Top AI Repos — open-source AI, indexed and scored

Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.

Date	Stars
2026-07-24	6925
2026-07-25	6926
2026-07-28	6926
2026-07-30	6926
2026-08-06	6926

Format	Resource	Length	Progress
Book	Designing Machine Learning Systems	386 pgs	✅
	Designing Data-Intensive Applications	616 pgs	⬜
Udacity	A/B Testing	5/5	⬜
	Authentication & Authorization: OAuth	4/4	✅
	Client-Server Communication	5/5	⬜
	Designing RESTful APIs	5/5	✅
	HTTP & Web Servers	3/3	✅
	Networking for Web Developers	5/5	✅
Udemy	AWS Certified Developer - Associate 2018		✅
Datacamp	A/B Testing in Python	4h	⬜
	Customer Analytics & A/B Testing in Python	4h	✅
	Machine Learning Monitoring Concepts	2h	✅
	MLOps Concepts	2h	✅
Neetcode	System Design for Beginners		✅
	System Design Interview		✅

Format	Resource	Length	Progress
Datacamp	Foundations of Probability in Python	5h	✅
	Introduction to Statistics	4h	✅
	Introduction to Statistics in Python	4h	✅
	Hypothesis Testing in Python	4h	✅
	Statistical Thinking in Python (Part 1)	3h	✅
	Statistical Thinking in Python (Part 2)	4h	✅
	Experimental Design in Python	4h	✅
	Practicing Statistics Interview Questions in Python	4h	⬜
edX	Essential Statistics for Data Analysis using Excel		✅
MIT	MIT 18.06 Linear Algebra, Spring 2005	36/36	✅
Udacity	Intro to Inferential Statistics	23/23	✅
	Eigenvectors and Eigenvalues	1/1	✅
	Linear Algebra Refresher	8/8	⬜
Youtube	Essence of linear algebra	16/16	⬜

Format	Resource	Length	Progress
Book	Grokking Algorithms	254 pgs	✅
Udacity	Intro to Data Structures and Algorithms		✅
Neetcode	Algorithms and Data Structures for Beginners		✅
	Advanced Algorithms	1/7	⏳
Youtube	Sliding Window Technique - Algorithmic Mental Models	36m	✅

Format	Resource	Length	Progress
Udacity	Intro to relational database	4/4	✅
	SQL for Data Analysis	10/10	⬜
	Database Systems Concepts & Design		⬜
Datacamp	Database Design	4h	✅
	Introduction to Databases in Python	4h	⬜
	Intro to SQL for Data Science	2h	✅
	Intermediate SQL	4h	✅
	Joining Data in SQL	4h	✅
	Data Manipulation in SQL	4h	⬜
	Exploratory Data Analysis in SQL	4h	⬜
	Applying SQL to Real-World Problems	4h	⬜
	Analyzing Business Data in SQL	4h	⬜
	Reporting in SQL	4h	⬜
	Data-Driven Decision Making in SQL	4h	⬜
	NoSQL Concepts	2h	✅
	Introduction to MongoDB in Python	3h	⬜

Format	Resource	Length	Progress
Book	Refactoring UI	213 pgs	⬜
Pluralsight	UX Fundamentals		✅
Course	How to Visualize Value		✅
	Series: K-12 Figma Design Basics		✅
Youtube	How to Make Your Website Not Ugly: Basic UX for Programmers	48m	✅
Article	Create an illustration in Figma design		✅

Format	Resource	Length	Progress
Udacity	Linux Command Line Basics	30/30	✅
	Shell Workshop	1/1	✅
	Configuring Linux Web Servers	3/3	✅
Datacamp	Introduction to Shell for Data Science	4h	✅
	Introduction to Bash Scripting	4h	✅
	Data Processing in Shell	4h	✅
MIT	The Missing Semester	11/11	✅
Article	A guide to manage your environment variables in a better way using direnv		✅

Format	Resource	Length	Progress
Udacity	Version Control with Git	6/6	✅
	GitHub & Collaboration		✅
	How to Use Git and GitHub	4/4	✅
Datacamp	Introduction to Git for Data Science	4h	✅
	Advanced Git	3h	✅
Youtube	How to Use Git Worktree \| Checkout Multiple Git Branches at Once	11m	✅

