Murat Akdeniz writes about AI infrastructure, developer tools, software architecture, and production engineering. He explores how modern systems work beneath their abstractions, turning complex topics—from LLM routing and RAG pipelines to testing and secure infrastructure—into practical guidance for engineers.
A practical walkthrough of NocoBase's Workflow plugin: the four key concepts (workflow, trigger, node, execution), the three trigger types, and how nodes form event-driven automation.
A practical deployment guide for running Windmill on your own infrastructure using Docker Compose, covering the server-worker-Postgres architecture, the bundled Caddy stack, and VPS sizing.
A pragmatic guide to choosing a Creative Commons or Open Data Commons license for a first Hugging Face dataset, grounded in EU publisher rights and attribution conventions.
A focused comparison of Caveman's in-band prompt discipline and Headroom's out-of-band local compression proxy, framed around where each tool intervenes in the agent-to-provider pipeline.
A pragmatic case for hiring 1099 contractors before W-2 employees when bootstrapping a SaaS, with guidance on which roles to externalize first and a TinyPilot case study.
An operational comparison of recursive, semantic, section-aware, and late chunking for RAG ingestion, with guidance on which method fits which document type.
A practical buyer's guide that compares Caveman variants against external alternatives, helping readers pick a compressed-output skill based on workflow, model, and stack rather than headline token-savings numbers.
Why pure vector search misses exact product codes and names, and how to assemble the three-stage production baseline: parallel BM25 and dense candidate generation, Reciprocal Rank Fusion to merge the ranked lists, and a cross-encoder reranker for final ordering — implemented with Qdrant and Cohere.
How to move PocketBase beyond standalone mode: embedding it as a Go library with custom hooks, routes, and migrations — plus why JavaScript handlers can't see variables declared outside their scope.
A layer-by-layer breakdown of the tools a solo SaaS founder actually pays for in 2026 — Toggl, Linear, daily.dev, SwipeGrowth, trial tracking, and the AI stack — with itemized monthly costs and free-tier-first upgrade rules.
Shipping one RAG deployment to many organizations forces a hard question: one shared Qdrant collection with tenant filters, or a separate collection per customer? This guide compares both isolation strategies, explains how Qdrant's filterable HNSW keeps tenant data fast and separated, and maps how…
Ragas doesn't just write questions — it builds a knowledge graph from your documents and then evolves simple prompts into reasoning, conditioning, and multi-context challenges. Here is what the generator LLM, the critic LLM, and the embedding model are actually doing inside testset generation, and…
Your VPS doesn't have to show a single open port to the public internet. Here's how to move SSH, admin panels, and databases onto Tailscale tailnet IPs only, close every inbound firewall rule, and write ACLs that assume your devices are already compromised — with a minimal two-server setup as the…
A Dockerized RAG stack can start anywhere from three to eight containers before you ask a single question — and knowing exactly what each one does turns confusion into control. This walkthrough maps the Gradio, Ollama, Milvus, and NVIDIA NIM containers, follows a PDF through the four-stage…
Tailscale SSH claims port 22 on your tailnet IP, swaps authorized_keys for control-plane-managed node keys, and distributes host keys automatically — which makes your bastion host look suddenly redundant. Here is exactly what changes under the hood, plus the honest cases where you should still keep…
Appium, Espresso, XCUITest, Detox, and Maestro solve mobile UI testing with five completely different architectures. This comparison breaks down each framework's synchronization model, setup cost, and flakiness trade-offs so mobile QA teams can pick the right tool for their platform, accuracy…
VRAM — not clock speed — decides which models your machine can actually run, and that single fact reshapes how every dollar should be spent. This guide walks through GPU tiers, the CPU's underrated role, and multi-GPU decisions with real 2026 street prices and benchmarks, so your local AI…
Running vllm serve doesn't start one program — it spawns a tree of API servers, engine cores, GPU workers, and sometimes a DP Coordinator, all wired together with ZMQ sockets. This walkthrough traces vLLM's V1 multi-process architecture from the CLI to the first forward pass on the device, step by…
Most engineers obsess over chunking strategies and vector databases, yet the pipeline that feeds them is where the real engineering lives. This guide maps all 11 stages of an automated docs ingestion pipeline — sitemap URL discovery, HTML cleaning, embedding, vector upserts, and incremental refresh…
When you call a model through LiteLLM, the model string you send is just a public name for a pool of deployments — and the Router decides which one actually answers. This deep dive traces that decision end to end: model_name matching, multi-provider model groups, routing groups vs. access groups…
From Claude Code's 1M-token terminal refactors to Devin's fully delegated pull requests, we ranked the ten best AI coding agents of 2026 on real benchmark scores, pricing, and autonomy. Here's which agent fits your workflow — and which failure modes the demos never show you.