Production-grade systems and the full picture. RAG, evals, cost, security, and long-running agents.
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9 min readDecide whether DGX Spark fits your private-agent roadmap based on NVIDIA-stated capability, operational capacity, and data boundary—not marketing slogans.
8 min readDesign a realistic single-node Spark inference path—memory budget, software stack, failure modes, and explicit criteria for keeping a cloud fallback.
8 min readLink two DGX Sparks using NVIDIA’s documented QSFP/ConnectX-7 path, verify SSH and RoCE readiness, and know how to roll back network changes safely.
10 min readPlan a NemoClaw evaluation on DGX Spark, understand current onboarding and policy layers, and define the execution evidence required before real data or credentials are used.
10 min readEvaluate whether an experimental dual-Spark DeepSeek-V4-Flash serving path is viable before designing n8n and Hermes workflows around it.
12 min readSet up a portable Codex + Claude Code + Cursor workflow where design, review, and implementation hand off through markdown contracts and CLI runs.
15 min readSet up a Linear-backed multi-agent coding workflow where Claude, Cursor, and Codex claim issues, work in parallel, review each other, and close work with an auditable trail.
11 min readDesign a secure document-ingestion pipeline for RAG with permission metadata, OCR quality checks, source freshness, retention rules, deletion behavior, and ingestion tests.
10 min readBuild a production AI failure-mode register with controls for hallucination, stale context, prompt injection, unsafe tool use, and weak fallbacks.
10 min readDesign a company knowledge RAG with permission-aware retrieval, source ownership, leakage controls, and refusal behavior.
9 min readDecide when to buy, configure, extend, or build an AI system based on workflow fit, data control, cost, capability, and strategic value.
9 min readMeasure AI adoption using workflow ROI, quality, risk controls, and maturity levels instead of tool usage vanity metrics.
9 min readCreate a practical AI governance baseline for an SME using AI tools, automations, or customer-facing systems in the EU.
9 min readDecide whether a customer voice agent is appropriate and design the first rollout with disclosure, escalation, testing, and monitoring.
10 min readChoose a private AI deployment pattern based on data sensitivity, capability needs, cost, latency, and operational capacity.
9 min readDesign a repository-aware AI coding workflow that improves delivery speed without weakening review, security, tests, or ownership.
13 min readA working architect's view of the 2026 LLM stack — the model tiers, inference providers, orchestration layers, evaluation tooling, and the trade-offs that actually matter when shipping production AI. Everything you wish someone had laid out before you started.
13 min readStructured outputs and function calling are the bridge from 'LLM that generates text' to 'system that does work'. In production, the patterns that matter are about schemas, error handling, idempotency, and graceful degradation — not just JSON mode.
12 min readSeparate system, developer, and user instructions and test production prompts as versioned system components.
13 min readMost eval suites look impressive but miss real regressions. Building evals that catch what matters requires careful dataset construction, sensitive metrics, judge calibration, and a culture of trust. The patterns from teams that get this right.
12 min readExtend ordinary observability with multi-step traces, attributable cost, prompt and model versions, evaluated quality signals, privacy controls, and workload-derived alerts.
14 min readRun a pinned minimal stdio MCP server and use a production checklist to design and test a separate authenticated Streamable HTTP deployment.
12 min readMost MCP tools we see are technically correct and practically useless. LLMs ignore them, misuse them, or call them in unhelpful ways. The principles for designing tools LLMs adopt naturally, with examples of common failures and their fixes.
12 min readA production RAG pipeline is six stages, each with specific patterns that determine quality. The architecture, the choices at each stage, and the iterative evaluation discipline that distinguishes RAG that works from RAG that disappoints.
12 min readClassic chunk-based RAG has limits. Graph RAG, agentic RAG, and long-context RAG each break those limits in different ways. When each is the right tool, how they actually work, and the production trade-offs that matter.
12 min readPrompting, RAG, and fine-tuning are the three big levers for adapting LLMs to your problem. Each is right for some problems and wrong for others. A framework for choosing, the realistic costs of each, and the production patterns where combining them shines.
13 min readDecide whether parameter-efficient tuning is justified, govern the data, pin a reproducible experiment, compare held-out and safety results, and benchmark serving before deployment.
13 min readInfinite or pseudo-infinite loops are a costly agent failure mode. This guide shows how to bound work, detect lack of progress, and terminate safely.
11 min readCompare agent frameworks against one representative workflow, explicit operational requirements, and an exit-cost review instead of relying on popularity or opinion.
12 min readLarge context windows are capacity limits, not quality guarantees. Build position, distractor, retrieval, latency, and cost tests for the workload you actually run.
12 min readLong-running agents need an owned persistence design: provenance, confirmation, tenant isolation, retrieval tests, retention, correction, and verifiable deletion.
12 min readEvaluate a narrow computer-use workflow with runtime-enforced scope, human approval, independent result checks, security testing, and measured unit economics.
14 min readThreat-model an LLM workflow and add concrete controls for untrusted content, retrieval, tool calls, authorization, monitoring, and incident response.
12 min readBuild a trace-based inference cost model, optimize the largest measured contributors, and prove that each change preserves task quality.
11 min readAt what scale does self-hosting beat API calls? The actual math, the operational realities, and the patterns that distinguish teams who should self-host from teams who should keep paying for managed inference.
13 min readModel usage distribution, contribution margin, failure handling, support, and retention before choosing a price. This worksheet replaces unsupported market ranges with auditable inputs.
