Tendril
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Creators · Ages 14–17
The full LLM pipeline, agentic AI with OpenClaw + Ollama, subscription-tier literacy, and a real capstone.
Meet your guide: Atlas — a minimal octahedron
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Chapters
Modules · 18
Everything comes together. Design, code, test, secure, and ship a production-quality agent with open-source code you can fork today.
Flux Pro vs. Flux Dev. Midjourney vs. Stable Diffusion. The choice affects product architecture, cost, and what's possible. Here's the honest tradeoff.
Assemble the four or five AI tools that actually belong in your daily life. A tested template for the stack that earns its keep.
Claude Projects, ChatGPT Projects, Notion AI, Perplexity Spaces. How persistent context changes AI from search box to actual assistant.
Perplexity Comet is a full web browser that treats AI as a first-class citizen. It reads, summarizes, and acts on pages you visit.
Flux Dev is the LoRA-friendly middle tier of the Flux family. Here is how to train a style on your own art without renting a farm.
Generative imagery, 3D garment sim, and on-demand pattern-making have collapsed the front end. Taste is still the scarce resource.
Codex CLI is OpenAI's open-source terminal coding agent. Look at how it compares to Claude Code, what it does uniquely, and why it matters to non-Anthropic shops.
Consensus searches 200M+ academic papers and gives evidence-based answers. Deep look at how researchers use it, what it does differently from Perplexity, and its limits.
Elicit automates slow parts of academic research: finding papers, extracting data, building literature matrices. Look at what it saves PhDs 20 hours a week.
Debate, amplification, weak-to-strong, process supervision. Research on how humans supervise models smarter than them.
Some capabilities grow smoothly with scale. Others seem to appear out of nowhere. Telling them apart is a whole research program. The Big Question Is AI capability a smooth climb or a staircase?
Even accurate data can encode an unjust history. The COMPAS recidivism tool shows what happens when AI learns from a biased past.
Every labeled dataset has mistakes. Studies have found error rates of 3 to 6 percent in famous benchmarks like ImageNet. Noisy labels confuse models and mislead evaluations.
Saying the average is 50,000 dollars can mean three different things. Picking the wrong kind of average is how statistics starts lying to you.
Mean tells you the center. Variance and standard deviation tell you the spread. Without both, you are missing half the story.
A single weird value can distort your entire analysis. But outliers are also where the most interesting stories live. Knowing when to remove them is an art.
Thousands of companies you have never heard of trade your personal data every second. Understanding this invisible market is understanding modern privacy. Brokers and AI training Much training data for specialized models (ad targeting, credit scoring, risk assessment) comes from brokers.