whu, backend developer
People's Republic of China
Joined November 2015
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husiwen retweeted
GitHub Trends Weekly : LoopKit/Loop github.com/LoopKit/Loop
husiwen retweeted
Linear Algebra: Problems Book by H. D. Ikramov
present #problembook closely follows the structure of the book by V. Voyevodin; contains problems for seminar classes, home-assignments, tests and examinations
#soviet #mathematics #algebra #problemsolving
mirtitles.org/2018/05/10/lin…
husiwen retweeted
The History os x86: From 4004 to 486
🔗pikuma.com/blog/history-of-x…
Why is x86 little-endian? Why are registers called AX, BX, CX, and DX? Why do DOS addresses use the format A000:0000, and why does the modern PC still wakes up pretending to be a chip from 1978?
husiwen retweeted
12 Best Statistics Courses in 2026 (Ranked by What You Actually Need, for AI) — list compiled by @tut_ml in the quoted post below ⤵️
husiwen retweeted
"Learning Mathematics: Why Memory, Practice, and Technique Matter More than Talent"
To learn mathematics it is essential to memorise a great quantity of information, rules, simplifications and problem-solving tricks. There is no other way. Theory has its use, but in the right dose.
It is like learning a language.
If you concentrate too much on the grammar, you will never speak it fluently.
algebrica.org/learning-mathe…
husiwen retweeted
Reinforcement Learning and Optimal Control: amzn.to/4iprVIE
Amazon Summary: "The book discusses solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are known by several essentially equivalent names: reinforcement learning, approximate dynamic programming, and neuro-dynamic programming. They underlie, among others, the recent impressive successes of self-learning in the context of games such as chess and Go. One of the aims of the book is to explore the common boundary between artificial intelligence and optimal control, and to form a bridge that is accessible by workers with background in either field."
husiwen retweeted
Nearly every law of physics is a differential equation.
Orbits, waves, heat flow, radioactive decay, quantum states, turbulence and the expansion of the universe are all solutions of the twelve equations here.
husiwen retweeted
"The Little Book of Generative AI Foundations" is a recent, free resource for anyone interested in the math behind modern generative AI systems (models that learn from data and generate new content such as text, images, audio, and video).
The book develops the foundations of generative modeling progressively, starting from Principal Component Analysis (PCA) and probabilistic PCA, and moving through variational autoencoders (VAEs), diffusion models, normalizing flows, autoregressive models, Generative Adversarial Networks (GANs), Wasserstein GANs, and energy-based models.
What makes it particularly interesting is its focus on mathematical derivations while still building an intuitive understanding of how these different families of generative models work and how they are connected. It's a very interesting resource if you want to understand the mathematics behind generative AI rather than simply how to use these models.
arxiv.org/pdf/2605.29713
husiwen retweeted
👩🏫 UNA PROFESORA DE STANFORD EXPLICA CÓMO DOMINAR LOS PROCESOS DE DECISIÓN DE MARKOV
Una clase de 83 minutos, gratuita y directamente desde Stanford.
🧠 En la lección aprenderás:
• Diferencias entre problemas de búsqueda y entornos estocásticos.
• Evaluación de políticas y recurrencias de valores Q.
• Cómo funciona la iteración de valores.
• Límites de convergencia en grafos cíclicos.
Todo explicado desde los fundamentos de los Markov Decision Processes (MDP).
🔥 Si estás aprendiendo inteligencia artificial, aprendizaje por refuerzo o toma de decisiones algorítmica, es una clase para guardar.
📌 Guárdala y mírala cuando tengas tiempo.
Una joya gratuita de Stanford.
husiwen retweeted
The course Foundations of Computing by Charles D. Allison is on sale on Leanpub! Its suggested price is $179.00; get it for $89.00 with this coupon: leanpub.com/c/foundationsofc… #computer_programming #computer_science
husiwen retweeted
Fundamentals of Physics by B. N. Ivanov
The principle underlying the preparation of this course can be summarized as follows: “From atom to matter”; intended for those who wish to acquire a deeper knowledge of physical phenomena
#physics #mir #soviet
mirtitles.org/2018/04/21/fun…
husiwen retweeted
No More Helloworlds. Build a Real C++ App: Master Real C++ Development: Architecture, Git, Testing & CMake in One Practical Workflow by Nikolai Kutiavin 📖 on Leanpub!
