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In this tutorial, we build a Meta-Agent that designs other agents automatically from a simple task description. We implement a system that analyzes the task, selects tools, chooses a memory architecture, configures a planner, and then instantiates a fully working agent runtime. We go beyond static agent templates and instead build a dynamic, self-configuring architecture […]
The race to build autonomous AI agents has hit a massive bottleneck: data. While frontier models like Claude Code and Codex CLI have demonstrated impressive proficiency in terminal environments, the training strategies and data mixtures behind them have remained closely guarded secrets. This lack of transparency has forced researchers and devs into a costly cycle
Meta has acquired Moltbook, the Reddit-esque simulated social network made up of AI agents that went viral a few weeks ago. The company will hire Moltbook creator Matt Schlicht and his business partner, Ben Parr, to work within Meta Superintelligence Labs. The terms of the deal have not been disclosed. As for what interested Meta
Meta acquires Moltbook, the AI agent social network Read More »
In finance, achieving operational automation by integrating agentic AI requires a data-centric foundation to drive real value. Financial infrastructure provider SEI has engaged IBM to modernise its internal operations via AI and automation. The joint initiative focuses on process redesign and targeted system updates to deliver consistent client experiences, building a modern and data-enabled foundation
Agentic AI in finance speeds up operational automation Read More »
In this tutorial, we build an advanced agent system that goes beyond simple response generation by integrating an internal critic and uncertainty estimation framework. We simulate multi-sample inference, evaluate candidate responses across accuracy, coherence, and safety dimensions, and quantify predictive uncertainty using entropy, variance, and consistency measures. We implement risk-sensitive selection strategies to balance confidence
The era of the ‘Copilot’ is officially getting an upgrade. While the tech world has spent the last two years getting comfortable with AI that suggests code or drafts emails, ByteDance team is moving the goalposts. They released DeerFlow 2.0, a newly open-sourced ‘SuperAgent’ framework that doesn’t just suggest work; it executes it. DeerFlow is
In the fast-moving world of agentic workflows, the most powerful AI model is still only as good as its documentation. Today, Andrew Ng and his team at DeepLearning.AI officially launched Context Hub, an open-source tool designed to bridge the gap between an agent’s static training data and the rapidly evolving reality of modern APIs. You
In the frantic arms race of ‘AI for code,’ we’ve moved past the era of the glorified autocomplete. Today, Anthropic is double-downing on a more ambitious vision: the AI agent that doesn’t just write your boilerplate, but actually understands why your Kubernetes cluster is screaming at 3:00 AM. With the recent launch of Claude Code
With all the change that’s happened in the past decade, a few key things remain the same across health care and life sciences. Clinical trials remain the engine behind every new therapy, care delivery systems determine whether patients receive timely and effective treatment and health care payers must steward finite […] The post Agentic AI
Andrej Karpathy released autoresearch, a minimalist Python tool designed to enable AI agents to autonomously conduct machine learning experiments. The project is a stripped-down version of the nanochat LLM training core, condensed into a single-file repository of approximately ~630 lines of code. It is optimized for execution on a single NVIDIA GPU. The Autonomous Iteration
