Subject
42 entries
Tutorial
Bookmarks
How to Build an Agent: Minimal Code-Editing Agent in <400 Lines
A practical tutorial showing how to build a fully functional code-editing agent in under 400 lines of code. Worth reading as a minimal reference implementation that strips the problem down to its essential loop.
Semantic Search and Q&A with GPT-3 and Datasette
Simon Willison's tutorial on building Q&A over documentation using GPT-3, embeddings, and Datasette — an early practical guide to semantic search that Willison built for his own blog. One of the first clearly explained end-to-end RAG implementations from a respected practitioner.
How to Call an AI Friend Using GPT-3 with Twilio Voice
A tutorial for building a callable AI 'friend' using GPT-3, Twilio Voice, and Twilio Functions — demonstrating the phone-based AI assistant pattern before it became common. The call-able AI interface predates consumer voice assistants built on LLMs.
Stable Diffusion Parameters Guide
A practical overview of the key parameters for controlling AI image generation in Stable Diffusion — steps, CFG scale, sampler, seed, and dimensions. A useful reference from the early days when these knobs were being collectively figured out.
A Complete Introduction to Prompt Engineering
Mihail Eric's comprehensive introduction to prompt engineering for LLMs — covering few-shot prompting, chain-of-thought, instruction tuning, and evaluation. Published in late 2022 when prompt engineering was emerging as a recognized discipline.
Running Your Own AI Image Generator with Latent Diffusion
A practical guide to running latent diffusion image generation locally — no credits, no limits, full control. Requires a Linux system, CUDA GPU (tested on RTX 3090), and 32GB RAM. From August 2022, right before Stable Diffusion made this accessible to everyone.
How to Play with the GPT-3 Language Model
Simon Willison's practical guide to getting started with GPT-3 — written in June 2022 when most developers still hadn't touched it. One of the clearest early explainers on the OpenAI API and what the model could actually do.
Introduction to Graph Neural Networks with JAX/jraph
DeepMind's interactive introduction to Graph Neural Networks using JAX and jraph — covers message passing, graph classification, and node prediction with runnable code. One of the clearest practical GNN tutorials available as a Google Colab notebook.
Aptos FullNode + Identity Docker Guide
A Docker-based guide to running an Aptos FullNode with identity configuration — practical setup guide for the Aptos testnet period before mainnet launch. Aptos (founded by ex-Meta/Diem engineers) was one of the most anticipated new L1s of 2022.
Running a Bitcoin Lightning Network Node
A guide to running a Bitcoin Lightning Network node — covers the LND or c-lightning stack, channel management, and liquidity. Lightning enables near-instant BTC payments by routing transactions off-chain through a network of payment channels.
Running an Ethereum Full Node on a Raspberry Pi 4
A guide to running an Ethereum full node on a Raspberry Pi 4 — a self-sovereign approach to blockchain access that avoids trusting centralized RPC providers like Infura. Technical tutorial covering hardware, software, and sync process.
Writing an OS in Rust
Philipp Oppermann's 'Writing an OS in Rust' is a series of detailed blog posts walking through building a minimal OS kernel in Rust from scratch — bootloader, VGA output, interrupts, memory management. The gold standard for learning both Rust and OS internals simultaneously.
LTR with Bees: Learning to Rank in Apache Solr
Christine Poerschke's tutorial using a bee-related dataset to demonstrate learning-to-rank in Apache Solr — end-to-end example from feature extraction through model training to serving. Concrete and dataset-grounded, unlike most abstract LTR tutorials.
Probable Points and Credible Intervals: Bayesian Decision Theory
Part 2 of Rasmus Baath's gentle intro to Bayesian decision theory — covering how credible intervals and probable points are used to make decisions under uncertainty. One of the cleaner elementary treatments of the Bayesian decision framework.
Pandas Pivot Table Explained
A step-by-step tutorial on using Pandas pivot tables for business data analysis from the Practical Business Python blog. Pivot tables are the single most useful tool for quickly summarizing and reshaping tabular data, and this covers the full API clearly.
Simple CSV Data Wrangling with Python
District Data Labs tutorial on CSV data wrangling with Python, covering the basics of loading, cleaning, and transforming tabular data before analysis. A foundational skill that every data scientist spends far more time on than they'd like.
Data in Practice
Data in Practice is a tutorial blog by Daniel Baumgartel covering coding, algorithms, data science, machine learning, and distributed computing — practical implementations with working code. A representative example of the practitioner-written technical blogs that shaped the 2014 data science self-education ecosystem.
An Introduction to Gradient Descent and Linear Regression
A clear walkthrough of gradient descent applied to linear regression, with code — a good foundational tutorial connecting the mathematical update rule to a concrete implementation. One of the most-linked introductions to the algorithm for newcomers to ML.
An Introduction to Programming in Go
Caleb Doxsey's free online book introducing the Go programming language — a clean, concise introduction covering types, functions, concurrency primitives, and the standard library. Go had just turned four years old in 2014 and was attracting serious attention for systems and network programming.
Understanding Multi-Armed Bandit Algorithms
DataBozo's conceptual explanation of multi-armed bandit algorithms — epsilon-greedy, UCB, and Thompson Sampling compared from first principles. Aimed at practitioners who want to understand the tradeoffs before implementing.
