Criteo is betting that ChatGPT-style agents will become a major source of product discovery. Through experiments with LLMs, it wants to use its commerce data infrastructure to power recommendations that sit behind them. The company, historically associated with ad retargeting, is attempting to reposition itself for an AI-driven commerce era, and its latest demos suggest the company now sees large language models - not just retailers or demand-side platforms - as the next major distribution channel for advertising.
We may want the model to query a database, call an external API, or perform calculations. Function calling (also known as tool calling) allows you to define functions (with schemas) that the model can call as part of its response. For us, this seems like a first step towards enabling more complex interactions with LLMs, where they can not only generate text but also perform actions based on that text.
The MarkItDown library lets you quickly turn PDFs, Office files, images, HTML, audio, and URLs into LLM-ready Markdown. In this tutorial, you'll compare MarkItDown with Pandoc, run it from the command line, use it in Python code, and integrate conversions into AI-powered workflows. By the end of this tutorial, you'll understand that: You can install MarkItDown with pip using the specifier to pull in optional dependencies.
While AI Answers give our users quick summaries, Ask Brave provides longer answers, follow-ups, and a chat mode enhanced with Deep Research, and most importantly, contextually relevant enrichments such as videos, news articles, products, businesses, shopping, and more - in the right place, at the right time," said Chief of Search at Brave, Josep M. Pujol, in a statement. "Search makes it possible, LLMs glue it together. We anticipate that Ask Brave will generate millions more daily AI-powered answers with this powerful combination of search and chat, and look forward to deploying more useful AI-powered search tools for our users.
The latest release of the Agent Development Kit for Java, version 0.2.0, marks a significant expansion of its capabilities through the integration with the LangChain4j LLM framework, which opens it up to all the large language models supported by the framework. Before integrating LangChain4j, ADK for Java only supported two models, Google Gemini and Anthropic Claude. This contrasted with the Python ADK, which offered broader support via via LiteLLM.
Traditional pipelines fall short when building agents that must respond to dynamic, evolving contexts. Gravity tackles this by embracing event-driven architecture and modular orchestration. Agents are modeled as independent services that react to discrete events, allowing the system to flexibly coordinate multiple actors across different stages of a task. Technologies like Temporal, pub/sub messaging, or custom orchestrators can be used to handle event sequencing and retries. The key is decoupling logic.
