The blog post details how to use Google's Gemini Pro and other large language models (LLMs) for creative writing, specifically focusing on generating poetry. The author demonstrates how to "hallucinate" text with these models by providing evocative prompts related to existing literary works like Shakespeare's Sonnet 3.7 and two other poems labeled "o1" and "o3." The process involves using specific prompting techniques, including detailed scene setting and instructing the LLM to adopt the style of a given author or work. The post aims to make these powerful creative tools more accessible by explaining the methods in a straightforward manner and providing code examples for using the Gemini API.
The core message of "Just Write" is to overcome the paralysis of perfectionism and the fear of judgment by simply starting to write. Don't get bogged down in elaborate outlines or editing; instead, prioritize consistent writing practice to develop your skills and discover your voice. The more you write, the easier it becomes, and the better your writing will be. Embrace imperfection, focus on quantity over quality initially, and view writing as a process of iterative refinement. Over time, this consistent effort will lead to significant improvement and unlock your creative potential.
Hacker News users generally agreed with the core message of "Just Write," emphasizing the importance of consistent writing for skill development and idea generation. Several commenters shared their personal experiences with writing streaks and the positive impact it had on their clarity of thought and ability to articulate ideas. Some cautioned against focusing solely on quantity over quality, suggesting a balance is needed. The idea of lowering the bar for publishing, embracing imperfection, and iterating based on feedback was also discussed. One commenter pointed out the parallels between writing and coding, highlighting the iterative nature of both. Another popular sentiment was the importance of finding a niche and writing about topics that genuinely interest the author.
Even if no one reads your blog, it's still valuable. Writing clarifies your thinking, solidifies your understanding of a topic, and acts as a personal record of your intellectual journey. It can serve as a sandbox for experimenting with ideas, a portfolio to showcase skills, and a springboard for future projects. Essentially, blogging is an act of learning and self-improvement, with the potential bonus of connecting with an audience down the line.
HN commenters largely agree with the author's premise that blogging, even without a large audience, has value. Several highlight the benefits of writing as a way to clarify thinking, consolidate knowledge, and improve writing skills. Some suggest that a blog can serve as a personal knowledge base, searchable archive, or a way to track personal growth. A few practical suggestions include focusing on niche topics and promoting the blog through relevant communities. The idea of writing primarily for oneself, with the potential for an audience as a secondary benefit, is a recurring theme. Some commenters share their own experiences of low-traffic blogs providing unexpected value, like attracting job offers or connecting with like-minded individuals. The overall sentiment is that the intrinsic rewards of blogging often outweigh the pressure of building a large readership.
Scroll, a zkEVM-based scaling solution for Ethereum, announced successful completion of their pre-alpha testnet, Scroll 5. This testnet focused on proving out the performance and stability of the network under a higher load of transactions, including complex DeFi interactions. They achieved significant performance improvements, demonstrating increased transaction throughput and decreased latency compared to previous testnets. The team is now working towards a permissioned alpha release, followed by a permissionless alpha later this year, with the ultimate goal of a mainnet launch on Ethereum.
Hacker News users discuss Scroll's announcement about expanding their zkEVM rollup's compatibility with existing Ethereum infrastructure and tools. Several commenters express skepticism about the viability and necessity of zkEVMs in general, questioning their complexity and potential security risks compared to optimistic rollups. Some point to the lack of readily apparent demand for zkEVM technology outside of specific niche use cases. Others voice concerns about the closed-source nature of Scroll's implementation, hindering community review and potentially impacting trust. Conversely, some commenters express excitement about the progress, particularly regarding the compatibility with existing tooling, viewing it as a positive step towards wider adoption of zk-rollups. A few users ask about the pricing model, but no definitive answers are provided in the comments.
Summary of Comments ( 26 )
https://news.ycombinator.com/item?id=43222027
Hacker News commenters discussed the accessibility of the "hallucination" examples provided in the linked article, appreciating the clear demonstrations of large language model limitations. Some pointed out that these examples, while showcasing flaws, also highlight the potential for manipulation and the need for careful prompting. Others discussed the nature of "hallucination" itself, debating whether it's a misnomer and suggesting alternative terms like "confabulation" might be more appropriate. Several users shared their own experiences with similar unexpected LLM outputs, contributing anecdotes that corroborated the author's findings. The difficulty in accurately defining and measuring these issues was also raised, with commenters acknowledging the ongoing challenge of evaluating and improving LLM reliability.
The Hacker News post titled "Making o1, o3, and Sonnet 3.7 Hallucinate for Everyone" (https://news.ycombinator.com/item?id=43222027) has several comments discussing the linked article about prompting language models to produce nonsensical or unexpected outputs.
Several commenters discuss the nature of "hallucination" in large language models, debating whether the term is appropriate or if it anthropomorphizes the models too much. One commenter suggests "confabulation" might be a better term, as it describes the fabrication of information without the intent to deceive, which aligns better with how these models function. Another commenter points out that these models are essentially sophisticated prediction machines, and the outputs are just statistically likely sequences of words, not actual "hallucinations" in the human sense.
There's a discussion about the potential implications of this behavior, with some commenters expressing concern about the spread of misinformation and the erosion of trust in online content. The ease with which these models can generate convincing yet false information is seen as a potential problem. Another commenter argues that these "hallucinations" are simply a reflection of the biases and inconsistencies present in the training data.
Some commenters delve into the technical aspects of the article, discussing the specific prompts used and how they might be triggering these unexpected outputs. One commenter mentions the concept of "adversarial examples" in machine learning, where carefully crafted inputs can cause models to behave erratically. Another commenter questions whether these examples are truly "hallucinations" or just the model trying to complete a nonsensical prompt in the most statistically probable way.
A few comments also touch on the broader ethical implications of large language models and their potential impact on society. The ability to generate convincing fake text is seen as a powerful tool that can be used for both good and bad purposes. The need for better detection and mitigation strategies is highlighted by several commenters.
Finally, some comments provide additional resources and links related to the topic, including papers on adversarial examples and discussions on other forums about language model behavior. Overall, the comments section provides a lively discussion on the topic of "hallucinations" in large language models, covering various aspects from technical details to ethical implications.