AI Coding2026
Turns a repo's code, docs, PDFs, images, and videos into a queryable multimodal knowledge graph for AI coding assistants. Uses deterministic AST extraction for code and LLM-based semantic extraction for other assets, exporting interactive HTML, JSON, and a human-readable audit report.
AI Model2026
Generates and iterates on long‑horizon agentic plans and code — designed to stay productive across many rounds of tool calls and experiments. Emphasizes iterative reasoning, stronger repo/terminal automation and code generation than GLM‑5, and can be served locally for research and autonomous-agent workloads.
AI Coding2026
Maps a codebase plus docs, PDFs, media and configs into a local, queryable knowledge graph; parses code with a local tree-sitter AST (no LLM), uses configurable backends for semantic extraction of non-code, and outputs graph.json, graph.html and a brief report.
AI Client2026
Compresses LLM/agent replies into a terse “caveman” style to cut output tokens (~65–75%) while preserving technical accuracy. Offers per-agent skills, intensity modes, memory-compression and middleware to lower token cost and extend usable context.
AI Model2026
Provides a compact GGUF export of a tuned Gemma‑4 26B variant for local inference, optimized for llama.cpp and Apple Silicon to deliver faster, less‑censored chat and coding outputs. Includes Q4_K_M quantization and a neutral embedded template for more reliable local deployments.
AI Coding2026
Turns a codebase into a live structural knowledge graph that coding agents can query in milliseconds. Bi-temporal, replay-aware indexing of symbols and relationships performed locally with zero LLM API calls; Rust-native, MCP-native integrations and fast incremental updates.
AI Model2026
A Mixture-of-Experts instruct-capable LLM (295B total, 21B active) designed for long-context reasoning, code/agent workflows and instruction-following; released by Tencent Hy Team with safetensors weights on Hugging Face.
AI Model2026
Multimodal agent model for long-horizon coding, image-text understanding, and autonomous task orchestration. Built as a 1T-parameter Mixture-of-Experts with 256K context and native int4 quantization — intended for coding-driven design, persistent background agents, and swarm-style sub-agent workflows.
AI Agent2026
Generates editorial-quality diagrams as self-contained HTML files with inline SVG across 27 visual types; includes brand onboarding, agent-skill integrations (Claude Code, Codex, Pi), draw.io/Mermaid import, and static-first output with optional accessible motion.
AI Model2026
A healed 64-layer 'frankenmerge' that stacks two Qwen3.5-derived finetunes into an ~18B GGUF model for multilingual text generation, reasoning, and reliable code/frontend output. Healed with a 1000-step QLoRA to reduce layer-boundary artifacts and targeted to run on 12–16 GB GPUs.
AI Dataset2026
Provides 34k execution-style agent trajectories (11,766 issues) for supervised fine-tuning of code-focused LLMs. Each instance includes multi-step interactions, tool-call records, and final unified diffs; generated with Qwen3-Coder and released under permissive licenses for commercial use.
AI Dataset2026
Provides 316,427 Go source-code samples in JSONL focused on concurrency and backend idioms, enabling fine-tuning and evaluation of code models for completion, summarization, and static-analysis tasks.