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.
ICANN's blog post details the transition from the legacy WHOIS protocol to the Registration Data Access Protocol (RDAP). RDAP offers several advantages over WHOIS, including standardized data formats, internationalized data, extensibility, and improved data access control through different access levels. This transition is necessary for WHOIS to comply with data privacy regulations like GDPR. ICANN encourages everyone using WHOIS to transition to RDAP and provides resources to aid in this process. The blog post highlights the key differences between the two protocols and reassures users that RDAP offers a more robust and secure method for accessing registration data.
Several Hacker News commenters discuss the shift from WHOIS to RDAP. Some express frustration with the complexity and inconsistency of RDAP implementations, noting varying data formats and access methods across different registries. One commenter points out the lack of a simple, unified tool for RDAP lookups compared to WHOIS. Others highlight RDAP's benefits, such as improved data accuracy, internationalization support, and standardized access controls, suggesting the transition is ultimately positive but messy in practice. The thread also touches upon the privacy implications of both systems and the challenges of balancing data accessibility with protecting personal information. Some users mention specific RDAP clients they find useful, while others express skepticism about the overall value proposition of the new protocol given its added complexity.
OAuth2 is a delegation protocol that lets a user grant a third-party application limited access to their resources on a server, without sharing their credentials. Instead of handing over your username and password directly to the app, you authorize it through the resource server (like Google or Facebook). This authorization process generates an access token, which the app then uses to access specific resources on your behalf, within the scope you've permitted. OAuth2 focuses solely on authorization and not authentication, meaning it doesn't verify the user's identity. It relies on other mechanisms, like OpenID Connect, for that purpose.
HN commenters generally praised the article for its clear explanation of OAuth2, calling it accessible and well-written, particularly appreciating the focus on the "why" rather than just the "how." Some users pointed out potential minor inaccuracies or areas for further clarification, such as the distinction between authorization code grant with PKCE and implicit flow for client-side apps, the role of refresh tokens, and the implications of using a third-party identity provider. One commenter highlighted the difficulty of finding good OAuth2 resources and expressed gratitude for the article's contribution. Others suggested additional topics for the author to cover, such as the challenges of cross-domain authentication. Several commenters also shared personal anecdotes about their experiences implementing or troubleshooting OAuth2.
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.