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.
How Debezium replaces the outbox poller by reading the MySQL binlog or PostgreSQL WAL like a replica client, and why commit order plus aggregate_id as the Kafka key keeps events ordered per entity.
A deployment-focused comparison of Langfuse's two self-hosting paths — the low-scale Docker Compose stack and the Kubernetes Helm chart v2.0.0 — covering bundled-store changes, migration ordering, and the managed-vs-self-managed Postgres decision.
A hardware-aware framework for deciding between 4-bit and 8-bit serving precision: the highest-ROI format per GPU generation, the weight-versus-KV-cache split, small-model safety margins, checkpoint provenance, and the H100/H200/B200 selection tree.
A side-by-side architectural and operational comparison of the three flash-tier frontier MoE models — GLM-5.3-Flash's hybrid sparse+linear attention, DeepSeek V4 Flash's CSA+HCA compression, and Qwen3.8 Flash-Next's 51B engram — including their diverging self-hosting floors and workload-based…
Persona prompting is neither decoration nor a free win: measured evidence shows a consistent expertise-depth-for-clarity tradeoff, benefits that flip by question type, and ensembling frameworks such as multi-persona debate and Jekyll & Hyde for when a single role is not enough.
A roundup of the MCP servers built for multi-agent orchestration and autonomous workflows — swarm orchestrators, workflow engines, and durable execution substrates — plus what the context layer contributes and how these servers differ from plain tool servers.
MemGPT treats the LLM context window as RAM and external storage as disk, paging data between main context, recall storage, and archival storage through agent-directed tool calls under memory pressure — letting long-running agents exceed fixed window limits.
A hands-on guide to the mechanics of Windmill flows: exposing input variables, binding step inputs to flow inputs and previous steps' results, the last-step return rule with its two exceptions, and authoring workflows as code.
A coding-agent benchmark score belongs to a whole system — model, harness, and execution settings — not to the model alone. This guide breaks down the Artificial Analysis Coding Agent Index, SWE-bench Verified's grading mechanics, and the distortions that make rankings hard to read.
A week-by-week allocation system for solo full-stack founders: split hours across builder, marketer, seller, and operator roles by business stage, batch each role into protected blocks, and run a one-week cycle that ships something visible every Friday.
A task-by-task cheat sheet for Caveman's compression levels: which mode to run for debugging, code review, architecture discussion, quick fixes, and subagent traffic, grounded in what each level actually drops.
A five-element positioning framework — product, positioning, messaging, messenger, audience — translated into concrete Hugging Face dataset publishing decisions, from category choice and dataset cards to namespace strategy and the developer adoption journey.
GLM-5.3-Flash's mHC widens the transformer residual stream into multiple parallel lanes with learned, Sinkhorn-projected mixing matrices, confining every junction to the Birkhoff polytope so deep-stack training stays non-expansive by construction rather than by luck.
A technical walkthrough of the open-source autonomous BI agent's sandboxed scratchpad, its session/semantic/long-term memory layers, the credential vault model, and the open-source versus managed deployment split — plus how the analyst role shifts from query writer to steward and trainer.
A floor-by-floor planning guide for self-hosting GLM-5.3-Flash: the ~306 GiB FP8 weight budget, why two H200s still cannot serve it, KV-cache sizing at the 1M-token window, and the realistic service-layer role for a home NAS.
A revenue-ops oriented guide to the Slack, Notion, and Salesforce MCP servers, including the documented Sales Call Follow-up template that automates post-call admin end-to-end.
A practical, threat-focused guide to MCP tool poisoning and rug pull attacks, covering how poisoned descriptions and post-approval tool swaps compromise servers, plus the input validation, schema enforcement, and output sanitization controls that stop them.
A cost-class taxonomy of ClickHouse ALTER operations, with explicit guidance on which ones are safe to run during business hours on MergeTree versus SharedMergeTree.
A walk-through of the entire Outlines pipeline that converts a Pydantic model into a token-level finite state machine, plus the formal-language-theory boundary that limits what regex-constrained generation can enforce.
A step-by-step dissection of one iteration of the harness loop, from prompt assembly through tool execution and observability, grounded in Claude Code and OpenDev internals.
A side-by-side comparison of MindsDB's federated SQL approach against traditional ETL pipelines for AI workloads, covering freshness, latency, operational cost, and the breakpoints where each architecture fails.
A side-by-side look at BERT's 512-token EHR splitting, LangExtract's multi-pass chunking, and Proxy-Pointer's structure-aware tree for structured extraction on documents that exceed any model's context window.
A practical walkthrough of how the Hugging Face Hub stores datasets — from Git repository layout and Xet chunking to the Google Cloud backbone underneath.
A technical teardown of the agent loop, context assembly, and tool wiring inside Claude Code, Cursor, and Codex — the machinery that makes modern coding agents work.
GLM-5.3 Flash is more than a cheap coding model; its native multimodal training makes it practical for video, 3D, CAD, slides, financial earnings, Godot games, vision-driven UI, and computer-use work. This guide walks through the recommended prompt patterns the Z.ai docs use for each non-coding…
A platform-engineering brief on why Redis 7.2 is at end-of-life, what the post-2024 licensing split means, and how to choose between Redis 8, Valkey, and Redis Software.
