DeepTide is a cross-platform terminal AI coding agent. The Rust workspace (deeptide-core, deeptide-host, deeptide-cli, deeptide-gui) implements the production agent engine: an interactive REPL session with an agent loop, tool dispatch (bash, edit, web, git, memory, MCP, LSP, vision, cron, planning), cross-provider model routing with local-first/cloud-escalation policies, cost tracking, tokens-per-second telemetry, tool usage observability, durable memory with BM25 retrieval ranking, session import/checkpoint/rewind, sensitive-file read protection, and a theme system for syntax highlighting and diffs. The CLI and GUI share a common host layer, and the npm package downloads prebuilt Rust binaries. Architecturally it is a multi-crate Rust workspace plus supporting native components: native/ds4 is a hard-forked DeepSeek V4 Flash Metal inference engine for local LLM execution, and native/dsgo is a Swift gateway that translates between Anthropic and OpenAI protocols with SSE streaming. Primary entry points are crates/deeptide-cli/src/main.rs (CLI), crates/deeptide-gui/src/main.rs (eframe/egui GUI), and bin.js (Bun launcher that delegates to @paean-ai/zero-cli). External dependencies include DeepSeek/Anthropic/OpenAI-compatible APIs, local Metal GPU inference, and GitHub Releases for binary distribution. The repo also contains 50+ self-contained HTML5 Canvas pixel-art games and Three.js showcases under samples/, which appear to be AI-generated demo content demonstrating agent output quality rather than core product code. These share heavily duplicated boilerplate (seeded RNG, localStorage save/load, bilingual i18n, screen state machines). The core Rust code is well-tested with integration tests covering routing, cost tracking, memory ranking, tool result summarization, and slash commands; live API tests are marked #[ignore]. Hybrid note: while primarily an application, it contains research-oriented elements such as memory capture benchmarks (benchmarks/auto_capture_bench.py), prompt cache layout benchmarks (benchmarks/cache_memory_bench.py), and deterministic puzzle generation algorithms with uniqueness verification.