Provides ~12.29M execution‑free agentic coding trajectories (≈112B tokens) sampled from 122K GitHub PRs to mid‑train code and agent models. Uses bash-only actions (grep, git, sed, etc.) so it scales without Docker; trajectories are unverified and intended for mid-training rather than final SFT.
Terminal-native AI coding agent that runs as a single static Go binary and preserves long LLM sessions using DeepSeek-aware prefix caching. Config- and plugin-driven: supports multiple providers, separate planner/executor sessions, and CLI/TUI, desktop and VS Code integrations.
Terminal-first developer workspace with an agentic AI side-panel that runs against your API keys or local models. Bundles a native PTY terminal, CodeMirror editor with AI edit diffs, file explorer, git history/graph, and a web preview in a ~7–8MB desktop app with no telemetry.
Provides step-by-step guides to integrate DeepSeek V4 models (deepseek-v4-pro and deepseek-v4-flash) into 22 popular AI agents and coding-assistant tools. Each entry shows installation, configuration, and first-run steps for tools like Claude Code, Qwen Code, Codex, Cline, Deep Code, and more.
Terminal-native AI coding assistant optimized for the deepseek-v4 model. Provides configurable "thinking" modes and reasoning-intensity controls, agent skills for extensibility, MCP integration, and a shared config with a VSCode plugin.
Local-first AI agent workspace that unifies coding, writing, design, research and automation under one runtime shared between a desktop GUI and a terminal TUI. Features Agent Graph for long-running, auditable workflows, multi-provider model support, and local-by-default data storage.
Terminal-native AI coding agent that reads and edits code, runs shell commands, searches files, fetches web pages, and determines next steps from interactive feedback. Delivered as a single-binary TUI with video input, subagents, a plugin marketplace, and IDE (ACP) integration.
Runs an external, reviewable coding-agent harness that turns intent into repeatable software work: clarifies requirements, builds reviewed plans, executes in tmux-backed sessions, and collects durable verification. Ships Telegram/Discord delivery, a research REPL, and optional desktop-control tools; beta-stage.
Agentic LLM for long-horizon, environment-driven workflows: decomposes goals, generates and executes code/tool calls, evaluates outputs, and iterates. The Pro variant emphasizes coding and terminal execution and is published for use with sglang and multi-node H100 deployment.
Benchmark for long-horizon computer-use agents that must orchestrate GUI, CLI, and code operations within single trajectories across 114 real-world tasks. Evaluated on a real Ubuntu desktop and paired with a trajectory-aware judge that inspects deliverables, artifacts, and action traces—revealing a top PassRate of ~41.2%.
Provides GGUF quantized weights and runnable instructions to run CohereLabs' North-Mini-Code-1.0 (30B A3B MoE) locally via llama.cpp or vLLM; includes quant files, build/run notes, and recommended sampling and tool-use settings for agentic coding.
Routes natural-language requests to a single “first mate” agent that spawns and supervises multiple autonomous crewmates, each running in an isolated git worktree and producing finished PRs, approved local merges, or standalone investigation reports. Key features include visible session backends, disposable worktrees, explicit project modes, optional persistent secondmates, and an event-driven zero-token watcher.