Reading paths
Series
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94 published series
AI Agent Arxiv Digest
Posts in the AI Agent Arxiv Digest series
AI Daily
A daily digest of AI developments.
AI Agent GitHub Digest
Posts in the AI Agent GitHub Digest series
AI Engineer Interview Daily
Posts in the AI Engineer Interview Daily series
AI Framework Changelog
Posts in the AI Framework Changelog series
AI Agent Funding
Posts in the AI Agent Funding series
AI Model Tracker
Posts in the AI Model Tracker series
Product Builder 面試日練
A daily product builder interview drill rotating through seven topics by day of the week — product sense, metrics, strategy, AI product design, growth, technical PM, and behavioral — pulling the latest case studies and interview questions from the web.
AI Security Alert
Posts in the AI Security Alert series
AI Tool of the Day
Posts in the AI Tool of the Day series
AI Pricing Watch
Posts in the AI Pricing Watch series
AI Region Focus
Posts in the AI Region Focus series
AI Agent Weekly Review
Posts in the AI Agent Weekly Review series
Document Parsing in Practice
The three-layer ladder for turning documents into LLM-readable content — conversion, extraction, and parsing. From picking the right layer to comparing MarkItDown, anydoc, MinerU, and the rest.
RAG 技法大全
RAG taken apart into techniques you can compare one at a time: chunking and indexing, sparse and dense retrieval, ranking and fusion, agentic and advanced patterns, generation-side control, the failure modes real queries hit, and evaluation, cost and observability. One decision per post, assembled into a pipeline of your own.
AI Benchmark Watch
Posts in the AI Benchmark Watch series
OMP Internals Deep Dive
Posts in the OMP Internals Deep Dive series
跟成熟 coding agent 學設計
A Looplane-driven comparison of pi, OMP, OpenCode, Codex CLI, and Claude Code, from loops, workspaces, approvals, and verification through shipped baselines for memory, compaction, MCP, sandboxing, subagents, replay, LSP, cost tracking, and Agent as a Service, with production validation and runtime-parity gaps kept explicit.
Ask AI in Practice
Follow the real quidproquo Ask AI data path from indexing, hybrid retrieval, writing, and source gates through streaming, caching, incident analysis, and reproducible evaluation. Each post traces one responsibility and the boundary of what its evidence can prove.
Cloudflare AI Stack
Posts in the Cloudflare AI Stack series
Cloudflare Edge Platform
Posts in the Cloudflare Edge Platform series
AI Engineer 面試日練
A daily AI engineer interview drill rotating through seven topics by day of the week — ML fundamentals, deep learning, system design, LLM engineering, coding, paper reading, and behavioral — pulling the latest interview questions and resources from the web.
Looplane Architecture Notes
Follow one coding-agent task through Looplane: from the TUI, disposable workspace, prompt, and two runtime lanes through tool authority, the state/event lifecycle, MCP, subagents, SDK/IDE integrations, and finally Cloudflare remote execution. Each article traces one data flow, failure boundary, and test surface.
Reading Harvard CS50 AI
Posts in the Reading Harvard CS50 AI series
Global AI/CS Course Map
Posts in the Global AI/CS Course Map series
MIT 6.7960 Fall 2024 OCW Guide
Posts in the MIT 6.7960 Fall 2024 OCW Guide series
MIT 6.7960 導讀 (Fall 2024 OCW)
Posts in the MIT 6.7960 導讀 (Fall 2024 OCW) series
World-Class AI/CS Course Map
Posts in the World-Class AI/CS Course Map series
Search and Scraping in Practice
The full path for getting data in from outside: renting a cloud search API versus self-hosting one, choosing among the scraping tools, what to do when anti-bot defenses block you, and how to wire it all into a research pipeline. One decision per post.
搜尋與爬取實戰
The full path for getting data in from outside: renting a cloud search API versus self-hosting one, choosing among the scraping tools, what to do when anti-bot defenses block you, and how to wire it all into a research pipeline. One decision per post.
Statistics from Exams to ML/AI
A statistics learning path that starts from NTU IM exam preparation, builds through statistical inference and applied modeling, and connects each topic to ML/AI training, evaluation, experiments, and data workflows.
Harvard CS181 Weekly Guides
Posts in the Harvard CS181 Weekly Guides series
Global AI and CS Course Map
Posts in the Global AI and CS Course Map series
Meta-Harness 與 Agent 治理
Posts in the Meta-Harness 與 Agent 治理 series
認識 AI 模型
Posts in the 認識 AI 模型 series
Reading MIT 6.7960
Posts in the Reading MIT 6.7960 series
一個人的媒體公司
Posts in the 一個人的媒體公司 series
AI 模型家族
Tracing the evolution, architecture, licensing traps, and version selection of mainstream model families — Qwen, DeepSeek, Claude, GPT, Gemini, Llama, Mistral, GLM, Kimi — with pick guidance for agent developers.
