AI Starter: Ask, Inspect, Prove
Use a repeatable ask-inspect-prove loop without exposing private information or trusting confident output blindly.
3 complete lessons · 75 min · BeginnerIf this is your first visit, begin with the short path that is ready now. You can also search the map of upcoming topics.
Use a repeatable ask-inspect-prove loop without exposing private information or trusting confident output blindly.
3 complete lessons · 75 min · BeginnerSearch by skill or tool, then use publication status to see what you can study now.
A complete three-lesson bilingual path for using any AI assistant with clearer instructions and stronger verification.
A rebuilt ten-part foundation covering prompting, tools, agents, data, RAG, safety, and careers.
Plan, build, connect data, add authentication, test, and deploy a real website.
Streaming interfaces, tool calls, memory, RAG, scheduled work, guardrails, and deployment.
Modernize the recovered CLI guide around permissions, context, diffs, tests, and recovery.
Repository context, multi-file changes, verification, parallel work, and safe handoff.
Use Agent, codebase context, rules, and diff review while avoiding obsolete unsafe modes.
Reconcile the four recovered guides around current setup, context, subagents, costs, privacy, and skills.
Syntax, data structures, functions, files, OOP, errors, debugging, and maintainable Python.
Compilation, arrays, pointers, strings, structs, dynamic memory, and leak inspection.
JVM basics, encapsulation, inheritance, polymorphism, collections, errors, and streams.
Components, state, effects, data fetching, performance, context, routing, and custom hooks.
App Router, server/client boundaries, data, rendering, Server Actions, middleware, and deployment.
A product walkthrough covering setup, live practice, reports, and improvement planning.
Goals, context, constraints, examples, output contracts, iteration, and verification.
Agent loops, tool permissions, memory, checkpoints, failure modes, and human approval.
Compare assistants, research tools, document tools, and agents by evidence and risk.
Collection, quality, labeling, leakage, privacy, evaluation sets, and monitoring.
Chunking, retrieval, ranking, citations, evaluation, access control, and stale knowledge.
Sensitive data, bias, transparency, human review, incident response, and safer defaults.
Research, briefs, drafts, claims, brand voice, approval, disclosure, and measurement.
Task change, augmentation, evidence of skill, portfolio choices, and continuous learning.
Conversations, files, projects, research, images, privacy choices, and verification.
Long documents, projects, artifacts, structured analysis, privacy, and verification.
Multimodal tasks, files, research, Workspace connections, privacy, and verification.
Search modes, citations, source quality, follow-ups, files, and research verification.
Task boundaries, connected tools, checkpoints, sensitive data, deliverables, and review.
Source notebooks, grounded questions, notes, study outputs, sharing, and privacy.
Completions, chat, agents, repository context, review, privacy, and governance.
Workspace context, agent changes, terminal actions, review, recovery, and privacy.
Planning, generation, data, secrets, testing, deployment, costs, and ownership.
Compare Lovable, Bolt, v0, and Replit by workflow, portability, data, cost, and risk.
Triggers, actions, credentials, retries, approvals, logs, budgets, and failure recovery.
A goal-based path through foundations, prompting, research, privacy, and verification.
Plan, delegate, inspect, test, secure, and preserve AI-assisted software work.