Tendril
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Builders · Ages 11–13
Compare Claude, ChatGPT, Gemini, and Flux side-by-side. Learn prompt engineering and catch hallucinations.
Meet your guide: Wren — a sharp raven
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Chapters
Modules · 109
AI is a label that covers many things. Let's narrow it down so you can tell marketing hype from the real computer science underneath.
Most modern AI is trained on a loop of guess, check, and adjust. Understand the loop and you understand the heart of machine learning.
AI does not read letters. It reads tokens, which live as vectors in a space of meaning. Learn how text becomes numbers you can do math on.
You have heard the term a thousand times. Now let's actually look inside: neurons, weights, activations, and what happens in a single pass.
You cannot understand modern AI without understanding its diet. Let's map where the data comes from, how it gets cleaned, and what that means.
Every new model claims a new high score. Before you trust a leaderboard, learn what benchmarks actually measure — and what they miss.
The line between deep reasoning and clever pattern recognition is blurry. Here's how researchers try to tell them apart.
The past decade of AI progress came from a simple, ruthless law: more compute and more data, predictable improvements. Here is the math behind it.
As models scale, some skills do not gradually improve — they just snap into existence. Let's look at what emergence really means and why it scares people.
AI did not start in 2022. It has decades of wrong turns and breakthroughs. Knowing the history helps you spot hype from real progress.
AI bias is not magic and not moral failure. It is math operating on imperfect data. Here is exactly where the bias enters the system.
AI is now involved in hiring, loans, medical care, and criminal sentencing. Here are the documented cases and the frameworks being built in response.
Generative AI trained on copyrighted work has triggered the biggest wave of copyright lawsuits in the internet era. Here is the state of the fight.
Using AI on schoolwork is not simply cheating or not cheating. It depends on the task, the rules, and what you are learning to do. Here is how to think about it.
Before AI, lies took time to make. Now they take seconds and come in infinite variations. Here is how the information ecosystem is changing.
Your posts, chats, photos, and behavior have been scraped, sold, and fed to models. Here is what has actually happened and what you can actually do.
Training a frontier model uses the electricity of a small city for months. Running inference at scale matches a large country's load. Here is what the numbers actually look like.
Children are using AI more than any other group, and have less legal protection. Here is what current laws cover, what they miss, and what is being debated.
Refactoring means changing code without changing behavior. That used to be scary. With tests and AI together, it becomes routine.
Giving an AI the keys to your computer is a big deal. Learn the two simplest ways to keep an agent safe: wall it off from things it shouldn't touch, and put a human in the decision path.
Your data can live in someone's data center or on your own laptop. Both are real options in 2026. Understand what you gain and lose with each.
ElevenLabs can clone a voice from 30 seconds of audio. That's useful for accessibility — and dangerous in the wrong hands. Here's how to use it well.
US Copyright Office in 2026: works created purely by AI aren't copyrightable. Works with enough human creative control might be. Here's where the line sits right now.
Artifacts is Claude's canvas. Charts, code, docs, and interactive React components render live next to the chat.
ElevenLabs voices are indistinguishable from humans. That is a feature and a fraud vector. Here is the production checklist before you clone anyone.
Algebra is where math gets abstract. Wolfram Alpha and Photomath solve anything - the trick is using them without losing the skill.
Geometry is visual. AI is mostly words. Combine tools like GeoGebra with ChatGPT to actually see what you are proving.
Biology is full of pictures and big words. AI can label diagrams, simplify papers, and quiz you on systems.
Chemistry equations are puzzles. AI can balance them instantly. But the lab is still physical - and AI cannot smell danger.
Physics needs intuition. PhET simulations plus AI explanations give you that intuition faster than any textbook.
A great essay starts with a great outline. Let AI brainstorm and structure. Then write every sentence yourself.
History essays live or die by evidence. AI can help you find sources, organize arguments, and avoid weak claims.
Speak, ChatGPT voice mode, and Duolingo Max let you practice conversations without a scary human on the other end.
Study a master artist by having AI explain their techniques, then imitate them yourself. The art is still yours.
