AI Myths & Mental Models
Understand what AI is doing, where it fails, and which common myths waste time.
26 stories (12 articles · 14 videos)
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A few good first pieces before you browse the full feed.
6 min readWhat AI actually is (and isn't): a no-hype primer
AI in 2026 means one specific thing for most people — and it is not what the movies told you. A plain-English explanation of what is really going on, what it does well, and what it does not.
New to AI
6 min readHow AI generates answers: the mental model that makes prompting click
AI does not think the way you do. It generates likely continuations from context. A plain-English mental model that, once it clicks, makes every prompting tip in the world easier to understand.
New to AI
7 min readThe ten AI myths holding you back
The persistent beliefs about AI that keep otherwise sharp adults from even trying it. Each one addressed honestly — with what's true, what's exaggerated, and what to do about it.
New to AIMore in this topic
7 min readWhy AI explanations feel like learning—and often aren't
A fluent explanation can make a subject feel familiar before you can actually use it. These three tests reveal whether AI helped you learn or merely helped you follow along.
New to AI
69 minutesThe Agent Landscape - Lessons Learned Putting Agents Into Production
MLOps.community. Prosus's VP of AI and an AI engineer report what actually broke when they deployed agents across the group's portfolio companies: prompt-injection pen-testing before launch, an unsafe write when a Jira agent choked on human shorthand, stale context handled by making agents surface their assumptions, and fallback design that merged or killed agents once they added cognitive load. It reads like the article's failure-mode register replayed as a live postmortem.
Advanced
10 min readProduction AI failure modes: what breaks after the demo
AI systems usually fail in predictable ways: hallucination, stale context, sycophancy, prompt injection, unsafe tool use, schema drift, and weak fallbacks. A production failure-mode register for teams shipping real workflows.
Advanced
6 min readAI vs Google: when to search, when to ask
Search and AI assistants are not interchangeable. A practical guide to which tool fits which question — with side-by-side examples and the cases where you should use both.
New to AI
8 min readThe anatomy of a prompt: role, context, task, constraints, format
A five-part template that lifts answer quality from "meh" to "useful" on any prompt, in any AI tool. With worked examples, common mistakes, and the order that matters.
Beginner
10 min readChain-of-thought, self-critique, tree-of-thoughts — when to use each
Three reasoning techniques that genuinely improve AI output on hard problems — and the cost-benefit math of using them. With concrete prompts, side-by-side comparisons, and the gotchas modern reasoning models introduce.
Intermediate
11 min readChunking, reranking, and hybrid search: make RAG actually work
Most RAG implementations work poorly because they get three things wrong. A practical guide to chunking documents, reranking results, and combining keyword with semantic search — without becoming a search engineer.
Intermediate
10 min readPrompt engineering for reasoning models (o3, R1, Claude extended thinking)
Reasoning models are not fast models with extra steps. They reward different prompting, ignore some conventional patterns, and have their own pitfalls. A practical guide to working with them well.
Intermediate
7 min readPicking the right model for the job: a 2026 decision cheat sheet
Which model to reach for, by task type. GPT, Claude, Gemini, the reasoning models, and the open-weights options — sorted by what they are actually best at, with simple decision rules.
Beginner
6 min readWhy AI gives confident wrong answers: a beginner's guide to hallucinations
AI does not lie the way a person does. It can generate fluent false specifics because plausibility and verification are different jobs. Here is what is going on, why it happens, and how to avoid being burned.
New to AI
10 minutesLearn 80% of Perplexity in under 10 minutes!
Jeff Su. Jeff opens with the spectrum the article hinges on — ChatGPT and Gemini at the "creative" end, Perplexity and Google Search at the "accurate, real-time, sourced" end — and then shows you the third option in action. The Olympics-medals comparison in the middle is the cleanest "this is when you ask a chatbot, this is when you Google" demo we've seen. After watching, you'll have a third tool in your search routine and a working rule for picking between the three.
New to AI
29 minutesTree of Thoughts: Deliberate Problem Solving with Large Language Models (Full Paper Review)
Yannic Kilcher. Yannic walks through the original Tree-of-Thoughts paper from Princeton/DeepMind — the generate-score-prune-backtrack loop, why it lifts Game of 24 accuracy from 4% to 74%, and where the technique stops being worth its 10× token cost. Useful precisely because it forces you to see ToT as a search algorithm, not a magic prompt prefix.
