mirror of https://github.com/rohitg00/ai-engineering-from-scratch.git synced 2026-10-02 01:54:39 +08:00

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Rohit Ghumare d86fedaa51 fix: dark-mode mermaid fills, Apple Silicon Docker build, fnm unzip, ROADMAP rows, site chrome translations (#477 )

* fix: dark-mode mermaid fills, Apple Silicon Docker build, fnm unzip, ROADMAP rows, site chrome translations

- site/lesson.html: mermaidPreprocess handles 3-digit hex fills so fill:#dfd stays legible in dark mode (#433)
- 07-docker-for-ai: pin FROM to linux/amd64 and document why the cu124 layer fails on Apple Silicon (#476)
- 01-dev-environment: add unzip to the apt line, fnm's installer needs it (#473)
- ROADMAP.md: add Phase 11 lessons 16 and 17, 521 rows to 523
- site: translate interface strings for the eight CI languages via ui-i18n.js + ui-strings.js, picker dispatches aifs:lang, drift guarded by test_ui_i18n.js in CI (#469, #404)

* docs: describe fnm's unzip check and scope the CUDA image to x86_64 hosts

- 01-dev-environment: the installer checks for unzip up front and exits with its own message; macOS goes through Homebrew
- 07-docker-for-ai: the linux/amd64 pin means the image is for x86_64 Linux hosts with an NVIDIA GPU

* feat(i18n): generate site chrome translations in the translate pipeline

The hand dictionary becomes site/ui-strings.json: an English key list plus
per-language overrides. scripts/translate_ui_strings.py fills every other key
through the lesson translator's provider layer, reuses what is already
published, protects file names, paths, and commands behind the same
placeholders, and writes i18n/<lang>/ui.json. A new ui-strings job in
translate.yml runs it per CI language and publishes to the translations
branch with the same race-safe worktree loop, so a new label or a new
language needs no translation written by hand.

site/ui-i18n.js now fetches i18n/<lang>/ui.json from the translations branch
at runtime, caches it per language, and falls back to English until a language
is published. Tests cover source consistency, precedence, placeholder
protection, the page drift guard, and the fetch path.

* fix(i18n): retranslate keys whose override was removed and keep failed dictionary loads retryable

- ui.json now carries {strings, pinned}; a key that was pinned in the last publication but has no override now is translated again instead of reusing the old pin
- the runtime no longer caches a failed fetch, so a language published later is picked up on the next switch without a reload

* fix(i18n): refresh flat ui.json values once when pin provenance is missing

A flat published file predates the {strings, pinned} shape and cannot say
which values came from overrides, so every value in it counts as pinned and
is retranslated on the first run instead of being reused blindly.

2026-09-24 09:22:11 +05:30

53 KiB

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Roadmap

Status tracker for every phase and lesson. The status glyphs in this file feed the website (site/build.js parses them into site/data.js); do not change their shape.

Total estimated time: ~323 hours, at your own pace.

Legend: ✅ Complete · 🚧 In Progress · ⬚ Planned

Phase 0: Setup & Tooling — ✅ (~14 hours)

#	Lesson	Status	Est.
01	Dev Environment	✅	~75 min
02	Git & Collaboration	✅	~45 min
03	GPU Setup & Cloud	✅	~75 min
04	APIs & Keys	✅	~75 min
05	Jupyter Notebooks	✅	~75 min
06	Python Environments	✅	~75 min
07	Docker for AI	✅	~75 min
08	Editor Setup	✅	~75 min
09	Data Management	✅	~75 min
10	Terminal & Shell	✅	~45 min
11	Linux for AI	✅	~45 min
12	Debugging & Profiling	✅	~75 min

Phase 1: Math Foundations — ✅ (~23 hours)

#	Lesson	Status	Est.
01	Linear Algebra Intuition	✅	~45 min
02	Vectors, Matrices & Operations	✅	~75 min
03	Matrix Transformations & Eigenvalues	✅	~75 min
04	Calculus for ML — Derivatives & Gradients	✅	~45 min
05	Chain Rule & Automatic Differentiation	✅	~75 min
06	Probability & Distributions	✅	~45 min
07	Bayes' Theorem & Statistical Thinking	✅	~75 min
08	Optimization — Gradient Descent Family	✅	~75 min
09	Information Theory — Entropy, KL Divergence	✅	~45 min
10	Dimensionality Reduction — PCA, t-SNE, UMAP	✅	~75 min
11	Singular Value Decomposition	✅	~75 min
12	Tensor Operations	✅	~75 min
13	Numerical Stability	✅	~45 min
14	Norms & Distances	✅	~45 min
15	Statistics for ML	✅	~45 min
16	Sampling Methods	✅	~75 min
17	Linear Systems	✅	~75 min
18	Convex Optimization	✅	~75 min
19	Complex Numbers for AI	✅	~45 min
20	The Fourier Transform	✅	~75 min
21	Graph Theory for ML	✅	~45 min
22	Stochastic Processes	✅	~45 min

Phase 2: ML Fundamentals — ✅ (~21 hours)

