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Education & Learning

 About Learning

A log of things I'm learning

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Learning

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learning

A running log of things I'm learning to build strong core software engineering skills while also expanding my knowledge of adjacent technologies everyday.

Updated: Once a month | Current Focus: Generative AI

Generic Skills

High-level skills that translates into any domain and are independent of a stack

System Design

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

Maths

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

Data Structures and Algorithms

Format Resource Progress
Book Grokking Algorithms ✅
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 36:44 ✅

Data Modeling

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

UI/UX

Format Resource Progress
Book Refactoring UI ⬜
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 ✅

Linux & Command Line

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

Version Control

Format Resource Progress
Udacity Version Control with Git ✅
GitHub & Collaboration ✅
How to Use Git and GitHub ✅
Datacamp Introduction to Git for Data Science ✅
Advanced Git ✅
Youtube How to Use Git Worktree | Checkout Multiple Git Branches at Once ✅

Testing & Profiling

Format Resource Progress
Datacamp Unit Testing for Data Science in Python ✅
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) ✅
Youtube Debug Python inside Docker using debugpy and VSCode ✅

Technical Writing

Format Resource Progress
Book The Tech Resume Inside Out ✅
Course Google: Technical Writing One ✅
Google: Technical Writing Two ⬜

Programming

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

Development Environment

Format Resource Progress
DeepLearning.AI Build Apps with Windsurf’s AI Coding Agents 1h10m ✅
Pluralsight Using The Chrome Developer Tools ✅
Youtube Prompt Driven Development Series 9/9 ✅
How Anthropic Built Claude Code (w/ Boris Cherny) ✅
Docs VSCode Docs: Python Interactive window ✅
Article A Guide to Claude Code 2.0 and getting better at using coding agents ✅

Specialized Skills

Traditional Machine Learning

Format Resource Progress
Book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition ⬜
A Machine Learning Primer ✅
Grokking Machine Learning ✅
The StatQuest Illustrated Guide To Machine Learning ✅
Datacamp Ensemble Methods in Python ✅
Extreme Gradient Boosting with XGBoost ⬜
Clustering Methods with SciPy ✅
Unsupervised Learning in Python ✅
Supervised Learning with scikit-learn ✅
Machine Learning with Tree-Based Models in Python ✅
Linear Classifiers in Python ✅
Model Validation in Python ✅
Hyperparameter Tuning in Python ✅
HR Analytics in Python: Predicting Employee Churn ✅
Predicting Customer Churn in Python ✅
Dimensionality Reduction in Python ✅
Preprocessing for Machine Learning in Python ✅
Data Types for Data Science ✅
Cleaning Data in Python ✅
Feature Engineering for Machine Learning in Python ✅
Predicting CTR with Machine Learning in Python ✅
Intro to Financial Concepts using Python ✅
Fraud Detection in Python ✅
Segmentation and Clustering ✅
Intro to Python for Data Science ✅
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 ✅
Complete Anomaly Detection Tutorials Machine Learning And Its Types With Implementation ✅

Deep Learning

Format Resource Progress
Book Make Your Own Neural Network ✅
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 ✅
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 ✅

Natural Language Processing

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

Generative AI

LLM Theory

Format Resource Progress
Book Hands-On Large Language Models: Language Understanding and Generation ✅
Large Language Models: A Deep Dive: Bridging Theory and Practice ⬜
Build a Large Language Model (From Scratch) 246/581
The Hundred-Page Language Models Book ✅
Super Study Guide: Transformers & Large Language Models ✅
The Smol Training Playbook: The Secrets to Building World-Class LLMs ⬜
DeepLearning.AI Pretraining LLMs ✅
Reinforcement Learning from Human Feedback ✅
How Transformer LLMs Work ✅
Karpathy Intro to Large Language Models 1hr ✅
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 1hr30m ✅
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 ✅
A little guide to building Large Language Models in 2024 1h15m ✅
How I use LLMs 2h7m ✅
Simple Diffusion Language Models ✅
Zed Inferred: Diffusion Language Models ✅
Some open problems I am currently thinking about ✅
Rotary Positional Embeddings: Combining Absolute and Relative ✅
How Rotary Position Embedding Supercharges Modern LLMs [RoPE] ✅
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 ⬜