A troubleshooting-oriented look at five anti-patterns that quietly degrade LLM output — from stacking temperature and Top-P to misusing repetition penalties on reasoning models — with the reasoning and a safer alternative for each.
A practical walkthrough of Argilla's FeedbackDataset data model — Fields, Questions, Guidelines, and Records — aimed at engineers assembling preference data for DPO and RLHF.
A practical engineer's explainer on GPT-6 Astra's recurrent depth architecture, the looped-transformer design it implies, and why the chain-of-thought the API returns is now a compressed summary rather than a transcript.
A developer's guide to running open-source LLMs on Apple Silicon, explaining how Ollama, llama.cpp, and MLX-LM work together and why unified memory changes the math for local inference.
An architecture-focused comparison of supervisor, peer-to-peer, adversarial, and graph-based multi-agent coordination patterns, with trade-offs, failure modes, and selection criteria.
A practical walkthrough of how to structure collections, compose blocks, and wire actions in NocoBase — using task-management and application-catalog examples — and why the underlying data stays in standard relational tables.
GLM-5.3-Flash activates only 18B parameters per token, but all 320B weights must stay resident in VRAM — here is why 'active' and 'resident' are different things.
Headline math, three-week test results, and the burst-workload edge cases that decide whether switching to the GLM Coding Plan actually saves money versus Claude Max or Cursor Pro+.
A deep dive into the Memory MCP reference server's knowledge-graph persistence model, comparing it to vector stores and showing how to wire it into a client for long-term agent context.
A DevOps-oriented breakdown of Flowise's idle RAM footprint versus Dify, the four self-hosting paths (Docker, Railway/Northflank, VPS, Kubernetes), and a clear rule for when Kubernetes actually pays off.
An end-to-end architecture guide for synthetic data pipelines, covering ingestion/transformation/loading stages, batch vs. micro-batch vs. real-time patterns, and Lambda vs. Kappa trade-offs.
Benchmark numbers and architectural breakdown of vLLM versus Ollama on 128GB unified-memory workstations, explaining the throughput and latency gap through PagedAttention versus llama.cpp's memory model.
A step-by-step Sol→Astra migration playbook covering eval set construction, acceptance-gate scorecards, and prompt rewrites tuned to Astra's behavioral tendencies.
A developer's walk-through of Langflow's FastAPI/SQLAlchemy backend, Component class model, and the DAG engine that executes flows node by node.
Caveman, Headroom, and Graphify attack different parts of the token-cost problem — output voice, incoming context, and repeated file reads — so they stack cleanly when configured with attention to layer order and KV-cache prefix stability.
A developer's reference to the MCP Registry, covering the server.json schema, the OpenAPI spec, federation with sub-registries, and how the registry relates to npm and PyPI.
A mid-2026 cost-benefit breakdown comparing self-hosted Coolify to Vercel, Render, DigitalOcean App Platform, and Fly.io — including the workloads where the cloud still beats self-hosting.
A deployment-and-scale-driven comparison of Qdrant, Pinecone, Weaviate, Milvus, and Redis for production RAG, with a decision framework for teams choosing at the selection stage.
A blueprint for orchestrating multiple LLM agents — Diversity, Privacy, QA Curation, expert deliberation, reasoning models, and label-targeted generation — to build custom synthetic QA evaluation sets for RAG systems.
A walkthrough of a self-hosted CI/CD pipeline where GitHub Actions builds the Docker image, calls Coolify's deploy API, and runs a two-phase polling loop so failures surface in CI rather than disappearing silently.
A reproducible LlamaIndex pipeline that sweeps chunk sizes against latency, faithfulness, and relevancy using GPT-4 as an LLM judge, so RAG teams can ship chunk configurations backed by evidence.
A practical look at how Apache Arrow's zero-copy, memory-mapped storage lets Hugging Face Datasets handle multi-GB datasets on ordinary laptops, and when to choose Dataset over IterableDataset.
A guided tour of Coolify's internals — SSH-based Docker deploys, the Sentinel metrics agent, dynamic Traefik routing, the upgrade script, and the Laravel/Fastify/SvelteKit codebase.
A practical guide to choosing between Keras preprocessing layers and tf.image-based augmentation, and between offline pre-generation and online on-the-fly execution, based on dataset size, compute budget, and reproducibility requirements.
A diagnostic guide that maps three recurring failures of system prompts — conflicting signals, attention scarcity, and state erosion — to the structural fixes that resolve each one.
A practitioner's guide to using a knowledge graph as durable agent memory, with Graphiti's EntityNode and episode pipeline as the concrete read/write loop.
A practical breakdown of Milvus standalone's three containers — etcd, MinIO, and Milvus — including their internal roles, Compose wiring, and the scaling path that comes before Kubernetes.
A side-by-side comparison of Caveman output compression, provider prompt caching, and tiered model routing — with concrete per-million-token math and a recommended stack order.
A practical breakdown of the n8n AI Starter Kit's five Docker services, how n8n orchestrates while MCP processes AI tasks, and how to pick a hosting environment from laptop to datacenter.
A head-to-head comparison of Coolify and Dokploy across orchestration (plain Docker vs Docker Swarm), control plane (Laravel vs Next.js), and Traefik editability.
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.