AI is the new front page.
Here's how you get on it.
Deep, no-fluff guides on answer engine optimization, building with LLMs, and using AI as an operator — plus free prompt generators that run entirely in your browser. This whole hub is built with the same engine I build for clients.
15 ChatGPT Hacks Every Beginner Should Know
15 simple ChatGPT tricks for total beginners, each with a copy-paste prompt you can use right now. No tech background needed.
AEO vs SEO: What Actually Changes When AI Answers the Question
SEO ranks links a human clicks; AEO wins the citation a model quotes. Here's what changes, what stays, and how to run both.
AI Leverage: The Operator's Playbook
How a solo operator or small team turns AI into cheap senior labor you direct — where it pays off, where it wastes time, and how leverage compounds.
Browse by model
See all →Everything on a given model or tool, in one place — Claude, ChatGPT, Gemini, Perplexity and the rest.
Browse by topic
See all →Topic clusters — the through-lines that run across the whole library.
Tools that do the hard part for you
Prompt generators, an answer-engine scorecard, schema and llms.txt builders, a model picker — built to paste straight into your workflow. Everything runs in your browser at zero cost.
Explore everything
Every piece, searchable and filterable by format and topic. The content engine builds in the formats answer engines reward — pillars, comparisons, step-by-step guides, and FAQ.
11 Things You Didn't Know AI Could Do
11 surprising, genuinely useful things AI can do for regular life, from dinner ideas off a fridge photo to a patient tutor that never sighs.
15 ChatGPT Hacks Every Beginner Should Know
15 simple ChatGPT tricks for total beginners, each with a copy-paste prompt you can use right now. No tech background needed.
AI Prompts That Feel Like Cheating
10 copy-paste AI prompts for everyday wins: the awkward email, dinner from your fridge, a confusing contract, a hard conversation, and more.
Building With Claude: Strengths, Quirks, and How to Get the Most Out of It
How I build with Claude in production: where it shines, which tier to use, prompt caching, structured output, extended thinking, and the honest limits.
Building With GPT and the OpenAI Stack: A Practical Guide
Where GPT and the OpenAI ecosystem fit for builders: multimodal, function calling, ecosystem breadth, when to reach for it, and the honest tradeoffs.
Building With Gemini: Where Google's Model Fits
Where Gemini fits in a builder's toolkit: huge context, strong multimodal, Google ecosystem and data integration, and the honest tradeoffs to plan for.
Building With Grok (xAI): Where It Fits
An honest operator's take on xAI's Grok — its real-time and X-data edge, where you'd reach for it, and the tradeoffs to weigh.
ChatGPT vs Claude vs Gemini: Which Should a Normal Person Use?
A plain-English, no-hype comparison of the three big AI assistants for regular people. They're all free to start. Here's how to just pick one.
Claude vs ChatGPT for Everyday Use
Claude vs ChatGPT for normal daily use — writing, research, brainstorming, and coding help — with a clear decision rule per use case.
Getting Found and Cited on Perplexity
How Perplexity sources and cites answers, what content actually wins there, and how to show up and track it.
Getting Found in ChatGPT Search
How ChatGPT's search and browsing pull in sources, how to be the page it cites, and how to track whether it's working.
Getting Found in Google AI Overviews and AI Mode
How Google's AI Overviews and AI Mode pick and cite sources, how it overlaps with classic SEO, and how to monitor your presence.
Grok, Llama, and the Rest of the Field
An honest survey of the models beyond the big three — Grok, Llama, open weights, and the rest — and when a builder reaches for each.
How to Talk to AI So It Actually Helps
The one beginner skill that changes everything: how to ask AI for help so you get something useful back. Real before-and-after examples.
How to Use AI to Save Hours Every Week
Practical ways to use AI for email, planning, learning, decisions, and admin, with real examples and the time each one saves.
Multimodal AI: A Builder's Guide to Vision, Images, and Audio
What multimodal models actually do, where they earn their keep, and how to ship vision and structured extraction in production without surprises.
