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 · 61
From raw bytes to deployed model, every ML system follows the same ten-stage pipeline. Master it and you can read any architecture paper.
Emergent abilities make AI both more exciting and more dangerous. How do labs forecast what the next model will do — and what happens when they are wrong?
Open-source AI is both a technical movement and a political one. Understand the arguments so you can pick a stack and defend it.
Every AI breakthrough of the past decade rests on three interacting ingredients. Synthesize everything you have learned into one working model.
Anthropic publishes detailed prompt engineering guidance. Master the core patterns — Be Direct, Let Claude Think, and Chain Complex Prompts — to write production-grade prompts.
Use an AI to write, optimize, and debug your prompts. Meta-prompting is how top teams ship production prompts faster than humans alone could write them.
Before shipping, attack your own prompts. Inject, confuse, overload, and role-swap. If you don't find the holes, your users will.
Long system prompts are expensive. Prompt caching lets you reuse the prefix at up to 90% cost reduction and much lower latency. Here's how to architect prompts for caching.
The EU AI Act is the most sweeping AI law in the world. It will set the compliance floor for anyone who ships globally. Here is the architecture, the timeline, and what it gets right and wrong.
Red-teamers get paid to make AI misbehave. The field has grown into a real discipline — with its own methods, its own ethics, and its own unresolved questions.
Abstract jailbreak theory is less useful than real cases. Here are the techniques that worked on production models, what they taught us, and what is still unsolved.
If you ship AI, ethics is not abstract. It is a set of decisions you make with real trade-offs. Here is the working checklist serious builders actually use.
The AI coding tool market fragmented fast. Let's map the 2026 landscape honestly: who is for autocomplete, who is for agents, who wins on cost, and what the tradeoffs actually feel like.
Autocomplete is a suggestion. An agent is an actor. The mental model you bring to each is different, and conflating them is the number-one reason teams trip over AI coding.
Frontier models now read a million tokens of your codebase in one shot. That changes how we architect prompts, retrieval, and the cost curve of agentic work.
Agents ship working code that's also quietly insecure. Red-teaming means actively attacking your own code. Let's build the habits that catch real-world exploits before attackers do.
Code review is the highest-leverage touchpoint in a team. Automating the noise with AI frees humans to focus on the irreducibly human parts. Let's design the workflow.
Sub-agents turn Claude Code from a coding assistant into a small engineering team that works in parallel. Let's build a real sub-agent workflow end to end.
AI belongs in CI/CD too. From PR previews to rollback judgment calls, agents can operate inside your pipeline safely — if you scope them right.
AI coding bills surprise teams that don't watch them. Let's break down the real cost drivers, the levers that actually reduce them, and how to set guardrails before your CFO does.
The creators capstone. You scope, design, build, test, deploy, and document a real full-stack project using an agentic workflow — end to end.
LangGraph became the production favorite in 2026 for good reasons — explicit state, checkpointing, first-class MCP. Build a real agent end-to-end and learn why.
Computer Use lets Claude see your screen and use it — mouse, keyboard, apps. The capability is real, the gotchas are real. A hands-on look at what works in 2026.
A prototype agent and a production agent have the same LLM. What's different is everything around it — durable state, retries, idempotency, observability. The real engineering.
An agent is a new attack surface. Prompt injection, privilege escalation, data exfiltration — these are no longer theoretical. Learn the attacks and the defenses.
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.
Behind the glossy UIs, video models expose REST APIs. Here's how to call Sora, Veo, and Runway programmatically and build production pipelines.
ElevenLabs, Stable Audio, and Suno expose APIs for voice, SFX, and music. Here's how to compose them into a production audio pipeline.
Plan, build, and launch a real creative product using the full AI stack. This is the final deliverable of the Creative track.
Claude Pro vs Max. ChatGPT Plus vs Pro. Gemini AI Pro vs Ultra. Stop guessing which plan you need. Here's the full map.
Subscription spend on AI can silently hit $100/mo. Learn the usage signals that mean upgrade, and the vibes that just mean temptation.