Format	Resource	Length	Progress
Datacamp	Unit Testing for Data Science in Python	4h	✅
Youtube	Debug Python inside Docker using debugpy and VSCode	8m	✅
	Improving Your Tests With the Python Mock Object Library: Introducing Mock & Replacing Code	16m	✅
	Hot-swapping Symbols During Tests: Demystifying unittest.mock[.patch] - Bryce Beagle	30m	✅
Article	Introduction to Memory Profiling in Python		✅
	Profiling Python code with memory_profiler		✅
	How to Use "memory_profiler" to Profile Memory Usage by Python Code?		✅
	Concurrency For Starlette Apps (e.g FastAPI / FastHTML)		✅

Format	Resource	Progress
Course	Google: Technical Writing One	✅
	Google: Technical Writing Two	⬜
	Compound Content	✅
	Build Once, Sell Twice	✅
Book	Writing for Software Developers	✅

Format	Resource	Length	Progress
Book	The Tech Resume Inside Out	280 pgs	✅
	The Coding Career Handbook		✅

Format	Resource	Length	Progress
Book	Python 201	352 pgs	⬜
	Writing Idiomatic Python 3	332 pgs	✅
Datacamp	Writing Efficient Python Code	4h	✅
	Writing Functions in Python	4h	✅
	Object-Oriented Programming in Python	4h	✅
	Intermediate Object-Oriented Programming in Python	4h	✅
	Importing Data in Python (Part 1)	3h	✅
	Importing Data in Python (Part 2)	2h	✅
	Intermediate Python for Data Science	4h	✅
	Python Data Science Toolbox (Part 1)	3h	✅
	Python Data Science Toolbox (Part 2)	4h	✅
	Developing Python Packages	4h	✅
	Conda Essentials	2h	✅
	Working with Dates and Times in Python	4h	✅
	Command Line Automation in Python	4h	⬜
Youtube	Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit	2h24m	✅
Article	Python's many command-line utilities		⬜
	A Programmer’s Introduction to Unicode		✅
	Exposing string types to maximize user happiness		✅

Format	Resource	Length	Progress
DeepLearning.AI	Build Apps with Windsurf’s AI Coding Agents	1h30m	✅
Pluralsight	Using The Chrome Developer Tools		✅
Youtube	Prompt Driven Development Series	9/9	✅
	How Anthropic Built Claude Code (w/ Boris Cherny)	50m	✅
Docs	VSCode Docs: Python Interactive window		✅
Article	A Guide to Claude Code 2.0 and getting better at using coding agents		✅

Format	Resource	Length	Progress
Book	Book: Natural Language Processing with Transformers	400 pgs	✅
Stanford	CS224U: Natural Language Understanding \| Spring 2019	15/15	✅
	CS224N: NLP with Deep Learning \| Winter 2019	22/22	✅
CMU	Low-resource NLP Bootcamp 2020	8/8	✅
	Multilingual NLP 2020		✅
Datacamp	Feature Engineering for NLP in Python	4h	✅
	Natural Language Processing Fundamentals in Python	4h	✅
	Regular Expressions in Python	4h	✅
	RNN for Language Modeling	4h	✅
	Natural Language Generation in Python	4h	✅
	Building Chatbots in Python	4h	✅
	Sentiment Analysis in Python	4h	✅
	Machine Translation in Python	4h	✅
Article	The Unreasonable Effectiveness of Collocations		⬜
	FuzzyWuzzy: Fuzzy String Matching in Python		✅
	Transformers: Origins		⬜
	Understanding the Difference Between Embedding Layers and Linear Layers		✅

