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

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

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

Coding for Design and Analysis of Crystal Structures Using Pymatgen for Symmetry Analysis, Phase Diagrams, Surface Generation, and Synthesis of Materials Project

Coding for Design and Analysis of Crystal Structures Using Pymatgen for Symmetry Analysis, Phase Diagrams, Surface Generation, and Synthesis of Materials Project

header(“11. DISORDERED STRUCTURE -> ORDERED APPROXIMATION”) disordered = Structure( Lattice.cubic(3.6), [{“Cu”: 0.5, “Au”: 0.5}], [[0, 0, 0]], ) disordered.make_supercell([2, 2, 2]) print(“Disordered composition:”, disordered.composition) try: disordered_oxi = disordered.copy() disordered_oxi.add_oxidation_state_by_element({“Cu”: 1, “Au”: 1}) ordered_transform = OrderDisorderedStructureTransformation() ordered_candidates = ordered_transform.apply_transformation( disordered_oxi, return_ranked_list=3, ) for idx, cand in enumerate(ordered_candidates): s = cand[“structure”].copy() s.remove_oxidation_states() print(f”Ordered candidate {idx+1}: formula={s.composition.formula}, sites={len(s)}”) … Read more

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

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

Deploying a new machine learning model to production is one of the most important phases of the ML lifecycle. Even if the model works well in validating and testing data sets, directly replacing an existing production model can be risky. Offline experiments rarely capture the full complexity of a real-world environment—data distributions can change, user … Read more

Coding Implementation to Build an Uncertainty-Based LLM System with Self-Assessment, Self-Assessment, and Automated Web Research

Coding Implementation to Build an Uncertainty-Based LLM System with Self-Assessment, Self-Assessment, and Automated Web Research

In this tutorial, we build a large uncertainty-aware linguistic modeling system that not only generates answers but also estimates confidence in those answers. We use a three-stage logic pipeline where the model first generates feedback and a self-reported confidence and correction score. We then introduce a self-evaluation step that allows the model to self-criticize and … Read more

NVIDIA Releases Nemotron-Cascade 2: Open 30B MoE with 3B Operating Parameters, Brings Better Thinking and Powerful Agentic Capabilities

NVIDIA Releases Nemotron-Cascade 2: Open 30B MoE with 3B Operating Parameters, Brings Better Thinking and Powerful Agentic Capabilities

NVIDIA has announced the release of Nemotron-Cascade 2open weight 30B Mixed-Expert (MoE) model with 3B activated parameters. The model focuses on increasing ‘intellectual density,’ bringing advanced imaging capabilities to a fraction of the parameter scales used by frontier models. Nemotron-Cascade 2 is the second LLM with open access Gold Medal level performance in the 2025 … Read more

A Coding Implementation Demonstration of ClawTeam’s Multi-Agent Swarm Orchestration with OpenAI Function Calling

A Coding Implementation Demonstration of ClawTeam’s Multi-Agent Swarm Orchestration with OpenAI Function Calling

SWARM_TOOLS = [ { “type”: “function”, “function”: { “name”: “task_update”, “description”: “Update the status of a task. Use ‘in_progress’ when starting, ‘completed’ when done.”, “parameters”: { “type”: “object”, “properties”: { “task_id”: {“type”: “string”, “description”: “The task ID”}, “status”: {“type”: “string”, “enum”: [“in_progress”, “completed”, “failed”]}, “result”: {“type”: “string”, “description”: “Result or output of the task”}, }, … Read more

LlamaIndex Releases LiteParse: A CLI and TypeScript-Native Library for Spatial PDF Parsing in AI Agent Workflows

LlamaIndex Releases LiteParse: A CLI and TypeScript-Native Library for Spatial PDF Parsing in AI Agent Workflows

In the current state of Retrieval-Augmented Generation (RAG), the main bottleneck for developers is no longer the large language model (LLM) itself, but the data import pipeline. For software developers, converting complex PDFs into a format that LLM can consult is often a high-latency, often expensive task. LlamaIndex has just been launched LiteParseis an open-source, … Read more

Feature Set and Subscription Price

Feature Set and Subscription Price

Genora AI is designed to allow unrestricted speech while maintaining clarity and ease of use. It is designed for those who prefer an AI system that interacts dynamically and adjusts its behavior based on a conversational approach rather than remaining static. ⚡️ FAVORITE CONVERSATIONS ⚡️ Candy AI Try Candy AI Unfiltered Chat with AI GirlsPictures … Read more

Google Colab Now Has an Open-Source MCP (Model Context Protocol) Server: Use Colab Runtimes and GPUs from any local AI agent

Google Colab Now Has an Open-Source MCP (Model Context Protocol) Server: Use Colab Runtimes and GPUs from any local AI agent

Google has officially released the Colab MCP Serverthe implementation of the Model Context Protocol (MCP) that allows AI agents to interact directly with the Google Colab environment. This integration goes beyond simple code generation by giving agents structured access to create, modify, and execute Python code within cloud-managed Jupyter notebooks. This represents a transition from … Read more