Going beyond the chat window. When you'd reach for the API, how pricing actually works, and how to start building. The API is where AI becomes a building block The consumer app is the most polished version of an AI experience.
Opus 4.7 shipped in April 2026 with a bigger thinking budget and a 1M-token window at standard prices. Here is the architecture, the pricing math, and when the premium is actually worth it.
Databricks Assistant, Snowflake Cortex, and dbt Copilot draft pipelines in minutes. The edge is in modeling, governance, and knowing what business question to answer.
Microsoft Security Copilot, CrowdStrike Charlotte, and SentinelOne Purple accelerate defense. Attackers use the same models. The security engineer is the referee in an AI-vs-AI arms race.
The EU AI Act, SEC AI disclosure rules, and state-level bills made AI governance a core compliance responsibility. The role grew; it did not shrink.
A real job now: adversarially probing LLMs and multimodal systems for jailbreaks, prompt injection, data exfiltration, and harm.
The job climbed the ladder. Simple image labeling went to workflows; trained humans now do reinforcement learning from human feedback on hard tasks.
Cursor forked VS Code and rebuilt it around AI. It's now the de facto AI IDE for serious engineers. Deep dive on what makes it different, the Composer agent, and the $500/month enterprise pricing.
Galileo AI (now part of Google) generates high-fidelity UI mockups from prompts. Look at the acquisition, what happened to the product, and current Google Stitch equivalence.
ElevenLabs generates synthetic voices indistinguishable from human recordings. Deep dive on voice cloning, dubbing, the consent-and-ethics story, and pricing realities.
Writer is a full-stack enterprise AI platform with its own models (Palmyra), strict governance, and deep integrations. Look at who chooses it over ChatGPT Enterprise.
Gong records, transcribes, and analyzes every sales call to surface what works. Deep dive on what Gong actually does, the 'deal intelligence' features, and why it's $1,500+/seat/year.
Clay scrapes, enriches, and personalizes at scale for sales and marketing. Deep look at what it does, the Claygent agent, and pricing that starts at $149/month.
Vic.ai autonomously processes invoices, codes transactions, and speeds up AP teams. Deep look at what CFOs are buying and where it fails.
Harvey is the AI legal platform deployed at top law firms worldwide. Deep dive on what it does, why firms pay six-figures for seats, and the 2026 competitive landscape.
Streaming AI chat to production takes one framework and three env vars. Learn the deploy path that actually ships.
Tie it all together. A command-line tool that reads a file, calls Claude, and prints a summary. Real code, real errors, real polish.
Not toy examples. These are reward-hacking behaviors documented in production LLM training runs, with what each one taught.
A model that behaves well in training and differently in deployment. It is a theoretical concept with growing empirical hints. Here is the full picture.
The attacker does not need access to the model. They only need to put a few carefully chosen examples into its training data. Here is how that works and why it is unsolved.
Labs run dangerous-capability evaluations before release. Which results go public, and which stay private? The line is moving, and it matters.
Insurers price risk. As AI starts causing real losses, they are being forced to do it for AI. The resulting contracts are quietly becoming a major governance force.
The UK stood up the world's first government AI safety institute in November 2023. Its structure, scope, and access model are templates other nations are following.
China was the first major jurisdiction to regulate generative AI specifically. Its rules reflect a very different governance philosophy than the West, but the mechanics matter.
Could AI help someone build a bioweapon? It's a serious question with a boring, important answer. Here is what the evidence shows without the scare quotes.
The world's most influential 'leaderboard' for AI is not a test — it is humans voting blindly. Here is how that works.
A calibrated model's 70 percent means it is right 70 percent of the time. Most LLMs are not calibrated. Here is what that costs you.
Benchmarks measure what you ask. Red-teaming measures what breaks. Learn to test for failure modes, not capabilities. For AI, red teams probe for harmful outputs, jailbreaks, bias, leakage of training data, and dangerous capabilities.
Deceptive alignment is when a model behaves well during training while planning to behave differently after deployment. Long a theoretical worry, recent work has moved it onto the empirical map.