Subject
52 entries
Learning
Bookmarks
The Architecture of Open Source Applications
Four-volume series where experienced developers explain the architecture of major open-source applications — Git, nginx, Firefox, and 80+ others. One of the best resources for seeing how real systems are actually designed.
claude-supermemory: real-time learning for Claude Code
claude-supermemory connects Claude Code to Supermemory so the agent learns in real-time — preferences, patterns, and project context accumulate across sessions. The memory grows alongside the developer's work.
CommonCog Forum: business sensemaking community
CommonCog is a sensemaking community for business analysis — case studies of operators like Charlie Munger and Tom Murphy, decision frameworks, and expertise development. Run by Cedric Chin, who writes seriously about how business knowledge actually works.
The Best Way to Use AI for Learning: Heptabase Method
Alan Chan's five-step method for using AI to learn harder material: parse the full source, generate tailored study materials, discuss with AI in context, take notes in your own words, then visualize relationships. The argument is that AI enables learning more complex things, not just the same things faster.
How I Studied for My Ham Radio General Exam
Simon Willison documents how he used LLMs to study for the ham radio General license exam — using Claude to explain questions, generate practice problems, and fill in knowledge gaps. A practical case study in LLM-assisted learning for a technical domain.
Greg Egan's Foundations: Physics for SF Readers
Greg Egan's Foundations is a four-part series explaining Special Relativity, General Relativity, Black Holes, and Quantum Mechanics for science fiction readers — accessible with high school math, focused on deriving concrete predictions rather than history or philosophy.
Book List for Streetfighting Computer Scientists
Nick Black's curated reading list for developing deep, confrontational CS competence — covering C/C++/Rust, algorithms (Knuth, CLRS), systems (Stevens, Kerrisk), architecture, and theory. Explicitly excludes ML, quantum, and infosec.
From Python to Rust: YouTube Playlist
A YouTube playlist teaching Rust to developers who already know Python — bridges concepts across the two languages rather than teaching Rust from scratch. Useful for Python engineers who want to understand Rust's ownership and performance model.
pytudes: Peter Norvig's Python Skill-Building Programs
Peter Norvig's collection of Python programs of "considerable difficulty" designed to perfect specific programming skills — a curated library of well-crafted exercises in algorithms, puzzles, and problem-solving by one of the most respected programmers alive.
Derek Sivers' Book Notes
Derek Sivers' collection of book notes — concise summaries with his personal takeaways from hundreds of books across business, psychology, philosophy, and music. One of the most useful curated book note collections on the internet.
The Most Effective Way to Learn Vocabulary
A guide on the most effective vocabulary acquisition methods, grounding recommendations in cognitive science — spaced repetition, contextual learning, and retrieval practice over passive exposure. Applied linguistics meets learning science.
Molecular Notes: A PKM System
Molecular Notes is a PKM system built around three primitives: Sources, Atoms (established concepts), and Molecules (personal insights). Mining Atoms from Sources and combining them into Molecules creates quadratic knowledge growth through cross-linking.
Calmcode: Calm Python Video Lessons
Calmcode.io is a platform of short, clear video lessons for Python tools and data science libraries — 757 videos across 106 courses, designed around reducing skill anxiety rather than maximizing content density.
Grok Vi: Understanding Vim's Modal Editing Philosophy
grok_vi is a classic text explaining how to actually learn Vim by internalizing its modal editing philosophy rather than memorizing keybindings — the conceptual shift from editor as application to editor as language. The foundational mental model for anyone trying to become fluent in Vim.
South Park Commons: The Modern Junto Club
Wired's profile of South Park Commons, a San Francisco community for technologists in transition — people between jobs, exploring new ideas, or building something new. Framed as a modern Junto Club where learning together precedes building together.
How to Write More Clearly, Think More Clearly, and Learn Complex Material More Easily
Michael Covington's guide arguing that writing, thinking, and learning are deeply interconnected — you don't truly know something until you can write about it clearly. Practical advice on clear academic and technical writing aimed at amplifying intellectual capacity.
Nikita Voloboev's Knowledge Wiki
Nikita Voloboev's public knowledge wiki — a massive, open personal knowledge base covering programming, math, physics, tools, and philosophy. One of the most ambitious public examples of building a second brain as a living document.