Format	Resource	Length	Progress
Book	Hands-On Large Language Models: Language Understanding and Generation	424 pgs	✅
	Large Language Models: A Deep Dive: Bridging Theory and Practice	600 pgs	⬜
	Build a Large Language Model (From Scratch)	472 pgs	✅
	The Hundred-Page Language Models Book	100 pgs	✅
	Super Study Guide: Transformers & Large Language Models	350 pgs	✅
	The Smol Training Playbook: The Secrets to Building World-Class LLMs		⬜
Courses	Stanford CS336: Language Modeling from Scratch	5/18	⏳
DeepLearning.AI	Pretraining LLMs	1h29m	✅
	Reinforcement Learning from Human Feedback	1h32m	✅
	How Transformer LLMs Work	1h44m	✅
Karpathy	Intro to Large Language Models	1h	✅
	Let's build the GPT Tokenizer	2hr13m	✅
	Let's reproduce GPT-2 (124M)	4hr1m	✅
	Deep Dive into LLMs like ChatGPT	3h31m	✅
Youtube	5 Years of GPTs with Finbarr Timbers	55m	✅
	A Hackers' Guide to Language Models	1h30m	✅
	Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)	1h44m	✅
	LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU	1h10m	✅
	CMU Advanced NLP Fall 2024 (14): Ensembling and Mixture of Experts	1h5m	✅
	A little guide to building Large Language Models in 2024	1h15m	✅
	How I use LLMs	2h7m	✅
	Simple Diffusion Language Models	15m	✅
	Zed Inferred: Diffusion Language Models	1h2m	✅
	Some open problems I am currently thinking about	30m	✅
	Rotary Positional Embeddings: Combining Absolute and Relative	11m	✅
	How Rotary Position Embedding Supercharges Modern LLMs [RoPE]	13m	✅
	Large Language Models in Five Formulas	58m	✅
	What I Learned From Implementing LLM Architectures From Scratch (And How to Get Started)	52m	✅
	LLM Building Blocks & Transformer Alternatives	27m	✅
	Everything I Learned Training Frontier Small Models	20m	✅
Article	You could have designed state of the art Positional Encoding		✅
	From Digits to Decisions: How Tokenization Impacts Arithmetic in LLMs		✅
	SolidGoldMagikarp (plus, prompt generation)		✅
	Sampling for Text Generation		✅
	First Token Cutoff LLM sampling		✅
	The Big LLM Architecture Comparison		✅
	From GPT-2 to gpt-oss: Analyzing the Architectural Advances		✅
	A Visual Guide to Mamba and State Space Models		⬜
	Patterns and Messages - Part 1 - The Missing Subscript		✅
	How text diffusion works		✅
	The Illustrated Evo 2		⬜
	Interpreting the Prediction of BERT Model for Text Classification		✅
	2025: The year in LLMs		✅
	Things we learned about LLMs in 2024		✅
	Synthetic pretraining		✅
	The Novice's LLM Training Guide		✅
	The Potential of RLMs		✅
	The Curious Case of the bos_token		⬜
	Transformer Math 101		✅

Format	Resource	Length	Progress
Book	A Little Bit of Reinforcement Learning from Human Feedback	280 pgs	✅
DeepLearning.AI	Post-training of LLMs	1h26m	✅
Youtube	How DeepSeek Changes the LLM Story	49m	✅
	Speculations on Test-Time Scaling (o1)	47m	✅
	DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning	1h19m	✅
	MIT EI seminar, Hyung Won Chung from OpenAI. "Don't teach. Incentivize."	35m	✅
	Group Relative Policy Optimization (GRPO) - Formula and Code	24m	✅
	How to approach post-training for AI applications	22m	✅
	Building Cursor Composer with Sasha Rush	19m	✅
	The RLHF Book Course	5/5	⬜
	Full Workshop: Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han	2h42m	✅
	Special Topics in Kernels, RL, Reward Hacking in Agents — Daniel Han, Unsloth	2h20m	✅
	Everything Is a Rollout — Alex Shaw + Ryan Marten, Terminal-Bench, Harbor	21m	✅
Article	Scaling test-time compute - a Hugging Face Space by HuggingFaceH4		✅
	DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs		✅
	The Illustrated DeepSeek-R1		✅
	A Visual Guide to Reasoning LLMs		✅
	GRPO in DeepSeek-R1		✅
	PPO & GRPO for LLM Alignment		✅
	Introduction to LLM Post-Training Techniques		✅
	Understanding Transformers... (beyond the Math)		✅
	SFT, RL, and On-Policy Distillation Through a Distributional Lens		✅

