Browsing: Coding

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.…

best_C = best[“params”][“C”] best_solver = best[“params”][“solver”] final_pipe = Pipeline([ (“scaler”, StandardScaler()), (“clf”, LogisticRegression( C=best_C, solver=best_solver, penalty=”l2″, max_iter=2000, random_state=42 )) ]) with mlflow.start_run(run_name=”final_model_run”) as final_run: final_pipe.fit(X_train, y_train)…

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”:…

is one of the earliest and most iconic games in the history of digital entertainment. In its classic form, the game simulates a table tennis match…

In the high-stakes world of AI, ‘Context Engineering’ has emerged as the latest frontier for squeezing performance out of LLMs. Industry leaders have touted AGENTS.md (and…

In this tutorial, we implement an end-to-end Practical Byzantine Fault Tolerance (PBFT) simulator using asyncio. We model a realistic distributed network with asynchronous message passing, configurable…

def normalize_ws(s: str) -> str: return re.sub(r”\s+”, ” “, s).strip() RAW_DOCS = [ { “doc_id”: “trulens_core”, “title”: “TruLens core idea”, “text”: “TruLens is used to track…

this past month, a social network run entirely by AI agents was the most fascinating experiment on the internet. In case you haven’t heard of it,…

In this tutorial, we design a practical image-generation workflow using the Diffusers library. We start by stabilizing the environment, then generate high-quality images from text prompts…

In this tutorial, we build a production-ready agentic workflow that prioritizes reliability over best-effort generation by enforcing strict, typed outputs at every step. We use PydanticAI…