AutoAgent is an autonomous framework designed for agent engineering, functioning similarly to autoresearch but focused on building and iterating on agent harnesses. The system allows a user to assign a task to an AI agent, which then autonomously modifies system prompts, tools, agent configurations, and orchestration over time. By running benchmarks and checking scores, the meta-agent performs a hill-climbing optimization, keeping improvements and discarding failures. The core workflow involves programming via a Markdown file called program.md, which provides context and directives to the meta-agent, while the meta-agent directly edits the agent.py harness file. This approach minimizes manual engineering by allowing the agent to optimize its own performance through continuous, automated experimentation.
This project guides you from data inspection and preprocessing to crafting an end-to-end application for aircraft localization based on crowdsourced air traffic control communication data (ADS-B). It utilizes Apache Spark, Modin, ensemble methods, TabNet, Apache Kafka, Flask, and Leaflet.js.
Meshquake monitors real-time earthquakes via USGS and sends alerts over Meshtastic mesh radios.
MarkItDown is a utility for converting various files to Markdown, including PDF, PowerPoint, Word, Excel, Images, Audio, HTML, text-based formats, and ZIP files.
This article explains how to run inference on a YOLOv8 object detection model using Docker and create a REST API to orchestrate the process. It includes code implementation and a detailed README in the author's GitHub repository for running the API via REST with Docker.
This is a hands-on guide with Python example code that walks through the deployment of an ML-based search API using a simple 3-step approach. The article provides a deployment strategy applicable to most machine learning solutions, and the example code is available on GitHub.
tensorflow jupyter):
Download the training zip file from drive, extract it
docker run --rm -it -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 -v /Users/foo/Learn/python/training:/home/jovyan/ jupyter/tensorflow-notebook:latest
Download the training zip file from drive, extract it
docker run --rm -it -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 -v /Users/foo/Learn/python/training:/home/jovyan/ jupyter/tensorflow-notebook:latest