My Recurse Center Syllabus
Ashley Blewer's self-directed syllabus from her Recurse Center batch — a structured plan for a self-directed coding retreat covering audio/video formats, networking, compilers, and systems programming. A useful template for independent deep-learning programs.
Free Resources to Get a Blockchain Job — For Non-Coders
A free curriculum guide for non-technical people to learn enough blockchain/web3 to get a job in the space within 6 months. Covers communities, certifications, content creation, and non-engineering roles available in crypto.
Jupyter Notebooks Gallery — notebook.community
notebook.community is a curated gallery of publicly shared Jupyter notebooks — a discovery layer for interesting notebooks covering machine learning, data analysis, visualization, and scientific computing. Good for finding worked examples.
Data Science Interview Questions and Answers
A community-maintained GitHub repo of data science and ML interview questions and answers — covering statistics, machine learning theory, algorithms, and coding. A useful study guide and signal for what interviewers actually test.
My Younger Self
My Younger Self is a platform collecting career and life advice that experienced professionals wish they had known earlier — interview-format stories organized by career stage and domain. Structured mentorship-at-scale.
Top Machine Learning Books Recommended by Experts (2020)
MentorCruise's expert-recommended machine learning book list for 2020 — recommendations from actual ML practitioners and researchers, covering foundational math, statistical learning, deep learning, and applied ML.
Machine Learning Roadmap
Daniel Bourke's visual roadmap connecting the core concepts of machine learning, what to learn first, and what tools to use — built as a community-oriented guide for self-learners entering the field in 2020.
Your Ultimate Learning Path to Become a Data Scientist in 2020
Analytics Vidhya's structured learning path to become a data scientist in 2020 — a step-by-step curriculum covering statistics, Python, ML algorithms, and tools, with specific resource recommendations at each stage.
Changing Education: How Bootcamps Outperform University
Liz Abinante's argument for how coding bootcamps outperform university education for software development — focusing on applied skills, tight feedback loops, and job-readiness. Written by someone who ran and taught at bootcamps at the height of the coding bootcamp boom.
Understanding Bayes: How to Become a Bayesian in Eight Easy Steps
Alexander Etz's eight-step guide to adopting Bayesian thinking in statistics — covers prior selection, Bayes factors, and the core philosophical shift from frequentist null-hypothesis testing. A practical on-ramp for scientists trained in classical statistics.
Clojure for the Brave and True
Daniel Higginbotham's free online book for learning Clojure — covers functional programming, the JVM, macros, and concurrency with a humor-forward style. One of the most recommended Clojure introductions for programmers coming from imperative languages.
5 Mistakes Programmers Make When Starting in Machine Learning
Jason Brownlee's list of five mistakes programmers make when transitioning into machine learning — over-focus on theory, skipping problem definition, ignoring data quality, neglecting model evaluation, and treating ML as a programming challenge. The practitioner's onramp.
Why's (Poignant) Guide to Ruby
_why the lucky stiff's infamous illustrated guide to Ruby — part tutorial, part surrealist comic, part programming philosophy. Became a cult object after _why's mysterious disappearance from the internet in 2009.
Ruby on Codecademy
Codecademy's interactive Ruby track — browser-based exercises covering variables, loops, methods, OOP, and blocks. The accessible on-ramp to Ruby for non-programmers in 2014.
Data Science Book Resources (Data Science Central)
Data Science Central's curated page of data science books and reading materials circa 2013 — a community-aggregated list covering statistics, machine learning, programming, and big data. A snapshot of the self-education canon for the emerging data science field.
ABC: Always Be Coding
David Byttow's Medium post arguing that the best way to get a software engineering job at top companies is to code constantly — side projects, open source, and deliberate practice rather than cramming interview prep. The ABC (Always Be Coding) philosophy.
Hadoop Illuminated — Free Open-Source Hadoop Book
Hadoop Illuminated is a free, open-source book on Apache Hadoop — a community-maintained guide covering HDFS, MapReduce, Hive, Pig, and the broader Hadoop ecosystem. One of the better free learning resources at a time when the Hadoop ecosystem was evolving faster than formal textbooks.
