostgreSQL. I built Google and GitHub auth, repo pulling with private repo support, and minimal code analysis — translating GitHub's Linguist project from Ruby to Python. I also rebuilt the AI functionality from scratch using Celery with Redis Streams, created a Helm package for deploying all three services to Kubernetes. Left: Disagreements on some architectural decisions and practices on the tech stack. ### Aug 2024 — Freelance Full-Stack Engineer on XLRT Technologies: Next.js, TypeScript, RabbitMQ, Python, TinyMCE, PostgreSQL A finance platform using LLMs to analyze company reports — quarterly, annual, internal documents — and help banks make credit approval decisions. XLRT could parse report files, extract financial information, and calculate metrics like revenue growth, debt ratios, and profitability stats — all displayed on per-company dashboards with heavy use of charts and tables. Left: Complexity outpaced the budget and team capacity. ### Jan 2024 — Freelance Full-Stack Engineer on ICOMS Technologies: Next.js, Laravel, FastAPI, PostgreSQL, Kubernetes, TypeScript A visual website builder competing with Webflow, Framer, and studio.design. Started by fixing frontend bugs, but quickly moved into the core — building a separate library of customizable components and rearchitecting the project. Automated custom domain assignment where users could assign their domain, get DNS configuration instructions, and the system would automatically create Kubernetes resources: SSL certificates, ingress rules, and routing. Was also among the first to try generating UI with ChatGPT — back on GPT-3.5 Turbo. Left: Couldn't come to an agreement on project budget with the owners. ### Jun 2023 — Built Asy AI for Prezident Tech Awards Technologies: FastAPI, Next.js, shadcn, Tailwind CSS, PostgreSQL, Python An early attempt at unifying AI agents before the world had a word for them. An AI-enabled Zapier — a workflow builder with a visual editor where developers could build and host their own agents, and users could grant those agents permissions to the platforms they wanted automated. Built a Shopping Agent, Lexpert Agent (legal AI), and a Mail Manager Agent integrated with Gmail. Prepared for the Prezident Tech Awards at awards.gov.uz — a competition with a $1 million prize fund. Left: The presentation didn't succeed — the concepts were too early for business at that time. ### Dec 2021 — Lead Backend & DevOps Engineer at BotNot.io (YoFi) Technologies: Python, Node.js, TypeScript, JavaScript, React, AWS, GCP, MongoDB, DynamoDB, Apache Airflow, BigQuery Built backend infrastructure and ML pipelines for YoFi — an AI platform helping e-commerce merchants combat bots, fraud, and unauthorized resale. Engineered serverless applications across AWS and GCP. Developed ML models predicting customer order scores. Orchestrated 20+ specialized microservices, led data pipeline construction improving ML workflow efficiency by 40%. Directed a high-availability backend infrastructure with 99.9% uptime. Left: Paused contract to focus on diploma work. The company later laid off part of the team for cost optimization. ### Sep 2019 — Lead Full-Stack Engineer at I-Edu Group Technologies: Vue.js, Nuxt.js, Laravel, Python, Node.js, PHP, Docker Maintained over 10 OJS (Open Journal Systems) installations for universities across Uzbekistan. The goal: increase visibility of Uzbek researchers in global scholar systems. Built scienceweb.uz — a unified platform with SEO optimization, indexing, and integrations with Scopus, Web of Science, ORCID, Google Scholar, and Crossref for automatic DOI resolution. Entire stack built singlehandedly — Nuxt.js/Vue.js frontend, Laravel backend, with Python and Node.js modules. Grew to 14,000+ publications from 4,000+ researchers. Left: YoFi offered orders of magnitude better compensation and an entrance into ML. ### Mar 2018 — Assistant of Manager at Gross Insurance Technologies: Vue.js, Laravel, PHP, Node.js, JavaScript Not an engineering job — but the one that taught me how businesses actually work. Hired for economic and legal work — verifying insurance coverage, handling claims, managing authorizations. Built a