Data Science in Python — Yhat Tutorial
Yhat's end-to-end data science tutorial in Python using pandas for data manipulation and scikit-learn for modeling. One of the cleaner introductory pipelines from 2014, before this kind of content became ubiquitous.
Introducing R
Alyssa Frazee's introduction to R for people who don't yet know they need it — a gentle, motivated tour of why R is the right tool for statistical computing. Written by a biostatistician who uses it daily.
Vowpal Wabbit Tutorial for the Uninitiated
Zinkov's beginner tutorial for Vowpal Wabbit — the fast online learning library from Microsoft Research. Shows the input format, basic invocation, and when VW's out-of-core learning beats batch methods.
UFLDL Tutorial — Stanford
Andrew Ng's Stanford UFLDL Tutorial — the primary self-study resource for deep learning before MOOCs existed. Teaches sparse autoencoders, PCA, CNNs, and deep belief networks with mandatory from-scratch implementation exercises.
Statistical Analysis Made Easy in Python
Randy Olson's tutorial on statistical analysis in Python using SciPy stats and pandas — t-tests, ANOVA, chi-squared, and more. A practical bridge from R's built-in stats to Python's ecosystem in 2012.
A Pandas Cookbook — Julia Evans
Julia Evans's hands-on pandas cookbook — eight chapters of real-dataset exercises covering groupby, merging, text ops, and timestamp handling. The go-to resource that made pandas approachable before the official docs caught up.
Deep Learning Tutorials — DeepLearning.net
The canonical deep learning tutorial site from the Montreal group — code-first walkthrough of core architectures (RBM, DBN, CNN, LSTM) using Theano. Written by the lab around Yoshua Bengio and became the standard reference before fast.ai existed.
A Not-So-Basic Neural Network in Python
Daniel Rodriguez's practical tutorial on implementing a non-trivial neural network in Python from scratch — going beyond the toy perceptron examples to show backpropagation and training on real data. A mid-2013 hands-on coding reference.
Digital Sound Processing Tutorial for the Braindead
Olli Niemitalo's 1998 beginner DSP tutorial covering filters, FFT, interpolation, and audio synthesis techniques. Honest about its approximations but remains a clear, practical entry point into audio signal processing.
Deep Learning 101
Introductory survey of deep learning for non-specialists — covers hierarchical representation learning, RBMs, autoencoders, and the four core obstacles. Written to help readers mentally filter hype from substance in 2013.
Intro to pandas Data Structures
Greg Reda's introduction to pandas data structures — Series, DataFrame, and Index — written in 2013 when pandas was still new enough to need a clear on-ramp. A canonical early tutorial that helped many data scientists learn the library.
Machine Learning Tutorial: The Naive Bayes Text Classifier
DatumBox's tutorial on the Naive Bayes text classifier — explaining the math, the conditional independence assumption, and how to implement it from scratch. A standard first classifier for anyone learning NLP and ML simultaneously.
Getting Started With Python For Data Science (Kaggle)
Kaggle's Getting Started With Python For Data Science guide — a practical on-ramp covering the core libraries (NumPy, pandas, matplotlib, scikit-learn) oriented around Kaggle competition workflows. The canonical starting point for competition-driven ML learning.
Teaching a Computer to Read: NLP Hacking in Python
Scripted's NLP hacking tutorial in Python — covering tokenization, part-of-speech tagging, named entity recognition, and text classification with NLTK and scikit-learn. An applied introduction to NLP for data scientists.
Random Forests Algorithm
An introduction to the Random Forests algorithm — explaining how ensembling many decorrelated decision trees reduces variance and produces a robust classifier. A standard explainer from the Data Science Central era.
Flipping a Coin: Bayesian Updating of Probability Distributions
A walkthrough of Bayesian probability updating using coin flipping — showing how a prior distribution over coin bias is updated with each flip observation. The cleanest possible introduction to Bayesian reasoning as a process.
How D3 Selections Work
Mike Bostock's definitive explanation of D3's selection model — how data joins, enter/update/exit selections, and subselections actually work under the hood. Essential for anyone who wants to go beyond copying D3 examples to understanding why they work.
An Advanced Guide to HTML & CSS
Shay Howe's free Advanced HTML & CSS guide — the companion to his introductory course, covering complex selectors, responsive design, transitions, transforms, and performance. A practical reference from the early responsive web era.
Getting Started with HyperDex
A practitioner's getting-started guide to HyperDex — the NoSQL database based on hyperspace hashing — showing how to install, configure, and run basic operations. Pairs with the academic paper as the hands-on entry point.
Getting Started with Amazon EC2
Paul Stamatiou's 2012 getting-started guide to Amazon EC2 — launching instances, SSH access, security groups, and basic server setup. Representative of the era when 'cloud' meant learning EC2 from scratch with no managed services.
Beginning Node.js and Express
An introductory Node.js and Express tutorial from 2012 — part of the wave of content that helped JavaScript developers cross from browser scripting to server-side web development. Node.js was two years old and just starting to find its production audience.
The Node Beginner Book
The Node Beginner Book is a free, comprehensive tutorial that teaches server-side JavaScript with Node.js by building a real web application — not a toy. Written for experienced developers from other languages who need to unlearn some assumptions about JavaScript before they can use it well.