Format	Resource	Length	Progress
DeepLearning.AI	How Diffusion Models Work	46m	✅
	Prompt Engineering for Vision Models	1h32m	⬜
	Building Multimodal Search and RAG	1h32m	✅
Youtube	Lesson 9A 2022 - Stable Diffusion deep dive	41m	✅
	AI Visions Live \| Merve Noyan \| Open-source Multimodality	54m	✅
	Sander Dieleman - Generative modelling through iterative refinement	1h4m	✅
Article	Pinecone: Embedding Methods for Image Search	0/8	⏳
	Understanding Multimodal LLMs		✅
	Computer-Using Agent		✅
	Flow Matching in 5 Minutes		✅
	Understanding Patch Embeddings for Vision Transformers (ViT)		✅
	Diffusion models are autoencoders		✅
	Diffusion Language Models		✅
	Guidance: a cheat code for diffusion models		✅
	Perspectives on diffusion		✅
	The geometry of diffusion guidance		✅
	Diffusion is spectral autoregression		✅
	Generative modelling in latent space		✅

Format	Resource	Progress
Article	Speech AI models: an introduction	✅
	Voice AI & Voice Agents - An Illustrated Primer	⬜
	Neural audio codecs: how to get audio into LLMs	⬜

Format	Resource	Length	Progress
DeepLearning.AI	Quantization Fundamentals with Hugging Face	1h14m	✅
	Quantization in Depth	2h20m	⬜
	Introduction to On-Device AI	1h19m	⬜
Youtube	CMU Advanced NLP Fall 2024 (11): Distillation, Quantization, and Pruning	1h4m	⬜
Article	A Visual Guide to Quantization		⬜
	QLoRA and 4-bit Quantization		⬜
	Understanding AI/LLM Quantisation Through Interactive Visualisations		⬜
	LLM.int8() and Emergent Features		⬜
	Quantization from the ground up		⬜

Format	Resource	Length	Progress
Book	Inference Engineering	275 pgs	✅
	Programming Massively Parallel Processors: A Hands-on Approach	Ch. 2	⏳
	The Algebra of Speed		⬜
DeepLearning.AI	Efficiently Serving LLMs	2h40m	✅
Udacity	Intro to Parallel Programming	299/458	⏳
Youtube	GPU Puzzles: Let's Play	1h15m	⬜
	Deploying Fine-Tuned Models	2h28m	✅
	Building Machine Learning Systems for a Trillion Trillion Floating Point Operations	1h	✅
	How GPT, Claude, and Gemini are actually trained and served – Reiner Pope	2h13m	✅
	Chip design from the bottom up – Reiner Pope	1h20m	✅
	Street Fighting Transformers	25m	✅
	Slaying OOMs with PyTorch FSDP and torchao	49m	✅
	Distributed Training with PyTorch: complete tutorial with cloud infrastructure and code	1h12m	✅
	How DDP works \|\| Distributed Data Parallel	3m	✅
	FSDP Explained	11m	✅
	Lecture 48: The Ultra Scale Playbook	44m/3h3m	⏳
	Invited Talk: PyTorch Distributed (DDP, RPC) - By Facebook Research Scientist Shen Li	1h7m	✅
	Unit 9 \| Techniques for Speeding Up Model Training	12/12	✅
	Optimizing Model Training End-to-End: A Tiny MoE Case Study Lambda	19m	✅
Article	How to make LLMs go fast		✅
	In the Fast Lane! Speculative Decoding - 10x Larger Model, No Extra Cost		⬜
	Accelerating Generative AI with PyTorch II: GPT, Fast		⬜
	Harmonizing Multi-GPUs: Efficient Scaling of LLM Inference		⬜
	Multi-Query Attention is All You Need		⬜
	Transformers Inference Optimization Toolset		⬜
	LLM Inference Series: 3. KV caching explained		⬜
	LLM Inference Series: 4. KV caching, a deeper look		⬜
	LLM Inference Series: 5. Dissecting model performance		⬜
	Transformer Inference Arithmetic		✅
	llama.cpp guide - Running LLMs locally, on any hardware, from scratch		✅
	Domain specific architectures for AI inference		⬜
	SBTB 2023: Charles Frye, Parallel Processors: Past & Future Connections Between LLMs and OS Kernels	32m	✅
	Compiling ML models to C for fun		⬜
	How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: a Worklog		⬜
	Inside vLLM: Anatomy of a High-Throughput LLM Inference System		⬜
	Understanding LLM Inference Engines: Inside Nano-vLLM (Part 1)		⬜
	Understanding LLM Inference Engines: Inside Nano-vLLM (Part 2)		⬜
	Defeating Nondeterminism in LLM Inference		✅
	Prompt caching: 10x cheaper LLM tokens, but how?		⬜
	Attention Mechanism: From Math to GPU		⬜
	Making Deep Learning Go Brrrr From First Principles		✅
	What Shapes Do Matrix Multiplications Like?		✅
	Strangely, Matrix Multiplications on GPUs Run Faster When Given "Predictable" Data!		✅
	A Short Guide to PyTorch DDP		✅
	Scaling Deep Learning with PyTorch: Multi-Node and Multi-GPU Training Explained (with Code)		✅
	Accelerating PyTorch Model Training		✅
	Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow		✅
	Distributed data parallel training in Pytorch		✅
	Training on Multiple GPUs		✅

