Browsing: build

Wix launches ChatGPT integration for website creation Users can build and manage sites via natural languageNew app offers enterprise-grade features by defaultTop website builder Wix has…

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a…

class EverMemAgentOS: def __init__( self, workdir: str = “/content/evermem_agent_os”, db_name: str = “evermem.sqlite”, embedding_model: str = “sentence-transformers/all-MiniLM-L6-v2”, gen_model: str = “google/flan-t5-small”, stm_max_turns: int = 10, ltm_topk:…

In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning…

Are you struggling to balance generative AI safety with accuracy, performance, and costs? Many organizations face this challenge when deploying generative AI applications to production. A…

In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory.…

Customer service teams face a persistent challenge. Existing chat-based assistants frustrate users with rigid responses, while direct large language model (LLM) implementations lack the structure needed…

INSTANCE_I = int(np.clip(INSTANCE_I, 0, len(X_test)-1)) x = X_test.iloc[INSTANCE_I].values y_true = float(y_test.iloc[INSTANCE_I]) pred = float(model.predict([x])[0]) iv = explainer.explain(x, budget=int(BUDGET_LOCAL), random_state=0) baseline = float(getattr(iv, “baseline_value”, 0.0)) main_effects =…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…

def create_marker_cluster_map(): “””Create a map with marker clustering for large datasets””” np.random.seed(123) n_locations = 5000 lats = np.random.uniform(25, 49, n_locations) lons = np.random.uniform(-125, -65, n_locations) values…