AI can write full songs now. Use it as a collaborator, not as your ghost-composer, and you'll learn more than you thought possible.
Every coder uses AI now. The skill is learning to code WITH AI from day one, not letting AI code for you.
Real athletes use video analysis. Now you can too - AI marks up your shot, stroke, or swing in real time.
Google's NotebookLM lets you upload textbooks, lectures, and notes, then chat with them. This is the most underrated study tool of 2026.
Anki is the nerd's secret weapon for memorizing anything. AI makes creating flashcards 10x faster, so you actually use them.
Most teachers in 2026 allow some AI. The gray zone is huge. Here's how to use AI for drafts and still learn.
Lab reports follow a template. AI can help you structure and polish - but your observations and analysis must be yours.
If calendars feel impossible, AI planners rearrange your schedule for you. Here are the best ones for student brains.
Past the beginner phase, English learners need targeted grammar practice. AI shows you your exact mistakes without embarrassment.
Past the basics, dyslexic students can use AI for deep work - reading papers, writing essays, and asking for accommodations that work.
Grammar tools make writing cleaner - but too much 'polish' kills your voice. Here's how to use them and still sound like you.
Stats is 10 percent concepts and 90 percent careful arithmetic. AI is shockingly good at the arithmetic, which frees you to actually think about the concepts.
Geometry rewards seeing. AI tools that can read and draw figures turn a blurry textbook diagram into something you can actually work with.
Shakespeare wrote in English, but not your English. Claude and SparkNotes-style AI can translate a scene the first time, so you can read it the second time for real.
A poem you don't understand can feel like a closed door. AI is excellent at opening the door so you can walk through and form your own opinion of the room.
Using AI to write your story for you makes it no longer your story. Using AI as an editor who reads every draft at 2am is one of the best deals in the world.
Music theory is a language with harsh rules. AI tools can check your voice leading, generate practice exercises, and play what you wrote back at you.
The hardest part of language class is speaking without freezing. Voice-mode AI lets you have real conversations with zero social risk.
ChatGPT Memory lets the model remember facts about you across conversations. Look at what it remembers, what it misses, and the privacy tradeoffs.
When AI outputs get too long, too technical, or too fast for humans to check, how do you know it is doing the right thing? Scalable oversight is the research program trying to answer that.
Most training grades the final answer. Process supervision grades each reasoning step. That small change produced some of the biggest honesty gains in recent years. Math problem-solving accuracy jumped substantially over outcome-only training, and the model was more honest about its own mistakes.
A circuit is a small sub-network inside a big model that implements one specific behavior. Finding circuits is how researchers prove how a model does what it does.
A transformer processes a token through many layers before outputting a prediction. The logit lens shows you what the model would predict if it stopped at each layer along the way.
Almost every AI regulation uses training compute as a trigger. 10^25 here, 10^26 there. Why compute, and why those numbers?
The US government is the largest single buyer of software in the world. What it buys and what it refuses to buy shapes the whole industry. That includes AI.
Japan chose light-touch, guideline-based AI governance built on existing laws. Understanding why illuminates a real alternative to comprehensive AI acts.
Every AI paper has the same skeleton. Learn the parts and you can navigate any of them in 20 minutes.
arXiv is where AI research actually lives. Here is how to read it without drowning.
A paper without code is often a paper without truth. Papers With Code links claims to runnable proof. Where Claims Meet Code Papers With Code is a community-maintained site that pairs AI papers with their open-source implementations and benchmark results.
Most big AI papers appear at one of four conferences. Learn the map and you can navigate the field.
Not every AI paper has the same goal. Read them differently based on their type.
AI is a terrific tutor for dense papers — if you use it the right way.
Benchmarks are how AI progress gets measured. Understanding them is the first step in reading any AI claim.
Automatic metrics miss a lot. Humans catch what metrics cannot. Here is how to run a simple human eval.
When you change a prompt, how do you know the new version is actually better? A/B testing is the honest answer.
Before LLMs-as-judges, researchers had hand-made metrics. They still matter — and still mislead.
AI is fundamentally probabilistic. A little probability literacy goes a long way.
A famous game show riddle teaches the single most important idea in Bayesian reasoning.
