Qdrant TurboQuant Explained: Is TurboQuant the Silver Bullet?
as a tradeoff between memory and recall. The standard is Float32 with high fidelity and high memory cost. The basic solution is scalar quantization, which reduces each value to fewer bits (around 4× compression) with a slight recall loss. Although binary quantization pushes much harder, often reaching 32× compression, the retrieval result might become inconsistent […]
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 […]
Hermes Agent Ships Tool Search for MCP: Anthropic Evals Show 49% to 74% Accuracy Gain on Opus 4
Nous Research’s open-source Hermes Agent now ships a Tool Search feature. It directly addresses a growing bottleneck in AI agent systems: too many MCP tools filling up the context window. In this explainer article, we will breaks down what Tool Search does, how it works, and when to use it. The Problem: MCP Tools Are […]
Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient
“”“ Continuous batching = iteration-level scheduling + ragged (packed) batching. Two approaches are compared (both run BATCH_SIZE sequences concurrently, so the comparison is slot-for-slot fair): 1. Static batching (baseline): Prompts are processed BATCH_SIZE at a time. Each wave is padded to a common length and run together until the LONGEST request in […]
How to Use AgentTrove: Streaming 1.7M Agentic Traces and Building a Clean ShareGPT SFT Dataset in Python
def is_success(row): res = (row.get(“result”) or “”).lower() if res in (“resolved”, “success”, “pass”, “passed”, “correct”): return True rw = row.get(“reward”) try: return float(rw) >= 1.0 except (TypeError, ValueError): return False out_path = “agenttrove_clean_sft.jsonl” kept, scanned, SCAN, KEEP = 0, 0, 1500, 200 print(f”\n⏳ Scanning up to {SCAN} rows, keeping up to {KEEP} successful traces…”) with […]
NVIDIA Introduces X-Token: Projection-Guided Cross-Tokenizer KD That Outperforms GOLD by +3.82 Average Points on Llama-3.2-1B
Knowledge distillation (KD) transfers “dark knowledge” from a large teacher model to a smaller student. The student learns from the teacher’s full output probability distribution over tokens, not just correct answers. This is done via per-position Kullback–Leibler (KL) divergence over next-token probability distributions. This formulation requires a shared tokenizer. A practitioner committed to Llama-3.2-1B cannot […]
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) […]
Baseline Enterprise RAG, From PDF to Highlighted Answer
fastest way to understand what RAG is is to build the smallest version that actually works, run it on a real document, and look closely at what just happened. That’s this article. About a hundred lines of Python (no vector database, no framework, no agents) running on the Attention Is All You Need paper (Vaswani […]
RAG Is Burning Money — I Built a Cost Control Layer to Fix It
TL;DR a full working implementation in pure Python, along with benchmark results from a local setup. RAG systems do not fail only on quality. They can also become inefficient in terms of cost, often in ways that are not immediately visible. Every extra retrieved token has a cost. In my system, context over-fetching ranged from […]
OpenAI governance frameworks secure enterprise AI deployments
OpenAI’s latest governance frameworks offer enterprise leaders a structured blueprint for scaling safe and compliant AI deployments globally. The adoption of large language models has steadily progressed towards requiring sustainable, commercial-grade architecture. OpenAI has released its Frontier Governance Framework (FGF), documenting how the organisation addresses systemic risk assessment and mitigation. The framework maps directly to […]