Intermediate
25 minutesPrompting 101
Anthropic. Two members of Anthropic's Applied AI team build up a real-world prompt live, layering in role, structured sections and explicit thinking steps. It's the most credible "how to use chain-of-thought without ritualizing it" demo on YouTube, and it makes the article's "stop pasting Let's think step by step into everything" point concrete.
Intermediate
131 minutesHow I use LLMs
Andrej Karpathy. A follow-up where Karpathy actually sits in front of ChatGPT, Claude, Gemini and Perplexity and uses them like a power user. Watch this after the article and his "Intro" — you'll see the mental model put into practice in real time, including the small choices (which model, which mode, when to paste vs upload) that separate frustrated beginners from people who get useful answers on the first try.
New to AI
60 minutes[1hr Talk] Intro to Large Language Models
Andrej Karpathy. A founding OpenAI engineer giving the cleanest hour-long explanation of what an LLM actually is, in plain English. The "psychology of an LLM" section is exactly the mental model the article is trying to install — why these models hallucinate, why they're good at vibes and bad at counting, why prompting is closer to coaching than coding.
New to AI
3 minutesBuilding OpenAI o1
OpenAI. Three minutes with the o1 team describing the moment the model started questioning its own reasoning during RL training. Useful as the primary source for the article's claim that the chain-of-thought is now happening inside the model, not in your prompt.
Intermediate
28 minuteso1 - What is Going On? Why o1 is a 3rd Paradigm of Model + 10 Things You Might Not Know
AI Explained. The clearest non-hype explanation of why o1 (and by extension o3, R1, Claude extended thinking) layers reinforcement learning on top of next-token prediction to reward correct multi-step answers, and what that means for how you talk to it. Watch this and the article's "stop saying think step by step, start writing the spec" advice stops feeling arbitrary.
Intermediate
34 minutes"Generative AI" is not what you think it is
Acerola. A developer-essayist works through the "AI is just slop / AI is magical / AI is theft" trio of myths with patience and code on the screen. If the article's myth #4 or #7 ("it's just plagiarism," "it's just plagiarism but for art") nagged at you, this is the video that earns the right to disagree with you.
New to AI
12 minutesWhat We Get Wrong About AI (feat. former Google CEO)
Cleo Abram. Cleo Abram walks through both the "it will kill us all" and "more profound than fire" extremes, asks Eric Schmidt the awkward questions, and lands on a sober middle. The framing — "we're living inside a trolley problem" — is exactly the spirit of the article: neither dismiss the technology nor catastrophise it, just understand what it actually is.
New to AI
36 minutesAndrew Ng: Opportunities in AI - 2023
Stanford Online. Andrew Ng, one of the people who actually built the field, talking plainly about what AI is good at, what it isn't, and where the realistic opportunities sit. A useful counterweight to social-media takes — same calm, no-hype tone as the article, just with more depth on the "where is this actually going" question.
New to AI
8 minutesLarge Language Models explained briefly
3Blue1Brown. The cleanest eight-minute mental model of an LLM on YouTube. Grant Sanderson walks through "next-word prediction," parameters, training, and reinforcement learning from human feedback without ever drifting into hype. Watch it right after the article and the phrase "AI is just autocomplete on steroids" stops being a slogan and starts being a useful shorthand.
New to AI
10 minutesWhy Large Language Models Hallucinate
IBM Technology. Martin Keen sorts hallucinations into four named buckets — sentence contradictions, prompt contradictions, factual errors, nonsense — and walks through each on a lightboard. After the article gives you the why, this video gives you a vocabulary for spotting the type of mistake in the wild so you can decide how much to trust a given answer.
New to AI
36 minutesChatGPT with Rob Miles - Computerphile
Computerphile. A long, calm sit-down with AI-safety researcher Rob Miles on why a model that's trained to predict plausible text will always, structurally, sometimes invent facts. Recorded in early 2023, but the underlying mechanism it explains hasn't changed and the framing is still the cleanest way to internalise "it's a fluency engine, not a truth engine."
New to AI