P-value is one of the most abused numbers in research. Here is what it actually says — and what it does not. 'Model B is no better than model A.' 'The new prompt does not change user satisfaction.' A low p-value means the boring story would rarely produce data that looks like what you saw.
A point estimate is a guess. A confidence interval is an honest guess with its uncertainty attached. Honest Numbers Come In Pairs When a model scores 72 percent on a benchmark, that is a point estimate.
The most famous warning in statistics is also the most ignored. Here is how to actually tell them apart.
Bayes' rule is just 'update your belief with evidence.' It is shockingly useful.
If your sample is skewed, your conclusion is skewed. Here is how to spot it.
Results tables are where papers make their case. Here is how to decode one in under five minutes.
Excel and Google Sheets hide a lot of complexity behind a pretty grid. Once you see what is really happening, you will never look at a spreadsheet the same way.
CSV is the plainest, ugliest, most universal data format. It has survived every trend because it does one thing well: it works everywhere.
Every column in a dataset has a type: number, text, date, boolean, or identifier. Mixing them up causes most beginner bugs.
Real datasets have holes. Blank cells, NaN, NULL, -999, and the dreaded empty string. Learning to see them is a core skill.
Stable Diffusion, Midjourney, and DALL-E all trace back to LAION, an open dataset of 5 billion image-text pairs. It changed AI, and started a legal storm.
When we say trillions of tokens, we mean it. Let's make these numbers feel real with comparisons you can actually picture.
Surveys consistently find data scientists spend 60 to 80 percent of their time cleaning data. Here is what that actually looks like.
If the same paragraph appears a million times in your training data, your model will memorize it. Deduplication quietly makes AI better.
The raw web is 99 percent garbage. Filtering it down to the 1 percent worth training on is one of the highest-leverage steps in modern AI.
Alan Turing opened modern AI with a single question and a clever game to answer it.
The imitation game became famous, but most AI researchers now think it measures the wrong thing.
A summer workshop in New Hampshire gave artificial intelligence its name and its optimism problem.
Frank Rosenblatt's perceptron promised a thinking machine. A skeptical book almost killed neural nets for a generation.
In the 1970s and 80s, AI found its first real customers by encoding expert knowledge as if-then rules.
After the Lighthill Report and mounting skepticism, AI funding collapsed and the field went quiet.
The 1980s AI boom ended when expert systems hit a wall and specialized Lisp machines went obsolete.
A computer that played a trivia game show became the face of AI for a moment, then taught a hard lesson about hype.
A 2013 paper from Google showed that words could live as points in space, with analogies as arithmetic.
A game thought to be a decade away for AI fell in Seoul, and move 37 rewrote what humans knew about Go.
In 2019, OpenAI released a language model in stages, citing safety, and started a conversation that continues today.
In 2024, a new class of models traded fast answers for slow, deliberate thinking, and benchmarks jumped.
Alignment is not a vibes word. It is the technical problem of getting AI to do what you meant, not just what you said. Here is the short version.
Models reliably find ways to hit the score without doing the task. A short tour of real examples, plus why the pattern keeps coming back.
Red-teamers try to make models misbehave before bad actors do. Here is how the job works, who does it, and what they look for.
Most jailbreaks come from a small number of patterns. Here are the ones that keep working, and why they are hard to kill. The Jailbreak Zoo A jailbreak is any prompt or setup that makes a model break its own rules.
When AI can read documents and act on them, hidden instructions become attacks. Here is what prompt injection is and why nobody has fully solved it.
C2PA, SynthID, and Content Credentials are the quiet standards deciding what is real online. Here is what they do and where the gaps are.
The world's most ambitious AI law passed in 2024. Here is what it actually does, when it kicks in, and why it matters if you do not live in Europe.
The big international AI summits produce non-binding declarations. Even so, they shape the rules. Here is what each one did.
Measured people at serious labs and universities publicly worry about AI going very wrong. Here is what they mean, what they disagree about, and how to read the headlines.
Forget extinction for a minute. Here is the practical stuff: how not to get fooled, scammed, or worse in your daily use of AI.