Format	Resource	Length	Progress
DeepLearning.AI	Finetuning Large Language Models	1h35m	✅
OpenAI	GPT-4o mini Fine-Tuning Build Hour		✅
	Distillation Build Hour		✅
Youtube	Fine-Tuning with Axolotl	2h10m	✅
	Creating, Curating, and Cleaning Data for LLMs	54m	✅
	Best Practices For Fine Tuning Mistral	23m	✅
	Fine Tuning OpenAI Models - Best Practices	49m	✅
	When and Why to Fine Tune an LLM	1h56m	✅
	Napkin Math For Fine Tuning Pt. 1 w/Johno Whitaker	57m	✅
	Napkin Math For Fine Tuning Pt. 2 w/Johno Whitaker	1h4m	✅
	Fine Tuning LLMs for Function Calling w/Pawel Garback	1h32m	✅
	From Prompt to Model: Fine-tuning when you've already deployed LLMs in prod w/Kyle Corbitt	32m	✅
	Why Fine Tuning is Dead w/Emmanuel Ameisen	50m	✅
Article	Tokenization Gotchas		⬜
	Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)		⬜
	How to Generate and Use Synthetic Data for Finetuning		✅

Format	Resource	Length	Progress
Book	AI Engineering: Building Applications with Foundation Models	400 pgs	✅
	Designing Large Language Model Applications	325 pgs	⬜
Course	LLM Bootcamp - Spring 2023		✅
DeepLearning.AI	Building Systems with the ChatGPT API	1h55m	✅
	Building Generative AI Applications with Gradio	59m	✅
	LLMOps	1h31m	⬜
Youtube	A Survey of Techniques for Maximizing LLM Performance	45m	✅
	Building Blocks for LLM Systems & Products: Eugene Yan	17m	✅
	Building LLM Applications	0/8	⏳
	Getting the Most Out of Your LLM Experiments	48m	✅
Article	What We’ve Learned From A Year of Building with LLMs		✅
	Data Flywheels for LLM Applications		✅
	Emerging Architectures for LLM Applications		✅
	Patterns for Building LLM-based Systems & Products		✅
	LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy		✅
	Emerging UX Patterns for Generative AI Apps & Copilots		✅
	Pushing ChatGPT's Structured Data Support To Its Limits		✅
	GPTed: using GPT-3 for semantic prose-checking		✅
	Don't worry about LLMs		✅
	Data acquisition strategies for AI-first start-ups		✅
	All about synthetic data generation		✅
	The Synthetic Data Playbook: Generating Trillions of the Finest Tokens		⬜
	Reverse-engineering Claude's generative UI - then building it for the terminal		✅
	Redpoint 2026 Market Update		✅
	Designing synthetic datasets for the real world: Mechanism design and reasoning from first principles		✅

Format	Resource	Length	Progress
DeepLearning.AI	Automated Testing for LLMOps	1h12m	✅
	Red Teaming LLM Applications	1h29m	✅
	Evaluating and Debugging Generative AI Models Using Weights and Biases	1h	⬜
	Quality and Safety for LLM Applications	1h59m	⬜
Youtube	OpenAI: Evals Build Hour		✅
	Instrumenting & Evaluating LLMs	2hr33m	✅
	LLM Eval For Text2SQL	51m	✅
	A Deep Dive on LLM Evaluation	49m	✅
	ARC Prize Foundation @ MIT 2025	8/8	✅
Article	Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)		✅
	Your AI Product Needs Evals		✅
	Task-Specific LLM Evals that Do & Don't Work		✅
	Evaluation & Hallucination Detection for Abstractive Summaries		✅
	Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)		⬜
	Aligning LLM as judge with human evaluators		✅
	Hard-Earned Lessons from 2 Years of Improving AI Applications		✅
	Evaluating Long-Context Question & Answer Systems		⬜
	LLM Evals FAQ		✅
	Quantifying infrastructure noise in agentic coding evals		✅
	How we optimized Dash's relevance judge with DSPy		✅
	“It’s Hard to Eval” Is a Product Smell		✅

