acecue
A real-time interview copilot that detects questions and streams tailored answers through a web app and Chrome extension.
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163 public repositories, presented without hiding coursework, experiments, forks, or unfinished records.
A real-time interview copilot that detects questions and streams tailored answers through a web app and Chrome extension.
Reusable agent workflows for research, writing, iterative review, and evidence-gated project improvement.
A public-safe library of reusable agent skills, workflow templates, references, and routing guidance.
A multilingual, multi-model AI chat interface for users in China, deployed on Vercel with bring-your-own-key support.
Self-study implementations based on the AI Computing Systems labs, covering operators, a compact deep-learning framework, and style transfer.
A desktop AI development environment for coding with Chinese large-language-model providers.
Configuration and profile content for the appleweiping GitHub account.
The source repository for Weiping Yan's personal portfolio website.
A browser-based interactive 3D art museum with a navigable history timeline and first-person galleries.
Self-study security exercises based on ASU CSE365 and pwn.college materials, spanning Linux permissions, shellcode, debugging, and web vulnerabilities.
Self-study systems-security exercises based on ASU CSE466 and pwn.college materials, covering reverse engineering, memory corruption, and sandboxing.
Self-study C++ assignments based on Amirkabir University AP1400-2, covering object-oriented programming and data structures.
A public fork of ARIS, a Markdown-based workflow for autonomous machine-learning research and cross-model review.
A coursework project on mobile-robot perception, sensor-driven navigation, and decision logic.
Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞
An original browser-based voxel sandbox with procedural worlds, lighting, crafting, creatures, and local saves.
An open-source browser rhythm game with public-domain music and a dependency-free Web Audio piano.
A self-study NanoDB implementation based on Caltech CS122, covering storage, buffer management, B+ trees, query planning, and transactions.
Self-study exercises based on Cambridge programming-language semantics materials, including operational and mechanized treatments of small languages.
Evidence-gated agentic experimentation with deterministic causal tools, selective deployment, and a 3D control room.
A local-first AI coding agent that combines a command-line interface, local web UI, and DeepSeek model support.
A self-study BusTub database implementation based on CMU 15-445, covering buffer management, B+ trees, query execution, and concurrency.
A CPU-oriented Python and NumPy reimplementation based on CMU 15-462/662 Scotty3D, covering rasterization, mesh processing, path tracing, and animation.
Self-study work based on CMU 15-799 database-systems materials, focused on query optimization and DBMS internals.
Self-study analyses based on CMU 17-803 materials, covering empirical study design, statistics, and reproducible software-engineering research.
Self-study implementations based on CMU 10-708, covering inference, parameter learning, and structured probabilistic models.
Self-study implementations based on CMU 11-667, covering transformers, retrieval and tool use, in-context learning, calibration, and efficiency.
Self-study assignments based on CMU 11-711, including a compact BERT implementation for sentiment, paraphrase, and similarity tasks.
Self-study deep-learning systems based on CMU 11-785, including automatic differentiation, neural-network components, sequence models, and attention.
Self-study systems work based on CMU 11-868, covering attention kernels, memory and quantization techniques, and parallel training and serving.
A local-first content workflow for scoring posts, making blind predictions, reviewing outcomes, and evolving a rubric from evidence.
Self-study OCaml assignments based on Cornell CS3110, covering functional programming, modules, testing, and a course-scale final project.
Self-study C++ assignments based on Stanford CS106B, covering recursion, backtracking, abstract data types, trees, graphs, and algorithms.
Self-study assignments based on Stanford CS106L, focused on modern C++ including the STL, RAII, templates, iterators, and move semantics.
Self-study Rust exercises based on Stanford CS110L, including systems-programming practice, a ptrace debugger, and an asynchronous load balancer.
A self-study TCP/IP stack based on Stanford CS144, covering byte streams, reassembly, TCP send and receive paths, network interfaces, and routing.
Self-study parallel-computing assignments based on CMU 15-418 and Stanford CS149, using ISPC, pthreads, OpenMP, and CUDA.
Self-study Pintos operating-system projects based on UC Berkeley CS162, covering scheduling, system calls, user programs, and file systems.
Self-study networking projects based on UC Berkeley CS168, covering reliable transport, routing, and traceroute.
Self-study web-engineering assignments based on UC Berkeley CS169, using Ruby on Rails with behavior- and test-driven development.
Self-study work based on UC Berkeley CS170, combining written analysis with implementations of algorithms for tractable and intractable problems.
A self-study RookieDB implementation based on UC Berkeley CS186, covering B+ trees, joins, query optimization, concurrency, and recovery.
