The post shows that RAG hallucinations stem from four upstream "bricks", parsing, question parsing, retrieval, and generation, each handing the model the wrong context. By tightening contracts at every stage, the author eliminates confident but incorrect answers on real NIST and World Bank documents. The attached notebook lets you reproduce the fixes yourself.
NVIDIA's open‑source SkillSpector correctly flags a deliberately malicious AI agent skill, but it also drowns a perfectly benign automation in dozens of false positives. The article shows why the scanner’s single score is unreliable and argues that human audit remains essential before shipping third‑party skills.
Simulation lets developers generate photorealistic, physics‑grounded data at scale, bypassing slow, risky real‑world collection. NVIDIA’s overview maps a three‑computer pipeline, training cluster, GPU‑accelerated simulator, and edge robot, and surveys the major engines targeting humanoids, drones, and manipulators. The result is a data‑first workflow that powers foundation‑model training and rapid policy testing.
Running Claude coding agents nonstop can cut human review time dramatically. The article shows how to give agents full permissions, let them self‑verify code, and host them remotely so they keep working for over a day without human interruption.
A step‑by‑step LoRA fine‑tuning of the 7‑b OpenVLA model runs on free‑tier Colab in just 100 steps. The notebook verifies dataset loading, training metrics, and logs results to Weights & Biases, proving that robot vision‑language‑action models can be adapted without expensive hardware.
Grabette is an open-source, low-cost kit that records hand-held demonstrations using dual cameras and reconstructs 6-DoF trajectories, turning everyday motions into robot-ready datasets. By removing the need for a robot or teleoperation rig, it aims to democratize large-scale manipulation data collection.
You’re caught up.
That was all 6 stories in the 22 July issue.