Post-training (RLHF / RLVR)

Format Resource Progress
Book A Little Bit of Reinforcement Learning from Human Feedback ✅
DeepLearning.AI Post-training of LLMs ✅
Youtube How DeepSeek Changes the LLM Story ✅
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 videos ⬜
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) ✅

Multi-modality (Vision)

Format Resource Progress
DeepLearning.AI How Diffusion Models Work ✅
Prompt Engineering for Vision Models ⬜
Building Multimodal Search and RAG ✅
Youtube Lesson 9A 2022 - Stable Diffusion deep dive ✅
AI Visions Live | Merve Noyan | Open-source Multimodality 54m ✅
Sander Dieleman - Generative modelling through iterative refinement ✅
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 ✅

Multi-modality (Audio)

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 ⬜

Quantization

Format Resource Progress
DeepLearning.AI Quantization Fundamentals with Hugging Face ✅
Quantization in Depth ⬜
Introduction to On-Device AI ⬜
Youtube CMU Advanced NLP Fall 2024 (11): Distillation, Quantization, and Pruning ⬜
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 ⬜

Distributed Training

Format Resource Progress
Youtube 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 ✅
FSDP Explained ✅
Lecture 48: The Ultra Scale Playbook 3h3m 44:24
Invited Talk: PyTorch Distributed (DDP, RPC) - By Facebook Research Scientist Shen Li ✅
Unit 9 | Techniques for Speeding Up Model Training ✅
Article 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 ✅

Inference Engineering

Format Resource Progress
Book Inference Engineering ✅
Programming Massively Parallel Processors: A Hands-on Approach Ch. 2
The Algebra of Speed ⬜
DeepLearning.AI Efficiently Serving LLMs ✅
Udacity Intro to Parallel Programming 458 video 299/458
Youtube GPU Puzzles: Let's Play ⬜
Deploying Fine-Tuned Models 2h28m ✅
Building Machine Learning Systems for a Trillion Trillion Floating Point Operations 1h ✅
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 ⬜
Optimizing AI Inference at Character.AI ⬜
Optimizing AI Inference at Character.AI (Part Deux) ⬜
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 ✅
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 ⬜

Finetuning and Distillation

Format Resource Progress
DeepLearning.AI Finetuning Large Language Models ✅
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 ✅
When and Why to Fine Tune an LLM 1h56m ✅
Napkin Math For Fine Tuning Pt. 1 w/Johno Whitaker ✅
Napkin Math For Fine Tuning Pt. 2 w/Johno Whitaker ✅
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 ✅

AI Engineering

LLM System Design

Format Resource Progress
Book AI Engineering: Building Applications with Foundation Models ✅
Designing Large Language Model Applications ⬜
Course LLM Bootcamp - Spring 2023 ✅
DeepLearning.AI Building Systems with the ChatGPT API ✅
Building Generative AI Applications with Gradio ✅
LLMOps ⬜
Youtube A Survey of Techniques for Maximizing LLM Performance ✅
Building Blocks for LLM Systems & Products: Eugene Yan ✅
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 ✅

Evals and Guardrails

Format Resource Progress
DeepLearning.AI Automated Testing for LLMOps ✅
Red Teaming LLM Applications ✅
Evaluating and Debugging Generative AI Models Using Weights and Biases ⬜
Quality and Safety for LLM Applications ⬜
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 ✅
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 ✅

Context Engineering

Format Resource Progress
Book Prompt Engineering for LLMs ⬜
Course LLM Engineering - Structured Outputs ✅
Anthropic Anthropic Courses ⬜
Anthropic: The Claude in Amazon Bedrock Course ⬜
DeepLearning.AI Reasoning with o1 ✅
ChatGPT Prompt Engineering for Developers ✅
Youtube OpenAI: Reasoning with o1 Build Hour ✅
Prompt Engineering Overview 1hr4m ✅
Prompt Engineering Workshop 1h ✅
Context Engineering SF - August 20th, 2025 4/4 videos ✅
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 ✅

