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In this tutorial, we walk through an end-to-end implementation of an advanced machine learning pipeline using ZenML . We begin by setting up the environment and initializing a ZenML project, then define a custom materializer that enables seamless serialization and metadata extraction for a domain-specific dataset object. As we progress, we build a modular pipeline that performs data loading, preprocessing, and a fan-out hyperparameter search across multiple models. We evaluate each candidate, log rich metadata at every step, and use a fan-in strategy to select and promote the best-performing model. Throughout the process, we leverage ZenML’s model control plane, artifact tracking, and caching mechanisms to ensure full reproducibility, transparency, and efficiency. Copy Code Copied Use a different Browser import os, sys, subprocess, json, shutil from pathlib import Path def _sh(cmd, check=True): print(f"$ {' '.join(cmd)}") return subprocess.run(cmd, check=...

Web search and content retrieval have quietly become the most critical infrastructure decisions in AI agent development. An agent without reliable access to live web data is effectively operating on stale knowledge — a hard limitation for any production deployment handling research, lead enrichment, competitive intelligence, or real-time monitoring. In 2026, the ecosystem of search and fetch APIs has matured considerably, with purpose-built tools replacing the older pattern of wrapping raw Google SERP data and passing it directly into a language model. This article covers the leading search and fetch APIs based on evaluations across output format, agent-native design, token efficiency, free tier generosity, latency, and framework integrations.  TinyFish TinyFish is an important entrant in this space and among the most directly agent-native of the group. Its Search and Fetch endpoints are free with generous rate limits — one API key, no credit card. The free plan i...

A model can behave perfectly one moment and degrade the next—without any change to your data, pipeline, or logic. The root cause often lies in something far more subtle: how your input is tokenized. Before a model processes text, it converts it into token IDs, and even minor formatting differences—like spacing, line breaks, or punctuation—can produce entirely different token sequences. This phenomenon is known as tokenization drift: when small surface-level changes push your input into a different region of token space, leading to unpredictable shifts in model behavior. The impact goes deeper than just token IDs. During instruction tuning, models learn not only tasks but also the structure in which those tasks are presented—specific separators, prefixes, and formatting patterns. When your prompt deviates from these learned patterns, you are no longer operating within the model’s familiar distribution. The result isn’t confusion—it’s a model doing its best on inputs it was never op...

Mistral AI has been quietly building one of the more practical coding agent ecosystems in the open-source/weights AI space, and they are shipping its most significant infrastructure upgrade yet. Mistral team announced remote agents in Vibe , its coding agent platform, alongside the public preview of Mistral Medium 3.5 — a new 128B dense model that now serves as the default model in both Vibe and Le Chat, Mistral’s consumer assistant. What is Vibe, and Why Does It Matter? If you haven’t used it yet, Mistral Vibe is a coding agent accessible through a CLI (command-line interface) that lets an AI model work through software tasks on your behalf — writing code, refactoring modules, generating tests, investigating CI failures, and more. Think of it as a junior developer that never gets tired and can operate across your codebase. Until now, Vibe sessions ran locally, meaning the agent was tied to your laptop and your terminal. That changes today. Remote Agents: Th...

In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading and inspecting the dataset, examining its structure, categories, and conversational format to get a clear idea of the available information. We then build simple parsers to extract key components such as reasoning traces, tool calls, and tool responses, allowing us to separate internal thinking from external actions. Also, we analyze patterns such as tool usage frequency, conversation length, and error rates to better understand agent behavior. We also create visualizations to highlight these trends and make the analysis more intuitive. Finally, we prepare the dataset for training by converting it into a model-friendly format, making it suitable for tasks like supervised fine-tuning. Copy Code Copied Use a different Browser !pip -q install -U datasets pandas matplotlib seab...