Related reading
Show HN: Share your AI Setup, Learn from others
A Show HN post announcing mysetup.ai, a community‑focused site where users can list the LLM tools, agents, and workflows they use. The author shares a few example profiles and invites others to add theirs, but provides no technical details, design rationale, or measurable insights.
Hacker News front pagemysetup.ai1 minHN244138GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills
GraphSkillEvo encodes LLM agent skills as directed graphs and applies population‑based evolution (mutation, crossover) to optimise them. Across five benchmarks it beats the SkillOpt baseline, gaining up to 4% accuracy on GPT‑5.4‑nano.
Hugging Face Daily Papersarxiv.org1 minpaperAssessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026
Paper evaluates a standard 3‑D nnU‑Net on the new BraTS‑GoAT benchmark, training on 1,351 cases with five‑fold cross‑validation and test‑time mirroring. It reports Dice scores of 0.78/0.83/0.89 (ET/TC/WT) and shows a ~0.07 drop on heterogeneous validation, with limited benefit from ensembling or mirroring and failure linked to small, fragmented tumors.
Hugging Face Daily Papersarxiv.org1 minpaperScaling Telco Autonomy: Leveraging GNNs with Distributed GraphFlow
Google Cloud’s blog introduces Distributed GraphFlow (DGF), an open‑source Python library for building and scaling Graph Neural Networks (GNNs) on a Spanner‑backed digital twin of telecom networks. The post outlines the three‑layer architecture (digital twin on Spanner Graph, ML layer with DGF, AI agents) and highlights DGF’s high‑level API (5‑line example) and low‑level primitives, but provides…
Google Cloud Bloggoogle.com3 minCan MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model
This paper introduces a novel evaluation framework to assess the physical world reasoning capabilities of omni-modal generative models like MiniMax-H3. It found that MiniMax-H3 achieved an overall success rate of 41.97% across 517 instances, with significant performance variations depending on the input modalities and reasoning tasks.
Hugging Face Daily Papersarxiv.org2 minpaperBreaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models
Latent Interface Training (LIT) first learns a goal‑conditioned action prior without visual input, then adds a pose‑supervised latent interface as the only visual conditioning path. Applied to several vision‑language‑action models, LIT cuts vision‑action shortcuts and lifts LIBERO‑Plus success by 3.9–10.7 points and real‑world task success by 13.3–16.7 points under distribution shifts.
Hugging Face Daily Papersarxiv.org1 minpaper