#	Lesson	Status	Est.
01	What Is Machine Learning — Types & Taxonomy	✅	~45 min
02	Linear Regression from Scratch	✅	~75 min
03	Logistic Regression & Classification	✅	~75 min
04	Decision Trees & Random Forests	✅	~75 min
05	Support Vector Machines	✅	~75 min
06	K-Nearest Neighbors & Distance Metrics	✅	~75 min
07	Unsupervised Learning — K-Means, DBSCAN	✅	~75 min
08	Feature Engineering & Selection	✅	~75 min
09	Model Evaluation — Metrics, Cross-Validation	✅	~75 min
10	Bias, Variance & the Learning Curve	✅	~45 min
11	Ensemble Methods — Boosting, Bagging, Stacking	✅	~75 min
12	Hyperparameter Tuning & AutoML	✅	~75 min
13	ML Pipelines & Experiment Tracking	✅	~75 min
14	Naive Bayes — Multinomial, Gaussian, Bernoulli	✅	~75 min
15	Time Series Fundamentals	✅	~45 min
16	Anomaly Detection	✅	~75 min
17	Handling Imbalanced Data	✅	~75 min
18	Feature Selection	✅	~75 min

Phase 3: Deep Learning Core — ✅ (~15 hours)

#	Lesson	Status	Est.
01	The Perceptron — Where It All Started	✅	~45 min
02	Multi-Layer Networks & Forward Pass	✅	~75 min
03	Backpropagation from Scratch	✅	~75 min
04	Activation Functions — ReLU, Sigmoid, GELU & Why	✅	~45 min
05	Loss Functions — MSE, Cross-Entropy, Contrastive	✅	~45 min
06	Optimizers — SGD, Momentum, Adam, AdamW	✅	~75 min
07	Regularization — Dropout, Weight Decay, BatchNorm	✅	~75 min
08	Weight Initialization & Training Stability	✅	~45 min
09	Learning Rate Schedules & Warmup	✅	~45 min
10	Build Your Own Mini Framework	✅	~120 min
11	Introduction to PyTorch	✅	~75 min
12	Introduction to JAX	✅	~75 min
13	Debugging Neural Networks	✅	~75 min

Phase 4: Computer Vision — ✅ (~27 hours)

#	Lesson	Status	Est.
01	Image Fundamentals — Pixels, Channels, Color Spaces	✅	~45 min
02	Convolutions from Scratch	✅	~75 min
03	CNNs — LeNet to ResNet	✅	~75 min
04	Image Classification	✅	~75 min
05	Transfer Learning & Fine-Tuning	✅	~75 min
06	Object Detection — YOLO from Scratch	✅	~75 min
07	Semantic Segmentation — U-Net	✅	~75 min
08	Instance Segmentation — Mask R-CNN	✅	~75 min
09	Image Generation — GANs	✅	~75 min
10	Image Generation — Diffusion Models	✅	~75 min
11	Stable Diffusion — Architecture & Fine-Tuning	✅	~75 min
12	Video Understanding — Temporal Modeling	✅	~45 min
13	3D Vision — Point Clouds, NeRFs	✅	~45 min
14	Vision Transformers (ViT)	✅	~45 min
15	Real-Time Vision — Edge Deployment	✅	~75 min
16	Build a Complete Vision Pipeline	✅	~120 min
17	Self-Supervised Vision — SimCLR, DINO, MAE	✅	~75 min
18	Open-Vocabulary Vision — CLIP	✅	~45 min
19	OCR & Document Understanding	✅	~45 min
20	Image Retrieval & Metric Learning	✅	~45 min
21	Keypoint Detection & Pose Estimation	✅	~45 min
22	3D Gaussian Splatting from Scratch	✅	~90 min
23	Diffusion Transformers & Rectified Flow	✅	~75 min
24	SAM 3 & Open-Vocabulary Segmentation	✅	~60 min
25	Vision-Language Models (ViT-MLP-LLM)	✅	~75 min
26	Monocular Depth & Geometry Estimation	✅	~60 min
27	Multi-Object Tracking & Video Memory	✅	~60 min
28	World Models & Video Diffusion	✅	~75 min

Phase 5: NLP — Foundations to Advanced — ✅ (~30 hours)