Getting Started with Python for Data Scientists
Data Community DC's guide to getting started with Python for data scientists — the standard 2013 on-ramp to scientific Python covering NumPy, Pandas, matplotlib, and scikit-learn. Represents the moment when Python decisively won the data science language wars.
Aaron Swartz: howtoget
Aaron Swartz's notes on how to get what you want — a short, frank guide to the mechanics of creating opportunities, from reaching out cold to the right people to learning by doing. Characteristically blunt.
How to Implement an Algorithm from a Scientific Paper
Emmanuel Goossaert's guide to the specific challenges of implementing an algorithm from a research paper — reading notation, handling undefined edge cases, bridging the gap between mathematical description and working code. Practical advice for a surprisingly common problem.
Getting Started with Clojure
jrheard's beginner guide to getting started with Clojure — covering setup, REPL workflow, and the first concepts that trip people up. A 2013 onramp into Clojure during its peak growth period.
The Mathematical Hacker
Evan Miller's essay arguing that programmers who invest in mathematical fluency gain compounding advantages — because math enables them to evaluate methods rather than just apply them, and to work with uncertainty and probability naturally. The case for quantitative education in software.
A Crash Course in Computer Science: Reading List
A curated crash course in computer science reading list from 2012 — the canonical texts someone with practical programming experience would read to get the theoretical foundations they missed. SICP, CLRS, Dragon Book, and peers.
The 5-Minute Guide to C Pointers
A concise primer on C pointers — the concept that trips up most programmers learning C. The guide covers pointer declaration, dereferencing, pointer arithmetic, and common pitfalls, aimed at getting past the initial confusion quickly.
CameraSim: SLR Photography Demystified
CameraSim is a browser-based DSLR camera simulator that lets you adjust aperture, shutter speed, ISO, and focal length and see the effect on a simulated photo in real time. A practical tool for learning exposure without burning through film or battery.
You Have to Get Good Before You Get Better
Dan Shipper argues that the painful early phase of learning any skill — when you can see quality but can't produce it yet — is unavoidable and shouldn't be shortcut. Getting good requires accumulating bad reps, not waiting for a breakthrough.
Please Understand Learning to Code
A response to the early-2012 'everyone should learn to code' movement arguing that understanding what coding actually involves — not just advocating for it abstractly — is what matters. Coding literacy and professional programming are different skills with different demands.
A Set of Top Computer Science Blogs (2012)
A curated list of top computer science blogs from 2012, covering theory, systems, programming languages, and applied CS. A snapshot of where the serious technical discussion was happening before Twitter/X fragmented the discourse.
Coding Horror: Learn to Read the Source, Luke
Jeff Atwood's Coding Horror post arguing that reading source code directly — not documentation, not Stack Overflow — is the highest-leverage skill for programmers. The ability to trace behavior to its root in code is what separates competent from exceptional developers.
Never Stop Hacking
Randall Degges's manifesto on maintaining a hacker's curiosity as a professional programmer — the argument that the best developers never stop building things purely for learning, outside of their day job. About intrinsic motivation over career optimization.
First Hundred Days of Clojure
Niclas Meier's reflection after 100 days of learning Clojure — a practitioner's account of what the language demands and what it delivers, written when Clojure was still a relatively young and unusual choice. A snapshot of the functional programming learning curve.
Code School — Learn by Doing
Code School homepage bookmarked in 2012 — an early interactive coding education platform using a 'learn by doing' model with in-browser code challenges. Competed with Codecademy and later Treehouse in the first wave of interactive programming education.
10,000 Hours: How Long Is That?
A reflection on what Malcolm Gladwell's 10,000-hour rule actually means in concrete time — roughly 10 years of focused daily practice. Useful for calibrating ambitions about expertise and understanding why mastery requires a long-term commitment, not a sprint.
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.
Exploit Exercises (Exploit Education)
Exploit Education (formerly Exploit Exercises) provides progressively harder virtual machine challenges for learning binary exploitation, privilege escalation, and memory corruption — from Linux basics through heap exploitation and modern mitigations. The canonical self-study path for systems security.