Discover the Best AI Resources
Curated essentials, no noise — just what matters
Unmixes green‑screen pixels with a neural model to recover straight (unmultiplied) foreground color and a clean linear alpha for every pixel, preserving hair, motion blur and translucency. Produces VFX‑standard EXR outputs, supports optional AlphaHint generators (GVM/VideoMaMa) and Docker/consumer‑GPU optimizations.
Converts scene intent into production-ready Seedance 2.0 prompts, reference-role mappings, and IP-safe rewrites for multimodal (text/image/audio/video) video generation. Ships as a modular agent-skill OS with multilingual examples, troubleshooting tools, and pro filmmaker handoff artifacts.
Provides a set of task-focused agent “skills” — small folders of instructions that teach agents how to perform common Flutter development workflows (integration tests, widget previews, routing, localization). Maintained by the Flutter team to reduce mistakes and make repeatable dev tasks reliable.
Provides persistent, searchable memory for coding agents (Claude Code, Cursor, Gemini CLI, etc.), automatically capturing tool usage and session facts. Combines BM25, vector embeddings and a knowledge graph for hybrid retrieval, reducing token costs and re-explaining between sessions.
Provides a structured library of 754 cybersecurity skills (agentskills.io format) mapped to MITRE ATT&CK, NIST CSF, MITRE ATLAS, D3FEND and NIST AI RMF — so AI agents can load practitioner workflows and decision logic across 20+ platforms.
Provides a suite of Claude Code skills that guide the full academic pipeline—research, write, review, revise, and finalize—while enforcing integrity gates (citation verification, anti-hallucination checks) and keeping a human-in-the-loop workflow.
Orchestrates autonomous coding agents to run isolated implementation tasks end-to-end: spawn runs from project boards that produce CI results, PR review feedback, complexity analysis, and walkthrough videos, and safely land accepted PRs. Experimental engineering preview for trusted environments; best for teams using harness engineering.
Provides 6,000 runnable, operator-level PyTorch tasks for training and evaluating CUDA kernel generation models; each sample includes executable code, operator descriptors, and provenance tags, with execution-driven filtering to ensure reproducibility and contamination control.
Builds a local structural knowledge graph of a codebase so AI coding assistants read only the minimal, relevant code during reviews and daily tasks—reducing tokens used while providing blast-radius impact analysis, incremental updates, and MCP integrations.
Audits and reduces token waste in LLM sessions by compressing verbose outputs, checkpointing before compaction, and restoring lost context. Runs fully locally with zero telemetry and provides a live token dashboard plus plugins for Claude Code, OpenClaw and Codex.
Provides an MCP server and agent skills so AI agents can run keyword research, inspect SERPs, compare domains, and manage backlinks using your DataForSEO data. Self‑hostable TypeScript project with an optional hosted UI (openseo.so) and pay‑as‑you‑go data usage.
Local-first desktop workbench that scrapes job leads, filters low-quality postings, scores candidate fit with explainable rules and vector matching, and generates tailored resumes, cover letters, and outreach drafts while keeping data on-device.