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Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow […]
3 Questions: The pros and cons of synthetic data in AI Read More »
How do you get GPT-5-level reasoning on real long-context, tool-using workloads without paying the quadratic attention and GPU cost that usually makes those systems impractical? DeepSeek research introduces DeepSeek-V3.2 and DeepSeek-V3.2-Speciale. They are reasoning-first models built for agents and targets high quality reasoning, long context and agent workflows, with open weights and production APIs. The
Google CEO Sundar Pichai says we’re just a decade away from a new normal of extraterrestrial data centers Fortune
The nine people trying to stop AI from ruining the world The Verge
The nine people trying to stop AI from ruining the world – The Verge Read More »
The AI coding landscape just got a massive shake-up. If you’ve been relying on Claude 3.5 Sonnet or GPT-4o for your dev workflows, you know the pain: great performance often comes with a bill that makes your wallet weep, or latency that breaks your flow.This article provides a technical overview of MiniMax-M2, focusing on its
MiniMax-M2: Technical Deep Dive into Interleaved Thinking for Agentic Coding Workflows Read More »
In this tutorial, we build an advanced multi-page interactive dashboard using Panel. Through each component of implementation, we explore how to generate synthetic data, apply rich filters, visualize dynamic time-series trends, compare segments and regions, and even simulate live KPI updates. We design the system step by step so we can truly understand how each
How do you keep synthetic data fresh and diverse for modern AI models without turning a single orchestration pipeline into the bottleneck? Meta AI researchers introduce Matrix, a decentralized framework where both control and data flow are serialized into messages that move through distributed queues. As LLM training increasingly relies on synthetic conversations, tool traces
Why do current audio AI models often perform worse when they generate longer reasoning instead of grounding their decisions in the actual sound. StepFun research team releases Step-Audio-R1, a new audio LLM designed for test time compute scaling, address this failure mode by showing that the accuracy drop with chain of thought is not an
How can an AI system learn to pick the right model or tool for each step of a task instead of always relying on one large model for everything? NVIDIA researchers release ToolOrchestra, a novel method for training a small language model to act as the orchestrator- the ‘brain’ of a heterogeneous tool-use agent https://arxiv.org/pdf/2511.21689
In this tutorial, we build an advanced Agentic AI using the control-plane design pattern, and we walk through each component step by step as we implement it. We treat the control plane as the central orchestrator that coordinates tools, manages safety rules, and structures the reasoning loop. Also, we set up a miniature retrieval system,