Prompting Claude vs GPT: What Actually Differs
The prompting habits that carry between Claude and GPT, the ones that don't, and how each family wants to be steered in production.
Self-Hosting Open Models: Llama, Mistral, and When It's Worth It
The real case for running Llama and Mistral yourself — privacy, cost at scale, and control — versus the operational burden that eats the savings.
AEO vs GEO vs LLMO: Decoding the Acronyms (and What Actually Matters)
AEO, GEO, and LLMO are three labels for mostly the same job: getting cited inside AI answers. Here's what each emphasizes and the shared playbook underneath.
AEO vs Paid Ads: Where Should Your Next Dollar Go?
A side-by-side on cost curve, durability, trust, and speed so you know exactly where your next acquisition dollar should land.
AEO vs SEO: What Actually Changes When AI Answers the Question
SEO ranks links a human clicks; AEO wins the citation a model quotes. Here's what changes, what stays, and how to run both.
AI Coding Assistants Compared: Autocomplete vs Chat vs Agent
Three shapes of AI dev tool — inline autocomplete, chat-in-editor, and autonomous coding agents — compared by control, speed, trust, and best-fit work.
AI Leverage: The Operator's Playbook
How a solo operator or small team turns AI into cheap senior labor you direct — where it pays off, where it wastes time, and how leverage compounds.
AI Search & AEO: Frequently Asked Questions
Straight answers on AI search, getting cited by ChatGPT and Claude, schema, llms.txt, crawlers, and how to measure AEO.
AI for Operators: Frequently Asked Questions
Straight answers to the questions operators actually ask about AI: cost, headcount, where to start, quality, data safety, and ROI.
An AI Marketing Stack That Actually Ships
The practical AI marketing stack an operator actually runs: research, content production with AEO, repurposing, distribution, and measurement — honest about what to automate.
Answer Engine Optimization: The Complete Playbook
How to get your business cited inside ChatGPT, Claude, Perplexity, and Google AI answers — the mechanics, the process, and how to measure it.
Automating Real Work With AI (Without the Slop)
A practical guide to automating real work with AI: pick the right tasks, keep a human in the loop, build the automation step by step, and gate the quality.
Big Model vs Small Model: When Cheap and Fast Wins
Frontier model or small fast one? Quality, cost, latency, and reliability head to head, plus the fan-out-cheap, escalate-to-frontier pattern.
Build vs Buy: Custom AI vs Off-the-Shelf Tools
When to use an off-the-shelf AI SaaS tool and when to build your own on an API. A clear decision framework by company stage.
Building AI Agents That Actually Work
An agent is a loop: model, tools, memory, and a stopping condition. Here's how to build one that finishes the job instead of spiraling.
Building With AI: Frequently Asked Questions
Practical answers for builders: model choice, RAG vs fine-tuning, agents, hallucinations, evals, cost, latency, and getting started with an LLM.
Building With LLMs: An Operator's Field Guide
How I actually build with large language models: model tiers, prompting as spec, structured output, evals, guardrails, and what breaks in production.
Building an AI Content Engine From Scratch
The operator's blueprint for a real AI content engine: research substrate, draft, judge loop, AEO structure, schema, citation measurement, and feedback.
ChatGPT vs Perplexity vs Google AI Overviews: Where to Win Visibility
Three answer surfaces, three ways they source and cite. Where to focus to get cited by AI, what wins on each, and how to track your presence.
Chatbot vs Agent vs Copilot: Which AI Feature to Build
Three AI product patterns, three different risk profiles. A clear guide to which one to build and when each actually fits.
Claude vs GPT vs Gemini: Picking a Model as a Builder
Choosing an LLM to build on, not chat with: reasoning, tool use, context, cost tiers, and where each family actually wins.
Context Engineering: The Skill That Replaced Prompt Hacking
Managing the context window is the real craft now. What to put in, retrieval vs stuffing, ordering, caching, compaction, token budgets, and multi-turn memory.
