AI Model2026
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.
AI Agent2026
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.
AI Agent2026
Manages real tmux-backed terminals and AI agents as draggable nodes on an infinite pan/zoom canvas, with a Trello-style kanban view, persistent sessions that survive restarts, mobile companion support, and a browser Server Edition for self-hosting.
AI Model2026
A local, quantized Gemma 4 12B fine-tune packaged as GGUF quants that runs on ~4.5 GB VRAM. Optimized for coding and multi-step agentic tool use (read→reason→act→verify), ships multiple quant sizes (Q3_K_M–Q8_0) and MTP draft support; English-centric with trade-offs versus generalist models.
AI Dataset2026
Provides 2,170 reference-grounded evaluation samples across seven agent domains (MCP, Search, Terminal, SWE, Android, Web, OS) to score language world models on Format, Factuality, Consistency, Realism and Quality. Includes per-domain JSONL files, judge prompts and an evaluation script for reproducible scoring.
AI Model2026
Provides a GGUF-quantized local build of Ornith-1.0's 9B dense model for offline inference and terminal-focused coding agents. Supports OpenAI-compatible tool-calling, a 256K context window, and runs via llama.cpp or Ollama on a single high-memory GPU.
AI Agent Papers2026
Provides a terminal-style benchmark of 46 long-horizon tasks decomposed into fine-grained graded subtasks to produce dense intermediate rewards and partial credit, enabling evaluation of long-horizon planning, long-context management, and iterative debugging. Tasks typically require hundreds of episodes and minutes-to-hours of execution; baseline evaluations report high token and episode consumption with low pass rates, highlighting evaluation headroom.
AI Model2026
27B multimodal reasoning model built on Qwen3.5-27B that preserves the base model's native multi-token-prediction head, full vision tower, and a 1,048,576-token YaRN context window. Designed for agentic tool use, long-context reasoning, and research deployments; released under Apache-2.0.
AI Model2026
1B-parameter text-generation model tuned for conversational and agentic workflows with a focus on security and vulnerability-detection; suited for low-cost or on-prem/edge deployments and terminal-agent integrations.
AI Agent Papers2026
Reformulates long-horizon agent execution as explicit task-state management: a manager defines bounded subtasks, fresh-context executors run them, and read-only auditors verify outcomes. Shows large performance gains on WeaveBench, Terminal-Bench and OSWorld.
AI Dataset2026
Provides 37,484 validated command-line tasks generated by recursive task synthesis, each paired with searchable metadata and a sanitized, runnable package (instructions, solution, verifier, and optional Dockerfile).
AI Dataset2026
Provides per-decision training samples for RL-driven command-line LLM agents: each record pairs a task prompt plus terminal history with a teacher's next-action in Terminus-2 JSON. Around 31k verifier-passing samples from 630 ATCB tasks, formatted for NeMo Gym's terminus_judge and licensed CC-BY-4.0.