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News
Hugging Face Unveils Microduck: A $399 Open-Source 25 cm Biped You Train with Reinforcement Learning
2+ week, 1+ day ago (321+ words) Microduck is 25 cm tall, 14 cm wide, and under 800 g. It carries 15 motors across legs, neck, and head, plus an articulated beak that picks objects off the floor. Compute is a Rockchip RK3566 with an AI accelerator, 1 GB of RAM, and 32 GB of…...
Hugging Face Releases ml-intern: An Open-Source AI Agent that Automates the LLM Post-Training Workflow
4+ mon, 3+ week ago (883+ words) Hugging Face has released ml-intern, an open-source AI agent designed to automate end-to-end post-training workflows for large language models (LLMs). Built on the company’s smolagents framework, the tool can autonomously perform literature review, dataset discovery, training script execution, and iterative…...
Understanding the Layers of AI Observability in the Age of LLMs
8+ mon, 1+ hour ago (639+ words) Let us now understand the different layers of AI observability with the help of an example. This is why traces and spans are important. A trace represents the complete lifecycle of a single resume submission—from the moment the file…...
How to Build Supervised AI Models When You Don’t Have Annotated Data
10+ mon, 1+ week ago (356+ words) One of the biggest challenges in real-world machine learning is that supervised models require labeled data—yet in many practical scenarios, the data you start with is almost always unlabeled. Manually annotating thousands of samples isn’t just slow; it’s expensive,…...
A New Agency-Focused Supervision Approach Scales Software AI Agents With Only 78 Examples
11+ mon, 6+ day ago (832+ words) Do curated, tool-grounded demonstrations build stronger software agents than broad piles of generic instruction data? A team of researchers from Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) proposes LIMI (“Less Is More for Agency”), a supervised…...
Google AI Proposes ReasoningBank: A Strategy-Level I Agent Memory Framework that Makes LLM Agents Self-Evolve at Test Time
11+ mon, 1+ week ago (838+ words) How do you make an LLM agent actually learn from its own runs—successes and failures—without retraining? Google Research proposes ReasoningBank, an AI agent memory framework that converts an agent’s own interaction traces—both successes and failures—into reusable,…...
How to Build a Robust Advanced Neural AI Agent with Stable Training, Adaptive Learning, and Intelligent Decision-Making?
11+ mon, 4+ week ago (997+ words) In this tutorial, we explore the design and implementation of an Advanced Neural Agent that combines classical neural network techniques with modern stability improvements. We build the network using Xavier initialization for balanced gradient flow and add stable activations like…...