Discover the Best AI Resources
Curated essentials, no noise — just what matters
AI Dataset2026
Curates ~1.1M instruction–response examples for 'vibe coding' scenarios where developers prompt LLMs to produce implementation plans, architecture choices, and deployment steps. Covers conversation memory, prompt templates, model routing, streaming responses, and scaling considerations; Apache-2.0.
AI Dataset2026
A JSON-format text dataset of 'vibe-coding' prompt–response examples sized in the 1M–10M category. Packaged for Hugging Face Datasets with pandas/polars-ready structure; useful for fine-tuning or evaluation but lacks an explicit license and detailed provenance.
AI Dataset2026
Provides 4,659 agentic single-turn SFT training pairs extracted from Claude Fable‑5, formatted as a single-column parquet for Qwen-style fine-tuning. Includes explicit chain-of-thought (<think>) blocks, XML-serialized <tool_use> calls, PII redaction, and AGPL-3.0 licensing.
AI Model2026
Provides an open-weight native multimodal agent that understands text and images within a 1,048,576-token context window for long-horizon coding, visual reasoning, and tool-driven workflows. Uses a 2.8T-parameter Mixture-of-Experts architecture (KDA + AttnRes) with MXFP4 quantization; best suited for research and large-scale inference setups.
AI Dataset2026
A collection of 953 JSON-formatted Fable 5 interaction traces (includes chain-of-thought entries), published on Hugging Face under AGPL-3.0 — meant for fine-tuning or analyzing LLM behavior but subject to license and provenance constraints.
AI Model2026
Open-weights agentic coding model that layers Claude Fable‑5 tool‑use SFT onto a reasoning‑distilled Qwen3.6 base; emits <tool_use> XML for file edits, shell commands and reads when prompted as an agent. Designed for agentic coding workflows; AGPL‑3.0 licensed.
AI Agent2026
Provides a lightweight repository-exploration subagent for LLM coding agents: invoked on demand to run parallel read-only READ/GLOB/GREP calls and return compact file-path plus line-range citations so the main solver gets focused evidence instead of noisy reads.
Learns, maintains, and runs unified world models for Physical AI using a cross-embodiment pretraining curriculum and a hybrid linear temporal-attention architecture. Emphasizes long-horizon state persistence, theoretical bounds on error accumulation, and deployment-aware low-latency inference for real-world embodied agents.
Frames AI research as a trainable practice of reading, building, debugging, and fast feedback. The essay is most useful for researchers learning how to avoid hype-chasing, benchmark tunnel vision, and agent-induced blind spots.
AI Video Papers2026
Controllable long-horizon text/image-to-video generation that supports camera navigation, revisits, and promptable events across photorealistic and stylized domains. Introduces camera-aware positional encoding (E-PRoPE), memory-conditioned scene persistence, causal-forcing distillation, and RL alignment to retain camera control and reduce drift.
Language-conditioned robot policy that reuses a pretrained geometric foundation model and inserts a causal future predictor at an intermediate layer so the same backbone produces future 3D-aware features and action outputs, enabling geometry-aware temporal prediction with minimal architectural change.
AI Model2026
Fine-tuned full checkpoint of the Qwen3.6-27B base that produces structured, trace-style assistant outputs for code, technical reasoning, and instruction-following. Packaged for local GGUF conversion and local inference; not a LoRA adapter and not validated for production use.