ChatGPT Plus vs Claude Pro vs Google AI Pro: what to choose
A practical comparison of personal AI subscriptions across workflows, limits, research, coding, ecosystems, privacy, total cost, and switching rules.
Codex subscription vs API: choosing access, billing, and automation
A practical comparison of Codex through a ChatGPT plan versus your own OpenAI API key across billing, identity, limits, local and cloud tasks, CI, governance, observability, and migration.
Claude Pro vs Max vs Team vs Enterprise: which plan to choose
A practical comparison of Claude Pro, Max, Team, and Enterprise across usage, Claude Code, collaboration, identity, security, retention, governance, and total cost.
Claude Code subscription vs API: choosing access and billing
A practical comparison of Claude Code through Pro, Max, Team, or Enterprise and token-based access through Anthropic Console or a cloud provider across billing, identity, limits, automation, observability, and exit readiness.
ChatGPT Business vs Enterprise: which plan should a company choose?
A practical comparison of ChatGPT Business and Enterprise across team scale, identity lifecycle, security, retention, data residency, compliance logs, support, cost, and rollout decisions.
Google AI Plus vs Pro vs Ultra: which Gemini plan to choose
A practical comparison of Google AI Plus, Pro, and Ultra across Gemini limits, context, Deep Research, Notebook, Flow, storage, privacy, and the Google Workspace boundary.
Perplexity Free vs Pro vs Max: what to choose
A practical comparison of personal Perplexity plans across search, Research, models, files, Comet, Computer, privacy, limits, credits, and total cost.
Microsoft Copilot Free vs Personal vs Family vs Premium
A practical comparison of Microsoft Copilot Free and Microsoft 365 Personal, Family, and Premium across chat, Office apps, grounding, AI credits, agents, privacy, account boundaries, and total cost.
ChatGPT Memory vs Projects vs custom GPT knowledge
A practical comparison of ChatGPT Memory, Projects, and custom GPT knowledge: what persists across chats, how to bound context, where sources belong, and how to test deletion and leakage.
MCP tools vs resources vs prompts: what to use when
A practical comparison of MCP tools, resources, and prompts across control plane, discovery, schemas, permissions, freshness, UX, testing, and safe server design.
AI agent harness: how to design a reliable runtime
A practical guide to AI agent harnesses: execution loops, tools, sandboxes, durable state, context assembly, permissions, checkpoints, evals, observability, and recovery for long-running tasks.
OpenAI Responses vs Claude Messages vs Gemini Interactions API
A practical comparison of the main OpenAI, Anthropic, and Google APIs for production AI: state, tools, streaming, background jobs, portability, evaluation, and migration controls.
MCP Tasks vs synchronous tool calls: how to handle long-running operations
A practical choice between standard MCP tools/call and experimental MCP Tasks for long-running operations: capability negotiation, states, polling, cancellation, security, testing, and rollout.
MCP form vs URL elicitation: how to request user data safely
A practical choice between form and URL elicitation in MCP: capability negotiation, sensitive data, third-party OAuth, completion, phishing controls, testing, and rollout.
How to evaluate browser agents: a practical checklist
A reproducible release protocol for browser and computer-use agents: task state, visual grounding, trajectories, side effects, recovery, security, and risk-bounded rollout.
AI agent benchmarks: GAIA, WebArena, OSWorld, and SWE-bench
A practical guide to choosing an AI agent benchmark: what GAIA, WebArena, OSWorld, and SWE-bench actually test, how to read results, and how to carry an external signal into your own release gate.
AI agent trajectory evaluation: test the path, not only the result
A practical guide to evaluating AI agent trajectories: trace contracts, tool calls, permissions, retries, side effects, graders, failure taxonomy, and release gates.
How to evaluate prompt-injection defenses: a practical checklist
A reproducible protocol for testing prompt-injection defenses: threat modeling, source-to-sink fixtures, tool traces, data exfiltration, side effects, false positives, release gates, and rollback.
How to evaluate AI tool calling: a practical checklist
A reproducible protocol for evaluating function calling and tool use: tool selection, arguments, trajectory, side effects, retries, terminal state, cost, and a release gate.
How to evaluate LLM hallucinations: a practical checklist
A reproducible protocol for evaluating factuality and groundedness: claim types, verified evidence, abstention, calibration, long-form responses, risk slices, and a release gate.
How to evaluate an AI image generator: a practical checklist
A reproducible protocol for evaluating AI image generators across prompt adherence, editing, consistency, text rendering, rights, provenance, safety, cost, and rollback.
MCP Apps vs plain tool output: when an AI integration needs a UI
A practical choice between a standard text or structured MCP response and an interactive MCP App: value criteria, architecture, security, fallback behavior, testing, and rollout.
Evaluating RAG: retrieval, groundedness, and answer-quality metrics
A practical RAG evaluation system that separates retrieval from generation, links metrics to failure modes, calibrates LLM judges, and turns the eval set into a release gate.
Prompt caching in OpenAI, Anthropic, and Gemini: architecture and trade-offs
A practical guide to prompt caching: build a stable prefix, compare automatic and explicit caching, model the economics, protect data, and diagnose cache misses.
Knowledge graphs in RAG
How to integrate a governed knowledge graph into RAG: ontology, entity linking, traversal, text evidence, provenance, fact validation, and operational updates.
Human-in-the-loop for AI
A practical production architecture for human oversight: involve a person at a specific risk point with enough context for real, not ceremonial, control. Covers contracts, authority boundaries, failure modes, evaluation, and controlled rollout.
Product discovery for AI products
AI product discovery validates workflow value rather than a model demo: user need, evidence availability, acceptable errors, human oversight, baseline, economics, and a safe path to deployment.
Evaluating AI vendors
A practical system for selecting an AI vendor: from requirements and a controlled evaluation set to security, contractual safeguards, migration cost, and continuous monitoring after procurement.
RAG from scratch: from document to verified answer
A production RAG pipeline from ingestion, normalization, chunking, and embeddings through retrieval, reranking, grounded generation, citations, and evaluation.
Prompt engineering as a systems discipline
How to design instructions, context, examples, quality criteria, and checks so a prompt becomes part of a reliable system rather than a magic incantation.