Claude Code, Codex, and Copilot: AI Coding Agents in 2026
How the major coding agents differ in autonomy, environment access, and pricing — and how to pick one for your workflow.
Practical writing on AI, web analytics, link tracking, SEO, and building with Next.js and Supabase.
Topics: agents · ai · analytics · auth · http · llm · marketing · markets · nextjs · postgres · security · seo · supabase · tutorial
How the major coding agents differ in autonomy, environment access, and pricing — and how to pick one for your workflow.
How retrieval-augmented generation works end to end — embeddings, vector search, chunking, and the failure modes the demos never mention.
Why models can’t reliably tell instructions from data, what the lethal trifecta means for agents, and the mitigations that actually help.
A decision guide for getting domain knowledge into an LLM app — costs, failure modes, and the order you should actually try things in.
Why models see subwords instead of words, why code and non-English text cost more, and how token counts drive pricing, context, and speed.
Ollama, llama.cpp, and what your hardware can actually handle — model sizes, quantization, and when local beats the API.
Chipmakers investing in the labs that buy their chips: how circular financing works, why it spooks investors, and the dot-com parallel.
Metadata, sitemaps, structured data, OG images, and rendering choices — everything an App Router site needs to be properly crawlable.
Capability tiers, context windows, and pricing across the big three model families — and the questions that actually decide which one fits your product.
How per-token pricing actually works, why output tokens cost more, and how prompt caching and batch processing cut real bills.
An agent is a model in a loop with tools. What that means concretely, when agents beat single prompts, and where they still fail.
Why AI tools converged on one protocol for connecting models to data and tools, how MCP servers and clients work, and when to build your own.
Hyperscaler capital spending, AI’s share of index gains, and how to think about concentration risk — an explainer, not investment advice.
What utm_source, utm_medium, and utm_campaign actually do, how they flow into GA4, and the naming conventions that keep campaign reports clean.
How each HTTP redirect status code behaves, what it means for SEO and caching, and simple rules for choosing the right one.
A complete walkthrough of building a personal link shortener: schema design, redirect handling, click analytics, and deployment on Vercel.
Where the gclid parameter comes from, how offline conversion imports use it, and why stripping it breaks Google Ads attribution.
How search engines treat shortened links, when a 301 passes PageRank, and the cases where a shortener genuinely costs you.
RLS policies for common app shapes — per-user data, admin service roles, public read tables — with the pitfalls that cause silent empty results.
Next.js 16 renames middleware to proxy. What changed, what stayed the same, and a step-by-step migration with auth gating as the example.
Passwordless email sign-in with @supabase/ssr — auth callback routes, session refresh, protected routes, and the redirect gotchas.