Link: leanpub.com/nomorehelloworld…
#books #ebooks
husiwen retweeted
这简直就是一本干货满满的使用手册，并且图文并茂，易于理解。
《如何快速了解一个行业》告诉你只关心一件事：怎么更快看懂一个行业，赚到该赚的钱。
作者把复杂的行业研究拆得非常清楚：
不讲空话，不兜圈子，直接从“赚钱逻辑”出发。
🔗：pan.quark.cn/s/7a3c79a844b0
husiwen retweeted
on a more pragmatic note here is a 3h discussion with my man @justinskycak on how to learn technical topic like math 100% on your own
husiwen retweeted
The "High-Level System Design Handbook" by Aayush Soni (2026) is now in @ChapterPal's collection of free books.
The book is a comprehensive guide intended for software engineers, system architects, and technical leaders who design, scale, and maintain large-scale distributed platforms or prepare for technical design interviews.
The text assumes a baseline familiarity with programming, basic operating system mechanics such as processes and threads, fundamental data structures, relational database concepts, and networking protocols such as TCP and HTTP.
From this foundation, the book covers the full architectural lifecycle of distributed software, ranging from single-machine performance limits to planet-scale multi-region topologies and modern artificial intelligence platforms.
Read the book with an AI tutor: chapterpal.com/book/9bc8434f…
All book on ChapterPal are free to read with a free account.
The table of contents:
Here’s the ToC with all level-three headings removed.
Chapter 0: Prerequisites
* 0.0 Networking Fundamentals for System Design
* 0.1 Operating System Essentials for System Design
* 0.2 Data Structures for Distributed Systems
* 0.3 Database Fundamentals for System Design
* 0.4 API Design Basics: REST, GraphQL, gRPC, and the Hard Parts
* References
Chapter 1: Core Fundamentals
* 1.0 Scalability: Growing a System Without Breaking It
* 1.1 Latency and Throughput: The Two Numbers That Matter
* 1.2 Availability and Reliability: Nines, SLOs, and Staying Up
* 1.3 Consistency Models: What Readers Actually See
* 1.4 Back-of-the-Envelope Estimation
* 1.5 How to Approach a System Design Question
* 1.6 Trade-off Thinking
* References
Chapter 2: Building Blocks
* 2.0 Load Balancers: Spreading Traffic, Absorbing Failure
* 2.1 Reverse Proxies and API Gateways: The Smart Edge
* 2.2 Content Delivery Networks: Moving Bytes Closer to Users
* 2.3 Caching: From Browser to Database
* 2.4 SQL Databases: The Boring Technology That Wins
* 2.5 NoSQL Databases: Picking the Right Non-Relational Tool
* 2.6 Database Partitioning and Sharding: When One Node Is Not Enough
* 2.7 Database Replication: Keeping Copies in Sync
* 2.8 Message Queues and Streaming: Decoupling at Scale
* 2.9 Pub/Sub: Fan-Out and Event-Driven Systems
* 2.10 Real-Time Communication: WebSockets, SSE, and Long Polling
* 2.11 Rate Limiting: Protecting Systems from Themselves
* 2.12 Service Discovery and Service Mesh: Finding and Talking to Services
* 2.13 Blob and Object Storage: Storing the Big Stuff
* 2.14 Geospatial Indexing: Geohash, Quadtree, R-tree, S2, and H3
* 2.15 Edge Computing (Cloudflare Workers, Lambda@Edge, Deno Deploy)
* References
Chapter 3: Distributed Systems Theory
* 3.0 Consensus Protocols: How Distributed Systems Agree
* 3.1 Consistency Deep Dive: Linearizability, Serializability, and the Spectrum Between
* 3.2 Quorums and Replication: The Math of R + W > N
* 3.3 CAP and PACELC: The Tradeoff That Keeps Confusing People
* 3.4 Clocks and Ordering: Lamport, Vector, and Hybrid Logical Clocks
* 3.5 CRDTs: Conflict-Free Replicated Data Types
* 3.6 Distributed Transactions: 2PC, Saga, and When to Avoid Both
* 3.7 Idempotency and Exactly-Once: The Honest Truth About Delivery Guarantees