#	Lesson	Status	Est.
01	Text Processing — Tokenization, Stemming, Lemmatization	✅	~45 min
02	Bag of Words, TF-IDF & Text Representation	✅	~75 min
03	Word Embeddings — Word2Vec from Scratch	✅	~75 min
04	GloVe, FastText & Subword Embeddings	✅	~45 min
05	Sentiment Analysis	✅	~75 min
06	Named Entity Recognition (NER)	✅	~75 min
07	POS Tagging & Syntactic Parsing	✅	~45 min
08	Text Classification — CNNs & RNNs for Text	✅	~75 min
09	Sequence-to-Sequence Models	✅	~75 min
10	Attention Mechanism — The Breakthrough	✅	~45 min
11	Machine Translation	✅	~75 min
12	Text Summarization	✅	~75 min
13	Question Answering Systems	✅	~75 min
14	Information Retrieval & Search	✅	~75 min
15	Topic Modeling — LDA, BERTopic	✅	~45 min
16	Text Generation — Language Models Before Transformers	✅	~45 min
17	Chatbots — Rule-Based to Neural	✅	~75 min
18	Multilingual NLP	✅	~45 min
19	Subword Tokenization — BPE, WordPiece, Unigram, SentencePiece	✅	~60 min
20	Structured Outputs & Constrained Decoding	✅	~60 min
21	NLI & Textual Entailment	✅	~60 min
22	Embedding Models Deep Dive	✅	~60 min
23	Chunking Strategies for RAG	✅	~60 min
24	Coreference Resolution	✅	~60 min
25	Entity Linking & Disambiguation	✅	~60 min
26	Relation Extraction & Knowledge Graph Construction	✅	~60 min
27	LLM Evaluation — RAGAS, DeepEval, G-Eval	✅	~75 min
28	Long-Context Evaluation — NIAH, RULER, LongBench, MRCR	✅	~60 min
29	Dialogue State Tracking	✅	~75 min

Phase 6: Speech & Audio — ✅ (~18 hours)

#	Lesson	Status	Est.
01	Audio Fundamentals — Waveforms, Sampling, Fourier Transform	✅	~45 min
02	Spectrograms, Mel Scale & Audio Features	✅	~45 min
03	Audio Classification	✅	~75 min
04	Speech Recognition (ASR)	✅	~45 min
05	Whisper — Architecture & Fine-Tuning	✅	~75 min
06	Speaker Recognition & Verification	✅	~45 min
07	Text-to-Speech (TTS)	✅	~75 min
08	Voice Cloning & Voice Conversion	✅	~75 min
09	Music Generation	✅	~75 min
10	Audio-Language Models	✅	~45 min
11	Real-Time Audio Processing	✅	~75 min
12	Build a Voice Assistant Pipeline	✅	~120 min
13	Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC	✅	~60 min
14	Voice Activity Detection & Turn-Taking	✅	~45 min
15	Streaming Speech-to-Speech — Moshi, Hibiki	✅	~75 min
16	Voice Anti-Spoofing & Audio Watermarking	✅	~75 min
17	Audio Evaluation — WER, MOS, MMAU, Leaderboards	✅	~60 min

Phase 7: Transformers Deep Dive — ✅ (~14 hours)

#	Lesson	Status	Est.
01	Why Transformers — The Problems with RNNs	✅	~45 min
02	Self-Attention from Scratch	✅	~75 min
03	Multi-Head Attention	✅	~75 min
04	Positional Encoding — Sinusoidal, RoPE, ALiBi	✅	~45 min
05	The Full Transformer — Encoder + Decoder	✅	~75 min
06	BERT — Masked Language Modeling	✅	~45 min
07	GPT — Causal Language Modeling	✅	~75 min
08	T5, BART — Encoder-Decoder Models	✅	~45 min
09	Vision Transformers (ViT)	✅	~45 min
10	Audio Transformers — Whisper Architecture	✅	~45 min
11	Mixture of Experts (MoE)	✅	~45 min
12	KV Cache, Flash Attention & Inference Optimization	✅	~75 min
13	Scaling Laws	✅	~45 min
14	Build a Transformer from Scratch — The Capstone	✅	~120 min
15	Attention Variants — Sliding Window, Sparse, Differential	✅	~60 min
16	Speculative Decoding — Draft, Verify, Repeat	✅	~60 min

Phase 8: Generative AI — ✅ (~14 hours)

#	Lesson	Status	Est.
01	Generative Models — Taxonomy & History	✅	~45 min
02	Autoencoders & VAE	✅	~75 min
03	GANs — Generator vs Discriminator	✅	~75 min
04	Conditional GANs & Pix2Pix	✅	~75 min
05	StyleGAN	✅	~45 min
06	Diffusion Models — DDPM from Scratch	✅	~75 min
07	Latent Diffusion & Stable Diffusion	✅	~75 min
08	ControlNet, LoRA & Image Conditioning	✅	~75 min
09	Inpainting, Outpainting & Image Editing	✅	~75 min
10	Video Generation	✅	~45 min
11	Audio Generation	✅	~45 min
12	3D Generation	✅	~45 min
13	Flow Matching & Rectified Flows	✅	~45 min
14	Evaluation — FID, CLIP Score, Human Preference	✅	~45 min
19	Visual Autoregressive Modeling (VAR): Next-Scale Prediction	✅	~90 min

Phase 9: Reinforcement Learning — ✅ (~13 hours)

#	Lesson	Status	Est.
01	MDPs, States, Actions & Rewards	✅	~45 min
02	Dynamic Programming	✅	~75 min
03	Monte Carlo Methods	✅	~75 min
04	Temporal Difference — Q-Learning, SARSA	✅	~75 min
05	Deep Q-Networks (DQN)	✅	~75 min
06	Policy Gradient Methods — REINFORCE	✅	~75 min
07	Actor-Critic — A2C, A3C	✅	~75 min
08	Proximal Policy Optimization (PPO)	✅	~75 min
09	Reward Modeling & RLHF	✅	~45 min
10	Multi-Agent RL	✅	~45 min
11	Sim-to-Real Transfer	✅	~45 min
12	RL for Games	✅	~75 min