Claude Code Deep Dives
Posts in the Claude Code Deep Dives series
The RAG Techniques Compendium
RAG taken apart into techniques you can compare one at a time: chunking and indexing, sparse and dense retrieval, ranking and fusion, agentic and advanced patterns, generation-side control, the failure modes real queries hit, and evaluation, cost and observability. One decision per post, assembled into a pipeline of your own.
AI 日報
A daily digest of AI developments.
Self-Hosted Inference
Posts in the Self-Hosted Inference series
AI 頂會導讀
How AI top conferences are recognized, how submissions and review work, and how the flagship venues differ.
AI Conference Guide
Posts in the AI Conference Guide series
AI Model Families
Tracing the evolution, architecture, licensing traps, and version selection of mainstream model families — Qwen, DeepSeek, Claude, GPT, Gemini, Llama, Mistral, GLM, Kimi — with pick guidance for agent developers.
Choosing an Agent CLI
A comparison of terminal agents — Claude Code, Codex, Gemini CLI (now transitioned to Antigravity CLI), OpenCode, Pi, Cursor CLI, and Kiro — covering each one's design trade-offs, plans, and billing, closing with a cross-tool subscription comparison and multi-model routing. Pricing and model names rot fast, so every post carries its verification date and defers the perishable details to official pages.
Technology Choices in the AI Era
Adoption remains the primary criterion, augmented by five AI-era criteria — machine-readable docs, types, source-in-repo, data skeleton, and machine-callability — from frontend to backend, cloud to self-hosted.
Reading AI Top Conferences
How AI top conferences are recognized, how submissions and review work, and how the flagship venues differ.
Reading CMU 07-280
Posts in the Reading CMU 07-280 series
Reading CMU 11-785 Deep Learning
A lecture-by-lecture reading of CMU 11-785 Spring 2026 that separates its public 28-lecture teaching sequence from the restricted assignment workflow.
Reading Stanford CS124
A week-by-week reading of Stanford CS124: language models, text classification, information extraction, question answering, speech, and the full NLP pipeline.
Reading Stanford CS224N
A lecture-by-lecture reading of Stanford CS224N: word vectors, sequence models, Transformers, large language models, evaluation, and responsible NLP.
Reading Stanford CS224V
A unit-by-unit reading of one explicitly versioned Stanford CS224V offering: understanding, dialogue management, generation, evaluation, and deployment for conversational assistants.
Reading Stanford CS336
A lecture-by-lecture reading of Stanford CS336: tokenizers, data, scaling, training, parallelism, evaluation, and alignment across the full language-model pipeline.
Reading MIT 6.S191
Reading all nine lectures and three labs of MIT 6.S191 from the official 2026 videos, slides, and lab code without mixing in earlier offerings.
Private Corpus Pipeline
How private data enters indexes safely and continuously, remains subject to query-time authorization, and stays consistent when sources change or disappear—focused on the data lifecycle rather than RAG retrieval techniques.
Reading Stanford CS221
A lecture-by-lecture reading of Stanford CS221: search, Markov decision processes, machine learning, constraint satisfaction, and probabilistic models.
Reading Stanford CS224W
A lecture-by-lecture reading of Stanford CS224W: graph representation, network science, graph neural networks, knowledge graphs, and scalable graph learning.
Reading Stanford CS229
A chapter-by-chapter reading of Stanford CS229’s official 2026 notes, spanning supervised and deep learning, foundation models, LLM reasoning, and reinforcement learning across twenty-one chapters without pretending to reconstruct a single quarter’s lecture schedule.
Berkeley CS188 Spring 2026
Reading Berkeley CS188 Spring 2026 through Projects P0–P5, from search and decision making to probabilistic inference, reinforcement learning, and machine learning.
Berkeley CS189 Spring 2025
Posts in the Berkeley CS189 Spring 2025 series
Reading Berkeley CS285 Spring 2026
Reading Berkeley CS285 Spring 2026 in deep reinforcement learning through 25 lectures, nine discussions, five assignments, and their compute constraints.
Berkeley CS288 Spring 2026
Reading Berkeley CS288 Spring 2026 from n-grams through RAG, reasoning, and agents using its 18 public slide units and three assignments.
Reading CMU 07-380
Posts in the Reading CMU 07-380 series
Reading CMU 10-301 Machine Learning
Reading the 27 lectures of CMU 10-301/601 through its nine public Spring 2026 homework bundles and the practical limits for independent learners.
Reading Stanford CS107
A lecture-by-lecture reading of Stanford CS107: C, memory, assembly, data representation, and systems debugging from high-level code down to the machine.
Global AI and CS Course Maps
Posts in the Global AI and CS Course Maps series
Reading Stanford CS103
A lecture-by-lecture reading of Stanford CS103: discrete mathematics, logic, proofs, sets, computability, and the shared language they provide for later CS courses.
Reading Stanford CS109
A lecture-by-lecture reading of Stanford CS109: probability, random variables, inference, and simulation as the foundation used by machine learning and data science.
Reading Stanford CS111
A lecture-by-lecture reading of Stanford CS111: processes, threads, synchronization, virtual memory, file systems, and operating-system design trade-offs.