Self-study Pacman AI projects based on UC Berkeley CS188, covering search, games, reinforcement learning, probabilistic inference, and ML.
Self-study machine-learning assignments based on UC Berkeley CS189, covering classification, regression, trees, neural networks, clustering, PCA, and kernels.
Self-study NLP assignments based on Stanford CS224N, covering word vectors, dependency parsing, neural machine translation, and Transformer pretraining.
Self-study graph-learning assignments based on Stanford CS224W, covering node embeddings, message-passing networks, classification, and link prediction.
Self-study programming assignments based on Stanford CS229, covering regression, generative classifiers, SVMs, clustering, PCA, and reinforcement learning.
Self-study theory exercises based on Stanford STATS 214 and CS229M, covering concentration, complexity, margins, kernels, and online learning.
Self-study computer-vision assignments based on Stanford CS231N, covering classifiers, convolutional networks, captioning, and generative models.
Self-study deep-RL assignments based on UC Berkeley CS285, covering imitation, policy gradients, value methods, model-based learning, and offline RL.
Self-study projects based on Harvard CS50 AI, covering search, logic, probabilistic inference, optimization, machine learning, computer vision, and NLP.
Self-study Python exercises based on Harvard CS50P, spanning core language features, testing, file handling, regular expressions, and object-oriented design.
Self-study projects based on Harvard CS50x, using C, Python, SQL, and web technologies across introductory computer-science topics.
A self-study full-stack chat application based on UW–Madison CS571, with a REST API and React web and React Native clients.
Self-study projects based on UC Berkeley CS61A, covering functional abstraction, data structures, interpreters, and a Scheme implementation in Python.
Self-study Java projects based on UC Berkeley CS61B, covering data structures, algorithms, a version-control system, and a tile-based world generator.
Self-study computer-architecture projects based on UC Berkeley CS61C, covering C, RISC-V, pipelined processors, and parallel matrix computation.
Self-study systems exercises based on CMU 15-213 and CS:APP, covering data representation, assembly, caching, shells, memory allocation, and network proxies.
Self-study assignments based on UC San Diego CSE234, covering automatic differentiation, graph and operator optimization, and parallel machine-learning systems.
Self-study implementations based on the DeepLearning.AI Deep Learning Specialization, covering optimization, convolutional networks, and sequence models.
Public-safe Windows workstation scripts and examples for a D-drive-first, agentmemory-first development setup.
Self-study C projects based on Duke University’s Introductory C Programming Specialization, including card-game and image-data exercises.
Research code examining reduced-lead ECG reconstruction through feature-level recoverability and distribution-free calibration.
Self-study vision assignments based on Michigan EECS 498/598, covering classifiers, convolutional and recurrent networks, detection, and generative models.
A coursework prototype for embedded infant-cry detection and motor-controlled soothing logic.
Self-study simulator exercises based on ETH Zürich computer-architecture materials, covering caches, branch prediction, prefetching, and memory systems.
Self-study SystemVerilog labs based on ETH Zürich digital-design materials, focused on constructing and simulating MIPS and RISC-V processors.
Self-study generative-model implementations based on MIT 6.S184 materials, covering flow matching and score-based diffusion.
Self-study applications based on Helsinki’s Full Stack Open course: a React and Express blog app and an Apollo GraphQL library service.
Self-study graphics assignments based on GAMES101, covering rasterization, shading, Bézier curves, ray tracing, acceleration structures, and path tracing.
Self-study GAMES103 implementations covering rigid bodies, mass-spring cloth, finite-element elasticity, and shallow-water simulation.
Self-study GAMES202 rendering work covering soft shadows, radiance transfer, screen-space effects, energy compensation, and denoising.
Self-study functional-programming exercises based on the University of Helsinki Haskell MOOC, spanning the course’s sixteen topic sets.
Self-study operating-system labs based on HIT materials, modifying Linux 0.11 components such as boot, system calls, scheduling, IPC, terminals, and procfs.
A dependency-free, top-down open-city browser game inspired by classic crime-action games.
A procedural Three.js shark-survival arcade game delivered from a single HTML file without a build step.
Self-study Python implementations based on David MacKay’s information-theory text, covering coding, channels, error correction, MCMC, and EM.
Research code examining identifiability and anti-aliasing sample design for implicit neural representations with fixed Fourier features.
Self-study Rust assignments based on KAIST CS220, focused on programming-language principles and functional abstraction.
A self-study Rust compiler based on KAIST CS420, covering intermediate representation, optimization, and assembly generation for C.
Self-study Rust assignments based on KAIST CS431, covering lock-based and lock-free structures, hazard pointers, and concurrent hash maps.