Phase 10: LLMs from Scratch — ✅ (~26 hours)

#	Lesson	Status	Est.
01	Tokenizers — BPE, WordPiece, SentencePiece	✅	~45 min
02	Building a Tokenizer from Scratch	✅	~75 min
03	Data Pipelines for Pre-Training	✅	~75 min
04	Pre-Training a Mini GPT (124M)	✅	~120 min
05	Scaling — Distributed Training, FSDP, DeepSpeed	✅	~75 min
06	Instruction Tuning — SFT	✅	~75 min
07	RLHF — Reward Model + PPO Training	✅	~75 min
08	DPO — Direct Preference Optimization	✅	~75 min
09	Constitutional AI & Self-Improvement	✅	~45 min
10	Evaluation — Benchmarks, Evals, LM Harness	✅	~75 min
11	Quantization — INT8, GPTQ, AWQ, GGUF	✅	~75 min
12	Inference Optimization	✅	~75 min
13	Building a Complete LLM Pipeline	✅	~120 min
14	Open Models — Architecture Walkthroughs	✅	~45 min
15	Speculative Decoding and EAGLE-3	✅	~75 min
16	Differential Attention (V2)	✅	~60 min
17	Native Sparse Attention (DeepSeek NSA)	✅	~60 min
18	Multi-Token Prediction (MTP)	✅	~60 min
19	DualPipe Parallelism	✅	~60 min
20	DeepSeek-V3 Architecture Walkthrough	✅	~75 min
21	Jamba — Hybrid SSM-Transformer	✅	~60 min
22	Async and Hogwild! Inference	✅	~60 min
25	Speculative Decoding and EAGLE	✅	~75 min
34	Gradient Checkpointing and Activation Recomputation	✅	~70 min

Phase 11: LLM Engineering — ✅ (~19 hours)

#	Lesson	Status	Est.
01	Prompt Engineering — Techniques & Patterns	✅	~45 min
02	Few-Shot, Chain-of-Thought, Tree-of-Thought	✅	~45 min
03	Structured Outputs	✅	~75 min
04	Embeddings & Vector Representations	✅	~75 min
05	Context Engineering	✅	~75 min
06	RAG — Retrieval-Augmented Generation	✅	~75 min
07	Advanced RAG	✅	~75 min
08	Fine-Tuning with LoRA & QLoRA	✅	~75 min
09	Function Calling & Tool Use	✅	~75 min
10	Evaluation & Testing LLM Applications	✅	~45 min
11	Caching, Rate Limiting & Cost Optimization	✅	~45 min
12	Guardrails, Safety & Content Filtering	✅	~45 min
13	Building a Production LLM Application	✅	~120 min
14	Model Context Protocol (MCP)	✅	~75 min
15	Prompt Caching & Context Caching	✅	~60 min
16	Agent State Machines — Graphs, Nodes, Checkpoints	✅	~75 min
17	Agent Framework Tradeoffs	✅	~45 min

Phase 12: Multimodal AI — ✅ (~65 hours)

#	Lesson	Status	Est.
01	Vision Transformers and the Patch-Token Primitive	✅	~120 min
02	CLIP and Contrastive Vision-Language Pretraining	✅	~180 min
03	BLIP-2 and Q-Former as Modality Bridge	✅	~180 min
04	Flamingo and Gated Cross-Attention	✅	~120 min
05	LLaVA and Visual Instruction Tuning	✅	~180 min
06	Any-Resolution Vision: Patch-n'-Pack and NaFlex	✅	~120 min
07	Open-Weight VLM Recipes: What Actually Matters	✅	~180 min
08	LLaVA-OneVision: Single, Multi, Video	✅	~180 min
09	Qwen-VL Family and Dynamic-FPS Video	✅	~120 min
10	InternVL3 Native Multimodal Pretraining	✅	~120 min
11	Chameleon and Early-Fusion Token-Only	✅	~180 min
12	Emu3 Next-Token Prediction for Generation	✅	~120 min
13	Transfusion Autoregressive + Diffusion	✅	~180 min
14	Show-o and Discrete-Diffusion Unified	✅	~120 min
15	Janus-Pro Decoupled Encoders	✅	~120 min
16	MIO Any-to-Any Streaming	✅	~120 min
17	Video-Language Temporal Grounding	✅	~180 min
18	Long-Video Understanding at Million-Token Context	✅	~180 min
19	Audio-Language Models: Whisper to AF3	✅	~180 min
20	Omni Models: Thinker-Talker	✅	~180 min
21	Embodied VLAs: RT-2, OpenVLA, π0, GR00T	✅	~180 min
22	Document and Diagram Understanding	✅	~180 min
23	ColPali Vision-Native Document RAG	✅	~180 min
24	Multimodal RAG and Cross-Modal Retrieval	✅	~180 min
25	Multimodal Agents and Computer-Use (Capstone)	✅	~240 min

Phase 13: Tools & Protocols — ✅ (~43 hours)

