Discover the Best AI Resources
Curated essentials, no noise — just what matters
Real‑time full‑duplex speech‑to‑speech system that controls conversational role via text prompts and voice timbre via audio-conditioned embeddings. Built on Moshi; optimized for low-latency, persona-consistent spoken interactions.
Provides a set of versioned "skills" that codify UI design standards and automated checks for design engineers and AI agents. Includes a CLI to discover, install, and run skills like baseline UI rules, accessibility fixes, motion-performance tuning, and metadata corrections.
Intercepts and blocks destructive git, filesystem and CLI commands before they execute when run by AI coding agents. Offers sub-millisecond hook latency, 50+ modular rule packs, heredoc/inline-script AST scanning, agent-specific integrations and configurable bypass/allow-once workflows.
Runs an autonomous agent loop that uses AI coding tools (Amp or Claude Code) to implement PRD user stories iteratively, persisting context via git history, progress.txt and prd.json; designed for small, CI-backed tasks.
Compresses any context sent to LLMs (tool outputs, DB reads, RAG results, files, logs) to cut tokens by ~70–95% while preserving reversible originals; runs as a proxy or Python/TypeScript SDK with integrations for common agent frameworks.
Enables Pi to delegate tasks to focused child agents for code review, parallel audits, background jobs, and saved workflows. Supports foreground and background runs, session artifacts, worktree isolation, and built-in role agents to simplify orchestration.
Generates low-latency, streaming text-to-speech entirely on CPUs (no GPU or cloud API required), using an ~100M-parameter model with voice cloning and multilingual support. Optimized for low resource use (2 CPU cores, ~200ms to first audio chunk) — suited for local, privacy-sensitive, or embedded TTS.
Orchestrates low-latency, multi-stage pipelines for omni and multimodal models by running each stage with its own scheduler and using zero-copy shared memory for tensor transfer. Emphasizes per-stage bottleneck tuning and OpenAI-compatible streaming endpoints, suitable for TTS and multimodal serving.
Aggregates global news, infrastructure, military and market signals into an interactive map dashboard and synthesizes AI-generated intelligence briefs. Key features: local/remote LLM support, 3D globe + flat map, 35+ data layers, country instability index and client-side RAG/embeddings.