Topic
AI Myths & Mental Models
Understand what AI is doing, where it fails, and which common myths waste time.
24 stories (11 articles · 13 videos)
Start here
A few good first pieces before you browse the full feed.
6 min readArticle
What 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.
Explain what LLMs do, where they are useful, and when to verify their output before acting on it.
New to AI
6 min readArticle
How 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.
Use the next-token mental model to write better prompts without implying that models think like people.
New to AI
7 min readArticle
The 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.
Separate realistic AI capability from common myths so adoption decisions are calmer and more accurate.
New to AI
More in this topic
48 minutesVideo
How to Build Reliable AI Agents (Context + Evals Explained) | Tobias Leong, Axium
Arize AI. Explains why production agents fail when the system lacks the right context, evaluation data, tracing and domain expertise. It maps well to the article's failure-mode register because it makes reliability an engineering loop: separate retrieval from reasoning, define expected outcomes, evaluate tool calls, and trace failures before changing models.
Advanced
10 min readArticle
Production 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.
Build a production AI failure-mode register with controls for hallucination, stale context, prompt injection, unsafe tool use, and weak fallbacks.
Advanced
11 min readArticle
Chunking, 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.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
10 min readArticle
Prompt 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.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
10 min readArticle
Chain-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.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
7 min readArticle
Picking 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.
Turn the workflow into a small practical experiment with a clear quality check.
Beginner
8 min readArticle
The 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.
Build prompts with role, context, task, constraints, examples, and output format instead of relying on one-off wording tricks.
Beginner
6 min readArticle
AI 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.
Understand the idea well enough to try it safely in a low-risk setting.
New to AI
6 min readArticle
Why 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.
Recognize hallucination-prone tasks and use verification, search, or source-grounding before relying on specifics.
New to AI
3 minutesVideo
Building 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 minutesVideo
o1 - 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) is trained for correctness instead of next-token prediction, 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
29 minutesVideo
Tree 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 minutesVideo
Prompting 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
10 minutesVideo
Learn 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
34 minutesVideo
"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 minutesVideo
What 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
132 minutesVideo
How 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
36 minutesVideo
ChatGPT 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
10 minutesVideo
Why 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 minutesVideo
Andrew 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 minutesVideo
Large 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