#	Lesson	Status	Est.
01	The Tool Interface	✅	~45 min
02	Function Calling Deep Dive	✅	~75 min
03	Parallel and Streaming Tool Calls	✅	~75 min
04	Structured Output	✅	~75 min
05	Tool Schema Design	✅	~45 min
06	MCP Fundamentals: Stateless Requests and JSON-RPC	✅	~55 min
07	Building an MCP Server: Stateless Python and TypeScript	✅	~85 min
08	Building an MCP Client: Discovery, Routing, and Dual-Era Fallback	✅	~85 min
09	MCP Transports: stdio and Stateless Streamable HTTP	✅	~65 min
10	MCP Resources and Prompts: Addressable Context for Stateless Servers	✅	~60 min
11	MCP Model Input: Sampling Migration and Stateless MRTR	✅	~75 min
12	Explicit Scope and Stateless Elicitation	✅	~60 min
13	MCP Tasks Extension: Durable Work on a Stateless Core	✅	~90 min
14	MCP Apps on the Stateless Protocol	✅	~75 min
15	MCP Security: Poisoned Metadata, Routing, and MRTR State	✅	~60 min
16	MCP Authorization: CIMD, Issuer Binding, PKCE, and Step-Up	✅	~90 min
17	Stateless MCP Gateways and Registry Admission	✅	~75 min
18	MCP Auth in Production: Issuer-Bound Enrollment and Tokens	✅	~90 min
19	A2A Protocol	✅	~75 min
20	OpenTelemetry GenAI	✅	~75 min
21	LLM Routing Layer	✅	~45 min
22	Agent Skills: Portable Contract and Runtime Boundary	✅	~90 min
23	Capstone: Stateless Tool Ecosystem	✅	~120 min
24	Skill Discovery and Progressive Disclosure	✅	~105 min
25	Skill Invocation and Routing	✅	~105 min
26	Skill Permissions, Sandboxes, and Trust	✅	~120 min
27	Skill Evals, Packaging, and Portability	✅	~150 min
28	MCP Tool Contracts and Content	✅	~120 min
29	MCP Reliability, Cancellation, and Flow Control	✅	~120 min
30	MCP Registry Supply Chain: Admission, Drift, and Rollback	✅	~90 min
31	MCP Conformance Engineering: Versioning, Evidence, and Operations	✅	~100 min

Phase 14: Agent Engineering — ✅ (~55 hours)

#	Lesson	Status	Est.
01	The Agent Loop	✅	~60 min
02	ReWOO and Plan-and-Execute	✅	~60 min
03	Reflexion and Verbal Reinforcement Learning	✅	~60 min
04	Tree of Thoughts and LATS	✅	~75 min
05	Self-Refine and CRITIC	✅	~60 min
06	Tool Use and Function Calling	✅	~60 min
07	Agent Memory — Virtual Context and Memory Paging	✅	~75 min
08	Memory Blocks and Sleep-Time Compute	✅	~75 min
09	Hybrid Memory — Vector + Graph + KV	✅	~75 min
10	Skill Libraries and Lifelong Learning (Voyager)	✅	~75 min
11	Planning with HTN and Evolutionary Search	✅	~75 min
12	Anthropic's Workflow Patterns	✅	~60 min
13	Stateful Graph Orchestration — Durable Execution and Checkpoints	✅	~75 min
14	The Actor Model for Agents	✅	~75 min
15	Role-Based Agent Teams — Roles, Tasks, Processes	✅	~60 min
16	OpenAI Agents SDK — Handoffs, Guardrails, Tracing	✅	~75 min
17	The Harness as a Library — Subagents and Session Store	✅	~75 min
18	Production Agent Runtimes	✅	~45 min
19	Benchmarks — SWE-bench, GAIA, AgentBench	✅	~60 min
20	Benchmarks — WebArena and OSWorld	✅	~60 min
21	Computer Use — Claude, OpenAI CUA, Gemini	✅	~60 min
22	Voice Agents — Pipecat and LiveKit	✅	~60 min
23	OpenTelemetry GenAI Semantic Conventions	✅	~60 min
24	Agent Observability — Langfuse, Phoenix, Opik	✅	~45 min
25	Multi-Agent Debate and Collaboration	✅	~60 min
26	Failure Modes — Why Agents Break	✅	~60 min
27	Prompt Injection and the PVE Defense	✅	~75 min
28	Orchestration Patterns — Supervisor, Swarm, Hierarchical	✅	~60 min
29	Production Runtimes — Queue, Event, Cron	✅	~60 min
30	Eval-Driven Agent Development	✅	~60 min
31	Agent Workbench: Why Capable Models Still Fail	✅	~45 min
32	The Minimal Agent Workbench	✅	~45 min
33	Agent Instructions as Executable Constraints	✅	~50 min
34	Repo Memory and Durable State	✅	~60 min
35	Initialization Scripts for Agents	✅	~45 min
36	Scope Contracts and Task Boundaries	✅	~50 min
37	Runtime Feedback Loops	✅	~50 min
38	Verification Gates	✅	~55 min
39	Reviewer Agent: Separate Builder from Marker	✅	~55 min
40	Multi-Session Handoff	✅	~50 min
41	The Workbench on a Real Repo	✅	~60 min
42	Capstone: Ship a Reusable Agent Workbench Pack	✅	~75 min
43	Frame the Task Before the Agent Writes Code	✅	~60 min
44	Build an Evidence-Backed Execution Plan	✅	~65 min
45	Delegate Agent Work with Isolation and Merge Contracts	✅	~70 min
46	Turn Every Agent Correction into a System Improvement	✅	~65 min
47	Define the Outcome Before You Choose the Output	✅	~60 min
48	Discover the Workflow People Actually Perform	✅	~70 min
49	Map Assumptions and Resolve the Riskiest One First	✅	~65 min
50	Choose the Smallest Slice That Can Change the Decision	✅	~65 min
51	Write Specifications That Preserve Judgment	✅	~75 min
52	Design Success Metrics Before the Result Exists	✅	~70 min
53	Choose Prototype, Pilot, or Production Deliberately	✅	~70 min
54	Build a Feedback Ratchet with Ownership and Retirement	✅	~75 min

