The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment
In an earlier piece on the 2026 data mandate, I the EU AI Act, the Cyber Resilience Act, and the Data Act are pushing organizations for structural mandates to transition from reactive compliance towards a systemic Governance-by-Design. However, translating this architectural intent into daily business operations introduces a practical bottleneck: once the governance controls are […]
Building a Multi-Tool Gemma 4 Agent with Error Recovery
In this article, you will learn how to transform a basic tool-calling script into a resilient agent that gracefully handles failures from misbehaving tools, malformed model outputs, and unavailable services. Topics we will cover include: How to structure an iterative agent loop with a safety cap on iteration count. The four distinct categories of failure […]
Autonomous AI systems test governance in physical environments
Autonomous AI systems are beginning to move beyond software environments and into warehouses, delivery networks, and public spaces. The development is drawing attention to whether current AI rules cover systems that operate in physical environments. Most existing AI governance frameworks have focused on online harms and model outputs, including bias, misinformation, and harmful content. Embodied […]
Meet OmniVoice Studio: A Local, Open-Source Alternative to ElevenLabs
OmniVoice Studio — How to Use It 01 / 08 What Is OmniVoice Studio? OmniVoice Studio is an open-source desktop application for voice cloning, video dubbing, real-time dictation, and speaker diarization. Everything runs locally on your machine. No API keys, no cloud account, no subscription required. 646 languages supported for TTS via the default OmniVoice […]
Design a Complete Multimodal RLVR Pipeline with Open-MM-RL, Vision-Language Prompting, Reward Scoring, and GRPO Export
EXTRACT_PATS = [ r”\\boxed\{([^{}]+)\}”, r”final\s+answer\s*[:=]\s*([^\n]+)”, r”answer\s*[:=]\s*([^\n]+)”, ] def extract_final(text): if not text: return “” for p in EXTRACT_PATS: m = re.search(p, text, flags=re.IGNORECASE) if m: return m.group(1).strip().strip(“.,;”) lines = [l.strip() for l in str(text).strip().splitlines() if l.strip()] return lines[-1] if lines else “” def latex_to_sympy(s): s = (s or “”).strip().strip(“$”).strip() s = re.sub(r”^\\[\[\(]”, “”, s); s […]
Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving
Long-context inference makes the KV cache one of the main costs of serving LLMs. During autoregressive decoding, the cache grows with context length, batch size, and model depth. At high batch sizes and long contexts with 100K tokens across dozens of concurrent requests the KV cache consumes a large fraction of GPU memory. Compressing it […]
Step by Step Guide to Build and Compare FedAvg and FedProx Federated Learning on Non-IID CIFAR-10 with NVIDIA FLARE
CLIENT_SCRIPT += r”’ def main(): p = argparse.ArgumentParser() p.add_argument(“–num_sites”, type=int, default=3) p.add_argument(“–alpha”, type=float, default=0.3) p.add_argument(“–local_epochs”, type=int, default=1) p.add_argument(“–mu”, type=float, default=0.0) p.add_argument(“–max_samples”, type=int, default=4000) p.add_argument(“–batch_size”, type=int, default=64) p.add_argument(“–lr”, type=float, default=0.01) p.add_argument(“–data_root”, type=str, default=”/tmp/nvflare/data”) p.add_argument(“–results_dir”, type=str, default=”/tmp/nvflare/results”) p.add_argument(“–tag”, type=str, default=”fedavg”) args = p.parse_args() device = “cuda” if torch.cuda.is_available() else “cpu” tf = T.Compose([T.ToTensor(), T.Normalize((0.5, 0.5, 0.5), (0.5, […]
I Built My First ETL Pipeline as a Complete Beginner. Here’s How.
of my data engineering journey series. In part one, I shared my 12-month roadmap for transitioning from data analyst to data engineer. This is where the actual building begins. When I published my first article documenting my data engineering journey, something unexpected happened. People resonated with it. I had strangers reaching out saying they were […]
Can AI Write Your Code? | Towards Data Science
is no longer whether AI can write code, but whether we can trust the code it writes? Over the past few years, ChatGPT and other large language models have become increasingly common in the daily workflow of students, analysts, researchers, and data scientists. Many of us have already used AI tools to generate a Python […]
From TF-IDF to Transformers: Implementing Four Generations of Semantic Search
“Beauty will save the world”— Fyodor Dostoevsky A. Introduction did not emerge overnight. Today’s transformer-based systems can feel almost magical, capable of capturing context and even subtle relationships between ideas. But the origin of today’s semantic search systems is actually gradual. Before embeddings, transformers, and large language models, researchers used keyword matching, TF–IDF vectors, and […]