* 3.8 Failure Detection: Deciding a Node Is Dead
* 3.9 Consistent Hashing: Keys to Nodes Without Global Reshuffles
* 3.10 Merkle Trees and Anti-Entropy: Keeping Replicas in Sync Cheaply
* References
Chapter 4: Data Systems
* 4.0 Storage Engines: B-Trees, LSM-Trees, and Why Your Database Feels the Way It Does
* 4.1 OLTP vs OLAP: Row Stores, Column Stores, and Matching Shape to Workload
* 4.2 Data Warehouses and Data Lakes: Structure, Schema, and the Lakehouse
* 4.3 Stream vs Batch Processing: Lambda, Kappa, and the End of That Debate
* 4.4 Change Data Capture: Streaming the Database's Inner Monologue
* 4.5 Search Systems: Inverted Indexes, BM25, and Running Elasticsearch in Production
* 4.6 Time-Series Databases: Metrics, Events, and Retention at Scale
* 4.7 Graph Databases: Property Graphs, Cypher, and When Joins Are the Problem
* 4.8 Vector Databases: Embeddings, ANN Indexes, and the Retrieval Layer for AI
* 4.9 Key-Value Stores: Redis, Memcached, DynamoDB, and Picking the Right Hash Table
* References
Chapter 5: Architecture Patterns
* 5.0 Monolith vs Microservices: Team Topology, Conway's Law, and the Distributed System Tax
* 5.1 Event-Driven Architecture: Notifications, State Transfer, and Choreography
* 5.2 CQRS: Separating Reads from Writes Without Losing Your Mind
* 5.3 Event Sourcing: Events as the Source of Truth
* 5.4 Serverless: Functions, Cold Starts, and When FaaS Actually Saves Money
* 5.5 Backend for Frontend: Per-Client API Aggregation Done Right
* 5.6 Strangler Fig: Incremental Migration Without a Big Bang
* 5.7 Hexagonal and Clean Architecture: Keeping Business Logic Independent
* 5.8 Multi-Region Architecture: Active-Passive, Active-Active, and CRDTs
* 5.9 Multi-Tenancy: Silo, Pool, and the SaaS Isolation Spectrum
* 5.10 CRDT Applications (Yjs, Automerge, Local-First Software)
* References
Chapter 6: Reliability & Operations
* 6.0 Observability: Metrics, Logs, Traces, and the OpenTelemetry Standard
* 6.1 SLI, SLO, SLA, and Error Budgets: Making Reliability Quantitative
* 6.2 Resilience Patterns: Timeouts, Retries, Circuit Breakers, and Bulkheads
* 6.3 Graceful Degradation: When Partial Service Beats No Service
* 6.4 Auto-Scaling and Capacity Planning: From HPA to Predictive Scaling
* 6.5 Deployment Strategies: Blue-Green, Canary, Rolling, and Feature Flags
* 6.6 Chaos Engineering: Breaking Things on Purpose
* 6.7 Incident Management: From Detection to Blameless Postmortem
* 6.8 Health Checks and Readiness: Telling the Truth About Whether You're Up
* 6.9 Cost Optimization and FinOps
* 6.10 Platform Engineering: IDPs, Golden Paths, and DX
* References
Chapter 7: Security at Scale
* 7.0 Authentication vs Authorization: Identity, Permissions, and Access Models
* 7.1 OAuth 2.0 and OpenID Connect: Delegated Authorization and Identity Done Right
* 7.2 JWT Deep Dive: Signed Tokens, Claims, and the Revocation Problem
* 7.3 mTLS and Service-to-Service Authentication: SPIFFE, Service Mesh, and Zero Trust
* 7.4 Secrets Management: Vault, KMS, and the End of Secrets in Config Files
* 7.5 DDoS Protection and WAFs: Mitigating Volumetric and Application Attacks
* 7.6 Data Residency and Compliance Architecture (GDPR, DPDP, CCPA, Right-to-Erasure)
* 7.7 Supply Chain Security: SBOM, SLSA, Sigstore, and Defending Against xz-utils
* 7.8 Privacy-Preserving Systems (Differential Privacy, Federated Learning)
* 7.9 Post-Quantum Cryptography: Migrating to ML-KEM, ML-DSA, and a Crypto-Agile Future
* References
Chapter 8: Case Studies
* 8.0 Design a URL Shortener (TinyURL / bit.ly)
