GibberLink is an experimental project exploring direct communication between large language models (LLMs). It facilitates real-time, asynchronous message passing between different LLMs, enabling them to collaborate or compete on tasks. The system utilizes a shared memory space for communication and features a "turn-taking" mechanism to manage interactions. Its goal is to investigate emergent behaviors and capabilities arising from inter-LLM communication, such as problem-solving, negotiation, and the potential for distributed cognition.
Earthstar is a novel database designed for private, distributed, and offline-first applications. It syncs data directly between devices using any transport method, eliminating the need for a central server. Data is organized into "workspaces" controlled by cryptographic keys, ensuring data ownership and privacy. Each device maintains a complete copy of the workspace's data, enabling seamless offline functionality. Conflict resolution is handled automatically using a last-writer-wins strategy based on logical timestamps. Earthstar prioritizes simplicity and ease of use, featuring a lightweight core and adaptable document format. It aims to empower developers to build robust, privacy-respecting apps that function reliably even without internet connectivity.
Hacker News users discuss Earthstar's novel approach to data storage, expressing interest in its potential for P2P applications and offline functionality. Several commenters compare it to existing technologies like CRDTs and IPFS, questioning its performance and scalability compared to more established solutions. Some raise concerns about the project's apparent lack of activity and slow development, while others appreciate its unique data structure and the possibilities it presents for decentralized, user-controlled data management. The conversation also touches on potential use cases, including collaborative document editing and encrypted messaging. There's a general sense of cautious optimism, with many acknowledging the project's early stage and hoping to see further development and real-world applications.
Summary of Comments ( 16 )
https://news.ycombinator.com/item?id=43168611
Hacker News users discussed GibberLink's potential and limitations. Some expressed skepticism about its practical applications, questioning whether it represents genuine communication or just a complex pattern matching system. Others were more optimistic, highlighting the potential for emergent behavior and comparing it to the evolution of human language. Several commenters pointed out the project's early stage and the need for further research to understand the nature of the "language" being developed. The lack of a clear shared goal or environment between the agents was also raised as a potential limiting factor in the development of meaningful communication. Some users suggested alternative approaches, such as evolving the communication protocol itself or introducing a shared task for the agents to solve. The overall sentiment was a mixture of curiosity and cautious optimism, tempered by a recognition of the significant challenges involved in understanding and interpreting AI-generated communication.
The Hacker News post titled "GibberLink [AI-AI Communication]" sparked a discussion with several interesting comments. Many commenters explored the potential implications and limitations of the project.
One commenter highlighted the potential for emergent communication if two LLMs are trained to cooperate on a task, speculating that a novel communication protocol could arise. They also pointed out the current reliance on pre-training datasets influencing the LLMs' behavior, suggesting a need for a more isolated environment to truly observe emergent communication.
Another commenter drew parallels to biological evolution, suggesting that if the system were complex enough and the selection pressure strong enough, a new "language" might emerge. They also proposed an experiment where the communication channel is restricted, forcing the AIs to be more concise and potentially leading to faster development of a unique communication system.
Several comments touched upon the concept of compression in communication. One user proposed using the communication bandwidth as a regularization term in the loss function, encouraging the LLMs to develop a more efficient and potentially novel communication system. This idea of pushing the models towards compression resonated with other commenters who saw it as a key driver for the emergence of complex communication.
One commenter questioned the novelty of the approach, pointing out that similar research using reinforcement learning to evolve communication protocols has been conducted in the past. They provided a link to a 2017 paper as an example of prior work in this area.
Another commenter raised the issue of interpreting the emergent communication. Even if a seemingly novel communication protocol arises, understanding its meaning and whether it truly represents a new form of communication would be a significant challenge. They argued that the current focus on observing differences in character strings might be a misleading metric for judging the emergence of complex communication.
The discussion also touched upon the practical applications of such a system. While acknowledging the potential for scientific discovery, one commenter questioned the immediate practical utility of the project, suggesting that focusing on other aspects of AI development might yield more tangible benefits in the short term.
Finally, some commenters expressed skepticism about the claims of "AI communication," arguing that the observed behavior is simply a result of the models optimizing for a specific task and not a genuine form of communication. They emphasized the importance of distinguishing between complex pattern matching and true understanding.
In summary, the comments on the Hacker News post explore various facets of the GibberLink project, ranging from the potential for emergent communication and the role of compression to the challenges of interpretation and the practical implications of the research. The discussion reflects a mix of excitement, skepticism, and thoughtful consideration of the complexities of AI communication.