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

Generic transferable skills independent of a stack

System Design

Format	Resource	Progress
Book	Designing Machine Learning Systems	✅
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	✅

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	✅
Docs	VSCode Docs: Python Interactive window	⬜

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	✅

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	✅
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	⬜

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	✅
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	✅

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	⬜

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	✅

Parallel Computing

Format	Resource	Progress
Book	Programming Massively Parallel Processors: A Hands-on Approach	Ch. 2
	The Algebra of Speed	⬜
Udacity	Intro to Parallel Programming 458 video	299/458
Youtube	GPU Puzzles: Let's Play	⬜

Inference Optimization

Format	Resource	Progress
DeepLearning.AI	Efficiently Serving LLMs	✅
Youtube	Deploying Fine-Tuned Models 2h28m	✅
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	⬜
	Serverless LLM apps with Amazon Bedrock	⬜
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	✅
	The Novice's LLM Training Guide	⬜
	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	✅

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	✅
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	✅

Context Engineering

Format	Resource	Progress
Book	Prompt Engineering for LLMs	⬜
Anthropic	Anthropic Courses	⬜
	Anthropic: The Claude in Amazon Bedrock Course	⬜
DeepLearning.AI	Reasoning with o1	✅
	ChatGPT Prompt Engineering for Developers	⬜
Course	LLM Engineering - Structured Outputs	⬜
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	⬜
	Anyscale Endpoints: JSON Mode and Function calling Features	⬜
	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]	✅
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	⬜
	Retrieval 101	⬜
	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	⬜

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	✅
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	✅

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

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

Spacy

Format	Resource	Progress
Datacamp	Advanced NLP with spaCy	✅

Tensorflow & Keras

Format	Resource	Progress
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

Format	Resource	Progress
Course	Build Once, Sell Twice	✅

Search Engine Optimization (SEO)

Format	Resource	Progress
Course	Compound Content	✅

Technical Writing

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