tool for the branch that generated contracts and policies ready for printing — cut human-factor errors by over 70% and brought contract creation time from 10 minutes down to 30 seconds. Left: Wanted to spend more time coding. Started studying at TSUE, I-Edu Group came calling. --- ## Lab Infrastructure My personal playground — where I deploy, break, and rebuild things on my own terms. Infrastructure is a big part of my life. I'm obsessed with automation and Kubernetes — and when you combine Kubernetes with ArgoCD, you get a level of automation where I can write code, deploy it, assign a domain on the way, and make it available to internal or external traffic in minutes. ### Cluster Stats - Nodes: 9 - CPU Cores: 124 - RAM: 320 GB - Storage: 15 TB - Cost/mo: ~$200 ### Node Types - 4x ARM Nodes: 12 cores · 24 GB RAM · 768 GB NVMe each - 3x AMD EPYC Nodes (EPYC 9645): 12 cores · 32 GB RAM · 2 TB NVMe each - 2x x86 Nodes: 20 cores · 64 GB RAM · 3 TB storage each ### Two-Phase Architecture Everything starts with Ansible — it owns the metal. OS preparation, containerd, kubeadm, Calico CNI with eBPF, Longhorn for distributed storage, and ArgoCD bootstrap. Once ArgoCD is up, it takes over. Every component above the base layer is declared as an ArgoCD Application pointing at a private Git repo. Automated pruning, self-healing, sync-waves for dependency ordering. Ansible touches the machines, ArgoCD owns everything else. No overlap. Components: Ansible, kubeadm, containerd, Calico + eBPF, Longhorn, ArgoCD ### Network Stack Two separate Traefik instances — a public gateway for internet-facing services and a private one behind Twingate for internal tools. Gateway API v1 with wildcard TLS on *.aliboyev.com. External DNS automation through Cloudflare, cert-manager with Let's Encrypt DNS-01 challenges. Private services like ArgoCD, Longhorn, and Grafana are never exposed to the internet. Components: Traefik (dual-gateway), Twingate, Gateway API v1, cert-manager, External DNS, Cloudflare ### Observability Victoria Metrics for Prometheus-compatible metrics collection, paired with Grafana for dashboards. The full stack is deployed via ArgoCD with server-side apply for CRD-heavy resources. Components: Victoria Metrics, Grafana ### What Runs on It A mix of self-hosted services and personal projects. Harbor as a private container registry, n8n for workflow automation, Temporal for workflow orchestration, Vaultwarden for password management, Label Studio for data labeling, and BuildKit for container image builds. Each app follows a consistent pattern — ArgoCD Application, Helm values, optional extra manifests, and a secret bootstrapping script. Some apps run on custom Helm charts I wrote from scratch. Components: Harbor, n8n, Temporal, Vaultwarden, Label Studio, BuildKit --- ## Blog — Behind the Code How I think about building software, working with teams, and the tools I use. ### How I Use AI — and Where I Draw the Line (2026-03-01) Tags: AI, Workflow AI is a tool in my workflow, not a replacement for thinking. I use it to move faster on boilerplate, explore ideas, and catch things I'd miss. But the architecture, the decisions, the code review — that's still me. Here's how I think about it. ### In Praise of Boring Technology (2026-02-15) Tags: Engineering, Philosophy PostgreSQL, Python, Linux. Not exciting at a meetup, but incredibly reliable at 3 AM. I pick technology that lets me sleep at night, not technology that looks good on a slide. ### How I Work (2026-02-01) Tags: Work Style, Teams I write code in the morning, review in the afternoon, and think about architecture in the shower. I prefer async communication, short meetings, and long focus blocks. Here's what working with me looks like. ### Running Kubernetes at Home Changed How I Think About Infrastructure (2026-01-15) Tags: Infrastructure, Homelab When the cluster goes down at 2 AM and there's no on-call team — just you and dmesg — you learn fast. My homelab taught me more about reliability than any cloud certification. --- ## Contact - Email: hello@aliboyev.com - LinkedIn: https://linkedin.com/in/aaliboyev - GitHub: https://github.com/aaliboyev - Telegram: @aaliboyev