AI Automation Society

AI Engineering from Scratch is a free, open-source curriculum developed by rohitg00 for learning to build and ship AI systems end to end. Its 20-phase sequence covers development tooling, mathematical and machine-learning foundations, deep learning, NLP, computer vision, generative AI, LLM applications, agent engineering, Model Context Protocol (MCP), agent skills, reinforcement learning, and related topics, with implementations in Python, TypeScript, Rust, and Julia. The repository describes 511 lessons and approximately 329 hours of material; each lesson produces a reusable artifact such as a prompt, skill, agent, or MCP server. Learners are instructed to read the lesson documentation, type and run the code from the repository root, record command evidence and outputs, and make a small change before continuing. It provides routes for complete foundations, mathematics and machine learning, production LLM applications, agent engineering, MCP, agent skills, and Claude certification preparation. The repository is distributed under the MIT license and is accompanied by a website containing the same lesson content.

TeamAI is an open-source CLI for managing a team's skills, rules, documentation, agents, hooks, MCP configuration, environment settings, and knowledge across AI coding tools such as Claude Code, Codex, Cursor, CodeBuddy, WorkBuddy, and OpenCode. It is installed with npm as `teamai-cli` and uses a shared Git repository as the team's source of truth. The CLI distributes resources through an administrative `push` → review and merge → `pull` workflow. Session-start hooks can run `teamai pull` to synchronize the shared harness into project- or user-scoped local AI-tool directories; roles, tags, and subscribed source repositories control which resources members receive. TeamAI also provides a knowledge layer: `teamai import` and `teamai codebase` build a searchable team knowledge base and codebase graph, while `teamai recall` uses BM25 search with graph-boosted reranking and can deploy a recall subagent into supported AI tools. Code relationships are extracted with a WebAssembly tree-sitter parser for TypeScript/JavaScript, Python, and Go, with regex-based heuristic extraction as a fallback and for other languages. Additional commands support friction-based learning suggestions, privacy-scrubbed session summaries, maintenance of stale or low-confidence knowledge, usage digests, a web dashboard, member and role management, package and plugin installation, diagnostics, and uninstallation. The repository is maintained by Tencent and contributors and is licensed under MIT.

AI Design Skills is an MIT-licensed collection of Markdown design skills for AI coding tools such as Claude Code, Cursor, Codex, Windsurf, and Cline. Its skills provide coding agents with guidance for landing-page strategy, page structure, conversion copy, typography, spacing, visual systems, border radius, and motion. Each skill is stored as a self-contained folder with a SKILL.md file that can be copied into an agent's project-rules directory, added to a system prompt, or uploaded to a Claude.ai project. The repository currently documents a landing-page-design skill that asks intake questions, builds the page structure and copy, and defines the visual system; its design values are grouped in an editable section so users can change fonts, colors, spacing, and motion. The collection can be forked and used, modified, distributed, or used in commercial work under the MIT license.

AI Engineering Lab is a free, self-paced 24-week AI engineering course developed by Zorost Intelligence AI Lab. It takes learners from Python and machine learning through deep learning, LLM internals, prompt and context engineering, retrieval-augmented generation with vector search, quantization, LoRA and DPO fine-tuning, evaluation harnesses, coding-agent harnesses, AI agents, MCP, and cloud and Databricks platforms including Azure AI Foundry, Google Vertex AI, AWS Bedrock, and Databricks. The curriculum uses one continuous fictional freight-company case study. Each week pairs a concept with a runnable Python, SQL, or PySpark artifact; from Week 3 onward, the work includes a metric and an error note. The repository contains 43 runnable notebooks, publishes the full curriculum online, requires no signup or paid API key, and states that the first eight weeks require no GPU. It is MIT licensed.

AI Job Search is an open-source, local job-application framework built around Claude Code or compatible agent tools. Users create a career profile, search and deduplicate job postings, evaluate and rank matches against structured fit criteria, and run `/apply` to produce tailored CVs and cover letters in LaTeX. The application pipeline uses a drafter–reviewer process: an agent evaluates the posting and drafts the materials, a second reviewer critiques them, and the workflow revises the output; `/interview` supports interview preparation, and optional salary benchmarking is included. The included portal-search skills target Danish services such as Jobindex, Jobnet, and Akademikernes Jobbank, while the profiling, fit-evaluation, and application workflow is described as language- and country-agnostic and adaptable to other job boards. It requires Python 3.10 or later, Bun for the job-search CLI tools, and a LaTeX distribution with `lualatex` and `xelatex`; an optional `pdftotext` installation supports the ATS parseability check for compiled CVs. The repository states that the project is independent of and unaffiliated with Anthropic or Claude Code.