Discover the Best AI Resources
Curated essentials, no noise — just what matters
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.
AI Dataset2026
Provides 7,823 single-turn reasoning conversations generated by Anthropic's Claude Opus 4.7 and reformatted into Qwen-style chat templates for supervised fine-tuning (SFT). Includes explicit <think> chain-of-thought blocks and many long reasoning chains (avg ~4k tokens).
AI Model2026
Fine-tuned Qwen3.6-35B-A3B MoE that reproduces Claude Opus 4.7-style chain-of-thought with explicit <think>…</think> blocks. Offers sparse activation (256 experts, ~3B active params), 64k context, and GGUF builds for local inference; best for long, multi-step reasoning but may emit very long reasoning traces.
AI Model2026
An ~18B frankenmerge text-generation model that stacks two 32-layer Qwen3.5-based finetunes and ships as a 9.2GB Q4_K_M GGUF for efficient local inference. A 1000-step QLoRA heal reduces layer-boundary code corruption and targets coding, reasoning, multilingual chat, and 12–16GB GPU compatibility.
AI Dataset2026
Contains 8,124 reasoning conversations (extended-thinking + final responses) generated by Anthropic Claude Opus 4.7 for distillation into open-source LLMs. Each row stores the prompt, thinking trace, final answer and usage metadata; packaged under Apache‑2.0.
AI Agent2026
Runs goal-driven penetration tests by orchestration of an LLM agent and an MCP toolchain to perform reconnaissance, vulnerability discovery, exploitation, and structured PoC/report generation; supports multiple LLM providers and local MCP integrations; for authorized security testing only.
AI Dataset2026
Synthetic JSON dataset of model-generated prompts and step-by-step reasoning traces (≈90k rows, ~75M tokens) created with Claude Sonnet 4.6 and cross-checked by Gemini 3.1 Pro — intended for training or fine-tuning LLMs on natural reasoning, multi-domain code/math, and instruction following. Hosted on Hugging Face, MIT license.
AI Model2026
Generates English text matching pre-1931 style — a 13B language model trained on ~260B tokens of pre-1931 English, useful for historical-language generation and stylistic research. An instruction-tuned variant exists for interactive tasks.
AI Dataset2026
Provides 2,405 chain-of-thought reasoning traces generated by Claude Opus 4.7 for hard math, science, and formal problems. Each record pairs a problem with the model's full <think> working and a polished answer; available as parquet splits for non-commercial research under Anthropic's usage policy.
AI Model2026
Provides a GGUF-packaged, native-INT4 quantized build of the multimodal Kimi K2.6 model for image-text-to-text inference — packaged for local/self-hosted inference engines (vLLM, SGLang, KTransformers) to reduce footprint while keeping multimodal capabilities.
AI Dataset2026
Synthetic Korean-language persona dataset for training and evaluating conversational and generative models — 1M records (≈7M persona entries) with 26 fields aligned to South Korea’s demographic distributions. Built with NeMo Data Designer and released under CC BY 4.0.
AI Dataset2026
A 1.4M image–text style dataset for text-to-image generation and style transfer, produced by mapping 170K curated style prompts to 400K content prompts via Qwen-Image to yield strong intra-style consistency. Designed for training and evaluating style-aware generative models; license: other.