* 8.1 Design a Pastebin (Paste Sharing Service)
* 8.2 Design a Distributed Rate Limiter
* 8.3 Design a Distributed Key-Value Store (Dynamo / Cassandra / Riak)
* 8.4 Design a Notification System (Push, SMS, Email at Scale)
* 8.5 Design a Chat System (WhatsApp / Messenger / Signal)
* 8.6 Design a Social Media Feed (Twitter / Instagram / LinkedIn)
* 8.7 Design a Photo Sharing Service (Instagram)
* 8.8 Design a Web Crawler (Googlebot-style)
* 8.9 Design Search Autocomplete (Typeahead Suggestions)
* 8.10 Design a Video Streaming Service (YouTube / Twitch / TikTok)
* 8.11 Design Netflix (End-to-End)
* 8.12 Design a Ride-Hailing Service (Uber / Lyft)
* 8.13 Design Google Maps (Routing and Tile Rendering)
* 8.14 Design a File Sync Service (Dropbox / Google Drive)
* 8.15 Design Collaborative Editing (Google Docs / Figma / Notion)
* 8.16 Design a Distributed Cache (Memcached / Redis Cluster)
* 8.17 Design a Recommendation System (Netflix / YouTube / TikTok)
* 8.18 Design a Ticketing System (BookMyShow / Ticketmaster)
* 8.19 Design a Payment System (Stripe / PayPal)
* 8.20 Design a Stock Exchange (Matching Engine)
* 8.21 Design a Food Delivery Service (DoorDash / Swiggy)
* 8.22 Design a Metrics Pipeline (Prometheus / InfluxDB / Thanos)
* 8.23 Design Ad-Click Aggregation (Real-Time Stream Processing)
* 8.24 Design a Logging Platform (ELK / Loki / Splunk)
* 8.25 Design a Proximity Service (Nearby Friends / Yelp)
* 8.26 Design a Real-Time Leaderboard
* 8.27 Design a Unique ID Generator (Snowflake, ULID, TSID, UUIDv7)
* 8.28 Design a Hotel Reservation System (Booking.com / Airbnb)
* 8.29 Design a Distributed Job Scheduler (Airflow / Temporal / Distributed Cron)
* 8.30 Design ChatGPT (Conversational AI at Scale)
* 8.31 Design an Enterprise RAG System
* 8.32 Design a Coding Agent (Claude Code / GitHub Copilot / Cursor)
* 8.33 Design Perplexity (AI Search with Citations)
* 8.34 Design a Voice Agent (Alexa / Siri-Class Realtime)
* 8.35 Design a Content Moderation System at Scale
* 8.36 Design a Semantic Cache for LLM Applications
* 8.37 Design a Model Router and Gateway (OpenRouter / LiteLLM)
* 8.38 Design a Feature Flag Service (LaunchDarkly / Harness FME / Unleash)
* 8.39 Design a DNS Service (Cloudflare 1.1.1.1 / Google 8.8.8.8)
* 8.40 Design a Dating App (Tinder / Hinge / Bumble)
* 8.41 Design an Online Auction (eBay / Catawiki)
* 8.42 Design a Multi-Tenant SaaS Platform
* 8.43 Design a Video Conferencing System (Zoom / Google Meet)
* 8.44 Design an Email Service at Gmail Scale (1.8B Users, 300B Messages/Day)
* 8.45 Design Live Comments at Scale (FB Live / YouTube Live / Twitch Chat)
* 8.46 Design a Fraud Detection System (Stripe Radar / PayPal / Feedzai)
* 8.47 Design a Fitness Tracking Service (Strava / MapMyRun)
* 8.48 Design an Online Judge (LeetCode / Codeforces / HackerEarth)
* 8.49 Design a Price Tracking Service (CamelCamelCamel / Honey / Keepa)
* 8.50 Design an API Gateway at Scale (Kong / AWS API Gateway / Apigee / Envoy)
* 8.51 Design a CI/CD Platform (GitHub Actions / GitLab CI / CircleCI)
* 8.52 Design an Observability Platform (Datadog / New Relic / Honeycomb)
* 8.53 Design a Search Engine (Google-Scale / Brave Search)
* 8.54 Design a Brokerage Platform (Robinhood / E*TRADE / Interactive Brokers)
* 8.55 Design Channel-Scale Chat (Discord / Slack)
* References
Chapter 9: AI & ML System Design
* 9.0 LLM Serving Architecture (vLLM, TGI, TensorRT-LLM)
* 9.1 RAG Pipelines (Retrieval-Augmented Generation)
* 9.2 Vector Search at Scale (HNSW, IVF-PQ, DiskANN)
