Technical writing from our practice.

What each pattern actually is, why the three terms get confused, the table-format wars (Iceberg, Delta, Hudi), the broader data-platform landscape (object storage, file formats, query engines, ingestion, transformation, catalogs, governance, orchestration), when to use which pattern, the modern practices that have settled around each, and a realistic step-by-step implementation guide.

What RAG actually is, where it came from, the eight pillars of a production RAG system (ingestion, chunking, embeddings, vector stores, retrieval, re-ranking, generation, evaluation), the four architecture generations (naive, advanced, modular, agentic), Graph RAG and ColBERT and the 2026 frontier, the failure modes that keep teams up at night, and how to actually start.

The eight families of AI models that matter in production today — generalist LLMs, reasoning models, vision-language models, embeddings, image and video generation, speech and audio, code models, and classical tabular ML — plus the emerging families (small/on-device, time-series, domain-specialized, robotics, MoE, state-space). What each family is for, how they were trained, how the leading 2026 products compare, the serving stack underneath, the eval methodology, the cost economics, the safety landscape, and the modern practices that have settled around each.

What Acunetix is — DAST web vulnerability scanning, DeepScan crawler for SPAs, AcuSensor IAST hybrid, network scanning — its place in the Invicti Security product family, and where it sits next to Burp Pro, OWASP ZAP, and Invicti.

What agentic AI actually means in 2026 — the agent loop, the Model Context Protocol (MCP) as the universal tool interface, the framework landscape (LangChain / LangGraph / CrewAI / Pydantic AI / Claude Agent SDK / OpenAI Agents SDK), code and browser agents, multi-agent patterns, evaluation, and production concerns.

A pan-and-zoom map of the 75+ tools that define the modern AI/ML stack — foundation models, inference engines, agent frameworks, vector databases, MLOps, evaluation, enterprise platforms, cloud compute, and developer assistants. Click ⛶ to go fullscreen.

What inferencing actually is in 2026 — the prefill / decode split that defines LLM serving, the runtime engines that compete (vLLM, Triton, TGI, TensorRT-LLM, llama.cpp), the optimization techniques (quantization, speculative decoding, paged attention, multi-LoRA), the hardware spectrum, and the economics that make inference the bigger half of any AI budget.

What Spark actually is, the driver/executor model that defines every job, the four library layers (SQL / Streaming / MLlib / GraphX), Spark on Kubernetes, and where it sits in the modern data landscape.

What Argo Workflows is, the DAG-and-pod-per-step execution model, where it fits next to Tekton and Airflow, and the patterns that actually scale on real fleets.

What 'data scientist' actually means in 2026 after LLMs reshaped the field, the three paths the role has bifurcated into, the skill stack to learn, the resources worth your time, and an honest read on the job market.

What Datadog actually is once you strip the marketing, how the agent-and-SaaS architecture works, what each of the 20+ products gives you, where it earns the cost, and where it doesn't.

What Camunda is, why BPMN matters as an executable runtime and not just a notation, the Camunda 7 → Camunda 8 (Zeebe) rewrite, the external-task / job-worker model that defines C8, and where it sits next to Temporal and traditional BPM.