This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
This research introduces a novel robot operating system (ROS) framework designed to seamlessly integrate large language models (LLMs) into embodied artificial intelligence. The framework enables robots to interpret and execute natural language instructions with greater versatility and reliability.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
In 1988, while reading a book about UFOs at Xerox PARC, Rob Tow overheard a discussion about embedding barcodes in documents to link them to digital files. He found this idea visually unappealing and instead had an epiphany: he could embed digital data within halftone images in a way that was imperceptible to the human eye.
Tow's idea involved using tiny, angled halftone dots to represent binary data, with left-leaning dots for zeros and right-leaning dots for ones. This method allowed for the embedding of significant amounts of data without affecting the image's appearance. He calculated that this approach could store kilobits of data in a small area, far more than traditional barcodes.
Tow submitted an invention disclosure for this concept and later developed a variant that utilized color and intensity variations below the threshold of human perception. Both ideas were patented. The technology, named "DataGlyph," was developed further with additional features like binary morphology image conditioning and error correction.
The term "DataGlyph" was derived from "Glyph," a term popularized by the musician Prince to denote his identity after a legal dispute. The technology became a real product offered by Xerox under the name "Smart Paper."
Several patents were issued for the DataGlyph technology, including a basic patent (U.S. Patent no. 5,315,098) and others related to adaptive scaling and self-clocking glyph codes.
Tow's idea involved using tiny, angled halftone dots to represent binary data, with left-leaning dots for zeros and right-leaning dots for ones. This method allowed for the embedding of significant amounts of data without affecting the image's appearance. He calculated that this approach could store kilobits of data in a small area, far more than traditional barcodes.
Tow submitted an invention disclosure for this concept and later developed a variant that utilized color and intensity variations below the threshold of human perception. Both ideas were patented. The technology, named "DataGlyph," was developed further with additional features like binary morphology image conditioning and error correction.
The term "DataGlyph" was derived from "Glyph," a term popularized by the musician Prince to denote his identity after a legal dispute. The technology became a real product offered by Xerox under the name "Smart Paper."
Several patents were issued for the DataGlyph technology, including a basic patent (U.S. Patent no. 5,315,098) and others related to adaptive scaling and self-clocking glyph codes.
This paper details the reconstruction and execution of the Logic Theorist (LT), considered the first artificial intelligence program, originally created in 1955-1956. The authors built a new IPL-V interpreter in Common Lisp and faithfully reanimated LT from code transcribed from a 1963 RAND technical report. The reanimated LT successfully proved 16 of 23 theorems from Principia Mathematica, consistent with the original system's behavior. This work demonstrates "executable archaeology" as a method for understanding early AI systems, highlighting the challenges and insights gained from reconstructing and running historical code.
