Genesis AI Releases Nyx, Quadrants, and Genesis World 1.0 Physics Platform for Scalable Robotics Foundation Model Evaluation

Genesis AI released Genesis World 1.0. The platform consists of four components: the Genesis World physics engine, Nyx (a real-time path-traced renderer), Quadrants (a Python-to-GPU compiler), and a simulation interface. It is designed to accelerate robotics foundation model development through simulation-based evaluation. Robotics model development has two bottlenecks: data and iteration speed. The field has…

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Microsoft under fire for threatening security researcher with criminal investigation

After a security researcher published a series of unpatched bugs in Microsoft products, along with code to exploit them, the company is now threatening to take legal action and call the cops on them. Microsoft’s veiled threat reignites a long-running argument over what responsibility, if any, security researchers have to disclose vulnerabilities affecting large and…

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After Nvidia’s B not-acqui-hire, AI chip startup Groq reportedly elevating 0M

After Nvidia’s $20B not-acqui-hire, AI chip startup Groq reportedly elevating $650M

Groq is seeking to elevate $650 million in new funding from present traders, sources inform Axios, because it leans into its inference neocloud enterprise that depends on its homegrown AI chip and techniques. In December, Groq struck a type of not-an-acquisition agreements with Nvidia for a reported $20 billion, which concerned the departure of some…

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StepFun Releases Step 3.7 Flash: A 198B MoE Vision-Language Model for Coding Agents and Search Workflows

StepFun today released Step 3.7 Flash, a multimodal Mixture-of-Experts model targeting agentic use cases. It adds native vision input and improved tool-use reliability over Step 3.5 Flash. What is Step 3.7 Flash? Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder (ViT)…

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How one can Use AgentTrove: Streaming 1.7M Agentic Traces and Constructing a Clear ShareGPT SFT Dataset in Python

How one can Use AgentTrove: Streaming 1.7M Agentic Traces and Constructing a Clear ShareGPT SFT Dataset in Python

def is_success(row): res = (row.get(“consequence”) or “”).decrease() if res in (“resolved”, “success”, “go”, “handed”, “appropriate”): return True rw = row.get(“reward”) attempt: return float(rw) >= 1.0 besides (TypeError, ValueError): return False out_path = “agenttrove_clean_sft.jsonl” stored, scanned, SCAN, KEEP = 0, 0, 1500, 200 print(f”n⏳ Scanning as much as {SCAN} rows, protecting as much as {KEEP} profitable…

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