* 9.3 AI Agent Architectures (ReAct, Reflection, Planning, Tool Use, Memory)
* 9.4 Multi-Agent Orchestration (LangGraph, OpenAI Agents SDK, AutoGen, Swarm)
* 9.5 LLM Evaluation and Observability (Ragas, LangSmith, TruLens, LLM-as-Judge)
* 9.6 LLMOps and Prompt Engineering (Versioning, Guardrails, Red-Teaming)
* 9.7 LLM Cost Optimisation (Semantic Cache, Model Routing, Cascading, Prompt Caching)
* 9.8 LLM Safety and Guardrails (OWASP LLM Top 10, Prompt Injection, PII, Jailbreaks)
* 9.9 ML System Design Fundamentals
* 9.10 Feature Stores and Model Serving (Feast, Tecton, KServe, BentoML, MLflow)
* 9.11 Recommendation Systems Deep Dive (DLRM, Two-Tower, Embedding Retrieval, Cold Start)
* 9.12 Realtime AI and Voice Agents (Streaming Inference, WebRTC, LiveKit, Deepgram)
* 9.13 Multimodal AI Systems (CLIP, Whisper, LayoutLM, Document AI)
* 9.14 Data Infrastructure for AI (Embedding Pipelines, Chunking, Unstructured ETL, MCP)
* References
Chapter 10: Emerging Patterns
* 10.0 Green Computing (Carbon-Aware Scheduling, PUE, Sustainable Systems)
* References
Chapter 11: Interview Framework
* 11.0 Interview Frameworks Compared (RESHADED, PEDALS, ADEPT)
* 11.1 Requirements Scoping: Functional, Non-Functional, and MoSCoW
* 11.2 Diagramming Skills for System Design Interviews
* 11.3 Trade-off Articulation: Saying 'It Depends' Well
* 11.4 Company-Specific Interview Flavors (Amazon, Google, Meta, Netflix)
* 11.5 Design Doc Authoring: RFCs, ADRs, and the Staff Engineer's Written Output
* References
Trade-offs Library
* 1. Strong vs Eventual Consistency
* 2. ACID vs BASE
* 3. SQL vs NoSQL
* 4. Latency vs Throughput
* 5. CAP and PACELC Applied
* 6. Cache Strategies: Cache-Aside vs Write-Through vs Write-Behind
* 7. Batch vs Stream Processing
* 8. Load Balancer vs Reverse Proxy vs API Gateway
* 9. REST vs gRPC vs GraphQL
* 10. Polling vs Long-Polling vs SSE vs WebSockets vs Webhooks
* 11. Rate Limiting Algorithms: Token Bucket vs Sliding Window
* 12. Optimistic vs Pessimistic Concurrency Control
* 13. Partitioning Schemes: Range, Hash, Consistent Hash, Directory
* 14. B-tree vs LSM-tree Storage
* 15. Monolith vs Microservices
* 16. Replication Topologies: Leader-Follower, Multi-Leader, Leaderless
* 17. Distributed Transactions: 2PC vs Saga vs TCC
* 18. Push vs Pull (Fan-out, Messaging, Feed)
* 19. Lambda vs Kappa Architecture
* 20. Vertical vs Horizontal Scaling
* 21. Normalization vs Denormalization
* 22. Single-Region vs Multi-Region Deployment
* References
husiwen retweeted
The Principles of Deep Learning Theory by Daniel A. Roberts, Sho Yaida, and Boris Hanin is now in @ChapterPal's collection.
This textbook provides an effective theory of deep learning rooted in theoretical physics, statistical mechanics, and probability theory.
Designed for machine learning theorists, mathematically oriented practitioners, and physicists studying neural networks, it assumes a background in multivariable calculus, linear algebra, probability, and basic statistical mechanics concepts such as Gaussian integrals, partition functions, and perturbation expansions.
The core subject is the analytical characterization of how signals, statistics, and representations propagate through multilayer perceptrons, treating the multilayer perceptron as a minimal model to derive universal principles that extend to other deep architectures.
Read the book with an AI tutor: chapterpal.com/ebook/fb823e3…
husiwen retweeted
『システム思考の世界へ』をざっと読んだ。
良書だと思う。端的には、いかに複雑系を扱うか、という話。
ビジネスや組織、あるいは自分自身も一種の複雑系と捉えられる。それらのシステムを、短絡的な一元化・局所最適化に陥ることなく、いかに理解・制御すべきかが語られる。
bookmeter.com/books/23234406
