AI Agent2026
Build native desktop apps authored with declarative .native markup and TypeScript (or Zig) compiled to native code, with no browser or JS runtime in the binary. Ships a component catalog, deterministic rendering, hot reload, and an embedded automation server for AI agent workflows.
AI Dataset2026
Contains 4,006 newline-delimited JSONL agent-session traces recording assistant responses and tool calls from deepseek/deepseek-v4-pro — includes a training-ready tools schema snapshot and helpers for conversion to SFT/distillation workflows.
AI Model2026
Processes text and images to produce conversational, reasoning-focused multilingual outputs for agentic workflows. Built as a sparse MoE decoder (25B active / 218B total parameters) with 128K context and available in BF16/FP8/W4A4 quantizations to balance quality and deployability.
MCP Server2026
Lets AI agents produce expressive, polished charts from compact, human-editable semantic specs; the compiler infers layout, scales, and labels and emits Vega-Lite, ECharts, or Chart.js outputs, with an MCP server for agent-driven chart creation and rendering.
AI Coding2026
Applies an "ADHD-friendly" output style to coding assistants so answers lead with the next action, present numbered steps, and avoid burying results. Packaged as a reusable skill/plugin for coding-agent workflows (examples: Claude Code, Codex) and guided by a 10-rule style set.
AI Agent2026
Routes code-based AI agents through repeatable reverse-engineering and pentesting workflows and orchestrates local and remote tools (jadx, Frida, IDA, BurpSuite) so agents can triage APKs, binaries, JS, firmware, and CTFs without guessing the toolchain. Includes master routing rules, tool-index detection, MCP integration, and a field-journal for reusable lessons.
AI Agent2026
An A‑share–specialized fork of TradingAgents that runs a seven‑analyst multi‑agent investment research pipeline for China stocks, integrating free A‑share data connectors and LLM providers. Key features: mootdx/東財 data integrations, A‑share trading rules (T+1, limits), Streamlit UI, and Apache‑2.0 license.
AI Dataset2026
Pairs natural-language instructions with executable setup artifacts and Python reward functions to create verifiable computer-use agent tasks. Provides a Parquet task table for fast filtering plus a compressed archive of runnable task bundles; several web task endpoints are placeholders that require a local CUA-Gym-Hub deployment.
AI Model2026
A trillion-parameter reasoning model aimed at long-horizon, multi-step agent workflows and tool collaboration. Offers adjustable Reasoning Effort modes (high, xhigh), async RL training (IcePop), and very long context (128K→256K) for complex production scenarios.
AI Model2026
Multimodal 35B scientific foundation model for image+text-to-text reasoning and conversational workflows. Uses task-scaling and full-chain training (pretraining → RL) to boost domain scientific abilities while keeping general multimodal reasoning and agent skills.
AI Model2026
A GGUF-format 9B model derived from Qwen3.5, fine-tuned for agentic coding, tool-calling, reasoning and vision-capable multimodal prompts. Optimized for local 8‑bit inference on 16GB-class machines; community experimental release for research use.
AI Dataset2026
Provides 40 public Kubernetes incident scenarios (SRE subset) with ground-truth root-cause entities and offline cluster snapshots in JSONL format; designed to evaluate agentic root-cause diagnosis on alerts, events, traces and topology.