Discover the Best AI Resources
Curated essentials, no noise — just what matters
Runs open-source LLMs and multimodal models entirely on mobile devices for offline, private inference. Offers Agent Skills, Thinking Mode, Ask Image, audio scribe, model management and benchmarks, with Gemma 4 and Hugging Face integration.
Generates full-stack web apps with the backend included — database, auth, file uploads, real-time UIs, and background workflows — by writing code against Convex's reactive APIs. A fork of bolt.diy; bring your key for Claude, GPT, Gemini, or Grok.
Builds a table-of-contents tree index over long PDFs and uses LLM tree search to fetch relevant sections — no embeddings, chunking, or vector database. Hits 98.7% on FinanceBench, for financial, legal, and technical docs where relevance needs reasoning.
Builds, evaluates, and deploys multi-agent systems in Python, code-first. A graph-based runtime handles routing, fan-out/fan-in, loops, retries, and human-in-the-loop; a Task API covers agent-to-agent delegation, plus a CLI and web UI.
Provides ready-to-use sample agents for Google’s Agent Development Kit across Python, TypeScript, Go, Java, Kotlin, and Android, from simple assistants to multi-agent workflows.
Orchestrates AI coding agents around tasks, sessions, artifacts, reviews, and parallel Claude Code workflows so teams can manage complex codebase work with more visibility.
Open-source AI platform for knowledge workers: describe a multi-step task (reports, monitoring, workflows) and get back finished docs, dashboards, or apps from agents wired to your own data. Swap agents and LLMs freely; self-host anywhere.
Exposes enterprise databases to AI agents as vetted, query-restricted tools instead of raw connection strings. One MCP server fronts 20+ engines and centralizes connection pooling, IAM auth, and OpenTelemetry tracing, with prebuilt SQL tools.
Combines static code analysis with LLM reasoning to produce interactive architecture diagrams, component-level documentation, and navigable outputs for IDEs, CI, and docs. Emits Mermaid diagrams and incremental updates with CLI and editor integrations.
Exposes a local MCP server that lets LLMs (e.g., Claude Desktop) query decompiled Android app context from a modified JADX GUI—supporting class/method retrieval, resources, xrefs, and debugger hooks for interactive reverse engineering workflows.
Autonomously proposes hypotheses, runs experiments, analyzes results, and drafts workshop-level papers via an agentic tree-search pipeline. Unlike template-driven predecessors, it explores open-ended ML research paths but requires GPU/PyTorch and careful sandboxing due to execution of LLM-written code.
Provides 5 million instruction–response pairs for supervised fine-tuning of code LLMs, with inputs, outputs, unit tests, and automated LLM judgments. Uses hybrid automated/synthetic generation and is released under CC BY 4.0 for large-scale SFT workflows.