dify
Summary
A very popular production-ready platform for building agentic workflows and applications, with UI, orchestration, and deployment all in one. Given its star growth, it’s becoming a de facto choice for many teams moving beyond simple RAG bots. ([github.com](https://github.com/trending?since=daily))
Related Content
stable-diffusion-webui
stable-diffusion-webui by AUTOMATIC1111 is the de facto standard local web interface for Stable Diffusion, providing a massive feature set—txt2img, img2img, inpainting/outpainting, upscaling, LoRA/embeddings support, training utilities, and a huge extension ecosystem—on top of consumer GPUs. If you’re doing any kind of image generation or fine-tuning with Stable Diffusion in a local or lab environment, this is usually the first tool people reach for and the one most community workflows target. ([github.com](https://github.com/AUTOMATIC1111/stable-diffusion-webui?utm_source=openai))
OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification
OPV (Outcome-based Process Verifier) is a verifier model that inspects the rationale steps of long chains-of-thought via summarized outcomes, combining the strengths of outcome-based and process-based verification. Trained with an active learning loop, rejection fine-tuning, and RLVR, OPV reaches strong F1 on OPV-Bench and outperforms much larger models like Qwen3-Max-Preview at detecting reasoning errors.
Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving
This work presents a long-horizon reasoning agent for Olympiad-level math that uses an Outcome-based Process Verifier (OPV) to supervise and clean up very long chains-of-thought. By summarizing and checking reasoning segments rather than only final answers, and training OPV via iterative active learning and RLVR, the system achieves new SOTA on a held-out benchmark while reducing annotation cost.
Memory in the Age of AI Agents
A substantial survey that systematizes the fast-growing literature on ‘agent memory’—how agentic LLM systems store, retrieve, and evolve information over time. It proposes a taxonomy across forms (token, parametric, latent), functions (factual, experiential, working) and dynamics, and catalogs existing benchmarks and frameworks. If you’re building agent systems with nontrivial memory, this is quickly becoming the reference map of the territory.