Phase 15: Autonomous Systems — ✅ (~20 hours)

#	Lesson	Status	Est.
01	From Chatbots to Long-Horizon Agents (METR)	✅	~45 min
02	STaR, V-STaR, Quiet-STaR — Self-Taught Reasoning	✅	~60 min
03	AlphaEvolve — Evolutionary Coding Agents	✅	~60 min
04	Darwin Gödel Machine — Self-Modifying Agents	✅	~60 min
05	AI Scientist v2 — Workshop-Level Research	✅	~60 min
06	Automated Alignment Research (Anthropic AAR)	✅	~60 min
07	Recursive Self-Improvement — Capability vs Alignment	✅	~60 min
08	Bounded Self-Improvement Designs	✅	~60 min
09	Autonomous Coding Agent Landscape (SWE-bench, CodeAct)	✅	~45 min
10	Permission Modes for Autonomous Agents	✅	~45 min
11	Browser Agents and Indirect Prompt Injection	✅	~45 min
12	Durable Execution for Long-Running Agents	✅	~60 min
13	Action Budgets, Iteration Caps, Cost Governors	✅	~60 min
14	Kill Switches, Circuit Breakers, Canary Tokens	✅	~60 min
15	HITL — Propose-Then-Commit	✅	~60 min
16	Checkpoints and Rollback	✅	~60 min
17	Constitutional AI and Rule Overrides	✅	~60 min
18	Llama Guard and Input/Output Classification	✅	~45 min
19	Anthropic Responsible Scaling Policy v3.0	✅	~45 min
20	OpenAI Preparedness Framework and DeepMind FSF	✅	~45 min
21	METR Time Horizons and External Evaluation	✅	~60 min
22	CAIS, CAISI, and Societal-Scale Risk	✅	~45 min

Phase 16: Multi-Agent & Swarms — ✅ (~28 hours)

#	Lesson	Status	Est.
01	Why Multi-Agent	✅	~45 min
02	FIPA-ACL Heritage and Speech Acts	✅	~60 min
03	Communication Protocols	✅	~45 min
04	The Multi-Agent Primitive Model	✅	~60 min
05	Supervisor / Orchestrator-Worker Pattern	✅	~75 min
06	Hierarchical Architecture and Decomposition Drift	✅	~60 min
07	Society of Mind and Multi-Agent Debate	✅	~75 min
08	Role Specialization — Planner / Critic / Executor / Verifier	✅	~75 min
09	Parallel Swarm and Networked Architectures	✅	~60 min
10	Group Chat and Speaker Selection	✅	~60 min
11	Handoffs and Routines (Stateless Orchestration)	✅	~60 min
12	A2A — The Agent-to-Agent Protocol	✅	~75 min
13	Shared Memory and Blackboard Patterns	✅	~75 min
14	Consensus and Byzantine Fault Tolerance for Agents	✅	~75 min
15	Voting, Self-Consistency, and Debate Topology	✅	~75 min
16	Negotiation and Bargaining	✅	~75 min
17	Generative Agents and Emergent Simulation	✅	~75 min
18	Theory of Mind and Emergent Coordination	✅	~75 min
19	Swarm Optimization for LLMs (PSO, ACO)	✅	~75 min
20	MARL — MADDPG, QMIX, MAPPO	✅	~90 min
21	Agent Economies, Token Incentives, Reputation	✅	~75 min
22	Production Scaling — Queues, Checkpoints, Durability	✅	~75 min
23	Failure Modes — MAST, Groupthink, Monoculture, Cascading	✅	~75 min
24	Evaluation and Coordination Benchmarks	✅	~75 min
25	Case Studies and 2026 State of the Art	✅	~90 min

Phase 17: Infrastructure & Production — ✅ (~32 hours)

