Alek's Blog - ai

What is Application Security

aleks — Sat, 20 Jun 2026 00:00:00 +0000

The third part of the (D)DoS series — but a step sideways: this post covers attacks at the Application layer that affect confidentiality and integrity rather than availability. SQL Injection, Log4Shell, and similar vulnerabilities have a different threat model than the DDoS attacks covered in the technical part</a>.

How I work with Claude Code

aleks — Wed, 10 Jun 2026 00:00:00 +0000

Since 2025, I have been using AI tools as part of my daily work. Not as a replacement for thinking — as a productivity aid. An AI coding agent has become a regular part of how I work on infrastructure, blog posts, and software projects. Most of what follows likely applies to other agents as well — Cursor, Copilot, or whatever comes next. I happen to use Claude.
This post is not a tutorial and not a pitch. It is a reflection on what actually works — for me, on my projects — across months of real use, across 40+ projects, and across months of agent use.

LLM Inference on OVH MKS: Connect IDEs and Web UIs

aleks — Tue, 02 Jun 2026 00:00:00 +0000

The first four parts of this series deployed a vLLM inference endpoint at https://llm.YOUR_DOMAIN/v1</code>, protected by a Bearer token, running on an OVH RTX5000-28 GPU node. This part shows how to connect coding assistants, web UIs, and other OpenAI-compatible clients to that endpoint.
LLM Inference on OVH MKS: Terraform, Ansible, and Deployment aleks — Tue, 02 Jun 2026 00:00:00 +0000 Part 1</a> covered the architecture and use cases. This post walks through the complete Terraform and Ansible setup and a first deployment. LLM Inference on OVH MKS: LiteLLM API Gateway aleks — Tue, 02 Jun 2026 00:00:00 +0000 Parts 1–5 deployed a functional self-hosted LLM endpoint. This part adds LiteLLM</a> — an open-source proxy that exposes a unified OpenAI-compatible API across multiple LLM backends — in front of vLLM. The gateway layer brings per-user API keys, budget enforcement, and automatic fallback to commercial APIs when the local model is unavailable. LLM Inference on OVH MKS: The Complete Guide aleks — Tue, 02 Jun 2026 00:00:00 +0000 This is the index and reading guide for a six-part series on self-hosting LLM inference on a GPU-enabled Kubernetes node pool, using OVH Managed Kubernetes Service (MKS) as the concrete platform throughout. The series runs end to end: the decision of whether to self-host at all, provisioning the GPU infrastructure, serving a model behind an OpenAI-compatible API, wiring up observability and autoscaling, connecting real client tools, and finally putting a multi-user gateway in front of the whole thing. LLM Inference on OVH MKS: Introduction aleks — Tue, 02 Jun 2026 00:00:00 +0000 This post covers the decision context for self-hosting LLM inference on OVH MKS: when it makes sense, what the stack looks like, and what the costs are. Part 2</a> walks through the Terraform and Ansible setup. LLM Inference on OVH MKS: Prometheus, Grafana, and KEDA aleks — Tue, 02 Jun 2026 00:00:00 +0000 Part 1</a> covered the architecture and use cases. Part 2</a> walked through Terraform and Ansible. Part 3</a> covered models and the OpenAI API. This part adds observability (Prometheus + Grafana) and scale-to-zero autoscaling via KEDA. LLM Inference on OVH MKS: Models, AWQ, and OpenAI API aleks — Tue, 02 Jun 2026 00:00:00 +0000 Part 1</a> covered the architecture and use cases. Part 2</a> walked through the Terraform and Ansible setup. This post covers which models fit on the RTX5000-28’s 16 GB GPU VRAM, why AWQ quantization is required for 7B+ models, and how to use the OpenAI-compatible endpoint from your own code.