Information Retrieval / RAG

Format Resource Progress
Course Course: Fullstack Retrieval ⬜
Systematically Improving RAG ⬜
DeepLearning.AI Building and Evaluating Advanced RAG Applications ✅
Vector Databases: from Embeddings to Applications ✅
Advanced Retrieval for AI with Chroma ✅
Prompt Compression and Query Optimization ✅
Large Language Models with Semantic Search 1hr ✅
Building Applications with Vector Databases ✅
Knowledge Graphs for RAG ⬜
Preprocessing Unstructured Data for LLM Applications ⬜
Embedding Models: From Architecture to Implementation ✅
Retrieval Optimization - From Tokenization to Vector Quantization ✅
Youtube Systematically improving RAG applications ✅
Back to Basics for RAG w/ Jo Bergum ✅
Beyond the Basics of Retrieval for Augmenting Generation (w/ Ben Clavié) ✅
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] ✅
Graph Databases: When to Use Them (And When to Run Away) ✅
Personalization in the Era of LLMs ✅
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) ✅

Agentic Engineering

Format Resource Progress
DeepLearning.AI Agentic AI with Andrew Ng ✅
Building Agentic RAG with LlamaIndex ✅
Multi AI Agent Systems with crewAI ✅
Building Towards Computer Use with Anthropic ✅
Pydantic for LLM Workflows ✅
Practical Multi AI Agents and Advanced Use Cases with crewAI ⬜
LLMs as Operating Systems: Agent Memory ✅
Serverless Agentic Workflows with Amazon Bedrock ⬜
AI Agentic Design Patterns with AutoGen ⬜
AI Agents in LangGraph ⬜
Building Your Own Database Agent ⬜
Function-Calling and Data Extraction with LLMs 59m ✅
Evaluating AI Agents 2h16m ✅
Building AI Browser Agents ⬜
Course Berkeley: CS294/194-196 Large Language Model Agents 0/14 lectures ⬜
Huggingface: Agents Course Unit 1
Berkeley: Advanced LLM Agents MOOC 0/12 lectures ⬜
Youtube OpenAI: Assistants & Agents Build Hour ✅
OpenAI: Function Calling Build Hour ✅
How to Evaluate Agents: Galileo’s Agentic Evaluations in Action ✅
Agent Response | LangSmith Evaluation - Part 24 ✅
Single Step | LangSmith Evaluation - Part 25 ✅
Agent Trajectory | LangSmith Evaluation - Part 26 ✅
Evaluating Agents and Assistants: The AI Conference ✅
How to Build, Evaluate, and Iterate on LLM Agents ✅
How Claude Code Works - Jared Zoneraich, PromptLayer ✅
Build Agents That Run for Hours (Without Losing the Plot) — Ash Prabaker & Andrew Wilson, Anthropic ✅
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 ✅

Technical Skills (Libraries/Frameworks/Tools)

CSS

Format Resource 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 ✅
Course CSS for Javascript Developers ✅

Django

Format Resource Progress
Article Django, HTMX and Alpine.js: Modern websites, JavaScript optional ✅

HTML

Format Resource Progress
Codecademy Learn HTML ✅
Make a website ✅
Article Alternative Text ⬜

Langchain

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

JavaScript

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

Matplotlib

Format Resource Progress
Datacamp Introduction to Seaborn ✅
Introduction to Matplotlib ✅

MLFlow

Format Resource Progress
Datacamp Introduction to MLFlow ✅

Numpy

Format Resource Progress
Youtube Numpy Array Broadcasting In Python Explained ✅

Nexxt.JS

Format Resource Progress
Docs Start building with Next.js

Pandas

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

PyTorch

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

ReactJS

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Codecademy Learn ReactJS: Part I ✅
Learn ReactJS: Part II ✅
Course NexxtJS: React Foundations ⬜

Spacy

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Datacamp Advanced NLP with spaCy ✅

Tensorflow & Keras

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Course Deeplizard: Keras - Python Deep Learning Neural Network API ✅
Datacamp Introduction to TensorFlow in Python ✅
Deep Learning in Python ✅
Introduction to Deep Learning with Keras ✅
Advanced Deep Learning with Keras ✅
Udacity Intro to TensorFlow for Deep Learning ✅

Miscellaneous

Marketing

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Course Build Once, Sell Twice ✅

Search Engine Optimization (SEO)

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Course Compound Content ✅