A zero-install browser video-downloader interface backed by a Cobalt engine on Hugging Face Spaces.
Self-study packet-analysis labs based on Kurose and Ross networking materials, using Python and Scapy across common Internet protocols.
A local-first tool that organizes Chinese consumer-dispute materials into evidence, missing-item checks, and reviewable draft documents.
Self-study machine-learning assignments based on Hung-yi Lee’s public course materials, spanning supervised, generative, and reinforcement-learning topics.
Research code for comparing pointwise LLM relevance estimates and uncertainty-aware reranking under a shared candidate protocol.
A local-first personal health-record prototype that organizes source-grounded longitudinal data, beginning with glucose and metabolic records.
Self-study exercises based on MIT’s Missing Semester, covering shell tools, editors, data wrangling, version control, debugging, build systems, and security.
Self-study numerical checks and worked exercises based on MIT 18.01 and 18.02, covering single- and multivariable calculus.
Self-study NumPy implementations based on MIT 18.06, covering matrix factorizations, least squares, eigenvalue methods, SVD, PageRank, and the DFT.
Self-study Julia exercises based on MIT 18.330, covering foundational numerical-analysis methods and convergence behavior.
Self-study security labs based on MIT 6.1600, covering hashing, Merkle trees, cryptographic attacks, side channels, sandboxing, and fuzzing.
Self-study programming exercises based on MIT 6.006, covering data structures, graph algorithms, dynamic programming, and related algorithmic problems.
Self-study Python work based on MIT 6.007, covering LTI systems, Fourier analysis, sampling, transforms, filtering, modulation, and feedback.
Self-study Java projects based on MIT 6.031, focused on specifications, abstract data types, testing, parsers, graphs, and collaborative applications.
Self-study code and proof notes based on MIT 6.042J, covering number theory, graphs, counting, generating functions, probability, and random walks.
Self-study written and programming exercises based on MIT 6.046, focused on algorithm design, analysis, and implementation.
Self-study Python work based on MIT 6.050J, covering entropy, mutual information, coding, channel capacity, error correction, and maximum entropy.
Self-study Java exercises based on MIT 6.092, covering introductory programming, object-oriented design, collections, and basic application development.
Self-study Python exercises based on MIT 6.100L, covering introductory programming, problem decomposition, algorithms, and data structures.
Self-study distributed-systems labs based on MIT 6.824/6.5840, covering MapReduce, Raft, fault-tolerant key-value services, and sharding.
Self-study MIT 6.858 labs using Zoobar to explore memory safety, isolation, symbolic execution, browser security, and secure storage.
Self-study deep-learning assignments based on MIT 6.S7960, focused on implementing and analyzing neural-network components and training methods.
A self-study MERN social application based on MIT web.lab materials, with authentication, real-time chat, and a React, Express, MongoDB, and Socket.IO stack.
Self-study Machine Learning Specialization work covering regression, neural networks, trees, clustering, anomaly detection, recommendation, and deep Q-learning.
Self-study exercises based on the MLC course, covering TensorIR, model compilation, program optimization, and Apache TVM integration.
Self-study implementations based on Nand2Tetris, progressing from logic gates and a CPU to an assembler, virtual machine, Jack compiler, and operating system.
A self-study Needle framework based on CMU 10-414/714, covering autodiff, neural-network modules, optimization, array backends, and sequence models.
Self-study implementations based on Andrej Karpathy’s Neural Networks: Zero to Hero series, including micrograd, makemore, a compact GPT, and BPE tokenization.
Research code exploring trajectory- and layer-aware data selection for instruction tuning.
A Stardew-inspired pixel town that presents a local agent workspace through Phaser, React, and FastAPI.
A self-study compiler based on Nanjing University materials, covering lexing, parsing, semantics, IR, and code generation.
Self-study operating-system labs based on Nanjing University materials, spanning host-side tools, concurrency, process management, memory, and file systems.
Self-study static analysis based on Nanjing University materials and Tai-e, covering dataflow, call graphs, pointers, and taint analysis.
Self-study work based on NYU’s 2021 deep-learning course, covering backpropagation, CNNs, RNNs, attention, and energy-based models.
A coursework operating-systems project covering threads, interprocess communication, and condition variables.
An original browser-based 3D open-world driving sandbox built with procedural systems and a minimal dependency footprint.
An original browser island life simulation with farming, fishing, combat, customization, and procedural art.
Self-study assignments based on Peking University software-analysis materials, covering static and dynamic analysis, dataflow, and symbolic execution.
A self-study SysY compiler based on Peking University materials, lowering a C-like language through Koopa IR to RISC-V assembly.
