We introduce AgentSynth, a scalable and cost-efficient pipeline for automatically synthesizing high-quality tasks and trajectory datasets for generalist computer-use agents. Leveraging information asymmetry, AgentSynth constructs subtasks that are simple during generation but significantly more challenging when composed into long-horizon tasks, enabling the creation of over 6,000 diverse and realistic tasks. Our pipeline begins with an LLM-based task proposer guided by a persona, followed by an execution agent that completes the task and logs the trajectory. This process is repeated iteratively to form a sequence of subtasks, which are then summarized by a separate agent into a composite task of controllable difficulty. A key strength of AgentSynth is its ability to precisely modulate task complexity by varying the number of subtasks. Empirical evaluations show that state-of-the-art LLM agents suffer a steep performance drop, from 18% success at difficulty level 1 to just 4% at level 6, highlighting the benchmark's difficulty and discriminative power. Moreover, our pipeline achieves a low average cost of \$0.60 per trajectory, orders of magnitude cheaper than human annotations. Our code and data are publicly available at https://github.com/sunblaze-ucb/AgentSynth

While every data scientist has their own methods and approaches to conducting data science, there is one tool that nearly everyone in the field uses: Jupyter Notebook. Its ease of use makes it the perfect tool for prototyping, usually resulting in a script in which we preprocess the data, do a train/test split, train our model, and evaluate it. However, once we have a decent prototype, the subsequent iterations generally don't touch most of the code. Instead, we tend to focus on tweaking feature engineering parameters and tuning model hyperparameters. At this point, we really start experimenting, trying to answer questions such as "What happens if I increase the learning rate?" and "What's the optimal batch size?"

Since its beta release in 2017, DVC has become an essential tool for many data science teams. Its data versioning capabilities, reproducible pipelines, and experiment tracking features are at the core of our ecosystem of open MLOps tools. Today we are proud to launch a new product that extends how machine learning teams can use DVC: our extension for Visual Studio Code. With this extension, you get a full VS Code-native experimentation platform for your machine learning projects. Control your datasets and models, run experiments, view metrics, create plots, and much more.

These little ones, are extremelly funny, and they literally don't care about the cold . So, I decided to help them and build an Automatic Feeder using Azure IoT, a Wio Terminal and maybe some more devices. You can check the Azure IoT project here Azure IoT - Squirrel Feeder. Once the feeder was ready, I decided to add a new feature to the scenario, detecting when a squirrel is nearby the feeder. Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own image identifier models.

A developer's workflow is full of potential tangents, interruptions, and context switches. But the nature of programming makes accessing and maintaining uninterrupted flow states critical. However, there are many coding tools that can help developers do exactly that, working to protect their space for deep thinking and create more productive workflows. Here are the top 10 coding tools of 2022. Visual Studio Code is an integrated development environment made by Microsoft for Windows, Linux, and macOS.

Running your Python scripts is an important step in the development process, because it is in this manner that you'll get to find out if your code works as you intended it to. It is, also, often the case that we would need to pass information to the Python script for it to function. In this tutorial, you will discover various ways of running and passing information to a Python script. Running and Passing Information to a Python Script Photo by Andrea Leopardi, some rights reserved. The command-line interface is used extensively for running Python code.

Today we'll be talking about how to make an AI-powered chatbot using Rasa and Python. It doesn't matter if you have deep knowledge of python or are just a beginner in the world of coding! This article mainly focuses on the AI framework, Rasa, and a little bit of python. Before getting started, let me tell you the required software to be installed for the project. I am assuming that you already have Python 3.8 installed in your PC since Python 3.9 version doesn't work with rasa, it has some issue so I'll suggest you download version 3.8 if you don't have it. You can download the following two softwares from the link provided below (if you don't already have them on your PC, or you can continue with the article if you do).

Here is a list of 50 FREE resources for developers. Learn to use key GitHub features, including issues, notifications, branches, commits, and pull requests. Git is the leading version control tool, and it's essential for developers. Learn how to use Git to track your own changes and collaborate with others. Learn how to use GitHub to find open-source projects and tasks to contribute to.

Google Colab is one of the best tools online to utilize for the construction of machine learning projects. With the help of the wonderful tools that are available to us within the Google Colab environment, we are granted access and freedom to manipulate the Colab notebooks to achieve the best possible results for a particular task. Colaboratory (also known as Colab) is a free Jupyter notebook environment that runs in the cloud and stores its notebooks on Google Drive. Colab was originally an internal Google project; an attempt was made to open-source all the code and work more directly upstream, leading to the development of the "Open in Colab" Google Chrome extension, but this eventually ended, and Colab development continued internally. With just the help of your Gmail account, you are granted access to this free service for making the best utility of the Notebooks.

Python is an experiment in how much freedom programmers need. Too much freedom and nobody can read another's code; too little and expressiveness is endangered. Since its creation, Python has rapidly evolved into a multi-faceted programming language, becoming the choice of several diverse projects ranging from web applications to being deployed into Artificial Intelligence, Machine Learning, Deep Learning, and more. Python comes with numerous features such as its simplicity, enormous collection of packages and libraries, with relatively faster execution of programs, to list a few. For a programmer, a Code Editor or an IDE is the first point of contact with any programming language, making its selection one of the most crucial steps in the journey ahead.