#	Lesson	Status	Est.
01	Managed LLM Platforms — Bedrock, Azure OpenAI, Vertex AI	✅	~60 min
02	Inference Platform Economics — Fireworks, Together, Baseten, Modal	✅	~60 min
03	GPU Autoscaling on Kubernetes — Karpenter, KAI Scheduler	✅	~75 min
04	Serving Engine Internals — PagedAttention, Continuous Batching, Chunked Prefill	✅	~75 min
05	EAGLE-3 Speculative Decoding in Production	✅	~60 min
06	Prefix-Cache Serving — RadixAttention and KV Reuse	✅	~60 min
07	Hardware-Specialized Inference Compilation — FP8 and NVFP4 on Blackwell	✅	~75 min
08	Inference Metrics — TTFT, TPOT, ITL, Goodput, P99	✅	~60 min
09	Production Quantization — AWQ, GPTQ, GGUF, FP8, NVFP4	✅	~75 min
10	Cold Start Mitigation for Serverless LLMs	✅	~60 min
11	Multi-Region LLM Serving and KV Cache Locality	✅	~60 min
12	Edge Inference — ANE, Hexagon, WebGPU, Jetson	✅	~60 min
13	LLM Observability Stack Selection	✅	~60 min
14	Prompt Caching and Semantic Caching Economics	✅	~60 min
15	Batch APIs — the 50% Discount as Industry Standard	✅	~45 min
16	Model Routing as a Cost-Reduction Primitive	✅	~60 min
17	Disaggregated Prefill/Decode — NVIDIA Dynamo and llm-d	✅	~75 min
18	Production Serving Stack — KV Offloading and Cache-Aware Routing	✅	~60 min
19	AI Gateways — LiteLLM, Portkey, Kong, Bifrost	✅	~60 min
20	Shadow, Canary, and Progressive Deployment	✅	~60 min
21	A/B Testing LLM Features — GrowthBook and Statsig	✅	~60 min
22	Load Testing LLM APIs — k6, LLMPerf, GenAI-Perf	✅	~75 min
23	SRE for AI — Multi-Agent Incident Response	✅	~60 min
24	Chaos Engineering for LLM Production	✅	~60 min
25	Security — Secrets, PII Scrubbing, Audit Logs	✅	~60 min
26	Compliance — SOC 2, HIPAA, GDPR, EU AI Act, ISO 42001	✅	~60 min
27	FinOps for LLMs — Unit Economics and Multi-Tenant Attribution	✅	~60 min
28	Self-Hosted Serving Selection — Matching Engine to Hardware and Scale	✅	~45 min

Phase 18: Ethics, Safety & Alignment — ✅ (~31 hours)

#	Lesson	Status	Est.
01	Instruction-Following as Alignment Signal	✅	~45 min
02	Reward Hacking & Goodhart's Law	✅	~60 min
03	Direct Preference Optimization Family	✅	~60 min
04	Sycophancy as RLHF Amplification	✅	~45 min
05	Constitutional AI & RLAIF	✅	~60 min
06	Mesa-Optimization & Deceptive Alignment	✅	~75 min
07	Sleeper Agents — Persistent Deception	✅	~60 min
08	In-Context Scheming in Frontier Models	✅	~60 min
09	Alignment Faking	✅	~60 min
10	AI Control — Safety Despite Subversion	✅	~75 min
11	Scalable Oversight & Weak-to-Strong Generalization	✅	~60 min
12	Red-Teaming — PAIR & Automated Attacks	✅	~75 min
13	Many-Shot Jailbreaking	✅	~45 min
14	ASCII Art & Visual Jailbreaks	✅	~60 min
15	Indirect Prompt Injection	✅	~75 min
16	Red-Team Tooling — Garak, Llama Guard, PyRIT	✅	~75 min
17	WMDP & Dual-Use Capability Evaluation	✅	~60 min
18	Frontier Safety Frameworks — RSP, PF, FSF	✅	~75 min
19	Model Welfare Research	✅	~45 min
20	Bias & Representational Harm	✅	~60 min
21	Fairness Criteria — Group, Individual, Counterfactual	✅	~60 min
22	Differential Privacy for LLMs	✅	~60 min
23	Watermarking — SynthID, Stable Signature, C2PA	✅	~75 min
24	Regulatory Frameworks — EU, US, UK, Korea	✅	~75 min
25	EchoLeak & CVEs for AI	✅	~45 min
26	Model, System & Dataset Cards	✅	~60 min
27	Data Provenance & Training-Data Governance	✅	~60 min
28	Alignment Research Ecosystem — MATS, Redwood, Apollo, METR	✅	~45 min
29	Moderation Systems — OpenAI, Perspective, Llama Guard	✅	~60 min
30	Dual-Use Risk — Cyber, Bio, Chem, Nuclear	✅	~75 min

Phase 19: Capstone Projects — ✅ (~620 hours)