A fan-made browser pixel RPG with a large creature index, turn-based battles, peer-to-peer multiplayer, and original chiptune music.
Self-study Java assignments based on Princeton Algorithms I and II, spanning union-find, sorting, graphs, string algorithms, and compression.
An original isometric real-time strategy game with a deterministic simulation core and procedural art and audio.
Research code examining speech-watermark robustness under generative resynthesis and laundering transformations.
Research code exploring risk-sensitive calibration rewards for RLVR reasoning models.
A small coursework utility that derives a token from a supplied student number.
Self-study security exercises based on Syracuse SEED Labs, covering selected software, cryptography, network, and web-security topics.
A self-study compiler based on Shanghai Jiao Tong University materials, covering the standard stages for a C-like language.
Self-study work based on University of Toronto STA4273, covering variational methods, gradient estimators, and latent-variable models.
Self-study C++ systems projects based on Stanford CS110, including filesystems, processes, shells, thread pools, proxies, and MapReduce.
A self-study MERN photo application based on Stanford CS142, covering sessions, registration, uploads, comments, and activity feeds.
A self-study Cool compiler based on Stanford CS143, covering lexical analysis, parsing, semantic analysis, and MIPS code generation.
A self-study CPU renderer based on Stanford CS148, covering rasterization, Whitted ray tracing, bounding-volume hierarchies, and Monte Carlo path tracing.
Self-study assignments based on Stanford CS242, covering lambda calculus, type systems, and programming-language implementation.
A self-study RedBase relational database based on Stanford CS346, covering paged storage, B+ trees, catalogs, query execution, optimization, and aggregation.
Self-study numerical exercises based on Stanford EE364A, using CVXPY for linear, quadratic, second-order-cone, and semidefinite optimization.
Self-study assignments based on Columbia STAT8201, covering variational autoencoders, normalizing flows, GANs, autoregressive models, and diffusion.
Self-study work based on Berkeley OCF’s Linux System Administration DeCal, covering command-line operations, services, networks, and security.
An experimental collection of Terraria documentation and agent-oriented tooling.
Research code exploring temporal graph-to-language retrieval for sequence and time understanding in LLM-based recommendation.
Self-study labs based on MIT 6.5940, covering pruning, quantization, neural-architecture search, and efficient deep-learning techniques.
Self-study networking labs based on USTC materials and a top-down networking curriculum, covering sockets, protocols, and Mininet experiments.
Research code examining when latent higher-order network structure can be identified from edge-flow observations.
Research code exploring causal-propagation audits of hallucination traces across LLM decoding paradigms.
Research code for uncertainty-aware generative recommendation, organized around shared data, baselines, evaluation, and reproducibility utilities.
Self-study exercises based on UC Berkeley EECS 126, covering probability, coding, random graphs, stochastic processes, detection, and filtering.
Self-study security projects based on UC Berkeley CS161, covering memory safety, cryptography, network security, and vulnerable web applications.
Self-study code and notes based on UC Berkeley CS70, covering discrete mathematics, number theory, coding, matching, probability, and Markov chains.
Self-study Data 100 labs covering regression, optimization, classification, dimensionality reduction, clustering, and applied projects.
Self-study Python labs based on UC Berkeley EE120, covering convolution, LTI systems, sampling, Fourier analysis, biomedical signals, denoising, and feedback.
Self-study Python labs based on UC Berkeley EE16A and EE16B, covering imaging, localization, classification, dynamics, estimation, control, and matrix methods.
A coursework UML class-diagram editor with validation, undo and redo, and export support.
Independent bilingual five-campus UMN Gopher Assistant platform
Undergraduate study notes and course materials from the Netherlands.
Self-study USTC graphics work covering image and mesh processing, parameterization, deformation, simplification, rasterization, and path tracing.
A self-study Cminus-f compiler based on USTC materials, covering lexical and syntax analysis, LightIR and LLVM IR, and code generation.
Coursework base-station software with a live visualization dashboard for a multi-robot planetary-exploration project.
An open-source U.S. visa-appointment monitor designed around account safety, local credentials, and an optional control plane.
A multi-model deliberation system supporting council, debate, red-team, consensus, specialist, and tournament protocols.
An autonomous research workflow centered on phenomenon-driven discovery, early falsification, and evidence-gated progression.
A terminal-native DeepSeek coding agent with snapshots, session branching, model routing, sub-agents, diagnostics, and cost tracking, based on CodeWhale.
A personal knowledge base maintained through an LLM-assisted workflow.
Self-study xv6 labs based on MIT 6.S081/6.1810, covering system calls, virtual memory, traps, copy-on-write, networking, synchronization, filesystems, and mmap.
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