Open Source Society University (OSSU)

CS-Books is a maintained GitHub resource repository collecting computer-science books, study notes, articles, interview materials, and related learning links. It covers C and C++, Java, Python, Go, algorithms and data structures, operating systems, Linux, backend architecture, computer systems, networking, databases, frontend development, machine learning, deep learning, NLP, design patterns, assembly, Git, mathematics, and programmer skills. The repository organizes the materials by subject and states that it is updated gradually by its maintainer; its README also says that many links were collected from other online sources and invites reports of potentially infringing material for removal.

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.

OpenScience is an open-source AI workbench for scientific research developed by Synthetic Sciences. It runs as a browser-based workspace where a single research agent takes a goal through literature review, hypothesis formation, code writing and execution, experiments, analysis, and a written report. The agent can load domain skills, delegate bounded exploratory or execution work, query scientific databases, and preserve sessions as observable research traces. The local server hosts the workspace UI, agent runtime, skill library, and tool layer. The agent plans with a research harness and calls shell, editor, LSP, MCP, scientific database, and skill tools; sessions, skills, artifacts, and provenance are stored on disk. The workbench includes a file tree, code editor, terminal, session history, and inline rendering for molecules, structures, genomes, and plots. Its bundled skills cover training, evaluation, datasets, molecular and clinical biology, cheminformatics, papers and LaTeX, figures, and scientific runtimes, while database connectors provide direct access to services including UniProt, PDB, Ensembl, ChEMBL, PubChem, arXiv, OpenAlex, and Semantic Scholar. It supports frontier and open-weight models through user-provided keys, eligible ChatGPT or Codex access, local models, and optional Ace-managed models. Models are routed per request, allowing providers to be switched without changing the workspace. Extensibility includes LSP integration, MCP servers, plugins, custom agents and commands, experimental Python environments and BioNeMo adapters, and a TypeScript SDK. It is installed with the @synsci/openscience npm package or launched through npx; platform binaries and desktop installers are distributed through GitHub Releases. A free Synthetic Sciences account links an installation and can provide credential synchronization, private research graphs, enhanced search, and optional credit-backed models, while BYOK and local-model usage remain separate from Ace.

Open Science is an open-source, local-first, model-agnostic AI research workbench developed by AIPOCH for scientists and researchers. It runs on macOS, Windows, and Linux and supports computational and data-intensive research in fields including machine learning, statistics, life sciences, chemistry, materials science, physics, and environmental science. Users create projects and sessions, describe research goals in plain language, attach source files, select a model and approval mode, and inspect the agent’s tool activity. Scientific AI agents can read files, search the web, execute Python and R code, query scientific data sources, and produce reports, tables, figures, and other research artifacts. The workbench supports literature review, hypothesis development, code execution, data analysis, simulation, visualization, and related reproducible research workflows. Its first-run setup checks the environment, configures model-provider credentials, optionally prepares Python and R runtimes, and prepares an agent runtime such as Claude Code, OpenCode, or Codex; app-managed runtimes can be installed without Node.js, npm, or administrator privileges. Projects keep sessions, uploads, generated files, and preview state together. Conversations record agent answers and the commands, file reads, edits, searches, and connector calls behind them. Generated artifacts are stored as immutable, checksummed versions; their Provenance view can show producer code and execution history, referenced inputs, the observed environment inventory, the producing conversation branch, and version-scoped reviewer findings, marking unavailable evidence as unavailable rather than guessing.

The Horowitz Andreessen Academy is a selective residential, project-based school in San Francisco for ambitious young builders after high school or during the first two years of college. Its educational model replaces traditional grades, tests, and homework with three components: Pursuits, in which students choose personal projects, startup ideas, or research interests; short Courses taught by technology founders and operators; and Co-ops that provide hands-on experience at partner companies. Students use AI tools and other resources to build projects, explore startup ideas, work with companies, and develop professional and people skills. The Academy is building a network of founding and hiring partners that can provide curriculum input, compute and hardware access, mentoring, co-op placements, and employment opportunities. Its announced Founding Class Fellowship is a tuition-free, one-year program beginning in fall 2027; a separate two-year program is planned but was not yet offered or enrolling at the time of the announcement.