In the field of AI-generated media, the industry is shifting from probabilistic pixel synthesis to models capable of structural reasoning. Luma Labs recently released Uni-1a basic image model designed to deal with ‘objective gapBy implementing a pre-production thinking phase, Uni-1 changes the workflow from ‘engineering’ to the next order. Architecture: … Read more
import asyncio import json import io import contextlib import re from dataclasses import dataclass from typing import Callable, Awaitable import nest_asyncio nest_asyncio.apply() TOOL_DEFINITIONS = [ { “name”: “execute_code”, “description”: “Execute Python code in the Colab kernel. Returns stdout, results, or errors. State persists between calls.” “parameters”: { “type”: “object”, “properties”: { “code”: {“type”: “string”, “description”: … Read more
Val Kilmer returns to the screen. But not quite. Not in retro montage. Not in a flashback that went long ago. No, I’m talking about the real deal. Well, kind of. In this case, you will be healed by AI. I wouldn’t blame you if you are both surprised and disturbed by this news. The … Read more
When you type a query into a search engine, something has to decide which documents are really important – and how to rank them. BM25 (Best Matching 25)the algorithm that powers search engines like Elasticsearch and Lucene, has been the leading answer to that question for decades. It scores by looking at three things: how … Read more
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
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
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
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 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
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