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A Coding Implementation to Design Self-Evolving Skill Engine with OpenSpace for Skill Learning, Token Efficiency, and Collective Intelligence

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we explore OpenSpace, a self-evolving skill engine developed by HKUDS that makes AI agents smarter, more cost-efficient, and capable of learning from every task they perform. We walk through the complete lifecycle of OpenSpace: from installing and configuring an OpenAI model, to executing cold-start tasks where no prior skills exist, watching the […]

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This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B

ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Staff, Tech News, Technology

Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn to reason using a remarkably small number of trained parameters. The research team introduces TinyLoRA, a parameterization that can scale down to a single trainable parameter under extreme sharing settings. Using this method on a

This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B Read More »

Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Staff, Technology

World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, training these models directly from pixel data often leads to ‘representation collapse,’ where the model produces redundant embeddings to trivially satisfy prediction objectives. Current approaches attempt to prevent this by relying on complex heuristics: they

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Meta AI’s New Hyperagents Don’t Just Solve Tasks—They Rewrite the Rules of How They Learn

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, New Releases, Software engineering, Staff, Tech News, Technology

The dream of recursive self-improvement in AI—where a system doesn’t just get better at a task, but gets better at learning—has long been the ‘holy grail’ of the field. While theoretical models like the Gödel Machine have existed for decades, they remained largely impractical in real-world settings. That changed with the Darwin Gödel Machine (DGM),

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Luma Labs Launches Uni-1: The Autoregressive Transformer Model that Reasons through Intentions Before Generating Images

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Large Language Model, New Releases, Staff, Technology, Text to Image

In the field of generative AI media, the industry is transitioning from purely probabilistic pixel synthesis toward models capable of structural reasoning. Luma Labs has just released Uni-1, a foundational image model designed to address the ‘intent gap” inherent in standard diffusion pipelines. By implementing a reasoning phase prior to generation, Uni-1 shifts the workflow

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How BM25 and RAG Retrieve Information Differently?

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, RAG, Staff, Technology, Tutorials

When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for decades.  It scores documents by looking at three things:

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Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Reinforcement Learning, Staff, Technology, Tutorials

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL framework,

Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent Read More »

Meet GitAgent: The Docker for AI Agents that is Finally Solving the Fragmentation between LangChain, AutoGen, and Claude Code

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, New Releases, Open Source, Staff, Tech News, Technology, Uncategorized

The current state of AI agent development is characterized by significant architectural fragmentation. Software devs building autonomous systems must generally commit to one of several competing ecosystems: LangChain, AutoGen, CrewAI, OpenAI Assistants, or the more recent Claude Code. Each of these ‘Five Frameworks’ utilizes a proprietary method for defining agent logic, memory persistence, and tool

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A Coding Implementation for Building and Analyzing Crystal Structures Using Pymatgen for Symmetry Analysis, Phase Diagrams, Surface Generation, and Materials Project Integration

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, Quantum Machine Learning, Staff, Technology, Tutorials

In this tutorial, we explore the capabilities of the pymatgen library for computational materials science using Python. We begin by constructing crystal structures such as silicon, sodium chloride, and a LiFePO₄-like material, and then investigate their lattice properties, densities, and compositions. Also, we analyze symmetry using space-group detection, examine atomic coordination environments, and apply oxidation-state

A Coding Implementation for Building and Analyzing Crystal Structures Using Pymatgen for Symmetry Analysis, Phase Diagrams, Surface Generation, and Materials Project Integration Read More »

Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine Learning, Staff, Tech News, Technology, Tutorials

Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validation and test datasets, directly replacing the existing production model can be risky. Offline evaluation rarely captures the full complexity of real-world environments—data distributions may shift, user behavior can

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