# Introduction Everyone is building agents. Far fewer people can explain, precisely, why their agent loops forever, ignores a tool it was given, or confidently reports success on a task it never finished. The gap between shipping an agent and understanding one is where these five resources live, and every one of them is completely…
Think of a professional athlete. What separates elite performers is what happens between games: continuous refinement, adjusting to new opponents and sharpening skills based on what the last game exposed. Agentic AI works the same way. A model is no longer asked for an answer. It’s given a goal and has to keep adapting as…
If you’ve shipped an LLM-powered feature beyond a simple chat interface, you’ve already built parts of an agent harness. The retry logic around a failed tool call, the code assembling a system prompt at runtime — these are harness components. Most teams build these pieces incrementally as problems surface in production, which means the agent…
Sand, loose gravel, wet grass, and steep slopes can quickly expose the limits of a humanoid robot’s balance. Georgia Institute of Technology researchers developed a machine learning framework called “Learn to Teach” that trained a bipedal robot to navigate varied outdoor terrain and slippery indoor surfaces using one controller. Presented at the IEEE International Conference…
You can catch tube and pipe defects automatically: train Roboflow’s RF-DETR on labeled pipe imagery, and it will flag holes, cracks, and ruptures. Connect it to a Roboflow Workflow and every image gets a PASS, REVIEW, or FAIL verdict, with low-confidence detections routed to a person. Corrosion is one of the most persistent threats to…
We’re excited to partner with Adaption to make Together Fine-Tuning available in Adaptive Data. Adaption is co-founded by Sara Hooker and Sudip Roy, both former leaders at Cohere and Google DeepMind veterans. Adaptive Data addresses the data challenges of modern model training by helping teams analyze dataset structure, adapt examples, evaluate quality, and export model-ready…
OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation assets, and real-world telemetry into a shared, physically accurate view of the world. Until now, building a USD implementation has typically required adapting a large existing codebase— even for teams that…
Dive Brief: Venture capital funding for digital health startups spiked in the first half of the year, but investment is continuing to concentrate in a smaller number of firms, according to research published Monday by Rock Health. U.S. digital health companies raised $7.4 billion across 244 deals in the first half of 2026, blowing past…
, I will give my first impressions of the newest OpenAI model, GPT-5.6. The model was released a few days ago, and I’ve gotten the chance to test it extensively since the release and compare it against other models such as GPT-5.5, Opus 4.8, and Fable 5. I’ll give my judgment on the model, share…
Kimi K3, and what we can still learn from the pelican benchmark 16th July 2026 Chinese AI lab Moonshot AI announced Kimi K3 this morning, describing it as their “most capable model to date, with 2.8 trillion parameters”. It’s currently available via their website and API, but an open weight release is promised “by July…