Reading Stanford's Main-Line CS Courses
A map of Stanford CS core courses, from the degree foundations through AI, NLP, graph learning, and agents, with versioned course guides and prerequisites.
Reading Stanford CS224U
A unit-by-unit reading of a versioned Stanford CS224U offering: semantic representations, natural-language inference, question answering, and interactive language systems.
Reading Stanford CS228
A week-by-week reading of one explicitly versioned Stanford CS228 offering: probabilistic graphical models, exact and approximate inference, and parameter and structure learning.
Reading Stanford CS329Z
A lecture-by-lecture reading of Stanford CS329Z on agent engineering, written only as current official materials appear rather than treating a tentative syllabus as delivered instruction.
Reading Stanford CS161
A lecture-by-lecture reading of Stanford CS161, Winter 2026: algorithm design, correctness proofs, and complexity analysis across all eighteen public lecture units.
AEO, GEO, and AI Search
Writing for a reader that is now a model: from the SEO groundwork through answer engine optimization, what content structure and structured data actually buy, and whether the tracking tools can really measure visibility inside AI search.
AI Engineer Interview Prep
Preparing for AI engineer interviews across ten topics — ML fundamentals, system design, LLM application architecture, coding, paper reading, and behavioral. Each post focuses on one interview dimension with core concepts, common question patterns, and practical strategies.
Reading Stanford CS329A
A lecture-by-lecture reading of Stanford CS329A on self-improving AI systems, grounded in materials attributable to each official session and paused where evidence is missing.
品味修煉
Treating taste as judgment that can be observed, defended, and recalibrated, with a systematic practice for deciding what is worth making and what good work looks like when AI amplifies execution.
Product Builder Interview Prep
Preparing for product builder interviews across ten topics — product sense, metrics, strategy, execution, technical PM, growth, and AI product design. Each post focuses on one interview dimension with frameworks, case studies, and answer strategies.
AI Certification Prep
One preparation path per certification, built on the official exam guides: what each domain tests, which official material covers it, what to build, and the reasoning behind every schedule. Everything comes from official exam guides and certification pages — no exam-day accounts, no leaked questions.
Hermes Agent Documentation Guide
Reading Hermes Agent against the official Nous Research docs: install and upgrade, model providers and Nous Portal, the Tool Gateway, seven terminal backends, memory and skills, tools and plugins, the gateway and scheduling, the security model, and migrating from OpenClaw. Each post keeps the trade-offs and failure modes and leaves command details to the docs.
The Cloudflare Edge Stack
Every piece needed to build a full application on Cloudflare’s edge, read one at a time: the Workers execution model, where D1, KV and R2 each stop being the right answer, the framework layer of Hono and OpenNext, then Workers AI bindings and the domain and native-module problems that show up at deploy time.
CS146S: Ten Weeks of AI-Native Development
Reading Stanford CS146S "The Modern Software Developer" week by week — agent internals, context engineering, skills and customization, codebase readiness, code review, security, background agents, team-scale adoption, and the software factory. Each post is grounded in the course material and verifiable primary sources.
Reading Stanford CS230
A lecture-by-lecture reading of Stanford CS230, Autumn 2025 — what was taught, what has changed since, and where it agrees or disagrees with the practice written up elsewhere on this site.
文件解析實戰
The three-layer ladder for turning documents into LLM-readable content — conversion, extraction, and parsing. From picking the right layer to comparing MarkItDown, anydoc, MinerU, and the rest.
The Agent Production Line
Reading agents as a production line: where the concept ends, how model and harness divide the work, context and memory, enterprise cases, security, the protocol layer, and the three shapes of RAG.
Taiwan's Drone Industry, Taken Apart
Taking the drone industry apart into verifiable layers — from the industry map and the supply-chain gap, through endurance physics and flight-controller and radio-link source code, to Taiwan’s regulatory authority, procurement records and counter-drone deadlock. Every post starts from primary material.
Learning How to Learn
Auditing the evidence behind learning science alongside how generative AI is actually used — which practices hold up, which merely circulate, and what pen and paper still do better.
Browser Automation and MCP
The routes for putting a browser in an agent’s hands: the trade-offs between the Playwright, Puppeteer and Chrome DevTools MCP servers, vision-driven Midscene, and how the CLI agents differ in what they can drive natively. Focused on where each route breaks.
AI Agent Systems in Practice
A practical series on AI agent systems, covering context, harness design, workflows, and multi-agent collaboration.
Claude Code Automation Guide
A practical series on Claude Code workflows, including hooks, skills, remote agents, routines, and team-scale automation.
Reading the OpenClaw Docs
Reading the 300+ official docs of OpenClaw, a self-hosted AI gateway, across 32 posts — installation and platforms, model providers, the agent runtime and memory, 24+ chat channels, sandboxing and threat model, tools and automation, gateway operations, plugins, and the user interfaces.
Building NobodyClimb
A climbing-community product written up end to end: positioning, why it needed AI at all, the system architecture, and the RAG pipeline. The technique-level potholes live in the RAG compendium; this series is about how the decisions got made.