Discover the Best AI Resources
Curated essentials, no noise — just what matters
Installs ready-made Claude Code configs — subagents, slash commands, MCP integrations, hooks, and settings — from a catalog of 100+ components via one CLI command. Includes a real-time dashboard to monitor live sessions and token usage.
Model-compression toolkit for large LLMs/VLMs that integrates quantization (FP8/INT4/etc.), speculative decoding, token pruning and deployment hooks—designed for end-to-end performance on single/multi-GPU inference workflows and research-to-prod model optimization.
Runs named-entity recognition, text classification, structured-JSON parsing and relation extraction from one 205M-parameter encoder in a single CPU forward pass, using schemas with per-field regex validators. A larger 1B model is available via API.
Extracts structured data from unstructured text with LLMs, mapping every extraction to its exact character span in the source for visual review. Uses few-shot examples, schema enforcement, and multi-pass chunking to handle long documents.
Runs a six-month live experiment where ChatGPT manages a real-money micro-cap portfolio from $100, trading under strict rules with automated stop-losses. Each trade's rationale is logged; returns are benchmarked against the S&P 500 and Russell 2000.
Bundles Langflow, Docling, and OpenSearch into one installable package so you can ingest messy documents, run agentic retrieval with re-ranking, and chat over your own knowledge base. Ships Python/TS SDKs and a built-in MCP server at /mcp.
Spec-driven agentic dev platform that turns a prompt into requirements, a design doc, and sequenced tasks before any code is written, then implements from the spec. Runs across IDE, CLI, web, and mobile; validates output with property-based tests.
Centralized enterprise platform to manage org-wide MCP servers with a private MCP registry, security guardrails, cost controls, and observability. Offers a Kubernetes-native orchestrator, built-in RAG knowledge base, security sub-agents, and tools for governed AI adoption.
Exposes Google Analytics Admin and Data APIs as a local Model Context Protocol (MCP) server so LLMs can query accounts, run reports, funnels and realtime queries via standardized MCP tools. Intended for local prototypes and developer integrations; requires Google Cloud credentials.
An agentic framework that analyzes, plans, and executes multi-step video understanding and editing workflows using multimodal LLM-driven agents—features intent decomposition, graph-based workflow orchestration, and automated shot planning for long-form video tasks.
Detects, segments, and tracks every instance of an open-vocabulary concept in images and video from a text phrase or visual exemplar, not just one object per prompt. An 848M-param model reaching ~75-80% of human accuracy across 270K concepts.
Unifies agentic tasks, reasoning, and coding in a single MoE model with 355B total / 32B active parameters and a switchable thinking mode. A lighter 106B-param Air variant trades scale for efficiency; both ship MIT-licensed.