Format	Resource	Length	Progress
Book	Prompt Engineering for LLMs	250 pgs	⬜
Course	LLM Engineering - Structured Outputs		✅
Anthropic	Anthropic Courses		⬜
	Anthropic: The Claude in Amazon Bedrock Course		⬜
DeepLearning.AI	Reasoning with o1	1h44m	✅
	ChatGPT Prompt Engineering for Developers	1h40m	✅
Youtube	OpenAI: Reasoning with o1 Build Hour		✅
	Prompt Engineering Overview	1h4m	✅
	Prompt Engineering Workshop	1h	✅
	Context Engineering SF - August 20th, 2025	4/4	✅
Article	OpenAI Prompt Engineering		✅
	Prompting Fundamentals and How to Apply them Effectively		✅
	How I came in first on ARC-AGI-Pub using Sonnet 3.5 with Evolutionary Test-time Compute		✅
	Prompt Engineering(Liliang Weng)		✅
	Prompt Engineering 201: Advanced methods and toolkits		✅
	Optimizing LLMs for accuracy		✅
	Primers • Prompt Engineering		✅
	Guided text generation with Large Language Models		✅
	Effective context engineering for AI agents		✅

Format	Resource	Length	Progress
Course	Course: Fullstack Retrieval		⬜
	Systematically Improving RAG		⬜
DeepLearning.AI	Building and Evaluating Advanced RAG Applications	2h5m	✅
	Vector Databases: from Embeddings to Applications	1h5m	✅
	Advanced Retrieval for AI with Chroma	1h2m	✅
	Prompt Compression and Query Optimization	1h49m	✅
	Large Language Models with Semantic Search	1h22m	✅
	Building Applications with Vector Databases	1h23m	✅
	Knowledge Graphs for RAG	2h4m	⬜
	Preprocessing Unstructured Data for LLM Applications	1h22m	⬜
	Embedding Models: From Architecture to Implementation	1h	✅
	Retrieval Optimization - From Tokenization to Vector Quantization	1h43m	✅
Youtube	Systematically improving RAG applications	1h8m	✅
	Back to Basics for RAG w/ Jo Bergum	44m	✅
	Beyond the Basics of Retrieval for Augmenting Generation (w/ Ben Clavié)	48m	✅
	RAG From Scratch	14/14	✅
	CMU Advanced NLP Fall 2024 (10): Retrieval and RAG	1h17m	✅
	What You See Is What You Search: Vision Language Models for PDF Retrieval [Jo Bergum]	44m	✅
	Graph Databases: When to Use Them (And When to Run Away)	33m	✅
	Personalization in the Era of LLMs	20m	✅
	Intelligence Efficiency, Ben Geist \| Compile 26	14m	✅
	The Memory Problem, Baseten \| Compile 26	13m	✅
Article	Pretrained Transformer Language Models for Search - part 1		✅
	Pretrained Transformer Language Models for Search - part 2		✅
	Pretrained Transformer Language Models for Search - part 3		✅
	Pretrained Transformer Language Models for Search - part 4		✅
	How not to use BERT for Document Ranking		✅
	Understanding LanceDB's IVF-PQ index		✅
	A little pooling goes a long way for multi-vector representations		✅
	Levels of Complexity: RAG Applications		✅
	Systematically Improving Your RAG		✅
	Stop using LGTM@Few as a metric (Better RAG)		✅
	Low-Hanging Fruit for RAG Search		✅
	What AI Engineers Should Know about Search		✅
	Evaluating Chunking Strategies for Retrieval		✅
	Sentence Embeddings. Introduction to Sentence Embeddings		✅
	LambdaMART in Depth		⬜
	Guided Generation with Outlines		✅
	RAG tricks from the trenches		✅
	Understanding the BM25 full text search algorithm		⬜
	Arxiv: Ragas: Automated Evaluation of Retrieval Augmented Generation		✅
	Pinecone: Vector Databases in Production for Busy Engineers		✅
	Pinecone: Retrieval Augmented Generation		✅
	Pinecone: Faiss: The Missing Manual		✅
	Pinecone: Natural Language Processing for Semantic Search	0/13	⏳
	Guidance: Token Healing		⬜
	Deep Research System Card		✅
	Exploring OpenAI's deep research API model o4-mini-deep-research		✅
	Introduction to deep research in the OpenAI API		✅
	Lessons from building the best Deep Research (and how you can build better agents)		✅

