File Abstractions, Allocation, and FAT | Stanford CS111 Lecture 19
A file system maps durable byte collections onto disk blocks; contiguous, linked, and FAT allocation trade locality, growth, random access, and metadata cost.
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345 postsMultilevel Inodes, Index Walks, and Disk Scheduling | Stanford CS111 Lecture 20
The 4.3BSD inode uses direct, single-indirect, and double-indirect tiers so lookup depth scales with file size; FIFO, SPTF, SCAN, and CSCAN then trade seek cost, fairness, and wait time.
Block Cache, Free Bitmaps, and Delayed Allocation | Stanford CS111 Lecture 21
Block cache retains hot indexes, bitmap slack preserves placement choices, and fragments plus delayed allocation trade later, better information for locality.
Directory Lookup, Hard Links, and Symbolic Links | Stanford CS111 Lecture 22
Directories map text names to file-system-local inode numbers; hard links share inode identity and reference counts, while symlinks store paths and permit cross-filesystem references with loops and dangling targets.
From fsck and Ordered Writes to Write-Ahead Logging | Stanford CS111 Lecture 23
A single file-system operation updates several blocks, but a crash can occur between any two writes; this lecture compares how fsck, ordered writes, and write-ahead logging trade recovery time, performance, durability, and consistency.
Journaling, Transactions, and Checkpoints | Stanford CS111 Lecture 24
Lecture 24 continues from the WAL entry point into transactions, idempotent replay, and checkpoints, showing why consistency is not durability and why a journal does not replace fsync or backups.
Truth, Trust, and Technology—How Algorithms, Generative AI, and Deepfakes Reshape Trust | Stanford CS111 Lecture 25
Lecture 25 separates assumption, inference, and substitution as ways to establish trust, then examines how social recommendations, generative AI, and synthetic media amplify over-trust; the response is preserved provenance, independent validation, and coordinated responsibility.
Flash Translation Layers, Garbage Collection, and Wear Leveling | Stanford CS111 Lecture 26
Flash programs pages but erases whole units; an FTL hides the asymmetry with out-of-place mapping, then manages amplification through garbage collection, temperature segregation, wear leveling, and TRIM.
Trap-and-Emulate, Virtual I/O, and Nested Page Tables | Stanford CS111 Lecture 27
A VM expands the process interface into a machine interface; the hypervisor directly executes ordinary instructions, traps privileged operations, and virtualizes interrupts, I/O, and two-stage address translation.
Four Ideas Connecting Concurrency, Memory, and Storage | Stanford CS111 Lecture 28
Lecture 28 reduces the semester to concurrency, memory, and storage, then uses four ideas—virtualization, atomicity, locality, and layering—to explain how operating systems manage shared resources.
Berkeley AI/ML Course Guide: From CS61A to CS288, What Can You Actually Study Online? | Global AI/CS Course Map
Berkeley has no standalone undergraduate AI degree. A workable path builds on the CS BA or EECS BS foundation, enters through either CS188's broad AI curriculum or CS189's mathematical machine learning curriculum, then branches into deep learning, NLP, vision, or reinforcement learning. Many 2025–2026 courses are A3, but the newest class, the newest stable URL, and the best self-study edition are not always the same.
CMU's AI Degrees: The First U.S. AI Bachelor's Turned 'What Should AI Students Learn?' into Graduation Requirements
Stanford has no AI degree; AI is a track inside CS. CMU launched the first U.S. B.S. in Artificial Intelligence in 2018, divided AI into four clusters, required one course from each, and made ethics a graduation requirement. At the master's level, MSAII sits not in CS but in the Language Technologies Institute; 84 of its 195 units cover an innovation process ending in a fundable capstone. Two official-page conflicts emerged during verification: whether the AI Core has two or three courses, and whether MSAII totals 192 or 195 units.
CMU AI/ML Course Guide: The New 07-280 Core and a Public Self-Study Route | Global AI/CS Course Map
CMU's current BSAI now runs through 07-280 and 07-380 before branching into an NLP/vision core and four AI clusters. 07-380 debuted in Fall 2026, and the same semester launched a graduate-level 11-768 AI Agents course. The residual Spring 2026 materials for 07-280 and the complete 10-301/601 site already support self-study; retired 15-281 remains a useful legacy route.
The Conference as a Content Factory: AI Engineer's Structural Advantage
AI Engineer reached 600,000 YouTube subscribers in under three years not because it mastered video production, but because it barely needs to produce videos at all: recordings from eight conferences a year create an inexhaustible supply of YouTube material. The real constraint on content creation is structure, not skill.
A Global Map of AI and CS Courses: Which Ones Can You Actually Study in Public? | Global AI/CS Course Map
This map audits AI and CS courses at Stanford, CMU, MIT, UC Berkeley, Harvard, and National Taiwan University (NTU) in 2025–2026 using four access labels: A0 for a visible catalog entry, A1 for a public syllabus, A2 for partial materials, and A3 for a self-study-ready package. A course site or YouTube playlist can exist without giving outsiders access to the current videos, assignments, or starter code.
From Programming and Mathematics to AI: A Learning Path through Open Courses | Global AI/CS Course Map
MIT has offered Course 6-4, a formal BS in Artificial Intelligence and Decision Making, since 2022. For an outside learner, however, the current degree requirements, the 2025–2026 course sites, and the best OCW editions rarely line up. A workable route follows 6-4's programming, algorithms, linear algebra, and probability foundation, then selects among 6.S191, 6.3900, 6.4110, 6.7960, vision, and robotics according to what is actually public.
Mathematical Foundations of Computing: How to Prove, Then What Can't Be Proven | Stanford CS103 Course Overview
CS103 is Stanford's mathematical foundations of computing course: the first half teaches proof writing through sets, logic, graphs and induction, and the second half covers finite automata, Turing machines, decidability and P vs NP. Slides, problem sets, starter files and the homegrown handouts are all public, and the autograded problems can be checked locally; solutions, practice exams and lecture recordings sit behind Stanford login.
Computer Organization and Systems: From C and Pointers to x86-64 and Writing Your Own malloc | Stanford CS107 Course Overview
CS107 is Stanford's computer systems course: it goes from bits, C strings and pointers to x86-64 assembly and the runtime stack, and ends with writing your own heap allocator, across seven assignments. Lecture slides, assignment specs and worked lab solutions are public; the assignment starter code lives on Stanford's AFS and is out of reach without a campus account.
Probability for Computer Scientists: From Counting and Inference to Machine Learning | Stanford CS109 Course Overview
CS109 is Stanford's probability course for computer scientists: it runs from counting, conditional probability and random variables to inference, maximum likelihood estimation, logistic regression and deep learning. The interactive course reader and the in-class worksheets with answer keys are public, and every lecture comes with an official LLM learning guide; slides after Lecture 15, the problem-set system and the recordings are closed to outsiders.
Operating Systems Principles: From Threads and Virtual Memory to File Systems | Stanford CS111 Course Overview
CS111 is Stanford's operating systems principles course, organized into concurrency, memory management and file systems, with nine assignments. All 28 lecture handouts, the nine assignment specs and three years of midterms and finals with solutions are public; the starter code requires a login on Stanford's myth machines, and the recordings are on Canvas.