Conversational Search: How AI Changed What People Ask
Chat-style queries are longer, richer, and full of follow-ups. Here's how the questions changed — and how to write content that answers them.
Evals: How to Actually Know Your AI Works
Vibes-testing lies to you. Here's how I build eval sets, grade outputs, and run regression tests so I know a model change didn't quietly break things.
Guardrails: Shipping AI That Won't Embarrass You
Input and output validation, moderation, prompt-injection defense, grounding, human-in-the-loop, and logging — the layers that keep AI from going sideways in front of users.
How to Choose an LLM (and Switch Without Pain)
A practical decision process for picking an LLM: define the task, pick a tier, test on your data, and architect so switching is a config change.
How to Cut Your LLM Costs (Without Cutting Quality)
Prompt caching, batching, model routing, leaner context, output caps — the levers that drop your AI bill without touching output quality.
How to Get Cited by ChatGPT, Claude, and Perplexity
A do-this-now playbook for becoming the source AI answer engines quote — answer-first writing, extractable claims, clusters, and testing.
How to Track Whether AI Is Citing You
A repeatable method to measure AI-search visibility: build a prompt set, query the engines, log citations, score share-of-voice, and turn the gaps into content.
How to Write FAQ Pages That AI Actually Cites
Picking real questions, answer-first phrasing, FAQ schema, and structure — the mechanics that turn an FAQ page into a cited source.
In-House AI Content vs Hiring It Out
Build the AI content engine yourself or hire an agency? A clear breakdown of cost, control, quality, and what to never outsource.
Models & Capabilities: Frequently Asked Questions
Straight answers to the questions builders actually ask about LLMs: tokens, context windows, cost, hallucination, multimodality, and more.
Open-Weight vs Closed Models: What Builders Should Actually Use
Open-weight or closed API model for your product? Capability, cost, privacy, control, support, and total cost of ownership, decided by use case.
Programmatic AEO at Scale (Without Becoming Slop)
How to build hundreds of templated pages that stay genuinely useful and citable — the quality gates that separate leverage from spam.
Prompt Engineering for Production (Not Party Tricks)
Treat prompts as specifications, not magic words. Structure, structured output, evals, versioning, and the system prompts that run 10,000 times a day.
RAG vs Fine-Tuning vs Long Context: How to Give a Model Your Knowledge
Three ways to put your proprietary knowledge into an LLM — retrieval, fine-tuning, long context. What each costs, when each wins, how they combine.
Reasoning Models, Explained: When Thinking Longer Helps
What reasoning and extended-thinking models actually do, where step-by-step deliberation beats a fast answer, and when it's just burning money.
Schema & JSON-LD for AI Search: A Practical Setup
The schema types that actually help answer engines extract and cite you, with copy-pasteable JSON-LD, where to put it, and the mistakes that quietly break it.
Single Prompt vs Agent vs Workflow: Choosing the Right Shape
The three shapes of LLM apps — one call, an agent loop, a deterministic workflow. How they compare and how to pick the simplest one that works.
Structured Output and Tool Use, End to End
How I get reliable JSON out of a model, design tools it can call, validate and repair the output, and wire the whole thing into a real app.
The Frontier Model Landscape: A Builder's Map
A builder's map of the frontier LLM landscape: the families, the dimensions that matter, and why you should design to swap models.
Vector Search vs Keyword Search for RAG
Semantic embedding retrieval vs lexical keyword search for RAG — accuracy, cost, setup, failure modes, and why hybrid usually wins.
When Fine-Tuning Is Actually Worth It
The honest cases for fine-tuning versus prompting, RAG, and long context — plus the maintenance cost that's why most teams shouldn't start here.
llms.txt, robots, and AI Crawlers: The Technical AEO Setup
The copy-pasteable technical setup for AI answer engines: llms.txt, robots.txt crawler rules, JSON-LD schema, canonicals, and freshness signals.