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From the README

learning

Generic Skills

System Design

Maths

Data Structures and Algorithms

Data Modeling

Design Sense

Linux & Command Line

Version Control

Testing & Profiling

Technical Writing

Career

Programming

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Systems Engineering

Format	Resource	Length	Progress
DeepLearning.AI	Agentic AI with Andrew Ng	9h55m	✅
	Building Agentic RAG with LlamaIndex	54m	✅
	Multi AI Agent Systems with crewAI	3h1m	✅
	Building Towards Computer Use with Anthropic	1h47m	✅
	Pydantic for LLM Workflows	1h50m	✅
	Practical Multi AI Agents and Advanced Use Cases with crewAI	2h49m	⬜
	LLMs as Operating Systems: Agent Memory	1h	✅
	Serverless Agentic Workflows with Amazon Bedrock	1h	⬜
	AI Agentic Design Patterns with AutoGen	1h	⬜
	AI Agents in LangGraph	1h42m	⬜
	Building Your Own Database Agent	1h8m	⬜
	Function-Calling and Data Extraction with LLMs	59m	✅
	Evaluating AI Agents	2h16m	✅
	Building AI Browser Agents	1h5m	⬜
Course	Berkeley: CS294/194-196 Large Language Model Agents	0/14	⬜
	Huggingface: Agents Course	Unit 1	⏳
	Berkeley: Advanced LLM Agents MOOC	0/12	⬜
Youtube	OpenAI: Assistants & Agents Build Hour		✅
	OpenAI: Function Calling Build Hour		✅
	How to Evaluate Agents: Galileo’s Agentic Evaluations in Action	16m	✅
	Agent Response \| LangSmith Evaluation - Part 24	14m	✅
	Single Step \| LangSmith Evaluation - Part 25	6m	✅
	Agent Trajectory \| LangSmith Evaluation - Part 26	8m	✅
	Evaluating Agents and Assistants: The AI Conference	24m	✅
	How to Build, Evaluate, and Iterate on LLM Agents	1h2m	✅
	How Claude Code Works - Jared Zoneraich, PromptLayer	1h5m	✅
	Build Agents That Run for Hours (Without Losing the Plot) — Ash Prabaker & Andrew Wilson, Anthropic	1h15m	✅
Article	Tool Invocation - Demonstrating the Marvel of GPT's Flexibility		✅
	Introducing smolagents, a simple library to build agents		✅
	What Problem Does The Model Context Protocol Solve?		✅
	Don’t Build Multi-Agents		✅
	Coding Agents 101: The Art of Actually Getting Things Done		✅
	What makes Claude Code so damn good (and how to recreate that magic in your agent)!?		✅
	Anthropic: Building effective agents		✅
	Anthropic: Building Effective Agents Cookbook		✅
	Anthropic: Writing effective tools for agents — with agents		✅
	How System Prompts Define Agent Behavior		✅
	Components of A Coding Agent		✅

