UPDATED OCT 12, 2026
AI Model Landscape: Major Players and Model Families
A practical, non-ranking reference to the major AI model providers, their current model families, and where each commonly fits in an architecture.
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Late Night Development Field Notes — one-page, practical references on architecture, delivery, and AI systems. Free to download, no email required.
UPDATED OCT 12, 2026
A practical, non-ranking reference to the major AI model providers, their current model families, and where each commonly fits in an architecture.
Read the guideFIELD NOTE 01
The minimum files, commands, and decisions needed to make a project reproducible.
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Service, volume, health check, environment-variable, and reset-command examples.
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Rebuild, reopen, attach terminal, forward port, inspect logs, and recover from a broken container.
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A simple diagram and checklist for lint, typecheck, test, build, and review.
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The practical expand, deploy, backfill, contract pattern, plus rollback questions.
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A fill-in template for documenting context, options, tradeoffs, decision, and consequences.
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Questions to ask before connecting to an API, ERP, identity provider, or vendor platform.
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The core workflow for using AI without losing scope, verification, review, or reasoning.
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Scope, permissions, verification, and review rules for using AI coding agents responsibly.
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A quick guide for choosing chat, repo-aware agents, terminal workflows, or AI review.
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A structured prompt format for turning AI requests into scoped, reviewable development tasks.
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A concise template for preserving assumptions, tradeoffs, rejected options, risks, and proof.
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Instructions, commands, conventions, and boundaries that help humans and AI agents work safely.
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A plan, patch, test, review, commit workflow for keeping AI-assisted changes reviewable.
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A handoff pattern for shaping feature intent in chat and implementing safely in the repo.
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A terminal-first workflow for investigation, refactoring checkpoints, verification, and safety.
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A review checklist focused on bugs, regressions, missing tests, security, and behavior drift.
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A reusable operating model for AI-assisted development from work order to durable reasoning.
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The core distinctions that explain why containers are fast, small, and not a lightweight virtual machine.
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The run, inspect, stop, and cleanup commands that cover most day-to-day container work.
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The instructions that cover almost every image you will build, and how the layer cache actually works.
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Why containers lose data by default, and the volumes, networks, and env vars that fix that.
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The compose.yaml keys and commands that cover most multi-container local setups.
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The real differences between Docker and Podman, and when each is the better default.
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The everyday commands for looking inside a container and cleaning up without guessing.
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Where this series ends and the From Laptop to Production series picks up.
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Know what job you are asking AI to do before you judge it.
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The core terms that improve conversations and decisions: model, prompt, token, and context.
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