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Self Dev Learning Platform

A unified, searchable, cross-linked study platform for systems engineering and AI infrastructure. Nine 24-week master-level curricula, one navigation tree, one search box.

Start here

  • New? Read How to use this site - reading order, time budget, prerequisites.
  • Browsing? Open the Paths index and pick one.
  • Looking for a concept? Use search (top right) or open a cross-cutting topic - same concept across multiple paths.
  • Want hands-on first? The Labs index aggregates every weekly exercise across every path. The labs are the curriculum.
  • Picking a terminal project? The Capstones catalog shows every path's capstone tracks side-by-side.
  • Need a definition? The Glossary covers cross-domain terms.
  • Tracking your reading? Toggle "Mark as read" on any chapter page; see your progress. Saved in your browser only - no account.

The paths

Beginner mode

The Beginner mode: OFF/ON button (bottom-right of every page) hides the senior-reference paths and other advanced material below. Try it - the senior table on this page collapses while beginner paths stay visible.

Beginner paths - start here

Path Domain Length
Go From Scratch Never coded → ready to contribute to real OSS Go 4-6 months
Python From Scratch Never coded → ready to contribute to real OSS Python 4-6 months
Java From Scratch Never coded → ready to contribute to real OSS Java 4-6 months
Rust From Scratch Never coded → ready to contribute to real OSS Rust (includes the borrow checker) 4-6 months
Linux From Scratch Never opened a terminal → contributing to Linux-adjacent OSS 4-6 months
Containers From Scratch Heard of Docker → writing Dockerfiles, debugging containers, contributing 3-4 months
Kubernetes From Scratch Heard of K8s → deploying pods/services/Helm, debugging clusters, contributing 3-4 months
AI Systems From Scratch Heard of ChatGPT → tensors, training, LoRA fine-tuning, RAG, serving locally, contributing to AI OSS 4-6 months
AI Expert Roadmap From Scratch The 12-month career map: phases, math you actually need, specialization, portfolio, interviews, first 90 days Read in 3 hours; act over 12 months

Senior reference paths - once you have a foundation

Path Domain Length
Go Mastery Runtime, GMP, GC, concurrency, distributed systems 24 weeks
Java Mastery JVM, JIT, GC, Loom, modern idioms 24 weeks
Rust Mastery Ownership, async, unsafe, FFI 24 weeks
Python Mastery CPython, performance, concurrency, AI runtimes 24 weeks
Linux Kernel Kernel, mm, namespaces, eBPF, networking 24 weeks
Container Internals OCI, runtimes, supply chain 24 weeks
Kubernetes Control plane, kubelet, operators, day-2 24 weeks
AI Systems GPU, framework internals, training, inference 24 weeks
AI Expert Roadmap Math → ML → transformers → RAG → evals → fine-tuning → LLM ops 12 months

How the content is organized

Every path follows the same shape:

README          Overview, syllabus, exit criteria
00 Prelude      Philosophy, mental model, reading list
01–06 Months    Six themed months, ~4 weeks each
APPENDIX A      Production hardening
APPENDIX B      Reference patterns / data structures
APPENDIX C      Contributing upstream
CAPSTONE        Three terminal-project tracks

This regularity is the platform's leverage - once you've worked through one path, the structure of every other is already familiar.

Status

This is v0.1 - the skeleton release. All nine curricula are reachable and full-text searchable. Coming next:

  • v0.2: cross-topic indexes (GC, concurrency, observability, FFI), tag-based discovery, polish.
  • v0.3: per-week page splits - every weekly module gets its own URL.
  • v1.0: labs index, capstones catalog, glossary, progress checkboxes, GitHub Pages deploy.

Roadmap tracked in the repo.