Format	Resource	Length	Progress
Book	Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition	848 pgs	⬜
	A Machine Learning Primer	100 pgs	✅
	Grokking Machine Learning	320 pgs	✅
	The StatQuest Illustrated Guide To Machine Learning	304 pgs	✅
Datacamp	Ensemble Methods in Python	4h	✅
	Extreme Gradient Boosting with XGBoost	4h	⬜
	Clustering Methods with SciPy	4h	✅
	Unsupervised Learning in Python	4h	✅
	Supervised Learning with scikit-learn	4h	✅
	Machine Learning with Tree-Based Models in Python	5h	✅
	Linear Classifiers in Python	4h	✅
	Model Validation in Python	4h	✅
	Hyperparameter Tuning in Python	4h	✅
	HR Analytics in Python: Predicting Employee Churn	4h	✅
	Predicting Customer Churn in Python	4h	✅
	Dimensionality Reduction in Python	4h	✅
	Preprocessing for Machine Learning in Python	4h	✅
	Data Types for Data Science	4h	✅
	Cleaning Data in Python	4h	✅
	Feature Engineering for Machine Learning in Python	4h	✅
	Predicting CTR with Machine Learning in Python	4h	✅
	Intro to Financial Concepts using Python	4h	✅
	Fraud Detection in Python	4h	✅
	Segmentation and Clustering	5/5	✅
	Intro to Python for Data Science	4h	✅
edX	Implementing Predictive Analytics with Spark in Azure HDInsight		✅
Article	The wrong batch size is all it takes		✅
	A Gentle Introduction to Expectation-Maximization (EM Algorithm)		✅
	How to Use Out-of-Fold Predictions in Machine Learning		✅
	Stacking and Blending — An Intuitive Explanation		✅
Youtube	A walk through the isolation forest	23m	✅
	Complete Anomaly Detection Tutorials Machine Learning And Its Types With Implementation	36m	✅

Format	Resource	Length	Progress
Book	Make Your Own Neural Network	226 pgs	✅
Fast.ai	Practical Deep Learning for Coder (Part 1)		✅
	Practical Deep Learning for Coder (Part 2)	9, 13,14,17,18(48:10),19	⬜
Datacamp	Convolutional Neural Networks for Image Processing	4h	✅
Karpathy	Neural Networks: Zero to Hero		✅
Article	An overview of gradient descent optimization algorithms		✅
	Things that confused me about cross-entropy		✅
	Why is the ReLU function not differentiable at x=0?		✅
	Are CNNs invariant to translation, rotation, and scaling?		✅
	How to Control the Stability of Training Neural Networks With the Batch Size		✅
	A Visual Guide to Learning Rate Schedulers in PyTorch		✅

Format	Resource	Length	Progress
Pluralsight	CSS Positioning		✅
	Introduction to CSS		✅
	CSS: Specificity, the Box Model, and Best Practices		✅
	CSS: Using Flexbox for Layout		✅
	Code School: Blasting Off with Bootstrap		✅
Codecademy	Learn SASS	3h	✅
Course	CSS for Javascript Developers		✅

Format	Resource	Length	Progress
Codecademy	Learn HTML	7h	✅
	Make a website	4h	✅
Article	Alternative Text		⬜

Format	Resource	Length	Progress
Course	Pinecone: LangChain AI Handbook	0/11	⏳
DeepLearning.AI	LangChain for LLM Application Development	1h48m	⬜
	LangChain: Chat with Your Data	1h18m	⬜

Format	Resource	Length	Progress
Codecademy	Learn JavaScript		✅
	Jquery Track	4h	✅
Udacity	ES6 - JavaScript Improved	4/4	✅
	Intro to Javascript	7/7	✅
	Object Oriented JS 1	8/8	✅
	Object Oriented JS 2	4/4	✅
Udemy	Understanding Typescript		✅

Format	Resource	Length	Progress
Datacamp	Introduction to Seaborn	4h	✅
	Introduction to Matplotlib	4h	✅

Format	Resource	Length	Progress
Datacamp	Pandas Foundations	4h	✅
	Pandas Joins for Spreadsheet Users	4h	✅
	Manipulating DataFrames with pandas	4h	✅
	Merging DataFrames with pandas	4h	✅
	Data Manipulation with pandas	4h	✅
	Optimizing Python Code with pandas	4h	✅
	Streamlined Data Ingestion with pandas	4h	✅
	Analyzing Marketing Campaigns with pandas	4h	✅
	Analyzing Police Activity with pandas	4h	✅

Format	Resource	Length	Progress
Course	Deeplizard: Neural Network Programming - Deep Learning with PyTorch	20/20	✅
Datacamp	Introduction to Deep Learning with PyTorch	4h	✅
	Intermediate Deep Learning with PyTorch	4h	⬜
	Deep Learning for Text with PyTorch	4h	⬜
	Deep Learning for Images with PyTorch	4h	⬜
Article	PyTorch internals		⬜
	Taking PyTorch For Granted		⬜