#	Project	Status	Est.
01	Terminal-Native Coding Agent	✅	~35 hr
02	RAG over Codebase (Cross-Repo Semantic Search)	✅	~30 hr
03	Real-Time Voice Assistant (ASR to LLM to TTS)	✅	~30 hr
04	Multimodal Document QA (Vision-First)	✅	~30 hr
05	Autonomous Research Agent (AI-Scientist Class)	✅	~40 hr
06	DevOps Troubleshooting Agent for Kubernetes	✅	~30 hr
07	End-to-End Fine-Tuning Pipeline	✅	~35 hr
08	Production RAG Chatbot (Regulated Vertical)	✅	~30 hr
09	Code Migration Agent (Repo-Level Upgrade)	✅	~30 hr
10	Multi-Agent Software Engineering Team	✅	~40 hr
11	LLM Observability & Eval Dashboard	✅	~25 hr
12	Video Understanding Pipeline (Scene to QA)	✅	~30 hr
13	Stateless MCP Server with Registry and Governance	✅	~25 hr
14	Speculative-Decoding Inference Server	✅	~30 hr
15	Constitutional Safety Harness + Red-Team Range	✅	~25 hr
16	GitHub Issue-to-PR Autonomous Agent	✅	~30 hr
17	Personal AI Tutor (Adaptive, Multimodal)	✅	~30 hr
20	Agent Harness Loop Contract	✅	~90 min
21	Tool Registry with Schema Validation	✅	~90 min
22	JSON-RPC 2.0 Over Newline-Delimited Stdio	✅	~90 min
23	Function Call Dispatcher	✅	~90 min
24	Plan-Execute Control Flow	✅	~90 min
25	Verification Gates and the Observation Budget	✅	~90 min
26	Sandbox Runner with Denylist and Path Jail	✅	~90 min
27	Eval Harness with Fixture Tasks	✅	~90 min
28	Observability with OTel GenAI Spans and Prometheus Metrics	✅	~90 min
29	End-to-End Coding Agent on the Harness	✅	~90 min
30	BPE Tokenizer From Scratch	✅	~90 min
31	Tokenized Dataset with Sliding Window	✅	~90 min
32	Token and Positional Embeddings	✅	~90 min
33	Multi-Head Self-Attention	✅	~90 min
34	Transformer Block from Scratch	✅	~90 min
35	GPT Model Assembly	✅	~90 min
36	Training Loop and Evaluation	✅	~90 min
37	Loading Pretrained Weights	✅	~90 min
38	Classifier Fine-Tuning by Head Swap	✅	~90 min
39	Instruction Tuning by Supervised Fine-Tuning	✅	~90 min
40	Direct Preference Optimization from Scratch	✅	~90 min
41	Full Evaluation Pipeline	✅	~90 min
42	Large Corpus Downloader	✅	~90 min
43	HDF5 Tokenized Corpus	✅	~90 min
44	Cosine LR with Linear Warmup	✅	~90 min
45	Gradient Clipping and Mixed Precision	✅	~90 min
46	Gradient Accumulation	✅	~90 min
47	Checkpoint Save and Resume	✅	~90 min
48	Distributed Data Parallel and FSDP from Scratch	✅	~90 min
49	Language Model Evaluation Harness	✅	~90 min
50	Hypothesis Generator	✅	~90 min
51	Literature Retrieval	✅	~90 min
52	Experiment Runner	✅	~90 min
53	Result Evaluator	✅	~90 min
54	Paper Writer	✅	~90 min
55	Critic Loop	✅	~90 min
56	Iteration Scheduler	✅	~90 min
57	End-to-End Research Demo	✅	~90 min
58	Vision Encoder Patches	✅	~90 min
59	Vision Transformer Encoder	✅	~90 min
60	Projection Layer for Modality Alignment	✅	~90 min
61	Cross-Attention Fusion	✅	~90 min
62	Vision-Language Pretraining	✅	~90 min
63	Multimodal Evaluation	✅	~90 min
64	Chunking Strategies, Compared	✅	~90 min
65	Hybrid Retrieval with BM25 and Dense Embeddings	✅	~90 min
66	Cross-Encoder Reranker	✅	~90 min
67	Query Rewriting: HyDE, Multi-Query, and Decomposition	✅	~90 min
68	RAG Evaluation: Precision, Recall, MRR, nDCG, Faithfulness, Answer Relevance	✅	~90 min
69	End-to-End RAG System	✅	~90 min
70	Task Spec Format	✅	~90 min
71	Classical Metrics	✅	~90 min
72	Code Exec Metric	✅	~90 min
73	Perplexity and Calibration	✅	~90 min
74	Leaderboard Aggregation	✅	~90 min
75	End-to-End Eval Runner	✅	~90 min
76	Collective Ops From Scratch	✅	~90 min
77	Data Parallel DDP From Scratch	✅	~90 min
78	ZeRO Optimizer State Sharding	✅	~90 min
79	Pipeline Parallel and Bubble Analysis	✅	~90 min
80	Sharded Checkpoint and Atomic Resume	✅	~90 min
81	End-to-End Distributed Training	✅	~90 min
82	Jailbreak Taxonomy	✅	~90 min
83	Prompt Injection Detector	✅	~90 min
84	Refusal Evaluation	✅	~90 min
85	Content Classifier Integration	✅	~90 min
86	Constitutional Rules Engine	✅	~90 min
87	End-to-End Safety Gate	✅	~90 min

Total: 20 phases, 523 lessons | 523 complete | ~1,081 hours estimated

Want to help? Pick any ⬚ lesson and submit a PR. See CONTRIBUTING.md.

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