Cross-platform downloader that uses AI-assisted chunking and acceleration to improve success rates across protocols. Supports HTTP/FTP/M3U8/MPEG‑DASH/Magnet/BT plus YouTube/Bilibili parsing, a browser extension, aria2-compatible RPC and an Android client — aimed at multi-protocol, long-running and streaming download tasks.
Web-based resume editor with real-time preview, custom themes, dark mode, auto-save and PDF export, plus built-in AI-assisted writing and a custom model for polishing content. Open-source under Apache-2.0 but requires a commercial license for paid/enterprise use.
Brings an agentic chat experience to the terminal: describe a task in natural language and it plans, edits files, and runs commands to build the app. Written in Rust, ships on macOS and Linux. Now succeeded by the closed-source Kiro CLI.
Companion resources for Chip Huyen's AI Engineering book: chapter summaries, study notes, prompt examples, case studies, and a few analysis scripts. Focuses on engineering practices for adapting foundation models to production rather than step-by-step code tutorials.
Pan-cancer CT segmentation dataset for training and benchmarking medical-image segmentation models — packaged as a Hugging Face dataset with an estimated 10k–100k samples and linked arXiv references. Designed for model development and reproducible benchmarking; non-commercial license applies.
A human-verified subset of 500 SWE-bench test cases for evaluating models that resolve GitHub issues into PRs using unit-test verification. Contains problem statements and base commits (pre-fix) for reproducible unit-test based evaluation; suitable for benchmarking code-fix and issue-resolution capabilities.
Physics-aware simulated sensor dataset for training and evaluating autonomous-vehicle perception and control models. Includes multimodal sensor streams with physical-scene annotations intended for tasks that require grounding in real-world dynamics.
Compares standard human psychometric questionnaires (PVQ, BFI) with generation‑based profiling to test whether questionnaires predict real LLM responses. Finds big divergences: questionnaires exploit lexical cues and elicit alignment‑consistent answers, mischaracterizing LLM behavior on everyday queries.
Detects motion from Wi‑Fi channel state information (CSI) on cheap ESP32 boards and integrates natively with Home Assistant; offers an optional on‑device ML detector that requires no calibration.
Curated collection of 70 hands‑on cybersecurity projects, certification roadmaps and learning resources organized into Foundations/Beginner/Intermediate/Advanced tiers. Each project ships source code plus deep learn/ documentation; several focus on AI security (LLM prompt defenses, ML threat detection).
Defines a vendor-neutral JSON/YAML semantic model specification and tooling to exchange metrics, dimensions, lineage and other business semantics across analytics, AI and BI platforms; includes a core spec, validators, converters (dbt, GoodData, Salesforce) and example models.
Collects ~200,000 human responses to 20 visual/semantic association questions (e.g., Bouba–Kiki), with per-response image options and demographic metadata — useful for cross‑cultural perception and evaluation of multimodal systems, but not guaranteed as a rigorously controlled experimental sample.